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酪氨酸激酶抑制剂引起的肝损伤机制研究进展

网络出版时间：２０２３－０８－２８０９：２５：３４　网络出版地址：ｈｔｔｐｓ：／／ｌｉｎｋ．ｃｎｋｉ．ｎｅｔ／ｕｒｌｉｄ／３４．１０８６．ｒ．２０２３０８２５．１００２．００６酪氨酸激酶抑制剂引起的肝损伤机制研究进展刘慧慧，魏静瑶，张丽珍，冯进伟，刘瑞娟，田　鑫（郑州大学第一附属医院药学部，河南郑州　４５００５２）收稿日期：２０２２－０３－１７，修回日期：２０２２－０６－２１基金项目：国家自然科学基金资助项目（Ｎｏ８１９０３７２０）作者简介：刘慧慧（１９９８－），女，硕士生，研究方向：药理学，Ｅｍａｉｌ：ｌｈｈ１８５３８２７７７８１＠１６３．ｃｏｍ；刘瑞娟（１９８８－），女，博士，副主任药师，研究方向：临床药理学，通信作者，Ｅｍａｉｌ：ｆｃｃｌｉｕｒｊ＠ｚｚｕ．ｅｄｕ．ｃｎ；田　鑫（１９７５－），女，博士，教授，博士生导师，研究方向：药理学，通信作者，Ｅｍａｉｌ：ｔｉａｎｘ＠ｚｚｕ．ｅｄｕ．ｃｎｄｏｉ：１０．１２３６０／ＣＰＢ２０２２０３０５２文献标志码：Ａ文章编号：１００１－１９７８（２０２３）０９－１６１３－０５中国图书分类号：Ｒ０５；Ｒ３４５５７；Ｒ５７５；Ｒ９７７３摘要：酪氨酸激酶抑制剂（ｔｙｒｏｓｉｎｅｋｉｎａｓｅｉｎｈｉｂｉｔｏｒｓ，ＴＫＩｓ）为一类靶向抑癌基因相关受体酪氨酸激酶的小分子化合物，通过阻断下游的信号通路发挥抗癌作用。

ＴＫＩｓ广泛用于癌症的治疗，对于部分肿瘤显示出较传统化疗药物更好的疗效。

然而，ＴＫＩｓ引起的药物性肝损伤是其在临床应用中面临的难题之一。

笔者通过查阅国内外相关文献，对ＴＫＩｓ的分类、临床应用及其引起肝损伤的机制等进行综述，以期为阐明ＴＫＩｓ肝损伤的机制和寻找有效的防治手段提供一定的参考。

关键词：酪氨酸激酶；酪氨酸激酶抑制剂；药物性肝损伤；靶向药物；机制；靶点开放科学（资源服务）标识码（ＯＳＩＤ）：酪氨酸激酶（ｔｙｒｏｓｉｎｅｋｉｎａｓｅｓ，ＴＫｓ）对于肿瘤细胞的信号转导、细胞增殖、转移和凋亡发挥着重要作用［１］，以ＴＫｓ作为靶点进行相关药物研发是当前抗肿瘤药物研究的热点。

I’screen AFLA M 1 Milk 产品说明书

1. Identification of the substance/preparation and of the Company/Undertaking 1.1 Product identifiers Product nameI’screen AFLA M 1 Milk – HU0040001, HU00400211. 2 Relevant identified uses of the substance or mixture and uses advised against In vitro diagnostic kit.1. 3 Company/Undertaking identification Eurofins Technologies Hungary Kft. Fóti út. 56.1047 Budapest, Hungary1. 4 Emergency telephonePlease, contact your local Poison Centre.2. Hazards Identification2.1 Classification : Known hazardous components according to Regulation (EC) No. 1272/2008: stop solution (sulfuric acid, 5≤x<15% wt/wt), classified as corrosive to the skin (category 1A).2.2 Label elementsPictogramStop solutionHazard Class andCategorySkin Corr. 1ASignal word Danger (Dng)Hazard statements H314Precautionary statementsP260 P264 P280P301+P330+P331 P303+P361+P353P363 P304+P340 P310P305+P351+P338P405 P501Hazard statements and precautionary statements full text in section 16. 2.3 Supplemental Hazard – none.3. Composition/information on ingredientsComponent Hazardoussubstance% (wt/wt)Componentclassification CAS No. EC No.Microtiter plate - - non-hazardous - -Standard Aflatoxin M1Sodium azideAflatoxin M1< 0,1%<0,01%Aquatic Acute 1Aquatic Chronic 1 26628-22-8 247-852-1Acute Tox. 2 6795-23-9 229-865-4Carc. 1BEnzyme Conjugate - - non-hazardous - - Washing buffer 20x - - non-hazardous - - Developing solution Citric acid < 5% Skin Irrit. 2 77-92-9 201-069-1Stop solution, 8ml/6ml Sulfuric acid 5≤x<15%(1M)Skin Corr. 1A 7664-93-9 231-639-5 4. First Aid Measures4.1 Description of first aid measuresIf inhaled: there is a minimal risk of inhalation. In case there appear to be symptoms of exposure, supply fresh air. Monitor respiration. If breathing becomes difficult, consult a doctor and give oxygen. Get medical aid.In case of skin contact: immediately flush with large amounts of water and soap. Remove all contaminated clothing and wash them before reusing. In presence of irritation, get medical aid.In case of eye contact:flush eyes with large amounts of water for at least 15 minutes. Insure adequate washing by keeping eyelids open with fingers. Get medical aid.If swallowed: STOP SOLUTION: DO NOT induce vomit. Do not administer anything if victim is unconscious. Rinse mouth with water. Get medical aid.4.2 Most important symptoms and effects, both acute and delayedSee section 2.2 and section 11.4.3 Indication of any immediate medical attention and special treatment neededNo data available.5. Firefighting MeasuresKit components are not flammable.5.1 Extinguishing media: Use water spray, alcohol-resistant foam, dry chemical or carbon dioxide. Keep surrounding materials cool with water spray.5.2 Special hazards arising from the mixture: Stop solution: sulphur oxides.5.3 Advice for firefighters: in case of fire, if necessary, wear approved self-contained breathing apparatus and appropriate protective clothing.5.4 Further information: Keep surrounding materials cool with water spray.6. Accidental Release Measures6.1 Personal precautions, protective equipment and emergency procedures:The small supplied volumes and packaging insure minimal risk of accidental release. In case of spill, wear protective clothing as indicated in section 8. Ensure adequate ventilation.6.2 Environmental precautions: avoid seepage into water course or sewage system.6.3 Methods and materials for containment and cleaning up: Absorb spill with inert absorbent material or absorbent paper. Discard all material into biohazard waste collection container for proper disposal. Wash the contaminated area taking care to avoid seepage into water course or sewage system.6.4 Further information: see section 13 for disposal.7. Handling and Storage7.1 Precautions for safe handlingThere are not special warnings if used according to instruction. Avoid skin and eyes contact. Avoid inhalation of vapour or mist. Wear appropriate personal protective equipment as specified in section 8.7.2 Conditions for safe storage, including any incompatibilitiesKeep products tightly sealed in their original containers. Store bottles between +2°C and +8°C. Avoid physical damage to containers. Do not expose to heat or direct light.The packaging guarantees the component isolation from incompatible material.7.3 Specific end usesIn vitro diagnostic reagents, as described in section 1.2.8. Exposure Controls/Personal Protection8.1 Control parametersComponents with workplace control parameters.Exposure workplace limit values for sulfuric acid (data refer to pure substance): 0,05 mg/m3 (TWA). Exposure workplace limit values for sodium azide (data refer to pure substance): 0,1 mg/m3 (TWA), 0,3mg/m3 (STEL).8.2 Exposure controls:Adhere to instructions and good laboratory practice. Use disposable latex or nitrile rubber gloves and protective lab coat. The selected protective gloves have to satisfy the specifications of EU Directive 89/686/EEC and the standard EN 374 derived from it. Use equipment for eye protection tested and approved under appropriate government standards such as NIOSH (US) or EN 166(EU). Have emergency shower and eye wash stations available. Always avoid direct contact of the solution with eyes, skin and clothing. Avoid inhalation. Avoid prolonged or repeated exposure.9. Physical and Chemical Properties10. Stability and reactivity10.1 Reactivity: no data available.10.2 Chemical stability: stable under the conditions for storage and handling described in the instructions. Keep in their original containers.10.3 Possibility of hazardous reactions: no data available.10.4 Conditions to avoid: heat, flame, sparks, direct light, incompatible materials.10.5 Incompatible materials: Aflatoxin M1 standards: metals. Stop solution: bases, halides, organic materials, carbides, nitrates, picrates, cyanides, chlorates, alkali halides, zinc salts, permanganates, hydrogen peroxide, azides, perchlorates, nitromethane, phosphorous, cyclopentadiene, cyclopentanone oxime, nitroaryl amines, hexalithium disilicide, phosphorous(iii) oxide, powdered metals.10.6 Hazardous decomposition products: no data available.11.Toxicological Information11.1 Information on toxicological effectsStop solution is classified as corrosive to the skin (category 1A).Causes severe skin burns and eye damage.Toxicological properties have not been further investigated.Further information: RTECS: WS5600000 (sulfuric acid).12. Ecological InformationThe components are furnished in volumes that do not represent hazard for the environment if used and disposed of correctly.This product contains no components considered to be either persistent, bioaccumulative and toxic (PBT) or very persistent and very bioaccumulative (vPvB).13. Disposal ConsiderationsUse the components according to good laboratory practice. Avoid release to the environment. Do not allow product to reach sewage system. Observe all international and local environmental regulations. Send surplus and non recyclable solutions to a licensed disposal company.14. Transport InformationThough some components are mentioned in legislation on transport of hazardous goods (sulphuric acid, with not more than 51% pure acid, UN2796), the volumes and the types of containers used with this product are such that this product is exempt from these norms.15. Regulatory InformationThis safety data sheet is in accordance with Regulation (EC) No. 1907/2006 and Regulation No. 453/2010.16. Other informationHazard statements and precautionary statements full textFull text of H-Statements referred to sections 2 and 3H314: Causes severe skin burns and eye damageSkin Corr. 1A Corrosive to the skin (category 1A)Precautionary statementsP260: Do not breathe dust/fume/gas/mist/vapours/sprayP264: Wash thoroughly after handlingP280: Wear protective gloves/protective clothing/eye protection/face protectionP301+330+331: is swallowed: Rinse mouth. Do NOT induce vomitingP303+361+353: if on skin (or hair): Take off immediately all contaminated clothing. Rinse skin with water/ showerP304+340: if inhaled: Remove person to fresh air and keep comfortable for breathingP305+P351+P338: If in eyes: rinse cautiously with water for several minutes. Remove contact lenses, if present and easy to do.Continue rinsingP310: Immediately call a POISON CENTER/doctorP363: Wash contaminated clothing before reuseP405: Store locked upP501: Dispose of contents to in accordance with local regulationIMPORTANT! Read the safety data sheets before the use and disposal of this product. Insure that this information is understood by the operators exposed to this product. Use this product for the intended purpose as indicated in the instruction manual.The above information is believed to be accurate and up to date. It is, however, liable to change due to the continuous modification of legislation and of standards and security data. Since the correct or incorrect use of this product is beyond our jurisdiction, this information cannot be expressed or implied to be comprehensive. Eurofins Technologies Hungary Kft. cannot be held responsible for any improper use of the product, including those uses that could violate current patents or other copyrights. Only the user is responsible for the evaluation of this product’s conformity and of the risks involv ed before use, and must adopt appropriate precautions towards self and other persons involved.。

iopamidol USP 41

2210Iohexol / Official MonographsUSP 41precipitate. Label it also to state its routes of administra-Residue on ignition 〈281〉: not more than 0.1%.tion. When the specific dose strength is not intended for Free aromatic amine—Transfer 500mg to a 25-mL volu-intrathecal use, label it to indicate “serious injury can oc-metric flask, and add 20mL of water, heating on a water cur if given by intrathecal route”.bath, if necessary, to effect solution. To a second 25-mL volumetric flask transfer 18.4mL of water and 1.6mL of a Standard solution prepared by dissolving a suitable quantity Change to read:of USP Iopamidol Related Compound A RS in water and diluting with water to obtain a solution having a concentra-•USP R EFERENCE S TANDARDS 〈11〉tion of 62.5µg per mL. To a third 25-mL volumetric flask ••(CN 1-May-2018)add 20mL of water to provide a blank. Treat each flask as USP Iohexol RSfollows. Place the flasks in an ice bath, protected from light,USP Iohexol Related Compound A RSfor 5minutes. [NOTE —In conducting the following steps,5-(Acetylamino)-N ,N ′-bis(2,3-dihydroxypropyl)-2,4,keep the flasks in the ice bath and protected from light as 6-triiodo-1,3-benzenedicarboxamide.much as possible until all of the reagents have been added.]USP Iohexol Related Compound C RSAdd slowly 1mL of hydrochloric acid, mix, and allow to N ,N ′-Bis(2,3-dihydroxypropyl)-5-nitro-1,3-stand for 5minutes. Add 1mL of sodium nitrite solution (1benzenedicarboxamide.in 50), mix, and allow to stand for 5minutes. Add 1mL of ammonium sulfamate solution (3 in 25), shake, and allow to stand for 5minutes. [Caution—Considerable pressure is pro-duced.] Add 1mL of N -(1-naphthyl)ethylenediamine dihy-drochloride solution (1 in 1000), and mix. Remove the flasks from the ice bath, and allow to stand in a water bath at Iopamidolabout 25° for 10minutes. Dilute with water to volume, mix,and without delay (about 5minutes from final dilution),concomitantly determine the absorbances of the solution from the substance under test and the Standard solution in 1-cm cells at the wavelength of maximum absorbance at about 500 nm, with a suitable spectrophotometer, against the prepared blank. The absorbance of the solution from the Iopamidol is not greater than that of the Standard solu-tion (0.02%).C 17H 22I 3N 3O 8777.09Free iodine—Transfer 2.0g to a stoppered, 50-mL centri-1,3-Benzenedicarboxamide, N ,N ′-bis[2-hydroxy-1-(hy-fuge tube, add sufficient water to dissolve, heating on a droxymethyl)ethyl]-5-[(2-hydroxy-1-oxopropyl)amino]-2,4,water bath, if necessary, to effect solution, and dilute with 6-triiodo-, (S )-.water to 25mL. Add 5mL of toluene and 5mL of 2N sulfu-(S )-N ,N ′-Bis[2-hydroxy-1-(hydroxymethyl)ethyl]-2,4,6-triiodo-ric acid, shake well, and centrifuge: the toluene layer shows 5-lactamidoisophthalamide [60166-93-0].no red color.Limit of free iodide—Transfer about 6.0g, accurately» Iopamidol contains not less than 98.0percent weighed, to a suitable container, dissolve in 50mL of water,and not more than 101.0percent of iopamidol,and add 2.0mL of 0.001 M potassium iodide. Titrate with calculated on the dried basis.0.001 N silver nitrate VS, determining the endpoint potenti-ometrically, using a silver indicator electrode and an appro-Packaging and storage—Preserve in well-closed, light-re-priate reference electrode. Perform a blank determination,sistant containers. Store at 25°, excursions permitted be-and make any necessary correction. Each mL of 0.001 N tween 15° and 30°.silver nitrate is equivalent to 126.9µg of iodide. Not more USP Reference standards 〈11〉—than 0.001% is found.USP Iopamidol RSFree acid or alkali—Dissolve 10.0g in 100mL of freshly USP Iopamidol Related Compound A RSboiled and cooled water. Using a pH meter and aN ,N ′-Bis-(1,3-dihydroxy-2-propyl)-5-amino-2,4,glass–calomel electrode system, determine the volume of 6-triiodoisophthalamide.0.01 N hydrochloric acid VS or 0.01 N sodium hydroxide VS C 14H 18I 3N 3O 6705.03to bring the pH of the test solution to 7.0: not more than USP Iopamidol Related Compound C RS1.37mL of 0.01 N sodium hydroxide, equivalent to a free 4-Chloro-N 1, N 3-bis(1,3-dihydroxypropan-2-yl)-5-(S)-acid content of 5mg of hydrochloric acid per 100g, or not lactamido-2,6-diiodoisophthalamide.more than 0.75mL of 0.01 N hydrochloric acid, equivalent C 17H 22ClI 2N 3O 8685.63to a free alkali content of 3mg of sodium hydroxide per Identification—100g, is required.A: Infrared Absorption 〈197K 〉.B: Heat about 500mg in a suitable crucible: violet vapors Delete the following:are evolved.C: The retention time of the major peak in the chromato-•Heavy metals, Method II 〈231〉: not more than 0.001%.gram of the Identification solution corresponds to that of the •(Official 1-Jan-2018)iopamidol peak observed in the chromatogram of the Sys-Related compounds—tem suitability solution, as obtained in the test for Related Solution A—Use water.compounds.Solution B—Prepare a filtered and degassed mixture of Specific rotation 〈781S 〉: between −4.6° and −5.2° (t =water and acetonitrile (1:1).20°; λ = 436 nm).Mobile phase—Use variable mixtures of Solution A and So-Test solution: 400mg per mL, in water, heating on a lution B as directed for Chromatographic system. Make ad-water bath, if necessary to effect solution, and passingjustments if necessary (see System Suitability under Chroma-through a membrane filter having a 3-µm or finer porosity.tography 〈621〉).Loss on drying 〈731〉—Dry it at 105° for 4hours: it loses System suitability solution—Dissolve accurately weighed not more than 0.5% of its weight.quantities of USP Iopamidol RS and USP Iopamidol Related Compound C RS in water, and dilute with water to obtain aUSP 41Official Monographs / Iopamidol 2211solution having concentration of about 20µg per mL of Table 1each.Relative Standard solution—Dissolve accurately weighed quantities Retention of USP Iopamidol RS and USP Iopamidol Related Compound Name Time Limit (%)C RS in water, and dilute with water to obtain a solution Monocarboxylic acid 10.10.1having concentrations of about 20µg per mL and 50µg per Iopamidol related compound B 20.60.1mL, respectively.Iopamidol related compound C 3 and 2-0.90.5*Test solution—Transfer about 0.5g of Iopamidol, accu-chloro derivative 4rately weighed, to a 50-mL volumetric flask, add water to Iopamidol 1.0—volume, and mix.2,3-Dihydroxypropyl isomer 5 1.10.1Identification solution—Dilute a suitable volume of the Test Diiodo derivative 6 1.20.1solution with water to obtain a solution with a concentration Acetyl analog 7 1.30.1of iopamidol of about 20µg per mL.Hydroxyethyl derivative 8 1.50.1Chromatographic system (see Chromatography 〈621〉)—The liquid chromatograph is equipped with a 240-nm detector O -Acetyl iopamidol 9 2.20.1and two 4.6-mm × 25-cm columns that contain packing N,N -Dimethylamino derivative 10 2.30.1L11, connected in series. The column temperature is main-*These peaks, appearing at a relative retention time of 0.9, are integratedtained at 60°, and the flow rate is about 2.0mL per minute.together to determine conformance.The chromatograph is programmed as follows.13-(1,3-Dihydroxypropan-2-ylcarbamoyl)-5-(S )-lactamido-2,4,6-triiodobenzoic acid.25-Glycolamido-N 1,N 3-bis(1,3-dihydroxy-2-propyl)-2,4,6-Time Solution A Solution B triiodoisophthalamide.(minutes)(%)(%)Elution 34-Chloro-N 1,N 3-bis(1,3-dihydroxypropan-2-yl)-5-(S )-lactamido-2,6-0–181000isocraticdiiodoisophthalamide.18–40100→620→38linear gradient 42-Chloro-N 1,N 3-bis(1,3-dihydroxypropan-2-yl)-5-(S )-lactamido-4,6-diiodoisophthalamide.40–4562→5038→50linear gradient 5N 1-(1,3-Dihydroxypropan-2-yl)-N 3-(2,3-dihydroxypropyl)-5-(S )-lactamido-2,4,45–5050→10050→0linear gradient 6-triiodoisophthalamide.50–60100isocratic6N 1,N 3-Bis(1,3-dihydroxypropan-2-yl)-5-(S )-lactamido-2,4-diiodoisophthalamide.Chromatograph the System suitability solution, and record 75-Acetamido-N 1,N 3-bis(1,3-dihydroxy-2-propyl)-2,4,6-triiodoisophthalamide.the peak responses as directed for Procedure: the resolution,8N 1-(1,3-Dihydroxypropan-2-yl)-N 3-(2-hydroxyethyl)-5-(S )-lactamido-2,4,6-triiodoisophthalamide.R, between iopamidol related compound C and iopamidol is 9(S )-5-(2-Acetoxypropanamido)-N 1,N 3-bis(1,3-dihydroxypropan-2-yl-not less than 2.0. Chromatograph the Standard solution, and carbamoyl)-2,4,6-triiodoisophthalamide.record the peak responses as directed for Procedure: the tail-10N 1-(1,3-Dihydroxypropan-2-yl)-5-(S )-lactamido-2,4,6-triiodo-N 3,N 3-ing factor for each peak is between 0.7 and 1.5, and the dimethylisophthalamide.relative standard deviation for replicate injections for eitherof the two peaks is not more than 2.0%. Chromatograph the Identification solution, and record the peak responses as Assay—Transfer about 300mg of Iopamidol, accurately directed for Procedure to obtain a chromatogram for Identifi-weighed, to a glass-stoppered, 125-mL conical flask, add cation test C.40mL of 1.25 N sodium hydroxide and 1g of powdered zinc, connect the flask to a reflux condenser, and reflux the Procedure—Separately inject equal volumes (about 20µL)mixture for 30minutes. Cool the flask to room temperature,of the Identification solution, the System suitability solution,rinse the condenser with 20mL of water, disconnect the the Standard solution, and the Test solution into the chro-flask from the condenser, and filter the mixture. Rinse the matograph, record the chromatograms, and measure the flask and the filter thoroughly, adding the rinsings to the peak area responses.filtrate. Add 5mL of glacial acetic acid, and titrate with 0.1Calculate the total percentage of iopamidol related com-N silver nitrate VS, determining the endpoint potentiometri-pound C and 2-chloro derivative in the portion of Iopamidol cally. Each mL of 0.1 N silver nitrate is equivalent to taken by the formula:25.90mg of C 17H 22I 3N 3O 8.100(C 1V/W )(r i /r S )in which C 1 is the concentration, in mg per mL, ofiopamidol related compound C in the Standard solution; V is the volume of the Test solution; W is the weight ofIopamidol InjectionIopamidol used to prepare the Test solution; r i is the total peak response for the iopamidol related compound C and » Iopamidol Injection is a sterile solution of2-chloro derivative obtained from the Test solution; and r S is Iopamidol in Water for Injection. It contains not the peak response for iopamidol related compound C ob-tained from the Standard solution.less than 95.0percent and not more than Calculate the total percentage of any other impurity in 105.0percent of the labeled amount ofthe portion of Iopamidol taken by the formula:iopamidol (C 17H 22I 3N 3O 8). It may contain small amounts of suitable buffers and of Edetate Cal-100(C 2V/W )(r i /r S )cium Disodium as a stabilizer. Iopamidol Injection in which C 2 is the concentration of iopamidol, in mg per intended for intravascular or intrathecal use con-mL, in the Standard solution; V and W are as previously de-tains no antimicrobial agents.fined; r i is the peak response for the individual impurity ob-tained from the Test solution; and r S is the peak response for Packaging and storage—Preserve Injection intended for iopamidol obtained from the Standard solution. In addition intravascular or intrathecal use in single-dose containers,to not exceeding the limits for each impurity shown in Table preferably of Type I glass, and protected from light.1, not more than 0.1% of any other individual impurity is Labeling—Label containers of Injection to direct the user to found; and not more than 0.20% of total impurities, other discard any unused portion remaining in the container andthan iopamidol related compound C and 2-chloro deriva-tive, is found.。

Atmer129

Food Contact Status ReportATMER 129The following food contact regulation references are applicable here:AustraliaIn the Australian standard AS 2070 on plastics materials for food contact use, it has been stated that new plastics produced in compliance with either the EC food contact regulations1 or the US food contact regulations2 are allowed for use in food contact applications in Australia. Reference should also be made to standard AS 2171 (Guide to manufacture of plastics items for food contact applications).I n addition, the use of plastics materials for food contact shall be in accordance with any relevant requirements set out in the Australia New Zealand Food Standards Code developed by ANZFA.EU generalATMER 129, as Glycerol Monostearate, are present on the incomplete list of additives, and the list of monomers allowed for use in the manufacture of materials and articles that come into contact with foods, No specific limitations are stated. (EC-Directive 2002/72/EC).For substances exempt from specific migration limits or other restrictions, a generic specific migration limit of 60 mg/kg or 10 mg/dm2 is applied. However the sum of all specific migration determined shall not exceed the overall migration limit.Except for the named joint regulations the following national food contact regulations are also applicable in the countries mentioned below:GermanyRecommendations BGVV ("Empfehlungen des Bundesinstitutes für gesundheitlichen Verbraucherschutz und Veterinärmedizin")Allowed like defined in EC regulations.- Allowed in mixed polymers containing PVC used in facilities that come into contact with drinking water (max. 3.0%).- Allowed in polyethylene used in facilities that come into contact with drinking water (max. 2%) (KTW-Empfehlungen).1The relevant European Commission directives for materials and articles intended tocome into contact with foodstuffs as set out by Commission Directives 89/109/EEC (Framework Directive) and 2002/72/EEC, 82/711/EEC and 85/572/EEC.2The relevant regulations of the United States of America Food and Drugs Administration as set out in the Code of Federal Regulations 21CFR Parts 170 to 199 and any subsequent amendments or revisions.JapanIn Japan there is no compulsory legislation but there are standards set by the associations for the PVC industry (JHPA) and the Oleofin and Styrene Plastics industry (JHOSPA) that have been generally recognised in Japan.United States of AmericaIn the Code of Federal Regulations 21: Food and Drugs the following references are applicable: §173.75 (c)(1) & (c)(5) Emulsifier in polymer dispersions for sugar refining (max. 7.5%)(max 0.70 ppm / 1.4 ppm in sugar juice or liqour produced resp.) §175.105 (c)(1) & (c)(5) Adhesivescoating.copolymerester§175.210 Acrylate§175.300 (b)(3)(ii)Resinous & Polymeric Coatings.§175.320 (b)(2) Resinous And Polymeric Coatings For Polyolefin Films§175.350 (d)(1) Vinyl Acetate/Crotonic Acid Copolymer§175.365 (c) Vinylidene Chloride Copolymer Coatings For PolycarbonateFilm.§175.380 (a) Xylene Formaldehyde Resins Condensed With 4,4'Isopropylidenediphenol Epichlorohydrin Epoxy Resins§175.390 (b)(1) Zinc-Silicon dioxide matrix coatings§176.170 (a)(1) & (4) Components Of Paper And Paperboard In Contact WithAqueous And Fatty Foods§176.170 (b)(1) Components Of Paper And Paperboard In Contact WithAqueous And Fatty Foods§176.180 (b)(1) Components Of Paper And Paperboard In Contact With DryFood§176.200 (d)(1) Defoaming Agents Used In Coatings§176.210 (d)(1) & (2) Defoaming Agents Used In The Manufacture Of Paper &Paperboard(d)Slimicides§176.300§177.1210 (a) & (b) (1) Closures With Sealing Gaskets For Food Containers§177.1240 (d)(1) & (3) 1,4-Cyclohexylene Dimethylene Terephthalate And 1,4-Cyclohexylene Dimethylene Isophthalate Copolymer§177.1320 (a)(1) (2) Ethylene-Ethyl Acrylate Copolymers§177.1345 (c) Ethylene/1,3-Phenylene Oxyethylene Isophthalate/TerephthalateCopolymercopolymers§177.1350 Ethylene-vinylacetate§177.1395 (b)(3) Laminate Structures For Use At Temperatures Between 120° F& 250° F.PolymersOlefin§177.1520(b)§177.1550 (b)(1) & (3) Perfluorocarbon Resins§177.1560PolyarylsulfoneResins(b)§177.1630 (e)(1) & (3) Polyethylene Phthalate Polymers§177.1632 (b)(1) Poly (Phenyleneterephthalamide) Resins§177.1635 (b) Poly (P-Methylstyrene) And Rubber Modified Poly ( P-Methylestyrene)§177.1640 (b) Polystyrene And Rubber Modified Polystyrene§177.1650 (a)(1) & (2) Polysulfide Polymer-Polyepoxy ResinsPolysulfoneResins(b)§177.1655§177.1970 (b) Vinyl Chloride-Lauryl Vinyl Ether Copolymers§177.1980 (b) Vinyl Chloride-Propylene Copolymers(USA 21CFR references, continued)§177.1990 (b) Vinylidene Chloride/Methyl Acrylate Copolymers§177.2000 (b) Vinylidene Chloride/Methyl Acrylate/Methyl MethacrylatePolymers§177.2400 (b)(1) & (3) Perfluorocarbon Cured Elastomers§177.2460 (b) Poly (2,6-Dimethyl-1,4-Phenylene) Oxide ResinsCopolymerPolyoxymethylene§177.2470(b)§177.2600 (c)(1) & (3) Rubber Articles Intended For Repeated UseAnimalGlue(c)§178.3120§178.3400 (c) Emulsifiers And/Or Surface Active Agents§184.1324 Generally Recognised as Safe (affirmed 21-2-1989)§582.1324 Generally Recognised as Safe for use as a miscellaneous and/orgeneral purpose food additive in animal feed§182.99 AdjuvantpesticidechemicalsforThe quantity of any food additive substance that may be added to food as a result of use in articles that contact food shall not exceed, where no limits are specified, that which results from use of the substance in an amount not more than reasonably required to accomplish the intended physical or technical effect in the food-contact article; shall not exceed any prescribed limitations; and shall not be intended to accomplish any physical or technical effect in the food itself, except as such may be permitted by regulations in parts 170 through 189 of this chapter.Version 3.0: Issued 30-10-2002 psra/lhdThe information and recommendations in this publication are to the best of our knowledge, information and belief accurateat the date of publication. Nothing herein is to be construed as a representation or a warranty, express or implied, as toany specific property, quality, use of condition of the Product. In all cases, it is the responsibility of the user to determinethe applicability of such information and recommendations, and the suitability of any products for their own particular purpose. All sales of these products shall be subject to ICI's standard Conditions of Sale.Uniqema is part of ICI Industrial Specialties, a business of ICI Chemicals & Polymers Ltd., registered in England No358535 Registered Office, The Heath, Runcorn, Cheshire, WA7 4QF. A subsidiary of ICI.。

MEAS-D-17-01640

MeasurementManuscript DraftManuscript Number: MEAS-D-17-01640Title: Identification of trace amounts of detergent powder in raw milk using a customized low-cost artificial olfactory systemArticle Type: Research PaperKeywords: Electronic nose; Detergent powder; Artificial olfactory system; MilkAbstract: One of the common concerns in quality assurance of raw milk is the use of antimicrobial agents for reducing the microbial population. For this purpose, different kinds of agents may be added to raw milk like detergents. In this study, an artificial olfactory machine (electronic nose) was developed based on eight metal oxide semiconductor sensors (MOS) and its ability to detect the presence of detergent powder in raw milk was investigated. Three features (area under the curve, relative response, and slope) were extracted from each sensor response and three baseline correction (differential, relative and fractional) techniques were also used. The multivariate analysis of variance (MANOVA), with regard to Wilk's λ and F values, showed that the feature of "area under the curve" along with differential baseline correction method is the best combination for distinguishing different levels of the adulteration in milk. Based on the results, analysis of main components with two PC1 and PC2 covered 91% of the data variance. The use of linear separation analysis for classification showed 69.7% accuracy as well. Also, from two types of support vector machines and four kernel functions, the support vector machine of nu-SVM type with kernel function of Radial Basis Function (RBF) had the best performance for identifying different levels of the adulteration in the milk. Furthermore, Adaptive Neuro-Fuzzy Inference System (ANFIS) method was also used for pattern recognition. Based on the data analysis, the results indicated the accuracy of 63.63% in the sample discrimination. It was concluded that the system is able to detect detergent powder in raw milk and can be applied as an effective, non-expensive and feasible method in dairy industry.Highlights (for review)*A customized low-cost electronic nose combined with pattern recognition methods was used toidentify different levels of detergent powder in raw milk.*Linear discriminant analysis (LDA), support vector machine (SVM) and Adaptive neuro-fuzzyinference system (ANFIS) were implemented.*The best combination of feature and baseline correction method was chosen based on theWilk’s λ and F values.*Good discrimination was found between the different levels of adulteration with pure milkexpect for the adulteration level of 0.03%.Graphical Abstract (for review)Identification of trace amounts of detergent powder in raw milk1 using a customized low-cost artificial olfactory system23Authors:45 Mojtaba Tohidi 1, Mahdi Ghasemi-Varnamkhasti 1*, Vahid Ghafarinia 2,6 Seyed Saeid Mohtasebi 3 , Mojtaba Bonyadian 478 1Department of Mechanical Engineering of Biosystems, Faculty of Agriculture , Shahrekord9 University, Shahrekord, Iran10 2Department of Electrical and Computer Engineering, Isfahan University of Technology,11 Isfahan, Iran12 3Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering13 and Technology, University of Tehran, Karaj, Iran14 4Department of Health and Food Quality Control, Faculty of Veterinary Medicine,15 Shahrekord University, Shahrekord, Iran1617 18 19 2021 22 23 24 25 26*Corresponding author: ghasemymahdi@ (Mahdi Ghasemi-Varnamkhasti)*ManuscriptClick here to download Manuscript: Manuscript-July.docxClick here to view linked References27Abstract28One of the common concerns in quality assurance of raw milk is the use 29of antimicrobial agents for reducing the microbial population. For this 30purpose, different kinds of agents may be added to raw milk likedetergents. In this study, an artificial olfactory machine (electronic nose) 3132was developed based on eight metal oxide semiconductor sensors (MOS) 33and its ability to detect the presence of detergent powder in raw milk wasinvestigated. Three features (area under the curve, relative response, and 3435slope) were extracted from each sensor response and three baseline 36correction (differential, relative and fractional) techniques were also 37used. The multivariate analysis of variance (MANOVA), with regard to 38Wilk’s λ and F values, showed that the feature of “area under the curve”39along with differential baseline correction method is the bestcombination for distinguishing different levels of the adulteration in 4041milk. Based on the results, analysis of main components with two PC1 42and PC2 covered 91% of the data variance. The use of linear separationanalysis for classification showed 69.7% accuracy as well. Also, from 4344two types of support vector machines and four kernel functions, the 45support vector machine of nu-SVM type with kernel function of Radial 46Basis Function (RBF) had the best performance for identifying different 47levels of the adulteration in the milk. Furthermore, Adaptive Neuro-48Fuzzy Inference System (ANFIS) method was also used for pattern 49recognition. Based on the data analysis, the results indicated the 50accuracy of 63.63% in the sample discrimination. It was concluded that 51the system is able to detect detergent powder in raw milk and can beapplied as an effective, non-expensive and feasible method in dairy 5253industry.54Keywords:Electronic nose; Detergent powder; Artificial olfactory55system; Milk561. Introduction5758Milk plays a significant role in the human nutrition due to its largeamounts of nutrients, such as carbohydrates, proteins, minerals and 5960vitamins. For this reason, global milk production and consumption has61increased [1]. The growing dairy market has always faced adulteration bydishonest producers and distributors. This issue is not only contrary to the 6263consumer protection law which has a negative economic impact, but can64also be a serious threat to the health of the community.65Different adulterations in milk are done due to various purposes such as66adding water in order to increase milk volume [2], vegetable oils to67increase on fat content [3, 4], mixing different animals milk [5], urea andwhey to change the protein content of milk [6, 7], melamine to increase the 6869content of milk nitrogen [8], neutralizing and inhibitor of microbial growth70in order to prevent milk spoilage and prolong shelf-life [9, 10].71Increasing milk microbial load due to inappropriate conditions of72production, storage and distribution could be led to an increase in milk73acidity which causes aging and loss of milk quality. Detergent powder is74one of the inhibitors of microbial growth that is added to raw milk as a75regulator or coating of milk acidity [11]. The use of detergents is harmful76to the human health and causes side effects such as indigestion, stomach77ulcers, imbalances in the absorption of organic matter such as calcium, 78phosphorus and iron, low blood pressure, abnormalities, anemia, dyspnea, 79kidney diseases and poisoning [12, 13].80General, adulteration in food and the hazards of it, especially when it is poisoned with chemical additives, is a major concern for food safety in the 8182most of the countries. Therefore, it is important to conduct regular 83assessments to control the quality of food. Various methods, such as physicochemical methods, gas chromatography (GC), and spectroscopic 8485techniques like mass spectrometry (MS), ultraviolet-visible 86spectroscopy(UV-Vis), and near-infrared spectroscopy (NIR), mid infrared 87spectroscopy (MIR), Fourier transforms of infrared spectroscopy(FT-IR) 88and Raman spectroscopy have been employed to evaluate the food quality 89and authenticity [14-21]. Most of these methods are costly and require specialist personnel and a rigorous sample preparation [22].9091Electronic nose is an innovative tool that can be used to characterize the 92odor of a foodstuff without separating and detecting its volatile compounds[23]. These tools are non-destructive and reliable and have the advantages 9394of easy-to-use, low-cost and high-speed [24]. In the last decade, the 95electronic nose has been successfully used in many works to recognize the 96authenticity of food or agricultural products including meat, honey, oils, 97saffron, juice, wine, coffee and pepper [25-36]. To the best of our 98knowledge, no research has so far been reported on the detection of 99detergent adulteration in raw milk using electronic nose. It means the 100novelty and originality behind of this research idea. Thus, the purpose of 101this study was to investigate the capability of an electronic nose based onmetal oxide gas sensors to detect the presence of detergent powder in raw 102103milk. Different pattern recognition methods were hence applied to sensor104responses to find the best chemometric solution.1051061072. Materials and Methods1082.1. Sample preparation109Fresh raw milk was obtained and kept at 4 °C. For each test, 50 ml of raw110milk was taken and stored in a special sampling container with a capacity111of 250 ml. Milk samples were mixed with different amounts of detergentpowder .The tested mixing ratios were 0, 0.03, 0.05, 0.1, 0.2 and 0.3 % by 112113weight. Also, 720 seconds before the beginning of the experiment with114electronic nose, the sampling chamber was placed in a water bath of 30 °Cto ensure uniformity of the test conditions and to achieve thermal 115116equilibrium [37]. This stabilization period was determined based on earlier117experiments.1181192.2. Experimental procedure120An artificial olfactory system based on eight metal oxide semiconductor121(MOS) gas sensors was designed and built in the laboratory. The system122was mainly composed of following parts: gas sensor array, sampling123vessel and sensory chamber, water bath, data acquisition board, patternrecognition systems, switching valves and connecting lines. The 124125schematic of the experimental set-up is shown in Fig. 1.a.126The sensor array was installed in a Teflon-based sensory chamber. Eight 127commercial gas sensors were including four metal oxide gas sensors of 128Figaro Engineering, Inc. (TGS2602, TGS2620, TGS813 and TGS822), 129three of HANWEI Electronics Co. (MQ3, MQ8 and MQ136), one of FISInc. (Sp3-aq2) and an integrated humidity and temperature sensor 130131(Sensirion SHT75 sensor). The sensor array is shown in Fig. 1.b. 132Additionally, the gas sensor names and their specifications are presentedin Table 1. The heater of all gas sensors were biased by 5 V according to 133134the recommended settings of manufacturers. Also the sensing resistors 135were connected to 5 V through a fixed load resistance. The voltage 136across this voltage divider was recorded as the sensor response.137The measurement process was sequenced in three phases: (1) odor 138concentration, (2) response measurement and (3) chamber purging. Inorder to avoid the effects of temperature variation on the sensor 139140responses, bottle of milk samples was placed in a water bath for 720s to 141fix its temperature at 30 °C. In the experiments pure oxygen gas wasused as the carrier and purging gas. The measurement procedure was 142143begun by cleaning the sensor chamber with a flow of carrier gas. To 144reach stable baseline values, the sensor array was exposed to oxygen for 145200 seconds. Then by switching the gas valves, the flow of oxygen was 146fed to the sampling bottle to carry the vapor of milk ingredients to the 147sensor chamber. The oxygen flow rate was set to 1.3 l/min to transfer 148sufficient milk headspace to the sensor chamber. The duration of this 149stage was 270 s. In the last stage, after recording the sensor responses, 150carrier gas was passed over the sensory chamber for 300 s in order torecover the sensor array. Therefore, the detection time for each sampling 151152cycle was 770 s (200 s for baseline purge, 270 s for sampling, and 300 s 153for cleaning the sensor chamber). All experiments in the laboratory were 154conducted at the temperature of 30 O C and 25–35% RH.155Sensors’output signals were recorded via data acquisition card (NI 156USB-6009, National Instruments Corporation, USA) with a samplingrate of 5 Hz, then these data were analyzed to extract useful information. 157158The data was used to create a database for training the artificial olfactory 159system. The database was organized in a matrix so that the rows andcolumns were the responses and the sensors, respectively.1601611622.3. Signal preparation and feature extraction163In the first step of signal processing, the responses were manipulated by 164their baseline values. This step was necessary for drift compensation, 165contrast enhancement and scaling. In this study, differential, relative andfractional techniques, as the most commonly used baseline correction 166167methods, were tested to find the best method in order to enhance the 168performance of the classification model [38].In differential method, the baseline value is deducted from the 169170dynamic response of the sensor in order to remove the additive 171noise or drift from the each original signal.Thus, the preprocessedresponse was obtained from Eq. (1).172173(1)174Relative method involves dividing the sensor response by the baseline. 175This method removes the effect of multiplicative drift , thus the 176dimensionless response can be achieved from Eq. (2).177(2)Fractional baseline correction method involves subtracting the baseline 178179from the sensor response and then dividing the result by the baseline Eq. 180(3).(3)181182Fractional measurements are not only dimensionless but also normalized 183since the resulting response is a per-unit change with respect to the 184baseline, which compensates for the sensors that have intrinsically large 185(or small) response levels.186In some cases, signals collected from the sensors have a little noise.Since noise may disrupt or reduce the performance of pattern recognition 187188techniques used for classification of pure milk and adulterated milk (in 189different levels), a moving average filter was used to reduce the noise.After baseline correction and noise reduction, feature extraction from 190191original response curves was performed. There are different types of 192feature extraction methods [39] that in this study three of them namely 193area under the curve, relative response, and slope were used. Trapezoidal 194approximation was used to calculate the area under the sensor response 195in measurement phase. Slope feature was obtained by calculating the 196difference between the maximum and minimum values of sensor 197response in measurement phase, then dividing it by the time. The 198difference between the maximum and minimum in sensor response was199divided by minimum of it, thus Relative response feature was calculated 200[39, 40].201Finally, in the last stage of preparation of signals, normalization is done. 202Normalization is used to achieve two objectives: first, this technique is 203used to compensate for sample-to-sample variations which may be 204caused due to sensor drift and the changes in analyte concentration. On 205the other hand, normalization acts on the entire database (for each 206sensor) and typically is used to compensate differences in sensor scaling.Equation (4) was used to normalize the data for each individual sensor in 207208the range (-1, 1), in which is the re sponse of sensor ‘‘s” to the k-th 209example in the database and is the normalized response of this sensor. 210Signal preparation and feature extraction was accomplished using 211Matlab software (The Mathworks Inc., Natick, MA, USA) and Microsoft 212Excel 2010.2132142152.4. Data analysis2162.4.1. Multivariate analysis of variance (MANOVA)Multivariate analysis of variance (MANOVA) is a procedure for 217218comparing multivariate sample means. MANOVA is similar to ANOVA, 219except that instead of one metric dependent variable, there are two ormore dependent variables [41]. In fact, the purpose of MANOVA is to 220221check the differences across multiple dependent variablessimultaneously. In this study, MANOVA was carried out on the data set 222223to test whether features obtained from the sensor response subjected to 224baseline correction methods(differential, relative and fractionaltechniques) can be used to make a distinction between non-adulterated 225226milk with adulterated milk (at different levels).Then the best 227combination of feature and baseline correction method was chosen based 228on the Wilk’s λ and F values.229An increase in F values or decrease in W ilk’s k value indicate s an 230increment in discrimination ability of the sensory array for distinguishing 231between unadulterated milk and different levels of adulterated milk. The 232MANOVA statistical test was performed using SPSS v21 software 233(International Business Machines Corporation, USA).2342.4.2. Principal component analysis (PCA)235236PCA is a linear unsupervised method that is often used as a first step of 237the data analysis to recognize the patterns in the database in amultivariate problem. This technique is a linear transformation of the 238239original variables into a new set of uncorrelated variables called 240Principal Components (PCs) with keeping most of the original 241information in the dataset [41]. In this study, this pattern recognition 242technique was used to visualize the differences or similarities between 243milk samples and reducing data dimensions from eight variables (eight 244sensors) to two or three PCs. The first PC describes the most variance in 245the data as possible and the second PCs explains the next largest 246variance in the data.2472482.4.3. Linear discriminant analysis (LDA)249LDA is a supervised technique that widely used for classification and 250feature extraction. This method models the difference between the 251classes of data, and 252tries to maximize the between-category variance and minimize the 253within-category variance via data projection from a high-dimensional 254space to a low-dimensional space. Thus it can be said, LDA is able to 255gather sensors information in order to improve the clarity of the classes. 2562572.4.4. Support vector machine (SVM)258Support vector machine is one of the supervised learning methods 259based on statistical learning theory that is used in both regression and 260classification issues. Although this technique initially was developed 261for linear classification of data, using kernel functions can also be used 262for non-linear data. Kernel functions map features for the originalspace to higher dimensional space and then provides linear algorithms 263264to work with higher dimensional feature space. SVM compared to 265some other classification methods such as neural networks, has lesstendency of over fitting [42].SVM has become popular nowadays and 266267is used in many studies for classification problems.268The Unscrambler 10.3 (CAMO AS, Trondheim, Norway) software was 269used for PCA, LDA and SVM analysis.2702712.4.5. Adaptive network-based fuzzy inference system (ANFIS)272Adaptive neuro-fuzzy inference system (ANFIS) is a combination of 273two computing methods of artificial neural network (ANN) and fuzzy 274inference system (FIS) that was first introduced by Jang [43] . In fact, 275the combination of ANNs learning capabilities with reasoning of fuzzy 276inference improves the capability of ANFIS [44]. Such framework 277makes the ANFIS modeling more systematic and less reliant on expert 278knowledge [45]. Without loss of generality, a summarized ANFIS 279model with two inputs x, y and one output z is explained the followingsentences. A common two fuzzy if-then rules as a first order Sugeno 280281model are expressed as [46]:282If x is A1 and y is B1, then Z1 = p1 x + q1 y+ r1283If x is A2 and y is B2, then Z2 = p2 x + q2 y+ r2284where x and y are the inputs, A i and B i are the fuzzy sets, Z i are the 285outputs, p i,q i, and r i are the design parameters that are determined 286during the training process. The general structure of this ANFIS model 287is shown in Fig.2. As seen, the ANFIS structure is composed of five 288layers that these layers with their associated nodes are described 289below.290Layer 1: in this layer, each node has a membership function. Theoutput of layer 1 is the fuzzy membership grade of the input, which are 291292given by:293where and are the degree of membership functions for the 294295fuzzy sets A i and B i that can be adopted with any fuzzy membership 296function such as Gaussian membership function, Bell-shapemembership function, Trapezoidal membership function and etc. For 297298example, if bell-shaped membership function is considered as the 299membership function, is given by300Where a i, b i and c i are the parameters of a Bell-shape membership 301function that can change the shape of the membership function and are 302known as premise parameters.303Layer 2, each node in this layer denotes the fuzzy inference rules. The 304T-norm operator with general performance, such as the AND, applies 305on the membership grades. The outputs are the so-called firingstrengths of the rules.306307308Layer 3, this layer is called as normalized layer. Nodes are also fixed 309and labeled with N. The outputs of this layer can be represented as 310Layer 4, each node in this layer is an adaptive node and the outputs of 311this layer are represented asThree modifiable parameters in this layer are referred to as 312313consequent parameters.Layer 5, in this layer is only single node that computes the final output as 314315the summation of all incoming signals (rules and firing strengths).316In this study, ANFIS modelling of detection of detergent powder in milkwas performed using MATLAB (R2013b, the MathWorks Inc., USA). 3173183193. Results and discussion3203.1. Signal smoothing and baseline correction321Signals generated by MOS sensors may have noise (Fig. 3.a). In order to 322remove the noise and improve the signals collected from the sensors, theMoving Average filter was applied to the raw signals (Fig. 3b). This 323324filter properly eliminates the signals noise and increases the signal-to-325noise ratio (SNR). Then, on the filtered data, three baseline correction 326methods including relative, differential and fractional were applied. The 327results are depicted in Figs. 4a-d for raw materials and three different 328baseline correction methods.3293303.2. MANOVA test331Using MANOVA statistical analysis, the separation capability of three 332different feature extraction methods was compared with three different 333baseline correction methods for pure milk and different levels of 334adulterated milk. Table 2 shows the values of this analysis. The 335contribution value of sensor s’set in the distinction between pure milk 336and different levels of adulterated milk was expressed based on p-value 337in the significance level of 0.05. Among nine combinations of baselinecorrection and feature extraction methods, most of these combinations 338339showed no significant difference between pure milk and adulterated milk 340for 0.03% level except three combinations of area and slope features 341with differential baseline correction method and Area feature along with 342relative baseline method. Based on Wilk's λ and F values, the 343combination of the Area feature with differential baseline correctionmethod and after that combination of Area feature with relative baseline 344345correction method is the best possible combination for the use in the 346separation of the different levels of the adulteration in milk. As shown inTable 2, the highest values of F (the lowest values of λ) were obtained 347348for Area-Differential combination of feature-baseline correction method. 349In this case, the most distinction between pure milk and different levels 350of adulteration in milk was achieved and hence was used for further 351analysis.3523533543.3. PCA and LDA results355The results of PCA analysis on the features set extracted from raw data 356selected by MANOVA analysis are shown in Fig.5. PCA score plots 357show information about patterns and relationship between different 358groups (different levels of adulteration in milk) in the main dataset. 359Fig.5.a. shows the first two main components that report 91% of 360variance in the dataset. As found, there is a clear distinction between 361pure milk and different levels of adulterated milk, except for the 362adulteration level of 0.03%, and it is graphically observed that indicator363points of the set and the points related to the pure milk set are 364overlapping. This may suggest that, in comparison to pure milk, the 365fingerprints of adulteration with a small amount of 0.03% has not 366caused very high changes in the sensors’responses that the PCAanalysis can distinguish this level of adulteration. But, since MANOVA 367368analysis showed the separation capability of this adulteration level, it is 369expected that the use of other classification and pattern recognitionmethods will have the ability to differentiate this level of adulteration 370371and therefore, this is evaluated using the LDA, SVM and ANFIS 372methods. In a similar work, Wang et al., (2010) employed an electronic 373nose including 18 MOS sensors for separation of three natural milk 374flavors and two synthetic flavorings and one enzyme induced milk 375flavoring. The results showed that the PCA method with a total ofvariance greater than 90% can distinguish between different groups 376377[47].378To illustrate the relationship between independent variables (sensors)with the main components, loading plots are also used. These plots are 379380generated by sensor coefficients in eigenvector equations. For each 381sensor, the high values of the loading on a PC indicate the greater 382contribution of the sensor with that PC [48].In Fig. 5.b, the loading 383values of these sensors are shown on PCs. As seen, there are two 384ellipses, the external ellipse and internal ellipse representing 100% and 38550% of the total variance of data, respectively. Therefore, two sensors of 386FIS, TGS822 have the lowest loading values and other sensors especially 387MQ8, TGS2602 sensors, have the highest loading coefficients and thus。

CODEX STAN 193-1995(CAC 标准食品和饲料中污染物和毒素通用标准 ,更新至2010)

1.2 1.2.1
DEFINITION OF TERMS General The definitions for the purpose of the Codex Alimentarius, as mentioned in the Procedural Manual, are applicable to the General Standard for Contaminants and Toxins in Food and Feed (GSCTFF) and only the most important ones are repeated here. Some new definitions are introduced, where this seems warranted to obtain optimal clarity. When reference is made to foods, this also applies to animal feed, in those cases where this is appropriate.
1
Processing aids are any substance or material, not including apparatus or utensils, and not consumed as a food ingredient by itself, intentionally used in the processing of raw materials, foods or its ingredients, to fulfil a certain technological purpose during treatment or processing and which may result in the non-intentional but unavoidable presence of residues or derivatives in the final product. Adopted 1995; Revised 1997, 2006, 2008, 2009; Amended 2009, 2010

NOM-121-SCT1-2009 (Mexico) - User Manual Requireme

Product Compliance Specialists Ltd Tel: +44 1844 273 277 The Malthouse, Malthouse Square, Fax: +44 1844 273 278 Princes Risborough Bucks, HP27 9AZ ************************************* United KingdomRegistered Office: 73 Southern Road, Thame, Oxon, OX9 2EDCompany Registration Number: 5101011 Registered in England and Wales Doc ref: RU1204005Regulatory UpdateMexicoFollowing publication of NOM-121-SCT1-2009 in Mexico (the Standard which introduced mandatory local testing for any 902 - 928MHz, 2.4GHz or 5725 – 5850MHz RF products) recent advice from PCS’s local contacts advises that COFETEL are now more strictly enforcing the User Manual requirements found in Section 4.6.1 and 4.6.2 of NOM-121-SCT1-2009.Accordingly, we advise that the following requirements should be adhered to with immediate effect:∙ The User Manual must be provided in Spanish language.∙ The User Manual must feature the following mandatory statement :“La operación de este equipo está sujeta a lassiguientes dos condiciones: (1) es posible que esteequipo o dispositivo no cause interferencia perjudicialy (2) este equipo o dispositivo debe aceptar cualquierinterferencia, incluyendo la que pueda causar suoperación no deseada.”The statement can be interpreted, as follows:The operation of this device is subject to the following two conditions:(1) this equipment or device must not cause harmful interference and(2) this equipment or device must accept any interference, includinginterference which could otherwise cause its undesired operation.Despite similarity to the FCC Regulatory statement (already included in most User Manuals for RF products, often in Spanish), COFETEL advisePage 2 of 2 Doc ref: RU1204005 re-using the FCC statement wording is not acceptable: the exact wording above (Spanish text) should be used.For additional information on the above article please contact:Jamie HarperProduct Compliance SpecialistsEmail: **************************************。

AOAC 991.43

Revised: March 1996 32.1.17AOAC Official Method 991.43Total, Soluble, and Insoluble Dietary Fiber in FoodsEnzymatic-Gravimetric Method, MES–TRIS BufferFirst Action 1991Final Action 1994Codex-Adopted—AOAC Method*(Applicable to processed foods, grain and cereal products, fruits, and vegetables.)Method Performance:See Table 991.43A for method performance data.A. PrincipleDuplicate samples of dried foods, fat-extracted if containing >10%fat, undergo sequential enzymatic digestion by heat stable α-amylase,protease, and amyloglycosidase to remove starch and protein. For total dietary fiber (TDF), enzyme digestate is treated with alcohol to precipi-tate soluble dietary fiber before filtering, and TDF residue is washed with alcohol and acetone, dried, and weighed. For insoluble and soluble dietary fiber (IDF and SDF), enzyme digestate is filtered, and residue (IDF) is washed with warm water, dried and weighed. For SDF,combined filtrate and washes are precipitated with alcohol, filtered,dried, and weighed. TDF, IDF, and SDF residue values are corrected for protein, ash, and blank.B. Apparatus(a ) Beakers.—400 or 600 mL tall-form.Table 991.43A Method Performance for Total, Soluble, and Insoluble Dietary Fiber in Foods (Fresh Weight Basis),Enzymatic-Gravimetric Method, MES-TRIS BufferFood Mean, g/100 g s r s R RSD r%RSD R % Barley 12.250.360.85 2.88 6.89High-fiber cereal 33.730.700.94 2.08 2.79 Oat bran 16.92 1.06 2.06 6.2612.17 Soy bran 67.14 1.01 1.06 1.50 1.58 Apricots 1.120.010.010.890.89 Prunes 9.290.130.40 1.404.31 Raisins 3.130.090.15 2.88 4.79 Carrots 3.930.130.13 3.31 3.31 Green beans 2.890.070.07 2.42 2.42 Parsley 2.660.070.14 2.635.26Soluble dietary fiber (SDF) Barley 5.020.400.628.0112.29 High-fiber cereal 2.780.440.5615.83 20.14 Oat bran 7.170.72 1.1410.04 15.90 Soy bran6.900.300.60 4.358.70 Apricots 0.530.020.02 3.77 3.77 Prunes 5.070.110.31 2.17 6.11 Raisins 0.730.050.16 6.8521.92 Carrots 1.100.070.18 6.3616.36 Green beans 1.020.080.117.8410.78 Parsley 0.640.030.10 4.6915.63 Insoluble dietary fiber (IDF) Barley 7.050.610.618.628.62 High-fiber cereal 30.520.440.71 1.44 2.33 Oat bran9.730.85 1.178.7412.02 Soy bran 60.530.700.70 1.16 1.16 Apricots 0.590.020.02 3.39 3.39 Prunes 4.170.070.09 1.68 2.16 Raisins 2.370.040.07 1.69 2.95 Carrots 2.810.090.16 3.20 5.69 Green beans 2.010.080.08 3.98 3.98 Parsley 2.370.120.24 5.0610.13 Total dietary fiber (SDF + IDF) Barley 12.140.390.70 3.21 5.77 High-fiber cereal 33.300.630.90 1.89 2.70 Oat bran 16.900.99 1.49 5.868.82 Soy bran 67.560.560.940.83 1.39 Apricots 1.120.020.02 1.79 1.79 Prunes 9.370.120.30 1.28 3.20 Raisins 3.100.050.18 1.61 5.81 Carrots 3.920.110.13 2.81 3.32 Green beans 3.030.090.12 2.97 3.96 Parsley 3.010.120.23 3.997.64(b ) Filtering crucible.—With fritted disk, coarse, ASTM 40–60µm pore size, Pyrex 60 mL (Corning No. 36060 Bchner, Corning,Inc., Science Products, Corning, NY 14831, USA, or equivalent).Prepare as follows. Ash overnight at 525° in muffle furnace. Let furnace temperature fall below 130° before removing crucibles.Soak crucibles 1 h in 2% cleaning solution at room temperature.Rinse crucibles with H 2O and then deionized H 2O; for final rinse,use 15 mL acetone and then air-dry. Add ca 1.0 g Celite to dry crucibles, and dry at 130° to constant weight. Cool crucible ca 1h in desiccator, and record weight, to nearest 0.1 mg, of crucible plus Celite.(c ) V acuum system.—V acuum pump or aspirator with regulating device. Heavy walled filtering flask, 1 L, with side arm. Rubber ring adaptors, for use with filtering flasks.(d ) Shaking water baths.—(1) Capable of maintaining 98 2°,with automatic on-and-off timer. (2) Constant temperature, adjust-able to 60°.(e ) Balance.—Analytical, sensitivity 0.1 mg.(f ) Muffle furnace.—Capable of maintaining 525 5°.(g ) Oven.—Capable of maintaining 105 and 130 3°.(h ) Desiccator .—With SiO 2 or equivalent desiccant. Biweekly,dry desiccant overnight at 130°.(i ) pH meter .—Temperature compensated, standardized with pH 4.0, 7.0, and 10.0 buffer solutions.(j ) Pipetters .—With disposable tips, 100–300 µL and 5 mL capacity.(k ) Dispensers.—Capable of dispensing 15 0.5 mL for 78%ethanol, 95% ethanol, and acetone; 40 0.5 mL for buffer.(l ) Magnetic stirrers and stir bars.C. ReagentsUse deionized water throughout.(a ) Ethanol solutions.—(1) 85%. Place 895 mL 95% ethanol into 1 L volumetric flask, dilute to volume with H 2O. (2) 78%. Place 821 mL 95% ethanol into 1 L volumetric flask, dilute to volume with H 2O.(b ) Heat-stable α-amylase solution.—Catalog Number A 3306,Sigma Chemical Co., St. Louis, MO 63178, USA, or Termamyl 300L, Catalog Number 361-6282, Novo-Nordisk, Bagsvaerd, Den-mark, or equivalent.(c ) Protease.—Catalog Number P 3910, Sigma Chemical Co, or equivalent. Prepare 50 mg/mL enzyme solution in MES/TRIS buff-er fresh daily.(d ) Amyloglucosidase solution .—Catalog Number AMG A9913,Sigma Chemical Co, or equivalent. Store at 0–5°.(e ) Diatomaceous earth.—Acid washed (Celite 545 AW, No.C8656, Sigma Chemical Co. or equivalent).(f ) Cleaning solution .—Liquid surfactant-type laboratory cleaner, designed for critical cleaning (Micro ®, International Prod-ucts Corp., Burlington, NJ 08601, USA, or equivalent). Prepare 2%solution in H 2O.(g ) MES .—2-(N -Morpholino)ethanesulfonic acid (No. M-8250,Sigma Chemical Co., or equivalent.)(h ) TRIS.—Tris(hydroxymethyl)aminomethane (No. T-1503,Sigma Chemical Co., or equivalent).(i ) MES–TRIS buffer solution.—0.05M MES, 0.05M TRIS, pH 8.2 at 24°. Dissolve 19.52 g MES and 12.2 g TRIS in 1.7 L H 2O.Adjust pH to 8.2 with 6N NaOH, and dilute to 2 L with H 2O.(Note: It is important to adjust pH to 8.2 at 24°. However, if buffer temperature is 20°, adjust pH to 8.3; if temperature is 28°,adjust pH to 8.1. For deviations between 20 and 28°, adjust by interpolation.)(j ) Hydrochloric acid solution.—0.561N . Add 93.5 mL 6N HCl to ca 700 mL H 2O in 1 L volumetric flask. Dilute to 1 L with H 2O.D. Enzyme PurityTo ensure absence of undesirable enzymatic activities and pres-ence of desirable enzymatic activities, run standards listed in Table 991.43B each time enzyme lot changes or at maximum interval of 6months.E. Sample Preparation and DigestionPrepare samples as in 985.29E (see 45.4.07) (if fat content of sample is unknown, defat before determining dietary fiber). For high sugar samples, desugar before determining dietary fiber by extract-ing 2–3 times with 85% ethanol, 10 mL/g, decanting, and then drying overnight at 40°.Run 2 blanks/assay with samples to measure any contribution from reagents to residue.Weigh duplicate 1.000 0.005 g samples (M 1 and M 2), accurate to 0.1 mg, into 400 mL (or 600 mL) tall-form beakers. Add 40 mL MES–TRIS buffer solution, pH 8.2, to each. Stir on magnetic stirrer until sample is completely dispersed (to prevent lump formation,which would make test material inaccessible to enzymes).Add 50 µL heat-stable α-amylase solution, stirring at low speed.Cover beakers with Al foil, and incubate in 95–100° H 2O bath 15min with continuous agitation. Start timing once bath temperature reaches 95° (total of 35 min is normally sufficient).Remove all beakers from bath, and cool to 60°. Remove foil.Scrape any ring from inside of beaker and disperse any gels in bottom of beaker with spatula. Rinse beaker walls and spatula with 10 mL H 2O.Add 100 µL protease solution to each beaker. Cover with Al foil,and incubate 30 min at 60 1° with continuous agitation. Start timing when bath temperature reaches 60°.Remove foil. Dispense 5 mL 0.561N HCl into beakers while stirring. Adjust pH to 4.0–4.7 at 60°, by adding 1N NaOH solution or 1N HCl solution. (Note: It is important to check and adjust pH while solutions are 60° because pH will increase at lower tempera-tures.) (Most cereal, grain, and vegetable products do not require pH adjustment. Once verified for each laboratory, pH checking procedure can be omitted. As a precaution, check pH of blank routinely; if outside desirable range, check samples also.)Add 300 µL amyloglucosidase solution while stirring. Cover with Al foil, and incubate 30 min at 60 1° with constant agitation. StartTable 991.43B Standards for Testing Enzyme Activity Standard Activity Tested Weight of Standard, gExpected Recovery, (%)Citrus pectin Pectinase 0.1–0.295–100 Arabinogalactan Hemicellulase 0.1–0.295–100β-Glucan β-Glucanase 0.1–0.295–100Wheat starch α-Amylase + AMG 1.0 0–1 Corn starch α-Amylase + AMG 1.00–1Casein Protease0.30–1timing once bath reaches 60°.F. Determination of Total Dietary FiberTo each digested sample, add 225 mL (measured after heating) 95% ethanol at 60°. Ratio of ethanol to sample volume should be 4:1. Remove from bath, and cover beakers with large sheets of Al foil. Let precipitate form 1 h at room temperature.Wet and redistribute Celite bed in previously tared crucible B(b), using 15 mL 78% ethanol from wash bottle. Apply suction to crucible to draw Celite onto fritted glass as even mat.Filter alcohol-treated enzyme digestate through crucible. Using wash bottle with 78% ethanol and rubber spatula, quantitatively transfer all remaining particles to crucible. (Note: If some samples form a gum, trapping the liquid, break film with spatula.)Using vacuum, wash residue 2 times each with 15 mL portions of 78% ethanol, 95% ethanol, and acetone. Dry crucible containing residue overnight in 105° oven. Cool crucible in desiccator ca 1 h. Weigh crucible, containing dietary fiber residue and Celite, to near-est 0.1 mg, and calculate residue weight by subtracting weight of dry crucible with Celite, B(b).Use one duplicate from each sample to determine protein, by method 960.52 (see 12.1.07), using N× 6.25 as conversion factor. For ash analysis, incinerate second duplicate 5 h at 525°. Cool in desiccator, and weigh to nearest 0.1 mg. Subtract weight of crucible and Celite, B(b), to determine ash weight.G. Determination of Insoluble Dietary FiberWet and redistribute Celite bed in previously tared crucible, B(b), using ca 3 mL H2O. Apply suction to crucible to draw Celite into even mat. Filter enzyme digestate, from E, through crucible into filtration flask. Rinse beaker, and then wash residue 2 times with 10 mL 70° H2O. Combine filtrate and water washings, transfer to pretared 600 mL tall-form beaker, and reserve for determination of soluble dietary fiber, H. Using vacuum, wash residue 2 times each with 15 mL portions of 78% ethanol, 95% ethanol, and acetone. (Note: Delay in washing IDF residues with 78% ethanol, 95% ethanol, and acetone may cause inflated IDF values.)Use duplicates to determine protein and ash as in F.H. Determination of Soluble Dietary FiberProceed as for insoluble dietary fiber determination through in-struction to combine the filtrate and water washings in pretared 600 mL tall-form beakers. Weigh beakers with combined solution of filtrate and water washings, and estimate volumes.Add 4 volumes of 95% ethanol preheated to 60°. Use portion of 60° ethanol to rinse filtering flask from IDF determination. Alterna-tively, adjust weight of combined solution of filtrate and water washings to 80 g by addition of H2O, and add 320 mL 60° 95% ethanol. Let precipitate form at room temperature 1 h.Follow TDF determination, F, from “Wet and redistribute Celite bed . . . .”I. CalculationsBlank (B, mg) determination:B = [(BR1 + BR2)/2] – P B – A Bwhere BR1 and BR2 = residue weights (mg) for duplicate blank determinations; and P B and A B = weights (mg) of protein and ash, respectively, determined on first and second blank residues. Dietary fiber (DF, g/100 g) determination:DF = {[(R1 + R2)/2] – P – A – B}/[(M1 + M2)/2] × 100 where R1 and R2 = residue weights (mg) for duplicate samples; P and A = weights (mg) of protein and ash, respectively, determined on first and second residues; B = blank weight (mg); and M1 and M2 = weights (mg) for samples.Total dietary fiber determination: Determine either by independent analysis, as in F, or by summing IDF and SDF, as in G and H. Reference: J. AOAC Int. 75, 395(1992).*Adopted as a Codex Defining Method for gravimetry/enzymatic di-gestion of total dietary fiber in infant formula and follow-up for-mula.。

超高效液相色谱-串联质谱法和胶体金快速定量法测定牛奶中黄曲霉毒素M1比较

2019年1月下摘　要：采用免疫亲和层析净化超高效液相色谱-串联质谱法和胶体金快速定量法测定牛奶中黄曲霉毒素M1含量，用这两种方法测定添加不同浓度黄曲霉毒素M1的牛奶，两种方法测定黄曲霉毒素M1牛奶样品的测定结果相对偏差小，相关性好。

胶体金快速定量法检测简便快速，能用于大批量样品的快速筛查。

关键词：牛奶；黄曲霉毒素M1；超高效液相色谱-串联质谱；胶体金快速定量1　前言黄曲霉毒素M1（AFM1）是由常见的黄曲霉菌和寄生曲霉菌产生的代谢产物，其物理化学性质稳定。

哺乳类动物摄入被AFB1污染的饲料后，在体内羟基衍生转化成AFM1。

AFM1的危害主要表现在致癌性和致突变性，对人及动物肝脏组织有破坏作用，可导致肝癌甚至死亡。

牛奶中的黄曲霉毒素主要来源于饲料。

黄曲霉毒素被世界卫生组织（WHO）的癌症研究机构划定为1类致癌物。

我国规定牛奶中黄曲霉毒素 M1含量不得超过0.5 μg/kg。

2　材料与方法2.1　材料与仪器PBS 清洗缓冲液（Charm公司）；AFM1、AFM1-13C17标准液（Romer公司）；AFM1免疫亲和柱（Romer公司）；玻璃纤维滤纸（Whatman公司）；ROSA黄曲霉毒素M1胶体金快速定量检测条（Charm公司，检测限0.5μg/kg）。

UPLC-Xevo TQ MS型超高效液相色谱-串联质谱仪（Waters公司）；孵育器、读数仪（Charm公司）。

2.2　方法2.2.1　免疫亲和层析净化超高效液相色谱-串联质谱法（1）样品前处理牛奶中的黄曲霉毒素M1含量测定一般按照GB 5009.24-2016《食品安全国家标准食品中黄曲霉毒素M族的测定》第一法同位素稀释液相色谱-串联质谱法进行测定。

（2）液相色谱条件色谱柱：CORTECS UPLC C18 Column，（1.6 μm，2.1 mm X 100 mm）；流动相A为乙腈；流动相B为0.2%甲酸水梯度洗脱；柱温为40℃；进样量为5μl。

Deformable Medical Image Registration

Deformable Medical Image Registration:A Survey Aristeidis Sotiras*,Member,IEEE,Christos Davatzikos,Senior Member,IEEE,and Nikos Paragios,Fellow,IEEE(Invited Paper)Abstract—Deformable image registration is a fundamental task in medical image processing.Among its most important applica-tions,one may cite:1)multi-modality fusion,where information acquired by different imaging devices or protocols is fused to fa-cilitate diagnosis and treatment planning;2)longitudinal studies, where temporal structural or anatomical changes are investigated; and3)population modeling and statistical atlases used to study normal anatomical variability.In this paper,we attempt to give an overview of deformable registration methods,putting emphasis on the most recent advances in the domain.Additional emphasis has been given to techniques applied to medical images.In order to study image registration methods in depth,their main compo-nents are identiﬁed and studied independently.The most recent techniques are presented in a systematic fashion.The contribution of this paper is to provide an extensive account of registration tech-niques in a systematic manner.Index Terms—Bibliographical review,deformable registration, medical image analysis.I.I NTRODUCTIOND EFORMABLE registration[1]–[10]has been,alongwith organ segmentation,one of the main challenges in modern medical image analysis.The process consists of establishing spatial correspondences between different image acquisitions.The term deformable(as opposed to linear or global)is used to denote the fact that the observed signals are associated through a nonlinear dense transformation,or a spatially varying deformation model.In general,registration can be performed on two or more im-ages.In this paper,we focus on registration methods that involve two images.One is usually referred to as the source or moving image,while the other is referred to as the target orﬁxed image. In this paper,the source image is denoted by,while the targetManuscript received March02,2013;revised May17,2013;accepted May 21,2013.Date of publication May31,2013;date of current version June26, 2013.Asterisk indicates corresponding author.*A.Sotiras is with the Section of Biomedical Image Analysis,Center for Biomedical Image Computing and Analytics,Department of Radi-ology,University of Pennsylvania,Philadelphia,PA19104USA(e-mail: aristieidis.sotiras@).C.Davatzikos is with the Section of Biomedical Image Analysis,Center for Biomedical Image Computing and Analytics,Department of Radi-ology,University of Pennsylvania,Philadelphia,PA19104USA(e-mail: christos.davatzikos@).N.Paragios is with the Center for Visual Computing,Department of Applied Mathematics,Ecole Centrale de Paris,92295Chatenay-Malabry,France,and with the Equipe Galen,INRIA Saclay-Ile-de-France,91893Orsay,France,and also with the Universite Paris-Est,LIGM(UMR CNRS),Center for Visual Com-puting,Ecole des Ponts ParisTech,77455Champs-sur-Marne,France. Digital Object Identiﬁer10.1109/TMI.2013.2265603image is denoted by.The two images are deﬁned in the image domain and are related by a transformation.The goal of registration is to estimate the optimal transforma-tion that optimizes an energy of the form(1) The previous objective function(1)comprises two terms.The ﬁrst term,,quantiﬁes the level of alignment between a target image and a source image.Throughout this paper,we in-terchangeably refer to this term as matching criterion,(dis)sim-ilarity criterion or distance measure.The optimization problem consists of either maximizing or minimizing the objective func-tion depending on how the matching term is chosen.The images get aligned under the inﬂuence of transformation .The transformation is a mapping function of the domain to itself,that maps point locations to other locations.In gen-eral,the transformation is assumed to map homologous loca-tions from the target physiology to the source physiology.The transformation at every position is given as the addition of an identity transformation with the displacementﬁeld,or.The second term,,regularizes the trans-formation aiming to favor any speciﬁc properties in the solution that the user requires,and seeks to tackle the difﬁculty associ-ated with the ill-posedness of the problem.Regularization and deformation models are closely related. Two main aspects of this relation may be distinguished.First, in the case that the transformation is parametrized by a small number of variables and is inherently smooth,regularization may serve to introduce prior knowledge regarding the solution that we seek by imposing task-speciﬁc constraints on the trans-formation.Second,in the case that we seek the displacement of every image element(i.e.,nonparametric deformation model), regularization dictates the nature of the transformation. Thus,an image registration algorithm involves three main components:1)a deformation model,2)an objective function, and3)an optimization method.The result of the registration algorithm naturally depends on the deformation model and the objective function.The dependency of the registration result on the optimization strategy follows from the fact that image regis-tration is inherently ill-posed.Devising each component so that the requirements of the registration algorithm are met is a de-manding process.Depending on the deformation model and the input data,the problem may be ill-posed according to Hadamard’s deﬁnition of well-posed problems[11].In probably all realistic scenarios, registration is ill-posed.To further elaborate,let us consider some speciﬁc cases.In a deformable registration scenario,one0278-0062/$31.00©2013IEEEseeks to estimate a vector for every position given,in general, scalar information conveyed by image intensity.In this case,the number of unknowns is greater than the number of constraints. In a rigid setting,let us consider a consider a scenario where two images of a disk(white background,gray foreground)are registered.Despite the fact that the number of parameters is only 6,the problem is ill-posed.The problem has no unique solution since a translation that aligns the centers of the disks followed by any rotation results in a meaningful solution.Given nonlinear and nonconvex objective functions,in gen-eral,no closed-form solutions exist to estimate the registration parameters.In this setting,the search methods reach only a local minimum in the parameter space.Moreover,the problem itself has an enormous number of different facets.The approach that one should take depends on the anatomical properties of the organ(for example,the heart and liver do not adhere to the same degree of deformation),the nature of observations to be regis-tered(same modality versus multi-modal fusion),the clinical setting in which registration is to be used(e.g.,ofﬂine interpre-tation versus computer assisted surgery).An enormous amount of research has been dedicated to de-formable registration towards tackling these challenges due to its potential clinical impact.During the past few decades,many innovative ideas regarding the three main algorithmic registra-tion aspects have been proposed.General reviews of theﬁeld may be found in[1]–[7],[9].However due to the rapid progress of theﬁeld such reviews are to a certain extent outdated.The aim of this paper is to provide a thorough overview of the advances of the past decade in deformable registration.Never-theless,some classic papers that have greatly advanced the ideas in theﬁeld are mentioned.Even though our primary interest is deformable registration,for the completeness of the presenta-tion,references to linear methods are included as many prob-lems have been treated in this low-degree-of-freedom setting before being extended to the deformable case.The main scope of this paper is focused on applications that seek to establish spatial correspondences between medical im-ages.Nonetheless,we have extended the scope to cover appli-cations where the interest is to recover the apparent motion of objects between sequences of successive images(opticalﬂow estimation)[12],[13].Deformable registration and opticalﬂow estimation are closely related problems.Both problems aim to establish correspondences between images.In the deformable registration case,spatial correspondences are sought,while in the opticalﬂow case,spatial correspondences,that are associ-ated with different time points,are looked for.Given data with a good temporal resolution,one may assume that the magnitude of the motion is limited and that image intensity is preserved in time,opticalﬂow estimation can be regarded as a small defor-mation mono-modal deformable registration problem.The remainder of the paper is organized by loosely following the structural separation of registration algorithms to three com-ponents:1)deformation model,2)matching criteria,and3)op-timization method.In Section II,different approaches regarding the deformation model are presented.Moreover,we also chose to cover in this section the second term of the objective function, the regularization term.This choice was motivated by the close relation between the two parts.In Section III,theﬁrst term of the objective function,the matching term,is discussed.The opti-mization methods are presented in Section IV.In every section, particular emphasis was put on further deepening the taxonomy of registration method by grouping the presented methods in a systematic manner.Section V concludes the paper.II.D EFORMATION M ODELSThe choice of deformation model is of great importance for the registration process as it entails an important compromise between computational efﬁciency and richness of description. It also reﬂects the class of transformations that are desirable or acceptable,and therefore limits the solution to a large ex-tent.The parameters that registration estimates through the op-timization strategy correspond to the degrees of freedom of the deformation model1.Their number varies greatly,from six in the case of global rigid transformations,to millions when non-parametric dense transformations are considered.Increasing the dimensionality of the state space results in enriching the de-scriptive power of the model.This model enrichment may be accompanied by an increase in the model’s complexity which, in turns,results in a more challenging and computationally de-manding inference.Furthermore,the choice of the deformation model implies an assumption regarding the nature of the defor-mation to be recovered.Before continuing,let us clarify an important,from imple-mentation point of view,aspect related to the transformation mapping and the deformation of the source image.In the in-troduction,we stated that the transformation is assumed to map homologous locations from the target physiology to the source physiology(backward mapping).While from a theoretical point of view,the mapping from the source physiology to the target physiology is possible(forward mapping),from an implemen-tation point of view,this mapping is less advantageous.In order to better understand the previous statement,let us consider how the direction of the mapping inﬂuences the esti-mation of the deformed image.In both cases,the source image is warped to the target domain through interpolation resulting to a deformed image.When the forward mapping is estimated, every voxel of the source image is pushed forward to its esti-mated position in the deformed image.On the other hand,when the backward mapping is estimated,the pixel value of a voxel in the deformed image is pulled from the source image.The difference between the two schemes is in the difﬁculty of the interpolation problem that has to be solved.In theﬁrst case,a scattered data interpolation problem needs to be solved because the voxel locations of the source image are usually mapped to nonvoxel locations,and the intensity values of the voxels of the deformed image have to be calculated.In the second case,when voxel locations of the deformed image are mapped to nonvoxel locations in the source image,their intensities can be easily cal-culated by interpolating the intensity values of the neighboring voxels.The rest of the section is organized by following coarsely and extending the classiﬁcation of deformation models given 1Variational approaches in general attempt to determine a function,not just a set of parameters.SOTIRAS et al.:DEFORMABLE MEDICAL IMAGE REGISTRATION:A SURVEY1155Fig.1.Classi ﬁcation of deformation models.Models that satisfy task-speci ﬁc constraints are not shown as a branch of the tree because they are,in general,used in conjunction with physics-based and interpolation-based models.by Holden [14].More emphasis is put on aspects that were not covered by that review.Geometric transformations can be classi ﬁed into three main categories (see Fig.1):1)those that are inspired by physical models,2)those inspired by interpolation and ap-proximation theory,3)knowledge-based deformation models that opt to introduce speci ﬁc prior information regarding the sought deformation,and 4)models that satisfy a task-speci ﬁc constraint.Of great importance for biomedical applications are the con-straints that may be applied to the transformation such that it exhibits special properties.Such properties include,but are not limited to,inverse consistency,symmetry,topology preserva-tion,diffeomorphism.The value of these properties was made apparent to the research community and were gradually intro-duced as extra constraints.Despite common intuition,the majority of the existing regis-tration algorithms are asymmetric.As a consequence,when in-terchanging the order of input images,the registration algorithm does not estimate the inverse transformation.As a consequence,the statistical analysis that follows registration is biased on the choice of the target domain.Inverse Consistency:Inverse consistent methods aim to tackle this shortcoming by simultaneously estimating both the forward and the backward transformation.The data matching term quanti ﬁes how well the images are aligned when one image is deformed by the forward transformation,and the other image by the backward transformation.Additionally,inverse consistent algorithms constrain the forward and backward transformations to be inverse mappings of one another.This is achieved by introducing terms that penalize the difference between the forward and backward transformations from the respective inverse mappings.Inverse consistent methods can preserve topology but are only asymptotically symmetric.Inverse-consistency can be violated if another term of the objective function is weighted more importantly.Symmetry:Symmetric algorithms also aim to cope with asymmetry.These methods do not explicitly penalize asym-metry,but instead employ one of the following two strategies.In the ﬁrst case,they employ objective functions that are by construction symmetric to estimate the transformation from one image to another.In the second case,two transformation functions are estimated by optimizing a standard objective function.Each transformation function map an image to a common domain.The ﬁnal mapping from one image to another is calculated by inverting one transformation function and composing it with the other.Topology Preservation:The transformation that is estimated by registration algorithms is not always one-to-one and cross-ings may appear in the deformation ﬁeld.Topology preserving/homeomorphic algorithms produce a mapping that is contin-uous,onto,and locally one-to-one and has a continuous inverse.The Jacobian determinant contains information regarding the injectivity of the mapping and is greater than zero for topology preserving mappings.The differentiability of the transformation needs to be ensured in order to calculate the Jacobian determi-nant.Let us note that Jacobian determinant and Jacobian are in-terchangeably used in this paper and should not be confounded with the Jacobian matrix.Diffeomorphism:Diffeomoprhic transformations also pre-serve topology.A transformation function is a diffeomorphism,if it is invertible and both the function and its inverse are differ-entiable.A diffeomorphism maps a differentiable manifold to another.1156IEEE TRANSACTIONS ON MEDICAL IMAGING,VOL.32,NO.7,JULY2013In the following four subsections,the most important methods of the four classes are presented with emphasis on the approaches that endow the model under consideration with the above desirable properties.A.Geometric Transformations Derived From Physical Models Following[5],currently employed physical models can be further separated inﬁve categories(see Fig.1):1)elastic body models,2)viscousﬂuidﬂow models,3)diffusion models,4) curvature registration,and5)ﬂows of diffeomorphisms.1)Elastic Body Models:a)Linear Models:In this case,the image under deforma-tion is modeled as an elastic body.The Navier-Cauchy Partial Differential Equation(PDE)describes the deformation,or(2) where is the forceﬁeld that drives the registration based on an image matching criterion,refers to the rigidity that quanti-ﬁes the stiffness of the material and is Lamésﬁrst coefﬁcient. Broit[15]ﬁrst proposed to model an image grid as an elastic membrane that is deformed under the inﬂuence of two forces that compete until equilibrium is reached.An external force tries to deform the image such that matching is achieved while an internal one enforces the elastic properties of the material. Bajcsy and Kovacic[16]extended this approach in a hierar-chical fashion where the solution of the coarsest scale is up-sam-pled and used to initialize theﬁner one.Linear registration was used at the lowest resolution.Gee and Bajscy[17]formulated the elastostatic problem in a variational setting.The problem was solved under the Bayesian paradigm allowing for the computation of the uncertainty of the solution as well as for conﬁdence intervals.Theﬁnite element method(FEM)was used to infer the displacements for the ele-ment nodes,while an interpolation strategy was employed to es-timate displacements elsewhere.The order of the interpolating or shape functions,determines the smoothness of the obtained result.Linear elastic models have also been used when registering brain images based on sparse correspondences.Davatzikos[18]ﬁrst used geometric characteristics to establish a mapping be-tween the cortical surfaces.Then,a global transformation was estimated by modeling the images as inhomogeneous elastic ob-jects.Spatially-varying elasticity parameters were used to com-pensate for the fact that certain structures tend to deform more than others.In addition,a nonzero initial strain was considered so that some structures expand or contract naturally.In general,an important drawback of registration is that when source and target volumes are interchanged,the obtained trans-formation is not the inverse of the previous solution.In order to tackle this shortcoming,Christensen and Johnson[19]pro-posed to simultaneously estimate both forward and backward transformations,while penalizing inconsistent transformations by adding a constraint to the objective function.Linear elasticity was used as regularization constraint and Fourier series were used to parametrize the transformation.Leow et al.[20]took a different approach to tackle the incon-sistency problem.Instead of adding a constraint that penalizes the inconsistency error,they proposed a unidirectional approach that couples the forward and backward transformation and pro-vides inverse consistent transformations by construction.The coupling was performed by modeling the backward transforma-tion as the inverse of the forward.This fact was also exploited during the optimization of the symmetric energy by only fol-lowing the gradient direction of the forward mapping.He and Christensen[21]proposed to tackle large deforma-tions in an inverse consistent framework by considering a se-quence of small deformation transformations,each modeled by a linear elastic model.The problem was symmetrized by consid-ering a periodic sequence of images where theﬁrst(or last)and middle image are the source and target respectively.The sym-metric objective function thus comprised terms that quantify the difference between any two successive pairs of images.The in-ferred incremental transformation maps were concatenated to map one input image to another.b)Nonlinear Models:An important limitation of linear elastic models lies in their inability to cope with large defor-mations.In order to account for large deformations,nonlinear elastic models have been proposed.These models also guar-antee the preservation of topology.Rabbitt et al.[22]modeled the deformable image based on hyperelastic material properties.The solution of the nonlinear equations was achieved by local linearization and the use of the Finite Element method.Pennec et al.[23]dropped the linearity assumption by mod-eling the deformation process through the St Venant-Kirchoff elasticity energy that extends the linear elastic model to the non-linear regime.Moreover,the use of log-Euclidean metrics in-stead of Euclidean ones resulted in a Riemannian elasticity en-ergy which is inverse consistent.Yanovsky et al.[24]proposed a symmetric registration framework based on the St Venant-Kir-choff elasticity.An auxiliary variable was added to decouple the regularization and the matching term.Symmetry was im-posed by assuming that the Jacobian determinants of the defor-mation follow a zero mean,after log-transformation,log-normal distribution[25].Droske and Rumpf[26]used an hyperelastic,polyconvex regularization term that takes into account the length,area and volume deformations.Le Guyader and Vese[27]presented an approach that combines segmentation and registration that is based on nonlinear elasticity.The authors used a polyconvex regularization energy based on the modeling of the images under deformation as Ciarlet-Geymonat materials[28].Burger et al.[29]also used a polyconvex regularization term.The au-thors focused on the numerical implementation of the registra-tion framework.They employed a discretize-then-optimize ap-proach[9]that involved the partitioning voxels to24tetrahedra.2)Viscous Fluid Flow Models:In this case,the image under deformation is modeled as a viscousﬂuid.The transformation is governed by the Navier-Stokes equation that is simpliﬁed by assuming a very low Reynold’s numberﬂow(3) These models do not assume small deformations,and thus are able to recover large deformations[30].Theﬁrst term of theSOTIRAS et al.:DEFORMABLE MEDICAL IMAGE REGISTRATION:A SURVEY1157Navier-Stokes equation(3),constrains neighboring points to de-form similarly by spatially smoothing the velocityﬁeld.The velocityﬁeld is related to the displacementﬁeld as.The velocityﬁeld is integrated in order to estimate the displacementﬁeld.The second term al-lows structures to change in mass while and are the vis-cosity coefﬁcients.Christensen et al.[30]modeled the image under deformation as a viscousﬂuid allowing for large magnitude nonlinear defor-mations.The PDE was solved for small time intervals and the complete solution was given by an integration over time.For each time interval a successive over-relaxation(SOR)scheme was used.To guarantee the preservation of topology,the Jaco-bian was monitored and each time its value fell under0.5,the deformed image was regridded and a new one was generated to estimate a transformation.Theﬁnal solution was the con-catenation of all successive transformations occurring for each regridding step.In a subsequent work,Christensen et al.[31] presented a hierarchical way to recover the transformations for brain anatomy.Initially,global afﬁne transformation was per-formed followed by a landmark transformation model.The re-sult was reﬁned byﬂuid transformation preceded by an elastic registration step.An important drawback of the earliest implementations of the viscousﬂuid models,that employed SOR to solve the equa-tions,was computational inefﬁciency.To circumvent this short-coming,Christensen et al.employed a massive parallel com-puter implementation in[30].Bro-Nielsen and Gramkow[32] proposed a technique based on a convolutionﬁlter in scale-space.Theﬁlter was designed as the impulse response of the linear operator deﬁned in its eigen-function basis.Crun et al.[33]proposed a multi-grid approach towards handling anisotropic data along with a multi-resolution scheme opting forﬁrst recovering coarse velocity es-timations and reﬁning them in a subsequent step.Cahill et al.[34]showed how to use Fourier methods to efﬁciently solve the linear PDE system that arises from(3)for any boundary condi-tion.Furthermore,Cahill et al.extended their analysis to show how these methods can be applied in the case of other regu-larizers(diffusion,curvature and elastic)under Dirichlet,Neu-mann,or periodic boundary conditions.Wang and Staib[35]usedﬂuid deformation models in an atlas-enhanced registration setting while D’Agostino et al. tackled multi-modal registration with the use of such models in[36].More recently,Chiang et al.[37]proposed an inverse consistent variant ofﬂuid registration to register Diffusion Tensor images.Symmetrized Kullback-Leibler(KL)diver-gence was used as the matching criterion.Inverse consistency was achieved by evaluating the matching and regularization criteria towards both directions.3)Diffusion Models:In this case,the deformation is mod-eled by the diffusion equation(4) Let us note that most of the algorithms,based on this transforma-tion model and described in this section,do not explicitly state the(4)in their objective function.Nonetheless,they exploit the fact that the Gaussian kernel is the Green’s function of the diffu-sion equation(4)(under appropriate initial and boundary condi-tions)to provide an efﬁcient regularization step.Regularization is efﬁciently performed through convolutions with a Gaussian kernel.Thirion,inspired by Maxwell’s Demons,proposed to perform image matching as a diffusion process[38].The proposed algo-rithm iterated between two steps:1)estimation of the demon forces for every demon(more precisely,the result of the appli-cation of a force during one iteration step,that is a displace-ment),and2)update of the transformation based on the cal-culated forces.Depending on the way the demon positions are selected,the way the space of deformations is deﬁned,the in-terpolation method that is used,and the way the demon forces are calculated,different variants can be obtained.The most suit-able version for medical image analysis involved1)selecting all image elements as demons,2)calculating demon forces by considering the opticalﬂow constraint,3)assuming a nonpara-metric deformation model that was regularized by applying a Gaussianﬁlter after each iteration,and4)a trilinear interpo-lation scheme.The Gaussianﬁlter can be applied either to the displacementﬁeld estimated at an iteration or the updated total displacementﬁeld.The bijectivity of the transformation was en-sured by calculating for every point the difference between its initial position and the one that is reached after composing the forward with the backward deformationﬁeld,and redistributing the difference to eachﬁeld.The bijectivity of the transformation can also be enforced by limiting the maximum length of the up-date displacement to half the voxel size and using composition to update the transformation.Variants for the contour-based reg-istration and the registration between segmented images were also described in[38].Most of the algorithms described in this section were inspired by the work of Thirion[38]and thus could alternatively be clas-siﬁed as“Demons approaches.”These methods share the iter-ative approach that was presented in[38]that is,iterating be-tween estimating the displacements and regularizing to obtain the transformation.This iterative approach results in increased computational efﬁciency.As it will be discussed later in this section,this feature led researchers to explore such strategies for different PDEs.The use of Demons,as initially introduced,was an efﬁcient algorithm able to provide dense correspondences but lacked a sound theoretical justiﬁcation.Due to the success of the algo-rithm,a number of papers tried to give theoretical insight into its workings.Fischer and Modersitzki[39]provided a fast algo-rithm for image registration.The result was given as the solution of linear system that results from the linearization of the diffu-sion PDE.An efﬁcient scheme for its solution was proposed while a connection to the Thirion’s Demons algorithm[38]was drawn.Pennec et al.[40]studied image registration as an energy minimization problem and drew the connection of the Demons algorithm with gradient descent schemes.Thirion’s image force based on opticalﬂow was shown to be equivalent with a second order gradient descent on the Sum of Square Differences(SSD) matching criterion.As for the regularization,it was shown that the convolution of the global transformation with a Gaussian。

The PRISMA Guideline

BMJ | onlineresearch methods& reportingintroductionSystematic reviews and meta-analyses are essential tools for summarising evidence accurately and reliably. They help clinicians keep up to date; provide evidence for policy makers to judge risks, benefits, and harms of healthcare behaviours and interventions; gather together and summarise related research for patients and their carers; provide a starting point for clinical practice guideline developers; provide summaries of previous research for funders wishing to support new research;1 and help editors judge the merits of publishing reports of new studies.2 Recent data suggest that at least 2500 new systematic reviews reported in English are indexed in Medline annually.3Unfortunately, there is considerable evidence that key information is often poorly reported in systematic reviews, thus diminishing their potential usefulness.3-6 As is true for all research, systematic reviews should be reported fully and transparently to allow readers to assess the strengths and weaknesses of the investigation.7 That rationale led to the development of the QUOROM (quality of reporting of meta-analysis) statement; those detailed reporting recommendations were published in 1999.8 In this paper we describe the updating of that guidance. Our aim is to ensure clear presentation of what was planned, done, and found in a systematic review.T erminology used to describe systematic reviews and meta-analyses has evolved over time and varies across different groups of researchers and authors (see box 1 at end of document). In this document we adopt the defini-tions used by the Cochrane Collaboration.9 A systematic review attempts to collate all empirical evidence that fits pre-specified eligibility criteria to answer a specific research question. It uses explicit, systematic methods that are selected to minimise bias, thus providing reliable findings from which conclusions can be drawn and deci-sions made. Meta-analysis is the use of statistical methods to summarise and combine the results of independent studies. Many systematic reviews contain meta-analyses, but not all.the QUorom statement and its evolution into prisma The QUOROM statement, developed in 1996 and published in 1999,8 was conceived as a reportingguidance for authors reporting a meta-analysis of ran-domised trials. Since then, much has happened. First, knowledge about the conduct and reporting of system-atic reviews has expanded considerably. For example, the Cochrane Library’s Methodology Register (which includes reports of studies relevant to the methods for systematic reviews) now contains more than 11 000 entries (March 2009). Second, there have been many conceptual advances, such as “outcome-level” assess-ments of the risk of bias,10 11 that apply to systematic reviews. Third, authors have increasingly used system-atic reviews to summarise evidence other than that pro-vided by randomised trials.However, despite advances, the quality of the con-duct and reporting of systematic reviews remains well short of ideal.3-6 All of these issues prompted the need for an update and expansion of the QUOROM state-ment. Of note, recognising that the updated statement now addresses the above conceptual and methodo-logical issues and may also have broader applicability than the original QUOROM statement, we changed the name of the reporting guidance to PRISMA (pre-ferred reporting items for systematic reviews and meta-analyses).development of prismaThe PRISMA statement was developed by a group of 29 review authors, methodologists, clinicians, medi-cal editors, and consumers.12 They attended a three day meeting in 2005 and participated in extensive post-meeting electronic correspondence. A consensus process that was informed by evidence, whenever pos-sible, was used to develop a 27-item checklist (table 1) and a four-phase flow diagram (fig 1) (also available as extra items on for researchers to down-load and re-use). Items deemed essential for transpar-ent reporting of a systematic review were included in the checklist. The flow diagram originally proposed by QUOROM was also modified to show numbers of identified records, excluded articles, and included stud-ies. After 11 revisions the group approved the checklist, flow diagram, and this explanatory paper.The PRISMA statement itself provides further details regarding its background and development.12ThisEmilia, Modena, Italy 2Centro Cochrane Italiano, Istituto Ricerche Farmacologiche Mario Negri, Milan, Italy 3Centre for Statistics in Medicine, University of Oxford, Oxford Hospital Research Institute, Ottawa, Ontario, Canada 5Annals of Internal Medicine, Philadelphia, Pennsylvania, USA 6Nordic Cochrane Centre, Copenhagen, Denmark Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece 8UK Cochrane Centre, Oxford 9School of Nursing and Midwifery, Trinity College, Dublin, Republic of Ireland 10Departments of Medicine, Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada Kleijnen Systematic Reviews, York Primary Care (CAPHRI), University of Maastricht, Maastricht, Netherlands 13Department of Epidemiology and Community Medicine, Faculty of Medicine, Ottawa, Ontario, Canada Correspondence to: alesslib@mailbase.itAccepted: 5 June 2009Cite this as: BMJ 2009;339:b2700doi: 10.1136/bmj.b2700The PRiSMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaborationAlessandro Liberati,1 2 Douglas G Altman,3 Jennifer Tetzlaff,4 Cynthia Mulrow,5 Peter C Gøtzsche,6 John P A Ioannidis,7 Mike Clarke,8 9 P J Devereaux,10 Jos Kleijnen,11 12 David Moher 4 13research methods & reportingaccompanying explanation and elaboration document explains the meaning and rationale for each checklist item. A few PRISMA Group participants volunteered to help draft specific items for this document, and four of these (DGA, AL, DM, and JT) met on several occasions to further refine the document, which was circulated and ultimately approved by the larger PRISMA Group. scope of prismaPRISMA focuses on ways in which authors can ensure the transparent and complete reporting of systematic reviews and meta-analyses. It does not address directly or in a detailed manner the conduct of systematic reviews, for which other guides are available.13-16W e developed the PRISMA statement and this explan-atory document to help authors report a wide array of systematic reviews to assess the benefits and harms of a healthcare intervention. W e consider most of the checklist items relevant when reporting systematic reviews of non-randomised studies assessing the benefits and harms of interventions. However, we recognise that authors who address questions relating to aetiology, diagnosis, or prog-nosis, for example, and who review epidemiological or diagnostic accuracy studies may need to modify or incor-porate additional items for their systematic reviews. how to use this paperW e modeled this explanation and elaboration document after those prepared for other reporting guidelines.17-19 T o maximise the benefit of this document, we encour-age people to read it in conjunction with the PRISMA statement.11W e present each checklist item and follow it with a published exemplar of good reporting for that item. (W e edited some examples by removing citations or web addresses, or by spelling out abbreviations.) W e then explain the pertinent issue, the rationale for including the item, and relevant evidence from the literature, when-ever possible. No systematic search was carried out to identify exemplars and evidence. W e also include seven boxes at the end of the document that provide a more comprehensive explanation of certain thematic aspects of the methodology and conduct of systematic reviews. Although we focus on a minimal list of items to con-sider when reporting a systematic review, we indicate places where additional information is desirable to improve transparency of the review process. W e present the items numerically from 1 to 27; however, authors need not address items in this particular order in their reports. Rather, what is important is that the information for each item is given somewhere within the report.the prisma checklistTitle and abstractItem 1: TitleIdentify the report as a systematic review, meta-analysis, or both.Examples “Recurrence rates of video-assisted tho-racoscopic versus open surgery in the prevention of recurrent pneumothoraces: a systematic review of ran-domised and non-randomised trials”20“Mortality in randomised trials of antioxidant supple-ments for primary and secondary prevention: system-atic review and meta-analysis”21Explanation Authors should identify their report as a systematic review or meta-analysis. T erms such as “review” or “overview” do not describe for readers whether the review was systematic or whether a meta-analysis was performed. A recent survey found that 50% of 300 authors did not mention the terms “systematic review” or “meta-analysis” in the title or abstract of their systematic review.3 Although sensitive search strategies have been developed to identify systematic reviews,22 inclusion of the terms systematic review or meta-analysis in the title may improve indexing and identification.W e advise authors to use informative titles that make key information easily accessible to readers. Ideally, a title reflecting the PICOS approach (participants, inter-ventions, comparators, outcomes, and study design) (see item 11 and box 2) may help readers as it provides key information about the scope of the review. Specify-ing the design(s) of the studies included, as shown in the examples, may also help some readers and those searching databases.Some journals recommend “indicative titles” that indicate the topic matter of the review, while others require declarative titles that give the review’s main conclusion. Busy practitioners may prefer to see the conclusion of the review in the title, but declarative titles can oversimplify or exaggerate findings. Thus, many journals and methodologists prefer indicative titles as used in the examples above.Item 2: Structured summaryProvide a structured summary including, as applicable, background; objectives; data sources; study eligibility cri-teria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings; funding for the systematic review; and systematic review registration number. Example “Context: The role and dose of oral vitamin D supplementation in nonvertebral fracture prevention have not been well established.Objective: T o estimate the effectiveness of vitamin D supplementation in preventing hip and nonvertebral fractures in older persons.Data Sources: A systematic review of English and non-English articles using MEDLINE and the Cochrane Controlled T rials Register (1960-2005), and EMBASE (1991-2005). Additional studies were identified by con-tacting clinical experts and searching bibliographies and abstracts presented at the American Society for Bone and Mineral Research (1995-2004). Search terms included randomised controlled trial (RCT), controlled clinical trial, random allocation, double-blind method, cholecalciferol, ergocalciferol, 25-hydroxyvitamin D, fractures, humans, elderly, falls, and bone density. Study Selection: Only double-blind RCT s of oral vita-min D supplementation (cholecalciferol, ergocalciferol) with or without calcium supplementation vs calcium supplementation or placebo in older persons (>60 years) that examined hip or nonvertebral fractures were included.BMJ| onlineBMJ | onlineresearch methods & reportingobjective of the review. Under a Data sources heading, they summarise sources that were searched, any lan-guage or publication type restrictions, and the start and end dates of searches. Study selection statements then ideally describe who selected studies using what inclusion criteria. Data extraction methods statements describe appraisal methods during data abstraction and the methods used to integrate or summarise the data. The Data synthesis section is where the main results of the review are reported. If the review includes meta-analyses, authors should provide numerical results with confidence intervals for the most important outcomes. Ideally, they should specify the amount of evidence in these analyses (numbers of studies and numbers of par-ticipants). Under a Limitations heading, authors might describe the most important weaknesses of included studies as well as limitations of the review process. Then authors should provide clear and balanced Con-clusions that are closely linked to the objective and find-ings of the review. Additionally, it would be helpful if authors included some information about funding for the review. Finally, although protocol registration for systematic reviews is still not common practice, if authors have registered their review or received a regis-tration number, we recommend providing the registra-tion information at the end of the abstract.T aking all the above considerations into account, the intrinsic tension between the goal of completeness of the abstract and its keeping into the space limit often set by journal editors is recognised as a major challenge.introduction Item 3: RationaleDescribe the rationale for the review in the context of what is already known.Example “Reversing the trend of increasing weight for height in children has proven difficult. It is widely accepted that increasing energy expenditure and reduc-ing energy intake form the theoretical basis for man-agement. Therefore, interventions aiming to increase physical activity and improve diet are the foundation of efforts to prevent and treat childhood obesity. Such lifestyle interventions have been supported by recent systematic reviews, as well as by the Canadian Paediat-ric Society, the Royal College of Paediatrics and Child Health, and the American Academy of Pediatrics. How-ever, these interventions are fraught with poor adher-ence. Thus, school-based interventions are theoretically appealing because adherence with interventions can be improved. Consequently, many local governments have enacted or are considering policies that mandate increased physical activity in schools, although the effect of such interventions on body composition has not been assessed.”33Explanation Readers need to understand the rationale behind the study and what the systematic review may add to what is already known. Authors should tell readers whether their report is a new sys-tematic review or an update of an existing one. If the review is an update, authors should state reasons for the update, including what has been added to the evidenceData Extraction : Independent extraction of articles by 2 authors using predefined data fields, including study quality indicators.Data Synthesis : All pooled analyses were based on random-effects models. Five RCTs for hip fracture (n=9294) and 7 RCT s for nonvertebral fracture risk (n=9820) met our inclusion criteria. All trials used cholecalciferol. Heterogeneity among studies for both hip and nonvertebral fracture prevention was observed, which disappeared after pooling RCT s with low-dose (400 IU/d) and higher-dose vitamin D (700-800 IU/d), separately. A vitamin D dose of 700 to 800 IU/d reduced the relative risk (RR) of hip fracture by 26% (3 RCT s with 5572 persons; pooled RR, 0.74; 95% con-fidence interval [CI], 0.61-0.88) and any nonvertebral fracture by 23% (5 RCT s with 6098 persons; pooled RR, 0.77; 95% CI, 0.68-0.87) vs calcium or placebo. No significant benefit was observed for RCT s with 400 IU/d vitamin D (2 RCT s with 3722 persons; pooled RR for hip fracture, 1.15; 95% CI, 0.88-1.50; and pooled RR for any nonvertebral fracture, 1.03; 95% CI, 0.86-1.24).Conclusions : Oral vitamin D supplementation between 700 to 800 IU/d appears to reduce the risk of hip and any nonvertebral fractures in ambulatory or institution-alised elderly persons. An oral vitamin D dose of 400 IU/d is not sufficient for fracture prevention.”23Explanation Abstracts provide key information that enables readers to understand the scope, processes, and findings of a review and to decide whether to read the full report. The abstract may be all that is readily available to a reader, for example, in a bibliographic database. The abstract should present a balanced and realistic assessment of the review’s findings that mirrors, albeit briefly, the main text of the report.W e agree with others that the quality of reporting in abstracts presented at conferences and in journal publications needs improvement.24 25 While we do not uniformly favour a specific format over another, we generally recommend structured abstracts. Structured abstracts provide readers with a series of headings per-taining to the purpose, conduct, findings, and conclu-sions of the systematic review being reported.26 27 They give readers more complete information and facilitate finding information more easily than unstructured abstracts.28-32A highly structured abstract of a systematic review could include the following headings: Context (or Back-ground ); Objective (or Purpose ); Data sources ; Study selection (or Eligibility criteria ); Study appraisal and Synthesis meth-ods (or Data extraction and Data synthesis ); Results ; Limita-tions ; and Conclusions (or Implications ). Alternatively, a simpler structure could cover but collapse some of the above headings (such as label Study selection and Study appraisal as Review methods ) or omit some headings such as Background and Limitations .In the highly structured abstract mentioned above, authors use the Background heading to set the context for readers and explain the importance of the review question. Under the Objectives heading, they ideally use elements of PICOS (see box 2) to state the primaryBMJ | onlineresearch methods & reportingpre-specifies the objectives and methods of the sys-tematic review. For instance, a protocol specifies out-comes of primary interest, how reviewers will extract information about those outcomes, and methods that reviewers might use to quantitatively summarise the outcome data (see item 13). Having a protocol can help restrict the likelihood of biased post hoc decisions in review methods, such as selective outcome reporting. Several sources provide guidance about elements to include in the protocol for a systematic review.16 38 39 For meta-analyses of individual patient-level data, we advise authors to describe whether a protocol was explicitly designed and whether, when, and how participating collaborators endorsed it.40 41Authors may modify protocols during the research, and readers should not automatically consider such modifications inappropriate. For example, legitimate modifications may extend the period of searches to include older or newer studies, broaden eligibility cri-teria that proved too narrow, or add analyses if the primary analyses suggest that additional ones are war-ranted. Authors should, however, describe the modifi-cations and explain their rationale.Although worthwhile protocol amendments are common, one must consider the effects that protocol modifications may have on the results of a systematic review, especially if the primary outcome is changed. Bias from selective outcome reporting in randomised trials has been well documented.42 43 An examination of 47 Cochrane reviews revealed indirect evidence for possible selective reporting bias for systematic reviews. Almost all (n=43) contained a major change, such as the addition or deletion of outcomes, between the pro-tocol and the full publication.44 Whether (or to what extent) the changes reflected bias, however, was not clear. For example, it has been rather common not to describe outcomes that were not presented in any of the included studies.Registration of a systematic review, typically with a protocol and registration number, is not yet common, but some opportunities exist.45 46 Registration may pos-sibly reduce the risk of multiple reviews addressing the same question,45-48 reduce publication bias, and provide greater transparency when updating systematic reviews. Of note, a survey of systematic reviews indexed in Medline in November 2004 found that reports of pro-tocol use had increased to about 46%3 from 8% noted in previous surveys.49 The improvement was due mostly to Cochrane reviews, which, by requirement, have a published protocol.3Item 6: Eligibility criteriaSpecify study characteristics (such as PICOS, length of follow-up) and report characteristics (such as years considered, language, publication status) used as criteria for eligibility, giving rationale.Examples T ypes of studies: “Randomised clinical trials studying the administration of hepatitis B vaccine to CRF [chronic renal failure] patients, with or without dialysis. No language, publication date, or publication status restrictions were imposed…”T ypes of participants: “Participants of any age withbase since the previous version of the review.An ideal background or introduction that sets context for readers might include the following. First, authors might define the importance of the review question from different perspectives (such as public health, individual patient, or health policy). Second, authors might briefly mention the current state of knowledge and its limitations. As in the above example, informa-tion about the effects of several different interventions may be available that helps readers understand why potential relative benefits or harms of particular inter-ventions need review. Third, authors might whet read-ers’ appetites by clearly stating what the review aims to add. They also could discuss the extent to which the limitations of the existing evidence base may be overcome by the review.Item 4: ObjectivesProvide an explicit statement of questions being addressed with reference to participants, interventions, comparisons, outcomes, and study design (PICOS).Example “T o examine whether topical or intralu-minal antibiotics reduce catheter-related bloodstream infection, we reviewed randomised, controlled trials that assessed the efficacy of these antibiotics for primary prophylaxis against catheter-related bloodstream infec-tion and mortality compared with no antibiotic therapy in adults undergoing hemodialysis.”34Explanation The questions being addressed, and the rationale for them, are one of the most critical parts of a systematic review. They should be stated precisely and explicitly so that readers can understand quickly the review’s scope and the potential applicability of the review to their interests.35 Framing questions so that they include the following five “PICOS” components may improve the explicitness of review questions: (1) the patient population or disease being addressed (P), (2) the interventions or exposure of interest (I), (3) the comparators (C), (4) the main outcome or endpoint of interest (O), and (5) the study designs chosen (S). For more detail regarding PICOS, see box 2.Good review questions may be narrowly focused or broad, depending on the overall objectives of the review. Sometimes broad questions might increase the applicability of the results and facilitate detection of bias, exploratory analyses, and sensitivity analyses.35 36 Whether narrowly focused or broad, precisely stated review objectives are critical as they help define other components of the review process such as the eligibility criteria (item 6) and the search for relevant literature (items 7 and 8).MethodsItem 5: Protocol and registrationIndicate if a review protocol exists, if and where it can be accessed (such as a web address), and, if available, provide registration information including the registra-tion number.Example “Methods of the analysis and inclusion criteria were specified in advance and documented in a protocol.”37Explanation A protocol is important because itBMJ | onlineresearch methods & reportingCaution may need to be exercised in including all identified studies due to potential differences in the risk of bias such as, for example, selective reporting in abstracts.60-62Item 7: Information sourcesDescribe all information sources in the search (such as databases with dates of coverage, contact with study authors to identify additional studies) and date last searched.Example “Studies were identified by searching electronic databases, scanning reference lists of articles and consultation with experts in the field and drug companies…No limits were applied for language and foreign papers were translated. This search was applied to Medline (1966 - Present), CancerLit (1975 - Present), and adapted for Embase (1980 - Present), Science Cita-tion Index Expanded (1981 - Present) and Pre-Medline electronic databases. Cochrane and DARE (Database of Abstracts of Reviews of Effectiveness) databases were reviewed…The last search was run on 19 June 2001. In addition, we handsearched contents pages of Jour-nal of Clinical Oncology 2001, European Journal of Cancer 2001 and Bone 2001, together with abstracts printed in these journals 1999 - 2001. A limited update literature search was performed from 19 June 2001 to 31 December 2003.”63Explanation The National Library of Medicine’s Medline database is one of the most comprehensive sources of healthcare information in the world. Like any database, however, its coverage is not complete and varies according to the field. Retrieval from any single database, even by an experienced searcher, may be imperfect, which is why detailed reporting is impor-tant within the systematic review.At a minimum, for each database searched, authors should report the database, platform, or provider (such as Ovid, Dialog, PubMed) and the start and end dates for the search of each database. This information lets readers assess the currency of the review, which is important because the publication time-lag outdates the results of some reviews.64 This information should also make updating more efficient.65 Authors should also report who developed and conducted the search.66In addition to searching databases, authors should report the use of supplementary approaches to identify studies, such as hand searching of journals, checking reference lists, searching trials registries or regula-tory agency websites,67 contacting manufacturers, or contacting authors. Authors should also report if they attempted to acquire any missing information (such as on study methods or results) from investigators or spon-sors; it is useful to describe briefly who was contacted and what unpublished information was obtained.Item 8: SearchPresent the full electronic search strategy for at least one major database, including any limits used, such that it could be repeated.Examples In text: “W e used the following search terms to search all trials registers and databases: immu-noglobulin*; IVIG; sepsis; septic shock; septicaemia; and septicemia…”68CRF or receiving dialysis (haemodialysis or peritoneal dialysis) were considered. CRF was defined as serum creatinine greater than 200 µmol/L for a period of more than six months or individuals receiving dialysis (haemodialysis or peritoneal dialysis)…Renal trans-plant patients were excluded from this review as these individuals are immunosuppressed and are receiving immunosuppressant agents to prevent rejection of their transplanted organs, and they have essentially normal renal function...”T ypes of intervention: “T rials comparing the benefi-cial and harmful effects of hepatitis B vaccines with adjuvant or cytokine co-interventions [and] trials com-paring the beneficial and harmful effects of immu-noglobulin prophylaxis. This review was limited to studies looking at active immunisation. Hepatitis B vaccines (plasma or recombinant (yeast) derived) of all types, dose, and regimens versus placebo, control vac-cine, or no vaccine…”T ypes of outcome measures: “Primary outcome measures: Seroconversion, ie, proportion of patients with adequate anti-HBs response (>10 IU/L or Sample Ratio Units). Hepatitis B infections (as measured by hepatitis B core antigen (HBcAg) positivity or persistent HBsAg positivity), both acute and chronic. Acute (pri-mary) HBV [hepatitis B virus] infections were defined as seroconversion to HBsAg positivity or development of IgM anti-HBc. Chronic HBV infections were defined as the persistence of HBsAg for more than six months or HBsAg positivity and liver biopsy compatible with a diagnosis or chronic hepatitis B. Secondary outcome measures: Adverse events of hepatitis B vaccinations…[and]…mortality.”50Explanation Knowledge of the eligibility criteria is essential in appraising the validity, applicability, and comprehensiveness of a review. Thus, authors should unambiguously specify eligibility criteria used in the review. Carefully defined eligibility criteria inform various steps of the review methodology. They influ-ence the development of the search strategy and serve to ensure that studies are selected in a systematic and unbiased manner.A study may be described in multiple reports, and one report may describe multiple studies. Therefore, we separate eligibility criteria into the following two com-ponents: study characteristics and report characteristics. Both need to be reported. Study eligibility criteria are likely to include the populations, interventions, compa-rators, outcomes, and study designs of interest (PICOS, see box 2), as well as other study-specific elements, such as specifying a minimum length of follow-up. Authors should state whether studies will be excluded because they do not include (or report) specific outcomes to help readers ascertain whether the systematic review may be biased as a consequence of selective reporting.42 43Report eligibility criteria are likely to include lan-guage of publication, publication status (such as inclu-sion of unpublished material and abstracts), and year of publication. Inclusion or not of non-English lan-guage literature,51-55 unpublished data, or older data can influence the effect estimates in meta-analyses.56-59。

MLL基因重排成人急性B淋巴细胞白血病和急性髓系白血病临床特征及预后危险因素分析

MLL 基因重排成人急性B 淋巴细胞白血病和急性髓系白血病临床特征及预后危险因素分析强萍丁凯阳［摘要］　目的　分析混合谱系白血病基因重排（MLL-r ）成人急性B 淋巴细胞白血病（B-ALL ）和急性髓系白血病（AML ）的临床特征、治疗疗效及预后危险因素。

方法　选取2017年1月至2021年11月中国科学技术大学附属第一医院（安徽省立医院）血液内科收治的17例MLL-r B-ALL 和25例MLL-r AML 为研究对象，回顾性分析患者临床特征、治疗疗效及总体生存率，使用COX 回归分析影响患者预后危险因素。

结果　MLL-r 成人急性白血病诊断时常表现为外周血白细胞异常升高、贫血和血小板减少，诊断时中位白细胞计数为46.83（8.13 ， 99.93）×109/L 。

MLL-r B-ALL 最常见的重排为MLL-AF4重排；MLL-r AML 最常见重排为MLL-AF9和MLL-AF6重排。

单纯化疗复发率高，异基因造血干细胞移植可以显著改善患者预后（P < 0.05）。

此外，多因素回归分析影响患者总生存期（OS ）的因素，诊断时白细胞计数≥100×109/L （HR =8.030，95%CI ： 1.327~48.594，P = 0.023）及异基因造血干细胞移植（HR =0.079，95%CI ： 0.015~0.423，P = 0.003）为影响MLL-r AML 患者OS 的因素；异基因造血干细胞移植（HR =0.054，95%CI ： 0.006~0.481，P = 0.009）为影响MLL-r B-ALL 患者OS 的因素。

结论　MLL-r 急性白血病诊断时常表现为白细胞异常升高，单纯化疗复发率高，总体预后不佳，异基因造血干细胞移植可改善患者预后。

［关键词］混合谱系白血病基因；急性淋巴细胞白血病；急性髓系白血病；造血干细胞移植doi:10.3969/j.issn.1000-0399.2023.04.002The clinical characteristics and prognostic risk factors of B-lymphoblastic leukemia and acute myelogenous leukemia with MLL gene rearrangementQIANG Ping ，DING KaiyangDepartment of Hematology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technol⁃ogy of China, Hefei, 230001, ChinaFunding project :Funding project: Anhui Health Research Project (No.AHWJ2021b078)Corresponding author:DINGKaiyang,****************[Abstract ] Objective To explore the clinical characteristics and prognostic risk factors of B-acute lymphoblastic leukemia (B-ALL) and acute myelogenous leukemia (AML) with mixed lineage leukemia gene rearrangement (MLL-r). Methods From January 2017 to Novem⁃ber 2021, the clinical data of 17 B-ALL patients and 25 AML patients with MLL gene rearrangement were retrospectively analyzed. Patientcharacteristics, treatment response, and overall survival were analyzed. Moreover, the prognostic factors in patients with MLL gene rearrange⁃ment were also evaluated based on a COX model. Results Leukocytosis was the main symptom of adult acute leukemia with MLL gene rear⁃rangement, and the median leukocyte count was 46.83(ranging from 8.13 to 99.93) ×109/L. MLL-AF4 was the most common partner gene in pa⁃tients with B-ALL, while MLL-AF9 rearrangement and MLL-AF6 rearrangement were the most common partner genes in patients with AML.Moreover, the prognostic factors in patients with MLL gene rearrangement were also evaluated based on a Cox model. WBC≥100×109/L (HR =8.030，95%CI ： 1.327~48.594，P = 0.023) and allogeneic hematopoietic stem cell transplantation (allo-HSCT) (HR =0.079，95%CI ： 0.015~0.423，P = 0.003) were risk factors for OS in AML patients, while allo-HSCT (HR =0.054，95%CI ： 0.006~0.481，P = 0.009) was a risk factor for OS in patients with B-ALL . Conclusions Leukocytosis is the main symptom of patients with MLL gene rearrangement. Patients present a high recurrence rate after treatment with conventional chemotherapy, and allo-HSCT can significantly improve the prognosis of these patients. ［Key words ］ Mixed-lineage leukemia ；Acute lymphoblastic leukemia ；Acute myelogenous leukemia ；Hematopoietic stem cell transplan⁃tation基金项目：安徽省卫生健康委科研项目（编号： AHWJ2021b078）作者单位：230001　安徽合肥　中国科学技术大学附属第一医院（安徽省立医院）血液内科通信作者：丁凯阳，****************本文引用格式：强萍，丁凯阳.MLL 基因重排成人急性B 淋巴细胞白血病和急性髓系白血病临床特征及预后危险因素分析［J ］.安徽医学，2023，44（4）：367-371.DOI ：10.3969/j.issn.1000-0399.2023.04.002混合谱系白血病（mixed-lineage leukemia，MLL）基因又称为KMT2A基因，位于11号染色体q23区域，编码转录辅激活子，在造血发育过程中发挥重要调节作用［1］。

Cell 1983 33 389-396 Cyclin Tim Hunt

* Present address MRC Laboratory of Molecular Biology. Hills Road, Cambridge. England. ’ Present address: Department of Anatomy, Harvard MedIcal School, Boston, Massachusetts 02115. * Present address: Bioscience Center, University of Minnesota, St. Paul, Minnesota 55455. ’ Present address: Scripps lnstltutlon of Oceanography, La Jolla, California 92093. I To whom correspondence should be addressed. Present address: Department of Biochemistry. Tennis Court Road, Cambndge CE2 lQW, England.
Fertilization of eggs or meiotic maturation of oocytes in many organisms is accompanied by an increase in the rate of protein synthesis programmed by maternal mRNA (Woodland, 1982). In addition, changes in the pattern of protein synthesis are found in almost every case studied recently; the list includes starfish (Rosenthal et al., 1982), clams (Rosenthal et al., 1980), frogs (Woodland, 1982), and mice (Schultz and Wassarman, 1977; McGaughey and Van Blerkom, 1977; Braude et al., 1979). Sea urchins appear to be the exception, according to the careful studies of Brandhorst (1976). He used two-dimensional gels to analyze patterns of protein synthesis in eggs of Lytechinus pictus before and after fertilization, and found essentially no qualitative differences. However, more recent studies have shown that there is at least some qualitative translational regulation in urchins, for the mRNA for histones is apparently stored in the female pronucleus (Venezky et al., 1981) and not translated until after the first cleavage (Wells et al., 1981). Neither the role of maternal mRNA nor the reasons for the existence of these striking examples of translational control are clear (Gross, 1968; Colman, 1983). However,

乳制品中AFT M_(1)和AFT M_(2)的系统适应性试验的验证

乳制品中AFT M_(1)和AFT M_(2)的系统适应性试验的验证牛丽丽
【期刊名称】《食品安全导刊》
【年(卷),期】2023()1
【摘要】为了准确测定黄曲霉毒素M_(1)(AFT M_(1))和黄曲霉毒素M_(2)(AFT M_(2))的含量,本文采用高效液相色谱法对AFT M_(1)和AFT M_(2)的系统适应性试验进行了验证。

在0~5 ng·mL^(-1),AFT M_(1)和AFT M_(2)均呈现良好的线性关系。

免疫亲和柱的验证、方法检出限和定量限均符合GB 5009.24—2016规定,AFT M_(1)和AFT M_(2)的回收率符合GB/T 27404—2008规定。

使用该方法对9个乳制品进行了测定,发现样品中AFT M_(1)和AFT M_(2)可准确测得。

【总页数】3页(P118-120)
【作者】牛丽丽
【作者单位】长治市综合检验检测中心
【正文语种】中文
【中图分类】R28
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