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丁酸盐在炎症性肠病中的免疫调节机制研究进展

doi :10.3969/j.issn.1002-7386.2024.09.026·综述与讲座·丁酸盐在炎症性肠病中的免疫调节机制研究进展崔馨月　石璠　郑丽红　王海强项目来源:国家中医药管理局第五批全国中医临床优秀人才研修项目(国中医药人教函[2022]1号);黑龙江中医药大学“优秀青年骨干教师”计划(编号:150********)作者单位:150040　哈尔滨市,黑龙江中医药大学(崔馨月、石璠);黑龙江中医药大学附属第四医院消化内科(郑丽红);黑龙江中医药大学附属第一医院消化二科(王海强)通信作者:王海强　E⁃mail:haiqiang915@ 【摘要】　炎症性肠病(IBD )是一组与肠道慢性炎症相关的异质性疾病,丁酸盐是肠道微生物群产生的关键代谢产物,能够调节免疫细胞的发育和功能,调节免疫功能并防止过度免疫反应,从而延缓IBD 的临床进展。

本文就丁酸盐在调节免疫功能方面改善IBD 作用机制的研究进展进行综述,旨在为IBD 的临床治疗提供新的选择。

【关键词】　炎症性肠病;丁酸盐;G 蛋白耦联受体;Th17细胞;Treg 细胞【中图分类号】　R 321.54 【文献标识码】　A 【文章编号】　1002-7386(2024)09-1397-06Research progress on the immunomodulatory mechanism of butyrate in inflammatory bowel diseases CUI Xinyue ∗,SHI Fan ∗,ZHENG Lihong ,et al.∗Heilongjiang University of Chinese Medicine ,Heilongjiang ,Harbin 150040,China【Abstract 】　Inflammatory bowel diseases (IBDs )are a group of heterogeneous diseases associated with chronic inflammation of the gut.Butyrate is a key metabolite produced by the gut microbiota that regulates the development and function of immune cells ,modulates immune function and prevents excessive immune responses ,thereby delaying the clinical progression of IBDs.This paper reviews the progress of research on the mechanism of butyrate in modulating immune function to improve IBD ,aiming to provide new options for the clinical treatment of IBD.【Key words 】　inflammatory bowel disease ;butyrate ;G⁃protein coupled receptor ;Th17cell ;Treg cell 炎症性肠病(inflammatory bowel disease,IBD)是一组与肠道慢性炎症相关的异质性疾病[1],常表现为腹痛、腹泻、血便、体重减轻等,在过去的10年里在全球范围内变得越来越普遍[2]。

The Body-Mass Index, Airflow Obstruction, Dyspnea, and Exercise Capacity Index in COPD

n engl j med 350;10march 4, 2004 The new england journal of medicine1005The Body-Mass Index, Airflow Obstruction, Dyspnea, and Exercise Capacity Index in Chronic Obstructive Pulmonary DiseaseBartolome R. Celli, M.D., Claudia G. Cote, M.D., Jose M. Marin, M.D., Ciro Casanova, M.D., Maria Montes de Oca, M.D., Reina A. Mendez, M.D.,Victor Pinto Plata, M.D., and Howard J. Cabral, Ph.D.From the COPD Center at St. Elizabeth’s Medical Center, Tufts University School of Medicine, Boston (B.R.C., V .P.P.); Bay Pines Veterans Affairs Medical Center, Bay Pines,Fla. (C.G.C.); Hospital Miguel Servet, Zara-goza, Spain (J.M.M.); H ospital Nuestra Senora de La Candelaria, Tenerife, Spain (C.C.); Hospital Universitario de Caracas and Hospital Jose I. Baldo, Caracas, Vene-zuela (M.M.O., R.A.M.); and Boston Uni-versity School of Public H ealth, Boston (H.J.C.). Address reprint requests to Dr.Celli at Pulmonary and Critical Care Medi-cine, St. Elizabeth’s Medical Center, 736Cambridge St., Boston, MA 02135, or at bcelli@.N Engl J Med 2004;350:1005-12.Copyright © 2004 Massachusetts Medical Society.backgroundChronic obstructive pulmonary disease (COPD) is characterized by an incompletely re-versible limitation in airflow. A physiological variable — the forced expiratory volume in one second (FEV 1 ) — is often used to grade the severity of COPD. However, patients with COPD have systemic manifestations that are not reflected by the FEV 1 . We hypoth-esized that a multidimensional grading system that assessed the respiratory and sys-temic expressions of COPD would better categorize and predict outcome in these pa-tients.methodsWe first evaluated 207 patients and found that four factors predicted the risk of death in this cohort: the body-mass index (B), the degree of airflow obstruction (O) and dys-pnea (D), and exercise capacity (E), measured by the six-minute–walk test. We used these variables to construct the BODE index, a multidimensional 10-point scale in which higher scores indicate a higher risk of death. We then prospectively validated the index in a cohort of 625 patients, with death from any cause and from respiratory caus-es as the outcome variables.resultsThere were 25 deaths among the first 207 patients and 162 deaths (26 percent) in the validation cohort. Sixty-one percent of the deaths in the validation cohort were due to respiratory insufficiency, 14 percent to myocardial infarction, 12 percent to lung can-cer, and 13 percent to other causes. Patients with higher BODE scores were at higher risk for death; the hazard ratio for death from any cause per one-point increase in the BODE score was 1.34 (95 percent confidence interval, 1.26 to 1.42; P<0.001), and the hazard ratio for death from respiratory causes was 1.62 (95 percent confidence inter-val, 1.48 to 1.77; P<0.001). The C statistic for the ability of the BODE index to predict the risk of death was larger than that for the FEV 1 (0.74 vs. 0.65).conclusionsThe BODE index, a simple multidimensional grading system, is better than the FEV 1at predicting the risk of death from any cause and from respiratory causes among pa-tients with COPD.The new england journal of medicine1006hronic obstructiv e pulmonarydisease (COPD), a common disease char-acterized by a poorly reversible limitationin airflow,1 is predicted to be the third most fre-quent cause of death in the world by 2020.2 Therisk of death in patients with COPD is often gradedwith the use of a single physiological variable, theforced expiratory volume in one second (FEV1).1,3,4However, other risk factors, such as the presenceof hypoxemia or hypercapnia,5,6 a short distancewalked in a fixed time,7 a high degree of functionalbreathlessness,8 and a low body-mass index (theweight in kilograms divided by the square of theheight in meters),9,10 are also associated with anincreased risk of death. We hypothesized that a mul-tidimensional grading system that assessed the res-piratory, perceptive, and systemic aspects of COPDwould better categorize the illness and predict theoutcome than does the FEV1 alone. We used datafrom an initial cohort of 207 patients to identifyfour factors that predicted the risk of death: thebody-mass index (B), the degree of airflow ob-struction (O) and functional dyspnea (D), and exer-cise capacity (E) as assessed by the six-minute–walk test. We then integrated these variables into amultidimensional index — the BODE index — andvalidated the index in a second cohort of 625 pa-tients, with death from any cause and death from859 outpatients with a wide range in the severityof COPD were recruited from clinics in the UnitedStates, Spain, and Venezuela. The study was ap-proved by the human-research review board at eachsite, and all patients provided written informed con-sent. COPD was defined by a history of smokingthat exceeded 20 pack-years and a ratio of FEV1 toforced vital capacity (FVC) of less than 0.7 measured20 minutes after the administration of albuterol.1All patients were in clinically stable condition andreceiving appropriate therapy. Patients who werereceiving inhaled oxygen had to have been takinga stable dose for at least six months before studyentry. The exclusion criteria were an illness otherthan COPD that was likely to result in death withinthree years; asthma, defined as an increase in theFEV1 of more than 15 percent above the base-linevalue or of 200 ml after the administration of a bron-chodilator; an inability to take the lung-functionand six-minute–walk tests; a myocardial infarctionwithin the preceding four months; unstable angi-na; or congestive heart failure (New York Heart As-sociation class III or IV).variables selected for the bode indexWe determined the following variables in the first207 patients who were recruited between 1995 and1997: age; sex; pack-years of smoking; FVC; FEV1,measured in liters and as a percentage of the pre-dicted value according to the guidelines of theAmerican Thoracic Society11; the best of two six-minute–walk tests performed at least 30 minutesapart12; the degree of dyspnea, measured with theuse of the modified Medical Research Council(MMRC) dyspnea scale13; the body-mass index9,10;the functional residual capacity and inspiratorycapacity11; the hematocrit; and the albumin level.The validated Charlson index was used to deter-mine the degree of comorbidity. This index hasbeen shown to predict mortality.14 The differenc-es in these values between survivors and nonsur-vivors are shown in Table 1.Each of these possible explanatory variableswas independently evaluated to determine its as-sociation with one-year mortality in a stepwise for-ward logistic-regression analysis. A subgroup offour variables had the strongest association — thebody-mass index, FEV1 as a percentage of the pre-dicted value, score on the MMRC dyspnea scale,and the distance walked in six minutes (general-ized r2=0.21, P<0.001) — and these were includ-ed in the BODE index (Table 2). All these variablespredict important outcomes, are easily measured,and may change over time. We chose the post-bron-chodilator FEV1 as a percent of the predicted value,classified according to the three stages identifiedby the American Thoracic Society, because it can beused to predict health status,15 the rate of exacer-bation of COPD,16 the pharmacoeconomic costs ofthe disease,17 and the risk of death.18,19 We chosethe MMRC dyspnea scale because it predicts thelikelihood of survival among patients with COPD8and correlates well with other scales and health-status scores.20,21 We chose the six-minute–walktest because it predicts the risk of death in patientswith COPD,7 patients who have undergone lung-reduction surgery,22 patients with cardiomyopa-thy,23 and those with pulmonary hypertension.24In addition, the test has been standardized,12 theclinically significant thresholds have been deter-mined,25 and it can be used to predict resource uti-cn engl j med 350; march 4, 2004n engl j med 350;10march 4, 2004 a multidimensional grading system in chronic obstructive pulmonary disease1007lization. 26 Finally, there is an inverse relation be-tween body-mass index and survival 9,10 that is not linear but that has an inflection point, which was 21 in our cohort and in another study. 10validation of the bode indexThe BODE index was validated prospectively in two ways in a different cohort of 625 patients who were recruited between January 1997 and January 2003. First, we used the empirical model: for each threshold value of FEV 1 , distance walked in six min-utes, and score on the MMRC dyspnea scale shown in Table 2, the patients received points ranging from 0 (lowest value) to 3 (maximal value). For body-mass index the values were 0 or 1, because of the unique relation between body-mass index and survival described above. The points for each varia-ble were added, so that the BODE index ranged from 0 to 10 points, with higher scores indicating a greater risk of death. In an exploratory analysis, the various components of the BODE index were as-signed different weights, with no corresponding increase in predictive value.study protocolIn the cohort, patients were evaluated with the use of the BODE index within six weeks after enroll-ment and were seen every three to six months for at least two years or until death. The patient and family were contacted if the patient failed to return for appointments. Death from any cause and from specific respiratory causes was recorded. The cause of death was determined by the investigators at each site after reviewing the medical record and death certificate.statistical analysisData for continuous variables are presented as means ± SD. Comparison among the three coun-tries was completed with the use of one-way analy-sis of variance. The differences between survivors and nonsurvivors in pulmonary-function variables and other pertinent characteristics were established with the use of t-tests for independent samples.To evaluate the capacity of the BODE index to pre-dict the risk of death, we performed Cox propor-tional-hazards regression analyses. 27 We estimat-ed the hazard ratio, 95 percent confidence interval,and P value for the BODE score, before and after adjustment for coexisting conditions as measured by the Charlson index. We repeated these analyses using the BODE index as the predictor of interest in*FVC denotes forced vital capacity, FEV 1 forced expiratory volume in one sec-ond, and FRC functional residual capacity.†Scores on the modified Medical Research Council (MMRC) dyspnea scale can range from 0 to 4, with a score of 4 indicating that the patient is too breathless to leave the house or becomes breathless when dressing or undressing.‡The body-mass index is the weight in kilograms divided by the square of the height in meters.§Scores on the Charlson index can range from 0 to 33, with higher scores indi- cating more coexisting conditions.*The cutoff values for the assignment of points are shown for each variable. The total possible values range from 0 to 10. FEV 1 denotes forced expiratory volume in one second.†The FEV 1 categories are based on stages identified by the American Thoracic Society.‡Scores on the modified Medical Research Council (MMRC) dyspnea scale can range from 0 to 4, with a score of 4 indicating that the patient is too breathless to leave the house or becomes breathless when dressing or undressing.§The values for body-mass index were 0 or 1 because of the inflection point in the inverse relation between survival and body-mass index at a value of 21.The new england journal of medicine1008dummy-variable form, using the first quartile as thereference group. These analyses yielded estimatesof risk similar to those obtained from analyses us-ing the BODE score as a continuous variable. Thus,we focus our presentation on the predictive charac-teristics of the BODE index and present only bivari-ate results for survival according to quartiles of theBODE index in a Kaplan–Meier analysis. The statis-tical significance was evaluated with the use of thelog-rank test. We also performed bivariate analysison the stage of COPD according to the validatedstaging system of the American Thoracic Society.3In the Cox regression analysis, we assessed thereliability of the model with the body-mass index,degree of airflow obstruction and dyspnea, and ex-ercise capacity score as the predictor of the time todeath by computing bootstrap estimates using thefull sample for the hazard ratio and its 95 percentconfidence interval (according to the percentilemethod). This approach has the advantage of notrequiring that the data be split into subgroups andis more precise than alternative methods, such ascross-validation.28Finally, in order to determine how much moreprecise the BODE index is than the FEV1 alone, wecomputed the C statistics29 for a model containingFEV1 or the BODE score as the sole independentvariable. We compared the survival times and esti-mated the probabilities of death up to 52 months.In these analyses, the C statistic is a mathematicalfunction of the sensitivity and specificity of theBODE index in classifying patients by means of theCox model as either dying or surviving. The nullvalue for the C statistic is 0.5, with a maximum of29patients (Tables 3 and 4) with all degrees of severityof COPD. The FEV1 was slightly lower among pa-tients in the United States than among those in Ven-ezuela or Spain. The U.S. patients also had morefunctional impairment, more severe dyspnea, andmore coexisting conditions. The 27 patients (4 per-cent) lost to follow-up were evenly distributed ac-cording to the severity of COPD and did not differsignificantly from the rest of the cohort with respectto any measured characteristic. There were 162deaths (26 percent) over a median follow-up of 28months (range, 4 to 68). The majority of patients(61 percent) died of respiratory insufficiency, 14percent died of myocardial infarction, 12 percentof lung cancer, and the rest of miscellaneouscauses. The BODE score was lower among survi-vors than among those who died from any cause(3.7±2.2 vs. 5.9±2.6, P<0.005). The score was alsolower among survivors than among those whodied of respiratory causes, and the difference be-tween the scores was larger (3.6±2.2 vs. 6.7±2.3,P<0.001).Table 5 shows the BODE index as a predictor ofdeath from any cause after correction for coexistingconditions. There were significantly more deathsin the United States (32 percent) than in Spain (15percent) or Venezuela (13 percent) (P<0.001). How-ever, when the analysis was done separately foreach country, the predictive power of the BODE in-dex was similar; therefore, the data are presentedtogether. Table 5 shows that the BODE index wasalso a predictor of death from respiratory causesafter correction for coexisting conditions (hazardratio, 1.63; 95 percent confidence interval, 1.48 to1.80; P<0.001). The Kaplan–Meier analysis of sur-*Because of rounding, percentages do not total 100. Thethree stages of chronic obstructive pulmonary disease(COPD) were defined by the American Thoracic Society.FEV1 denotes forced expiratory volume in one second.†Higher scores on the body-mass index, degree of airflowobstruction and dyspnea, and exercise capacity (BODE)index indicate a greater risk of death. Quartile 1 was de-fined by a score of 0 to 2, quartile 2 by a score of 3 to 4,quartile 3 by a score of 5 to 6, and quartile 4 by a scoreof 7 to 10.n engl j med 350; march 4, 2004n engl j med 350;10march 4, 2004 a multidimensional grading system in chronic obstructive pulmonary disease1009vival (Fig. 1A) shows that each quartile increase in the BODE score was associated with increased mor-tality (P<0.001). Thus, the highest quartile (a BODE score of 7 to 10) was associated with a mortality rate of 80 percent at 52 months. These same data are shown in Figure 1B in relation to the severity of COPD according to the staging system of the Amer-ican Thoracic Society. The C statistic for the ability of the BODE index to predict the risk of death was 0.74, as compared with a value of 0.65 with the use of FEV 1 alone (expressed as a percentage of the pre-dicted value). The computation of 2000 bootstrap samples for these data and estimation of the haz-ard ratios for death indicated that for each one-point increment in the BODE score the hazard ratio for death from any cause was 1.34 (95 percent confi-dence interval, 1.26 to 1.42) and the hazard ratio for death from a respiratory cause was 1.62 (95 per-the BODE index — and validated its use by show-ing that it is a better predictor of the risk of death from any cause and from respiratory causes than is the FEV 1 alone. We believe that the BODE index is useful because it includes one domain that quan-tifies the degree of pulmonary impairment (FEV 1 ),one that captures the patient’s perception of symp-toms (the MMRC dyspnea scale), and two indepen-dent domains (the distance walked in six minutes and the body-mass index) that express the systemic consequences of COPD. The FEV 1 is essential for the diagnosis and quantification of the respirato-ry impairment resulting from COPD. 1,3,4 In addi-tion, the rate of decline in FEV 1 is a good marker of disease progression and mortality. 18,19 Howev-er, the FEV 1 does not adequately reflect all the sys-temic manifestations of the disease. For example,the FEV 1 correlates weakly with the degree of dys-pnea, 20 and the change in FEV 1 does not reflect the rate of decline in patients’ health. 30 More impor-tant, prospective observational studies of patients with COPD have found that the degree of dyspnea 8 and health-status scores 31 are more accurate pre-dictors of the risk of death than is the FEV 1 . Thus,although the FEV 1 is important to obtain and essen-tial in the staging of disease in any patient with COPD, other variables provide useful information that can improve the comprehensibility of the eval-uation of patients with COPD. Each variable should*Plus–minus values are means ±SD.†Analysis of variance was used to calculate the P values.‡Scores on the modified Medical Research Council (MMRC) dyspnea scale can range from 0 to 4, with a score of 4 indicating that the patient is too breathless to leave the house or becomes breathless when dressing or undressing.§Scores on the Charlson index can range from 0 to 33, with higher scores indi-cating more coexisting conditions.¶Scores on the body-mass index, degree of airflow obstruction and dyspnea, and exercise capacity (BODE) index can range from 0 to 10, with higher scores indicating a greater risk of death.*The Cox proportional-hazards models for death from any cause include 162 deaths. The Cox proportional-hazards models for death from specific respira-tory causes include 96 deaths. Model I includes the body-mass index, degree of airflow obstruction and dyspnea, and exercise capacity (BODE) index alone. The hazard ratio is for each one-point increase in the BODE score. Model II includes coexisting conditions as expressed by each one-point increase in the Charlson index. CI denotes confidence interval.The new england journal of medicine1010correlate independently with the prognosis ofCOPD, should be easily measurable, and shouldserve as a surrogate for other potentially importantvariables.In the BODE index, we included two descriptorsof systemic involvement in COPD: the body-massindex and the distance walked in six minutes. Bothare simply obtained and independently predict therisk of death.7,9,10 It is likely that they share somecommon underlying physiological determinants,but the distance walked in six minutes contains adegree of sensitivity not provided by the body-massindex. The six-minute–walk test is simple to per-form and has been standardized.12 Its use as a clin-ical tool has gained acceptance, since it is a goodpredictor of the risk of death among patients withother chronic diseases, including congestive heartfailure23 and pulmonary hypertension.24 Indeed, thedistance walked in six minutes has been acceptedas a good outcome measure after interventions suchas pulmonary rehabilitation.32 The body-mass in-dex was also an independent predictor of the riskof death and was therefore included in the BODEindex. We evaluated the independent prognosticpower of body-mass index in our cohort using dif-ferent thresholds and found that values below 21were associated with an increased risk of death, anobservation similar to that reported by Landbo andcoworkers in a large population study.10The Global Initiative for Chronic ObstructiveLung Disease and the American Thoracic Societyrecommend that a patient’s perception of dyspneabe included in any new staging system for COPD.1,3Dyspnea represents the most disabling symptomof COPD; the degree of dyspnea provides informa-tion regarding the patient’s perception of illnessand can be measured. The MMRC dyspnea scale issimple to administer and correlates with other dys-pnea scales20 and with scores of health status.21Furthermore, in a large cohort of prospectively fol-lowed patients with COPD, which used the thresh-old values included in the BODE index, the scoreon the MMRC dyspnea scale was a better predictorof the risk of death than was the FEV1.8The BODE index combines the four variables bymeans of a simple scale. We also explored whetherweighting the variables included in the index im-proved the predictive power of the BODE index. In-terestingly, it failed to do so, most likely becauseeach variable included has already proved to be agood predictor of the outcome of COPD.Our study had some limitations. First, relative-ly few women were recruited, even though enroll-ment was independent of sex. It probably reflectsthe problem of the underdiagnosis of COPD inwomen. Second, there were differences among thethree countries. For example, patients in the UnitedStates had a higher mortality rate, more severe dys-pnea, more functional limitations, and more co-n engl j med 350; march 4, 2004n engl j med 350; march 4, 2004a multidimensional grading system in chronic obstructive pulmonary disease1011existing conditions than patients in Venezuela or Spain, even though the severity of airflow obstruc-tion was relatively similar among the patients as a whole. The reasons for these differences are un-known, because there have been no systematic com-parisons of the regional manifestations of COPD.In all three countries, the BODE index was the best predictor of survival, an observation that renders our findings widely applicable.Three studies have reported the effects of the grouping of variables to express the various do-mains affected by COPD.33-35 These studies did not include variables now known to be important pre-dictors of outcome, such as the body-mass index.However, as we found in our study, they showedthat the FEV 1, the degree of dyspnea, and exercise performance provide independent information regarding the degree of compromise in patients with COPD.Besides its excellent predictive power with re-gard to outcome, the BODE index is simple to cal-culate and requires no special equipment. This makes it a practical tool of potentially widespread applicability. Although the BODE index is a predic-tor of the risk of death, we do not know whether it will be a useful indicator of the outcome in clinical trials, the degree of utilization of health care re-sources, or the clinical response to therapy.We are indebted to Dr. Gordon L. Snider, whose guidance, com-ments, and criticisms were fundamental to the final manuscript.1.Pauwels RA, Buist AS, Calverley PM,Jenkins CR, Hurd SS. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease:NHLBI/WHO Global Initiative for Chronic Obstructive Lung Disease (GOLD) Work-shop summary. Am J Respir Crit Care Med 2001;163:1256-76.2.Murray CJL, Lopez AD. Mortality by cause for eight regions of the world: Global Burden of Disease Study. Lancet 1997;349:1269-76.3.Definitions, epidemiology, pathophys-iology, diagnosis, and staging. Am J Respir Crit Care Med 1995;152:Suppl:S78-S83.4.Siafakas NM, Vermeire P, Pride NB, et al. 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Am J Respir Crit Care Med 1998;157:1791-7.ndbo C, Prescott E, Lange P, Vestbo J,Almdal TP. Prognostic value of nutritional status in chronic obstructive pulmonary dis-ease. Am J Respir Crit Care Med 1999;160:1856-61.11.American Thoracic Society Statement.Lung function testing: selection of reference values and interpretative strategies. Am Rev Respir Dis 1991;144:1202-18.12.ATS Committee on Proficiency Stan-dards for Clinical Pulmonary Function Lab-oratories. ATS statement: guidelines for the six-minute walk test. Am J Respir Crit Care Med 2002;166:111-7.13.Mahler D, Wells C. Evaluation of clinical methods for rating dyspnea. Chest 1988;93:580-6.14.Charlson M, Szatrowski T, Peterson J,Gold J. Validation of a combined comor-bidity index. J Clin Epidemiol 1994;47:1245-51.15.Ferrer M, Alonso J, Morera J, et al. Chron-ic obstructive pulmonary disease stage and health-related quality of life. Ann Intern Med 1997;127:1072-9.16.Dewan NA, Rafique S, Kanwar B, et al.Acute exacerbation of COPD: factors associ-ated with poor treatment outcome. Chest 2000;117:662-71.17.Friedman M, Serby CW , Menjoge SS,Wilson JD, Hilleman DE, Witek TJ Jr. Phar-macoeconomic evaluation of a combination of ipratropium plus albuterol compared with ipratropium alone and albuterol alone in COPD. Chest 1999;115:635-41.18.Anthonisen NR, Wright EC, Hodgkin JE. Prognosis in chronic obstructive pulmo-nary disease. Am Rev Respir Dis 1986;133:14-20.19.Burrows B. Predictors of loss of lung function and mortality in obstructive lung diseases. Eur Respir Rev 1991;1:340-5.20.Mahler DA, Weinberg DH, Wells CK ,Feinstein AR. The measurement of dyspnea:contents, interobserver agreement, and phys-iologic correlates of two new clinical index-es. Chest 1984;85:751-8.21.Hajiro T, Nishimura K, Tsukino M, Ike-da A, Koyama H, Izumi T. Comparison of discriminative properties among disease-specific questionnaires for measuring health-related quality of life in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1998;157:785-90.22.Szekely LA, Oelberg DA, Wright C, et al.Preoperative predictors of operative mor-bidity and mortality in COPD patients under-going bilateral lung volume reduction sur-gery. Chest 1997;111:550-8.23.Shah M, Hasselblad V , Gheorgiadis M,et al. Prognostic usefulness of the six-min-ute walk in patients with advanced conges-tive heart failure secondary to ischemic and nonischemic cardiomyopathy. Am J Car-diol 2001;88:987-93.24.Miyamoto S, Nagaya N, Satoh T, et al.Clinical correlates and prognostic signifi-cance of six-minute walk test in patients with primary pulmonary hypertension: compari-son with cardiopulmonary exercise testing.Am J Respir Crit Care Med 2000;161:487-92.25.Redelmeier DA, Bayoumi AM, Gold-stein RS, Guyatt GH. Interpreting small dif-ferences in functional status: the Six Minute Walk test in chronic lung disease patients.Am J Respir Crit Care Med 1997;155:1278-82.26.Decramer M, Gosselink R, Troosters T,Verschueren M, Evers G. Muscle weakness is related to utilization of health care resourc-es in COPD patients. Eur Respir J 1997;10:417-23.27.Cox DR. Regression models and life-tables. J R Stat Soc [B] 1972;34:187-220.28.Harrell FE Jr, Lee KL, Mark DB. Multi-variate prognostic models: issues in devel-oping models, evaluating assumptions and adequacy, and measuring and reducing er-rors. Stat Med 1996;15:361-87.29.Nam B-H, D’Agostino R. Discrimina-tion index, the area under the ROC curve. In:Huber-Carol C, Balakrishnan N, Nikulin MS,Mesbah M, eds. Goodness-of-fit tests and。

甘薯蛋白抗癌作用研究进展

荧光显微镜观察细胞凋亡（ Hoechst 33258 核染色）：将细胞接种到24孔板上(1.2x105/孔)，托血清16-24 h后用不同浓度SPP处理细胞48 h ，吸去培养基，用PBS洗细胞3 次。然后用2.5% 的戊二醛固定细胞4 h，置摇床上用0.9% NaCL洗细胞3次。除去上清液后，用1μg/ml Hoechst 33258溶液在冰上避光处理细胞30min，然后用0.9% NaCL漂洗3次，甘油封闭后立即于荧光显微镜下(Olymus IX71)紫外光 (360 nm)激发，观察细胞核染色情况。
结果
结果
结果
结果
Fig 1 . Sporamin inhibited the wound closure of HT-29 cells in the presence of PMA. *, P＜0.05 vs. PMA alone; #, P＜0.05 vs. vehicle
划痕愈合实验
Fig 3. Sporamin suppressed the PMA-induced secretion of uPA in HT-29 cells after 16 h
Fig 2. Sporamin decreases adhesion of colorectal carcinoma HT-29 cells to the substrate in the presence of PMA in HT-29 cells after 16 h
Fig 4. Gelatin zymogram of conditioned medium from HT-29 cancer cells treated with 100 ng/ml PMA and various concentrations of sporamin during 4-h incubation period showing direct inhibitory effect of sporamin on MMP-9 and MMP-2 activity.

人乙醛脱氢酶2基因在毕赤酵母中的高效表达

０ＡＬ７ＤＨ２去除信号肽（－ａｔｒ。将酶切的质粒用ａＦｃｏ）
ＤＮＡ回收试剂盒回收，用。备
种。其中ＡＬＤＨ２处在线粒体内，现出基因多态表
性，是人体酒精代谢的关键酶在肝脏和胃中有引，
圈
２０ｏ７ｏ亿与生物互程０，Ｉ．１Ｖ．Ｎ２２
Ｃｈｍｉｔｙ＆Ｂｉｅｇｎｅｉｇｅｓｒｏｎｉｅｒｎ
人乙醛脱氢酶２基因在毕赤酵母中的高效表达
赵锦，玉凤，赵黄锟。明科，元欣。雷吴刘洋（汉工程大学绿色化工过程省部共建教育部重点实验室，北武汉４０７）武湖３０３
达过程中失活严重，难得到大量有活性的人ＡＬ很 —
ＤＨ２。
（）５将上一步的质粒转化至Ｅ．ｏｉＨ５，涂布ｃｌＤａ并在含氨苄青霉素的ＬＢ平板上。
（）菌落ＰＲ验证得到ＥｃｌＤａｐＩ９－６经Ｃ．ｏｉＨ５（ＰＣＫ
ＡＩＤＨ２ｄｅｓｇ一ｌｉｎ）。
作者在此研究了人ＡＩＤＨ２在毕赤酵母中的高效
表达。
１实验
１１材料、剂与仪器．试含有密码子优化的人ＡＤＬＨ２的质粒ｐＩ９－ＰＣＫ０ＡＤＨ２７Ｌ，自行构建。毕赤酵母Ｐｃｉｐｓｒｓｉａａｔｉｈｏ

红蓝撞色开展工作计划PPT模版

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FAME (费米)全自动酶免分析系统

•Nexgen Four全自动酶免分析系统•双臂四针，可并行运行4块96孔酶标板•系统由1个中央样品装载盘，2套独立三维加样臂、4个独立孵育舱、4×8通道读数器、2套洗板装置组成。

•拥有可实时互换的双重加样针(金属加样针和一次性加样头)，并行加样本速度>800孔/h，加试剂速度> 3 800孔/h。

•该仪器由内、外2套计算机管理，先进的运行软件基干WINDOWS XP和NET架构设计。

•仪器还可以增选全自动过敏原分析装置。

FAME (费米)全自动酶免分析系统•硬件上采用综合模块化设计，并行工作模式、双孵育双洗板、单读数模块•可容纳10块板，24个试剂位，30个密闭式孵育位•采用液体水平探测(LLD )、体积与重量传感、光学位置传感等技术，实现了真正的全过程控制( TPC )，尤其是专利的洗液传感器，确保了最佳洗板效果。

•软件上采用全自动GMP/GLP规范系统，如全面的系统跟踪记录（Traceability）与系统追溯（Traceability），标本／试剂加样校验( Sarnple verification) 及“自由任务管理”等功能。

VELISA 软件作为日常工作流程，您所作的只是提供一个电子数据l表格，包括样品的描述以及各种参数。

您只需要简单地点击确定您想做的分析，所有进一步的优化都由Hamilton 的VELISA 软件完成。

在您确定了这些参数以后，您将样品放置到工作站台面上，ELISA STAR let将完成您选择的实验。

优化资源管理的Dynamic Scheduler软件Hamilton的Dynamic Scheduler软件可以根据可用的系统资源，动态的调度多个并行任务，从而能够优化您ELISA 实验的通量。

iSWAP 机械手• 集成的台面内外微孔板控制工具• 可将板放置在孵育器，洗站及读板机上• 确认微孔板的运送条形码扫描器• 读取试管、微孔板以及载架上的条形码• 支持样品追踪• 确保正确的样品被载入，增强实验过程安全监控洗板机• 8通道同时进行洗涤• 每个通道都有液面探测功能• 四条液体处理线路（3条清洗，1条漂净）• 独立的吸液和放液钢针，避免交叉污染读板机• 8道LED酶标检测器• 波长范围340－750nm• 标配4个滤光片（405，450，492，620nm）• 其它波长滤光片根据需要选配孵育器• 成熟的Microlab F.A.M.E技术• 暗箱中共可容纳20块微孔板（4个培养仓，每个培养仓可放置5块微孔板）• 每个培养仓的温度均可独立控制• 培养仓的温度范围从环境温度到70℃标准化的实验器具STARlet的台面有30个轨道，可以灵活的放置各种实验器具，比如可放置2个微孔板载架（每个载架占6个轨道），1个吸头载架（占6个轨道）和12个32孔位的试管载架（占1个轨道。

应用新一代测序技术测定大室别藻苔虫线粒体基因组全序列

计划的基础，是由于它测序通量低、时费力，但费科学家们一直在努力寻求通量更高、度更快、格更速价
便宜的测序技术。自２００５年以来，Ｒｏｈ以ｃｅ公司的４４技术、ｌｍｉａ司的Ｓｌｘ技术和ＡＢ公５Ｉｕｎ公ｌｏｅａＩ
１引言
诞生于２Ｏ世纪７０年代的Ｓｎｅ法是最早被ａｇｒ
广泛使用的ＤＮＡ测序技术，是完成人类基因组也
群体遗传分化的理想材料Ｌ。线粒体基因组的获２Ｊ
得传统上通常是采取长ＰＲ结合鸟枪法或步移法Ｃ测序。虽然ＰＲ方法操作简单，样品需求量少，Ｃ对但是花费时间较长、工作量较大等问题，并且利用长ＰＲ扩增有时成功率不高，以获得目的片段ｌ。Ｃ难５］苔藓动物（ｒｏｏｎ）为重要的海洋无脊椎ｂｙｚａｓ作
关键词：室别藻苔虫；粒体基因组；一代测序技术；式ＰＲ；因排列大线新巢Ｃ基
中图分类号：１．１Ｑ９５８５文献标忘码：Ａ文章编号：２３４９（０２０—１６００５－１３２１）２０３ —７
动物门类，存６０余种。目前国际上较少，完成４个苔藓动仅物物种的线粒体全基因组，包括多室草苔虫Ｂｇｌｕｕａ
ｎｒｔａ栉口目苔虫Ｆｌｓｅｌｒｉｐｄ、ｅｉｎ、ｉｕｔｌｄａｈｓｉａ颈链血ｒｉ苔虫Ｗａｅｓｏａｓｂｏｑａａ和扇形管孔苔虫Ｔ一ｔｒｉｒｕｔｒｕｔｐ “ ｂｌｏａｆａｅｌｒｓ６。大室别藻苔虫Ｍｅｒｕｉｒｌｂｌｉ¨ ｐａ］ｍｂａ

100%活细胞UNES

100%活细胞UNES VEGNEOTOX原液10ml有盒。

1、暗沉+斑点+松弛——叶杜果+胶原蛋白。

2、暗沉+斑点+干燥缺水——叶杜果+玻尿酸。

3、暗沉+斑点+出油+粉刺——叶杜果+杜鹃花。

4、暗沉+斑点+凹洞+疤痕——叶杜果+胎盘素。

5、暗沉+斑点+皱纹——叶杜果+胜肽祛皱。

6、暗沉+斑点+暗黄+痘痘——叶杜果+左旋C。

1、原液一共分为8种。

100%多糖尿酸/玻尿酸原液（顶级保湿）。

100%杜鹃花原液（控油、收缩毛孔）。

100%去皱原液（强效去皱）。

100%胶原蛋白原液（紧致皮肤、增加弹性）。

100%胎盘素原液（修复受损、滋润）。

100%叶杜果美白皇后原液（强效美白淡斑）。

100%左旋C原液（美白去痘印）。

100%甘草精原液（抗过敏修复）。

肉毒杆菌原液（抗皱去皱）+胎盘原液（延缓衰老）。

油性皮肤毛孔大的最佳选择：杜鹃花原液（收缩毛孔、控油）+玻尿酸原液（补水，油性皮肤经常由于缺水，水油不平衡，导致毛孔更大，皮肤也变得敏感哦）。

痘痘、痘印、修复凹洞左旋维生素C是利用纳米技术,具有超强的渗透力,及抗氧化稳定性,可停留在真皮内长达六小时以上并缓慢释放左旋C而不会使左旋C氧化.具亲手性,亲脂性双重特性,能促进胶原蛋白合成，淡化黑色素沉淀，黑班，雀班，老人班，晒班等各类型班点及去皱，去痘去痘疤，修补缺损，增加肌肤靓白，弹性，尤其对于容易干燥，敏感发炎的肌肤具有独特的免疫保护作用。

看起来比较眼花缭乱，不过搭配方法其实很简单，都是两两一组搭配的，美容院有专门指导搭配的罗盘，只要告诉店主你的皮肤特征，我们用罗盘一转，就马上知道皮肤应该怎么搭配拉！2、一小瓶原液就是一个月的用量哦！每瓶配有专门的喷头，开封后套上即可，左旋c每次只能使用一滴哦！！其他的可以酌情使用两-三滴，由于活细胞原液的渗透效果非常好，所以不要担心不够用哦~~~。

3、使用的时候，将原液滴到虎口处（大拇指跟食指之间的地方），因为虎口是身体温度最低的地方之一，温度越高，原液吸收最快，所以不要用手心哦，手心温度高，原液就容易被手心都吸收拉！4、本品搭配可贝尔水嫩白面膜效果更赞！！本店是可贝尔广东代理，有授权书的哦~~。

Network Cell Info 2.0 用户手册说明书

2
Manual v.2, Mar. 8, 2017
Gauge Tab (2/2)
(1) SIM1 cells’ information (2) SIM2 cells’ information ** (for dual SIM phones with Android > 5) (3) WiFi network information (4) Band information (only for Android 7)
(b) Start/pause updates (c) Lock (unlock) the cursor in the center (d) Clear measurement markers and lines (e) Clear map completely (f) Save log files (save the database to the device storage) (g) Zoom in (h) Zoom out (i) Move cursor in the center
The top gauge "Serving Cell" provides the signal strength about the cell you are currently connected to. The other gauges "Neighbor #x" are the signal strengths of the neighbor cells you are most likely to switch to, if you move or if the network judges so.
(2) Bar graph
(3) Reset button to reset statistics counters

细胞结构英文版27张幻灯片

Needed to continue glucose breakdown for energy
NAD+
cytoplasm
fermentation
Electron carrier produced in glycolysis
fermentation lactate
when oxygen is available
Contains digestive enzymes to digest food in protists or destroy aging organelles
Endomembrane System
Rough Endoplasmic
Reticulum
Smooth Endoplasmic
Reticulum
Structure
Cilia and Flagella
Function(s)
• Move substances across cell surface in lungs, fallopian tubes
• Cell movement in sperm
Connections Between Cells
Citric acid cycle
Electron Transport chain
oxygen Outside cell
mitochondrion
2 ATP
2 ATP
32 ATP
Energy Yield from Glucose
Without Oxygen, Fermentation Occurs
Structure
Mitochondrion
Function
Captures energy from organic molecules, producing ATP

cell search操作流程

cell search操作流程摘要：1.cell search 操作流程概述2.cell search 操作流程的具体步骤3.cell search 操作流程的注意事项正文：一、cell search 操作流程概述cell search 操作流程，即细胞搜索操作流程，是一种用于检测和分析细胞结构的生物学实验技术。

该技术通过显微镜观察细胞，借助计算机分析系统对细胞结构进行定量和定位分析。

cell search 操作流程广泛应用于细胞生物学、生物医学和药物研发等领域，有助于科学家深入了解细胞结构和功能，为相关研究提供有力支持。

二、cell search 操作流程的具体步骤cell search 操作流程主要包括以下几个步骤：1.样本制备：首先，需要从生物体中提取细胞样本，并将其固定在载玻片上。

固定液可以防止细胞在实验过程中发生形态改变。

2.染色：将固定后的细胞样本进行染色，以便在显微镜下观察到细胞结构。

常用的染色方法包括荧光染色、免疫染色等。

3.扫描：使用激光共聚焦显微镜对染色后的细胞进行扫描，获取细胞结构的二维或三维图像。

4.图像处理：将扫描得到的图像输入计算机分析系统，进行图像处理和分析。

这一步通常包括去噪、分割、定位等操作，目的是提取出细胞结构信息。

5.分析：根据分析目的，对提取到的细胞结构信息进行统计和分析。

例如，可以计算细胞内各个结构的数量、大小、位置等参数，或者分析不同结构之间的相互关系。

6.结果输出：将分析结果以可视化的方式展示，便于科学家进行观察和分析。

常用的结果输出形式包括图像、图表等。

三、cell search 操作流程的注意事项在进行cell search 操作流程时，需要注意以下几点：1.样本处理：在制备细胞样本时，要确保细胞形态不变，以免影响实验结果。

2.染色方法：选择合适的染色方法，使细胞结构对比鲜明，便于观察和分析。

3.显微镜选择：使用合适的显微镜，确保获取的图像质量高，有利于后续图像处理和分析。

TD信息元素详解

信息元素功能性定义作者：李欣目录目录 (1)信息元素功能性定义 (11)1 核心网信息元素 (11)1.1 CN Information elements (11)1.2 CN Domain System Information (11)1.3 CN Information info (11)1.4 IMEI (11)1.5 IMSI (GSM-MAP) (11)1.6 Intra Domain NAS Node Selector (11)1.7 Location Area Identification (12)1.8 NAS message (12)1.9 NAS system information (GSM-MAP) (12)1.10 Paging record type identifier (12)1.11 PLMN identity (12)1.12 PLMN Type (12)1.13 P-TMSI (GSM-MAP) (12)1.14 RAB identity (12)1.15 Routing Area Code (12)1.16 Routing Area Identification (13)1.17 TMSI (GSM-MAP) (13)2 UTRAN 移动信息元素 (13)2.1 Cell Access Restriction (13)2.2 Cell identity (13)2.3 Cell selection and re-selection info for SIB3/4 (13)2.4 Cell selection and re-selection info for SIB11/12 (13)2.5 Mapping Info (14)2.6 URA identity (14)3 UE 信息元素 (14)3.1 Activation time (14)3.2 Capability Update Requirement (14)3.3 Cell update cause (15)3.4 Ciphering Algorithm (15)3.5 Ciphering mode info (15)3.6 CN domain specific DRX cycle length coefficient (15)3.7 CPCH Parameters (15)3.8 C-RNTI (15)3.9 DRAC system information (15)3.10 Void (16)3.11 Establishment cause (16)3.12 Expiration Time Factor (16)3.13 Failure cause (16)3.14 Failure cause and error information (16)3.15 Initial UE identity (16)3.16 Integrity check info (16)3.17 Integrity protection activation info (17)3.18 Integrity protection Algorithm (17)3.19 Integrity protection mode info (17)3.20 Maximum bit rate (17)3.21 Measurement capability (17)3.22 Paging cause (17)3.23 Paging record (17)3.24 PDCP capability (17)3.25 Physical channel capability (18)3.26 Protocol error cause (18)3.27 Protocol error indicator (18)3.28 RB timer indicator (18)3.29 Redirection info (18)3.30 Re-establishment timer (18)3.31 Rejection cause (18)3.32 Release cause (18)3.33 RF capability FDD (19)3.34 RLC capability (19)3.35 RLC re-establish indicator (19)3.36 RRC transaction identifier (19)3.37 Security capability (19)3.38 START (19)3.39 Transmission probability (19)3.40 Transport channel capability (20)3.41 UE multi-mode/multi-RAT capability (20)3.42 UE radio access capability (20)3.43 UE Timers and Constants in connected mode (21)3.44 UE Timers and Constants in idle mode (21)3.45 UE positioning capability (21)3.46 URA update cause (21)3.47 U-RNTI (21)3.48 U-RNTI Short (21)3.49 UTRAN DRX cycle length coefficient (21)3.50 Wait time (21)3.51 UE Specific Behavior Information 1 idle (21)3.52 UE Specific Behavior Information 1 interRAT (22)4 无线承载信息元素 (22)4.0 Default configuration identity (22)4.1 Downlink RLC STATUS info (22)4.2 PDCP info (22)4.3 PDCP SN info (22)4.4 Polling info (22)4.5 Predefined configuration identity (23)4.6 Predefined configuration value tag (23)4.7 Predefined RB configuration (23)4.8 RAB info (23)4.9 RAB info Post (23)4.10 RAB information for setup (23)4.11 RAB information to reconfigure (24)4.12 NAS Synchronization indicator (24)4.13 RB activation time info (24)4.14 RB COUNT-C MSB information (24)4.15 RB COUNT-C information (24)4.16 RB identity (24)4.17 RB information to be affected (24)4.18 RB information to reconfigure (25)4.19 RB information to release (25)4.20 RB information to setup (25)4.21 RB mapping info (25)4.22 RB with PDCP information (25)4.23 RLC info (25)4.24 Signaling RB information to setup (26)4.25 Transmission RLC Discard (26)5 传输信道信息元素 (26)5.1 Added or Reconfigured DL TrCH information (26)5.2 Added or Reconfigured UL TrCH information (27)5.3 CPCH set ID (27)5.4 Deleted DL TrCH information (27)5.5 Deleted UL TrCH information (27)5.6 DL Transport channel information common for all transport channels (27)5.7 DRAC Static Information (27)5.8 Power Offset Information (28)5.9 Predefined TrCH configuration (28)5.10 Quality Target (28)5.11 Semi-static Transport Format Information (28)5.12 TFCI Field 2 Information (28)5.13 TFCS Explicit Configuration (28)5.14 TFCS Information for DSCH (TFCI range method) (29)5.15 TFCS Reconfiguration/Addition Information (29)5.16 TFCS Removal Information (29)5.17 Void (29)5.18 Transport channel identity (29)5.19 Transport Format Combination (TFC) (29)5.20 Transport Format Combination Set (29)5.21 Transport Format Combination Set Identity (29)5.22 Transport Format Combination Subset (29)5.23 Transport Format Set (29)5.24 UL Transport channel information common for all transport channels (30)6 物理信道信息元素 (30)6.1 AC-to-ASC mapping (30)6.2 AICH Info (30)6.3 AICH Power offset (30)6.4 Allocation period info (30)6.5 Alpha (30)6.6 ASC Setting (30)6.7 Void (31)6.8 CCTrCH power control info (31)6.9 Cell parameters Id (31)6.10 Common timeslot info (31)6.11 Constant value (31)6.12 CPCH persistence levels (31)6.13 CPCH set info (31)6.14 CPCH Status Indication mode (31)6.15 CSICH Power offset (32)6.16 Default DPCH Offset Value (32)6.17 Downlink channelisation codes (32)6.18 Downlink DPCH info common for all RL (32)6.19 Downlink DPCH info common for all RL Post (32)6.20 Downlink DPCH info common for all RL Pre (32)6.21 Downlink DPCH info for each RL (32)6.22 Downlink DPCH info for each RL Post (33)6.23 Downlink DPCH power control information (33)6.24 Downlink information common for all radio links (33)6.25 Downlink information common for all radio links Post (33)6.26 Downlink information common for all radio links Pre (33)6.27 Downlink information for each radio link (33)6.28 Downlink information for each radio link Post (33)6.29 Void (33)6.30 Downlink PDSCH information (33)6.31 Downlink rate matching restriction information (34)6.32 Downlink Timeslots and Codes (34)6.33 DPCH compressed mode info (34)6.34 DPCH Compressed Mode Status Info (34)6.35 Dynamic persistence level (34)6.36 Frequency info (34)6.37 Individual timeslot info (35)6.38 Individual Timeslot interference (35)6.39 Maximum allowed UL TX power (35)6.40 Void (35)6.41 Midamble shift and burst type (35)6.42 PDSCH Capacity Allocation info (35)6.43 PDSCH code mapping (36)6.44 PDSCH info (36)6.45 PDSCH Power Control info (36)6.46 PDSCH system information (36)6.47 PDSCH with SHO DCH Info (36)6.48 Persistence scaling factors (36)6.49 PICH Info (36)6.50 PICH Power offset (37)6.51 PRACH Channelisation Code List (37)6.52 PRACH info (for RACH) (37)6.53 PRACH partitioning (37)6.54 PRACH power offset (37)6.55 PRACH system information list (37)6.56 Predefined PhyCH configuration (38)6.57 Primary CCPCH info (38)6.58 Primary CCPCH info post (38)6.59 Primary CCPCH TX Power (38)6.60 Primary CPICH info (38)6.61 Primary CPICH Tx power (38)6.62 Primary CPICH usage for channel estimation (38)6.63 PUSCH info (38)6.64 PUSCH Capacity Allocation info (38)6.65 PUSCH power control info (39)6.66 PUSCH system information (39)6.67 RACH transmission parameters (39)6.68 Radio link addition information (39)6.69 Radio link removal information (39)6.70 SCCPCH Information for FACH (39)6.71 Secondary CCPCH info (39)6.72 Secondary CCPCH system information (40)6.73 Secondary CPICH info (40)6.74 Secondary scrambling code (40)6.75 SFN Time info (40)6.76 SSDT cell identity (40)6.77 SSDT information (40)6.78 STTD indicator (40)6.79 TDD open loop power control (41)6.80 TFC Control duration (41)6.81 TFCI Combining Indicator (41)6.82 TGPSI (41)6.83 Time info (41)6.84 Timeslot number (41)6.85 TPC combination index (41)6.86 TSTD indicator (41)6.87 TX Diversity Mode (41)6.88 Uplink DPCH info (41)6.89 Uplink DPCH info Post (42)6.90 Uplink DPCH info Pre (42)6.91 Uplink DPCH power control info (42)6.92 Uplink DPCH power control info Post (42)6.93 Uplink DPCH power control info Pre (42)6.94 Uplink Timeslots and Codes (42)6.95 Uplink Timing Advance (42)6.96 Uplink Timing Advance Control (43)7 测量信息元素 (43)7.1 Additional measurements list (43)7.2 Cell info (43)7.3 Cell measured results (43)7.4 Cell measurement event results (44)7.5 Cell reporting quantities (44)7.6 Cell synchronization information (44)7.7 Event results (44)7.8 FACH measurement occasion info (45)7.9 Filter coefficient (45)7.10 HCS Cell re-selection information (45)7.11 HCS neighboring cell information (45)7.12 HCS Serving cell information (45)7.13 Inter-frequency cell info list (46)7.14 Inter-frequency event identity (46)7.15 Inter-frequency measured results list (46)7.16 Inter-frequency measurement (46)7.17 Inter-frequency measurement event results (47)7.18 Inter-frequency measurement quantity (47)7.19 Inter-frequency measurement reporting criteria (47)7.20 Inter-frequency measurement system information (47)7.21 Inter-frequency reporting quantity (47)7.22 Inter-frequency SET UPDATE (48)7.23 Inter-RAT cell info list (48)7.24 Inter-RAT event identity (48)7.25 Inter-RAT info (48)7.26 Inter-RAT measured results list (48)7.27 Inter-RAT measurement (49)7.28 Inter-RAT measurement event results (49)7.29 Inter-RAT measurement quantity (49)7.30 Inter-RAT measurement reporting criteria (49)7.31 Inter-RAT measurement system information (50)7.32 Inter-RAT reporting quantity (50)7.33 Intra-frequency cell info list (50)7.34 Intra-frequency event identity (50)7.35 Intra-frequency measured results list (50)7.36 Intra-frequency measurement (50)7.37 Intra-frequency measurement event results (51)7.38 Intra-frequency measurement quantity (51)7.39 Intra-frequency measurement reporting criteria (51)7.40 Intra-frequency measurement system information (51)7.41 Intra-frequency reporting quantity (52)7.42 Intra-frequency reporting quantity for RACH reporting (52)7.43 Maximum number of reported cells on RACH (52)7.44 Measured results (52)7.45 Measured results on RACH (52)7.46 Measurement Command (52)7.47 Measurement control system information (53)7.48 Measurement Identity (53)7.49 Measurement reporting mode (53)7.50 Measurement Type (53)7.51 Measurement validity (53)7.52 Observed time difference to GSM cell (53)7.53 Periodical reporting criteria (53)7.54 Primary CCPCH RSCP info (54)7.55 Quality measured results list (54)7.56 Quality measurement (54)7.57 Quality measurement event results (54)7.58 Quality measurement reporting criteria (54)7.59 Quality reporting quantity (54)7.60 Reference time difference to cell (54)7.61 Reporting Cell Status (55)7.62 Reporting information for state CELL_DCH (55)7.63 SFN-SFN observed time difference (55)7.64 Time to trigger (55)7.65 Timeslot ISCP info (55)7.66 Traffic volume event identity (55)7.67 Traffic volume measured results list (55)7.68 Traffic volume measurement (55)7.69 Traffic volume measurement event results (56)7.70 Traffic volume measurement object (56)7.71 Traffic volume measurement quantity (56)7.72 Traffic volume measurement reporting criteria (56)7.73 Traffic volume measurement system information (56)7.74 Traffic volume reporting quantity (56)7.75 UE internal event identity (56)7.76 UE internal measured results (57)7.77 UE internal measurement (57)7.78 UE internal measurement event results (57)7.79 UE internal measurement quantity (57)7.80 UE internal measurement reporting criteria (57)7.81 Void (58)7.82 UE Internal reporting quantity (58)7.83 UE Rx-Tx time difference type 1 (58)7.84 UE Rx-Tx time difference type 2 (58)7.85 UE Transmitted Power info (58)7.86 UE positioning Ciphering info (58)7.87 UE positioning Error (58)7.88 UE positioning GPS acquisition assistance (59)7.89 UE positioning GPS almanac (59)7.90 UE positioning GPS assistance data (59)7.91 UE positioning GPS DGPS corrections (59)7.92 UE positioning GPS ionospheric model (59)7.93 UE positioning GPS measured results (59)7.94 UE positioning GPS navigation model (60)7.95 UE positioning GPS real-time integrity (60)7.96 UE positioning GPS reference time (60)7.97 UE positioning GPS UTC model (61)7.98 UE positioning IPDL parameters (61)7.99 UE positioning measured results (61)7.100 UE positioning measurement (61)7.101 UE positioning measurement event results (61)7.102 Void (62)7.103 UE positioning OTDOA assistance data for UE-assisted (62)7.104 Void (62)7.105 UE positioning OTDOA measured results (62)7.106 UE positioning OTDOA neighbor cell info (62)7.107 UE positioning OTDOA quality (63)7.108 UE positioning OTDOA reference cell info (63)7.109 UE positioning position estimate info (64)7.110 UE positioning reporting criteria (64)7.111 UE positioning reporting quantity (64)7.112 T ADV info (65)8 其它信息元素 (65)8.1 BCCH modification info (65)8.2 BSIC (65)8.3 CBS DRX Level 1 information (65)8.4 Cell Value tag (65)8.5 Inter-RAT change failure (65)8.6 Inter-RAT handover failure (66)8.7 Inter-RAT UE radio access capability (66)8.8 Void (66)8.9 MIB Value tag (66)8.10 PLMN Value tag (66)8.11 Predefined configuration identity and value tag (66)8.12 Protocol error information (66)8.13 References to other system information blocks (66)8.14 References to other system information blocks and scheduling blocks (67)8.15 Rplmn information (67)8.16 Scheduling information (67)8.17 SEG COUNT (67)8.18 Segment index (67)8.19 SIB data fixed (67)8.20 SIB data variable (67)8.21 SIB type (67)8.22 SIB type SIBs only (67)9 ANSI-41 Information elements (68)10 Multiplicity values and type constraint values (68)信息元素功能性定义消息是由多个信息元素组合而成，信息元素根据其功能的不同划分为：核心网域信息元素、UTRAN 移动信息元素、UE 信息元素、无线承载信息元素、传输信道信息元素、物理信道信息元素和测量信息元素。

BD FACSLyric

1Alex Fainshtein, 1Edward Joe, 1Lori Apoll, 1Xiaoyang (Alice) Wang, 2Robert Sutherland1BD Biosciences, San Jose, California, USA; 2Laboratory Medicine Program, University Health Network, Toronto, CanadaAccurate Enumeration of CD34+ Cells with the BD®Stem CellEnumeration Kit on the BD FACSLyric™Flow CytometerIntroductionHematopoietic stem cells (HSCs)are CD34+and are responsible for engraftment in the bone marrow transplant setting.Enumerating CD34+HSCs in peripheral blood,leukapheresis and cord blood samples provides critical information to the transplant physicians.The number of viable CD34+cells present in the peripheral blood after mobilization and/or chemotherapy predicts the yield of CD34+cells in the leukapheresis product.Additionally,the number of CD34+cells collected predicts time to engraftment after transplantation.The infusion of a minimum of two million viable CD34+cells per kilogram patient weight generally enables rapid (10—12days to 500neutrophils/µL)and sustained engraftment in the auto-transplant setting.The International Society of Hematotherapy and Graft Engineering (ISHAGE)protocol for CD34+cell enumeration is the most widely used flow cytometric method in clinical laboratories.BD Biosciences has developed an algorithm for the BD FACSLyric™Flow Cytometer that closely follows the ISHAGE protocol.This study was performed to demonstrate the accuracy of the new BD Stem Cell Enumeration Assay algorithm used to enumerate CD34+HSCs.Materials and SamplesBD ®Stem Cell Enumeration Kit BD ®Stem Cell Control BD ® CS&T RUO Beads BD ®FC Beads 7-Color KitBD FACSLyric™Flow Cytometer BD FACSuite™Clinical Application BD Stem Cell Enumeration Assay moduleBD FACSDiva™Software BD CellQuest™Pro SoftwareSamples (50 total):•15 leukapheresis•15 mobilized leukapheresis •15 cord blood • 2 bone marrow• 3 normal peripheral bloodMethodThe samples were stained using the BD ®Stem Cell Enumeration Kit.The stained samples were acquired on the BD FACSLyric™Flow Cytometer using BD FACSuite™Clinical Application.The cytometer was set up using BD ®CS&T RUO Beads for daily performance quality control,BD ®FC Beads and stained BD ®Stem Cell Control for automated compensation.Analysis and ResultsAcquired samples were analyzed in parallel using three different methods. They were gated:A.Manually by BD experts, using BD gating templates(BD internal use) in BD FACSuite™Clinical ApplicationB.Automatically by the new BD Stem CellEnumeration Assay algorithm used in BD FACSuite™Clinical ApplicationC.Manually by an academic expert, using well-established sequential gating criteria and BD CellQuest™Pro Software Each analysis produced cell counts for:•Viable stem cells (CD34+)•Total stem cells (CD34+)•Viable CD45+ Cells •Total CD45+ CellsThe cell counts derived by the three methods were compared using regression analysis.Gating with BD CellQuest™ProManual, Academic Expert (C)Correspondence between “R” regions and populations LabelDefinitionViable CD45R1 and R8G2 (viable CD34+) R2 and “viable CD45”G3 (viable CD34+ and CD45dim)R3 and G2Viable stem cells R4 and G3BeadsR6Total stem cells R1 and R2 and R3Viable lymph R5 and R8Total CD45R1 and not beads DebrisR7Gating with BD FACSuite™Clinical ApplicationManual, BD Experts (A)New Algorithm (B)The BD template includes additional SSC-A vs. CD34 PE-A plots (*) that show the total rather than viable CD45+ cells. The total plot displays the same CD34+ gate, linked to the gate in the viable plot.Regression plots: BD ®Stem Cell Enumeration Assay vs. academic expert**Method A/B vs. Method C (Academic Expert)Method ABD Manual ResultsMethod BBD Stem Cell Enumeration Assay algorithm used in BD FACSuite™Clinical Application Av. BiasSt. Dev.Av. Bias St. Dev.Viable SCE -2.1% 6.3%-3.6%7.8%Total SCE -1.8%8.4%-3.9%9.7%Viable CD45+0.5%0.9%0.5%0.8%Total CD45+-0.1%0.1%0.1%0.2%The additional plot for total CD45+ helps to exclude non-stem cell particles (e.g., platelets), which can appear as a larger population in the CD45+ total plot than is represented in the viable CD45+ —see the example (right). This provides a means to ensure that results for total stem cell count and for stem cell viability are more robust,especially for bone marrow samples.Some of the algorithmic gates have a different construct from the traditional manual gates, but these produce results that are statistically equivalent to the manual results, as demonstrated by the regression analysis.The BD FACSLyric™Flow Cytometer is a Class 1 Laser Product.The BD FACSLyric™Flow Cytometer with the BD FACSuite™Clinical and BD FACSuite™Applications are CE marked in compliance with the European In Vitro Diagnostic Medical Device Directive 98/79/EC. The BD® Stem Cell Enumeration Kit is not available for use with the BD FACSLyric™Flow Cytometer in the United States.BD, the BD Logo, CellQuest, FACSDiva, FACSLyric and FACSuite are trademarks of Becton, Dickinson and Company or its affiliates. © 2021 BD. All rights reserved. BD-42639 (v1.0) 0921 / Formerly 23-19959-00The matched data show excellent concordance between all three methods (R 2= 0.99). Data derived from the algorithm generated slightly lower stem cell counts than BD experts, who in turn generated slightly lower counts than the academic expert.However , the differences were all below 5% for viable and total stem cells and less than 1% for viable and total CD45+ cells.The results demonstrate that the BD ®Stem Cell Enumeration Assay algorithm used in BD FACSuite™Clinical Application on the BD FACSLyric™System accurately enumerates CD34+ cells .ConclusionB D F AC S L y r i c ™S y s t e m a l g o r i t h mB D F AC S L y r i c ™S y s t e m a l g o r i t h mB D F AC S L y r i c ™S y s t e m a l g o r i t h mB D F AC S L y r i c ™S y s t e m a l g o r i t h m。

人类基因组概况ppt课件

A+T含量 G+C含量不能确定的碱基重复序列（不含异染色质）编码序列(基因）数目功能未知基因比例外显子最多的基因 SNP数量 SNP密度
2.91Gbp
54% 38% 9％ 35% 26588 42% Titin（234）约300万个 1/12500 bp
最长的染色体最短的染色体基因最多的染色体基因最少的染色体基因密度最大的染色体基因密度最小的染色体重复序列含量最高的染色体
It is essentially immoral not to get it (the human genome sequence) done as fast as possible.
James Watson
人类基因组计划的完成，使得我们今天有可能来探讨基因组的概，但我们仍然无法来谈论细节。
重复序列含量最低的染色体
编码外显子序列的比例基因的平均长度
2（240 Mbp） Y（19 Mbp） 1（2453） Y（104） 19（23/Mb） 13，Y（5/Mb） 19（57%）
2，8，10，13，18（36%）
1.1～1.4% 27 Kb
女平均男
染色体上距着丝粒越远，重组率越高
4. Francis S. Collins, Eric D. Green, Alan E. Guttmacher, Mark S. Guyer ：A Vision for the Future of Genomics Research. A blueprint for the genomic era. Nature Apr 24 2003: 835.
而 Celera 的测序样本来自5个人：分别属于西班牙裔、亚洲裔、非洲裔、美洲裔和高加索裔（2男3女），是从21个志愿者样本中挑选的。

纤维素结合域的酿酒酵母表面展示及其黏附位点初探

粉１／用于重组质粒的筛选。５ｇＬ，
ＣＡＡ培养基中，０ ℃ 、０／ｎ培养过夜。待菌３２０ｒｍｉ体生长到ＯＤｏ在２～５时，００Ｘｇ离心５ｍｉ０５０ｎ收集菌体。用２半乳糖的Ｙ — Ａ培养基重％ＮＢＣＡ新悬浮菌体，菌液ＯＤ使洲… 为０５～１０，０ｏ．．２ｃ条件下２０ｒｍｉ床培养６ｈ导基因表达。０／ｎ摇诱
１４稻草片的制备与酵母细胞孵育．
改进Ｗａｇ等 ¨ ｎ和ＭｃａｍｅＣｒｙ等的方法制备植物材料，对其进行酵母细胞孵育。具体做并法如下：整株稻草，离茎、片和叶鞘，茎的取剥叶在第二节上缘，下２厘米处截取长为３厘米的茎片节
接产物转化大肠杆菌Ｔａｓｏ，化后涂布于Ｌｒｎ５￣活Ｂ氨苄青霉素固体选择性培养基。挑取筛选出的单菌落经液体选择性培养基扩增后，提质粒，过抽通限制性酶切分析和测序（海桑尼公司）证重组上验质粒构建是否成功。１３酵母转化及诱导表达．采用醋酸锂法副将构建成功的质粒ｐＢ２ＭＣＤ转化酿酒酵母细胞ＥＹ１０转化后涂布于ＭＤ培Ｂ０，
面。利用免疫组化技术检测重组酵母细胞在稻草茎细胞壁上的黏附位点。结果表明：用酿酒利

A2O工艺活性污泥黏性膨胀原因及控制措施

DOI ：10.19965/ki.iwt.2023-0608第 44 卷第 4 期2024年 4 月Vol.44 No.4Apr.，2024工业水处理Industrial Water Treatment A 2O 工艺活性污泥黏性膨胀原因及控制措施赵晓娟，张智瑞，刘东洋，雷彬（中原环保股份有限公司，河南郑州 450000）［摘要］污泥黏性膨胀问题一直是A 2O 工艺运行控制的难点。

郑州市某污水处理厂在运行过程中出现污泥黏性膨胀问题，造成污泥沉降性能变差，SVI 逐渐提升至240 mL/g 左右，二沉池泥位持续升高。

从进水水质、水温、曝气量、浮渣等多方面综合分析引起污泥膨胀的原因，及时从剩余污泥排放量、污泥龄、溶解氧、回流比、水力停留时间等工艺参数调整运行工艺，使污泥膨胀问题得到一定程度的缓解。

为彻底消除生物池浮泥，又通过在二沉池配水井精准投加40 mg/L 的阳离子高分子絮凝剂，经过一段时间的药剂助沉，明显提高了污泥沉降性能，SVI 也逐渐下降至120 mL/g 左右的正常水平，解决了污泥黏性膨胀问题，消除了生物池浮泥，为解决污泥黏性膨胀提供了思路。

［关键词］ A 2O 工艺；污泥黏性膨胀；生物浮泥；絮凝剂［中图分类号］ X703.1 ［文献标识码］B ［文章编号］ 1005-829X （2024）04-0198-07Reasons and control measures for viscous bulking of activatedsludge in A 2O processZHAO Xiaojuan, ZHANG Zhirui, LIU Dongyang, LEI Bin (Central Plains Environment Protection Co., L td., Z hengzhou 450000, China )Abstract ： Sludge viscosity expansion has always been a difficulty in the operation control of A 2O process. The sludge viscosity expansion problem occurred in a sewage treatment plant in Zhengzhou City, resulting in poor sludge settling performance with SVI gradually increasing to about 240 mL/g and the mud level of the secondary sedimenta⁃tion tank continuing to rise. The reasons of sludge swelling were analyzed from the aspects of inlet water quality, wa⁃ter temperature, aeration rate and scum. And then some measures such as adjusting the residual sludge discharge, sludge age, dissolved oxygen value, reflux ratio, hydraulic residence time were timely adopted, so that the sludge bulking problem was alleviated to a certain extent. In order to eliminate the floating mud in the biological tank, 40 mg/L cationic polymer flocculant was added to the distribution well of the secondary sedimentation tank. After a pe⁃riod of time, the sedimentation performance of the sludge was obviously improved with the SVI gradually decreasing to the normal level of about 120 mL/g, indicating that the problem of sludge viscosity swelling and eliminated thefloating mud in the biological tank was solved. It provides a way to solve the viscous swelling of sludge.Key words ： A 2O process; sludge viscous bulking; biological floating mud; flocculant长期以来污泥膨胀是困扰采用活性污泥法处理工艺的城镇污水处理厂正常运行管理的突出难题〔1〕。

一种新的多肽——人S4核糖体蛋白10和编码这种多肽的多核苷酸[发明专利]

专利名称：一种新的多肽——人S4核糖体蛋白10和编码这种多肽的多核苷酸
专利类型：发明专利
发明人：毛裕民,谢毅
申请号：CN00115719.1
申请日：20000516
公开号：CN1323826A
公开日：
20011128
专利内容由知识产权出版社提供
摘要：本发明公开了一种新的多肽——人S4核糖体蛋白10,编码此多肽的多核苷酸和经DNA重组技术产生这种多肽的方法。

本发明还公开了此多肽用于治疗多种疾病的方法,如恶性肿瘤,血液病,HIV 感染和免疫性疾病和各类炎症等。

本发明还公开了抗此多肽的拮抗剂及其治疗作用。

本发明还公开了编码这种新的人S4核糖体蛋白10的多核苷酸的用途。

申请人：上海博德基因开发有限公司
地址：200092 上海市中山北二路1111号3号楼12层
国籍：CN
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RESONATOR LENGTH MEASUREMENT

专利名称：RESONATOR LENGTH MEASUREMENT发明人：DJUPSJÖBACKA, Anders,DAHLQUIST, Håkan 申请号：EP2009063208申请日：20091009公开号：WO10/040838P1公开日：20100415专利内容由知识产权出版社提供摘要：The invention provides a method and system for measuring the length of a reflective resonator, by analyzing an electromagnetic spectrum emitted therefrom. The emitted spectrum is used for estimating a first cavity length. This estimation is thereafter improved, by first computing at least one interference number for the spectrum, adjust this value to e.g. an integer or half-integer depending on the configuration of the resonator, and thereafter re-calculating the length of the resonator using the adjusted value of the interference number. The above is an efficient way of improving the accuracy in the determination of a physical property of a resonator.申请人：DJUPSJÖBACKA, Anders,DAHLQUIST, Håkan地址：Electrum 236 S-164 40 Kista SE,Sorterargatan 1 S-162 50 VällingbySE,Virebergsvägen 20 S-169 30 Solna SE,Lindaus Väg 8 S-165 72 Hässelby SE 国籍：SE,SE,SE,SE代理机构：HAAG, Malina更多信息请下载全文后查看。

activate格子原理

activate格子原理
格子激活原理是指通过某种方式刺激格子的活性，使其达到一定程度的活跃状态。

在科学与技术领域中，这一原理被广泛应用于多个领域，如生物学、物理学、电子学等。

在生物学中，格子激活原理常常被用于描述细胞内蛋白质与细胞间信号传递的过程。

细胞内的蛋白质网络形成了一个复杂的生物信息传递系统，其中的格子具有重要的作用。

通过刺激这些格子，可以促进细胞内信号的传导与调控，进而调整细胞的功能状态。

在物理学中，格子激活原理被应用于描述固体晶格中的原子活动。

固体晶格是由一系列排列有序的原子或离子组成的规则结构，其活性对材料的性质至关重要。

通过刺激晶格的活性，可以改变材料的导电性、热导性、机械性能等特性，从而实现材料的优化与调控。

在电子学中，格子激活原理常被用于描述半导体器件中的工作原理。

半导体器件中的格子，如硅或锗等材料的晶格，可以通过外加电场或电流的刺激而发生活跃状态的改变，从而实现电子的传导或封锁。

这一原理被广泛应用于电子元件的设计与制造，如晶体管、二极管等。

总而言之，格子激活原理是一种通过刺激格子的活性，使其达到一定程度的活跃状态的原理。

在不同领域中，这一原理被应用于解释和实现各种现象和功能，从而推动了科学技术的发展和应用。