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第33卷第6期暨南大学学报（医学版）Vol．33No．6 2012年12月Journal of Jinan University（Medicine Edition）Dec．2012应用双能X线吸收法测量人体身体成分的精密度肖泽宇，郭斌，弓健，唐勇进，徐浩（暨南大学附属第一医院核医学科，广东广州510632）［摘要］目的：应用双能X线吸收法（dual-energy X-ray absorptiometry，DXA）测量人体身体成分精密度，探讨DXA对人体身体成分和脂肪分布测量结果的重复性。
方法：DXA（GE Lunar Prodigy）对20名健康成年志愿者（男女各10名）进行10次全身重复扫描，应用专用分析软件计算全身骨量（total bone mineral content，TBMC），全身脂肪组织量（total fat mass，TFM），全身瘦组织量（total lean mass，TLM），全身脂肪含量百分数（total body fat percent-age，%TBF），腹部脂肪含量百分数（android fat percentage，%AF），臀部脂肪含量百分数（gynoid fat percentage，% GF），通过计算变异系数均方根（RMS-CV）和标准差均方根（RMS-SD）评价DXA对人体身体成分和脂肪分布测量的精密度。
结果：人体全身TBMC，TFM，TLM，%TBF的RMS-CV和RMS-SD分别为1.05%、1.95%、0.93%、1.98%和0.025、0.234、0.377、0.353。
脂肪分布测量结果（%AF、%GF）的RMS-CV和RMS-SD分别为2.51%、1.80%和1.153、1.101。
结论：DXA测量人体身体成分和脂肪分布具有良好的精密度。
［关键词］精密度；身体成分；脂肪分布；双能X线吸收法［中图分类号］R81［文献标志码］A［文章编号］1000－9965（2012）06－0587－04Precision of DXA for the measurements of human body compositionXIAO Ze-yu，GUO Bin，GONG Jiang，TANG Yong-jin，XU Hao（Department of Nuclear Medicine，the First Affiliated Hospital，Jinan University，Guangzhou510630，China）［Abstract］Aim：To measure the precision of body composition measurements and fat distribution in human using dual energy X-ray absorptiometry（DXA），and evaluate its reliability．Methods：Ten con-secutive total body scans were conducted with DXA（GE Lunar Prodigy）in20healthy adult volunteers（10 males，10females）．The software（enCORE，version10.50.086）was used to analyze total bone mineral content（TBMC），total lean mass（TLM），total fat mass（TFM），total body fat percentage（%TBF），android fat percentage（%AF），gynoid fat percentage（%GF），the precision was represented as the co-efficient of variation of root mean square（RMS-CV），standard deviation of root mean square（RMS-SD）．Results：RMS-CV were1.05%，1.95%，0.93%，1.98%and RMS-SD were0.025，0.234，0.377，0.353for TBMC，TFM，TLM and%TBF．RMS-CV were2.51，1.80and RMS-SD were1.153，1.101for%AF and%GF．Conclusion：DXA provided excellent precision for the measurements of body composi-tion and fat distribution．［Key words］precision；body composition；fat distribution；dual energy X-ray absorptiometry［收稿日期］2012－05－09［基金项目］广东省医学科研基金立项项目（A2008354）［作者简介］肖泽宇（1987－），男，医师，研究方向：临床核医学通讯作者：徐浩，男，教授，博士生导师，Tel：020－38688404，E-mail：txh@jnu．edu．cn身体成分（body composition）是指在人体总质量中，脂肪、瘦组织、骨矿物含量及其构成比例［1］。
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. Optimal assessment and management of chronic obstructive pulmonary disease (COPD). Eur Respir J 1995;8:1398-420.5.Nocturnal Oxygen Therapy Trial Group.Continuous or nocturnal oxygen therapy in hypoxemic chronic obstructive pulmonary disease: a clinical trial. Ann Intern Med 1980;93:391-8.6.Intermittent positive pressure breathing therapy of chronic obstructive pulmonary disease: a clinical trial. Ann Intern Med 1983;99:612-20.7.Gerardi DA, Lovett L, Benoit-Connors ML, Reardon JZ, ZuWallack RL. Variables re-lated to increased mortality following out-patient pulmonary rehabilitation. Eur Res-pir J 1996;9:431-5.8.Nishimura K, Izumi T, Tsukino M, Oga T. Dyspnea is a better predictor of 5-year sur-vival than airway obstruction in patients with COPD. Chest 2002;121:1434-40.9.Schols AM, Slangen J, Volovics L, Wout-ers EF. Weight loss is a reversible factor in the prognosis of chronic obstructive pulmo-nary disease. 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。
第47卷第1期Vol.47No.1计算机工程Computer Engineering2021年1月January2021基于姿态估计与GRU网络的人体康复动作识别闫航1，2，陈刚1，2，佟瑶2，3，姬波1，胡北辰1（1.郑州大学信息工程学院，郑州450001；2.郑州大学互联网医疗与健康服务协同创新中心，郑州450001；3.郑州大学护理与健康学院，郑州450001）摘要：康复锻炼是脑卒中患者的重要治疗方式,为提高康复动作识别的准确率与实时性,更好地辅助患者在居家环境中进行长期康复训练,结合姿态估计与门控循环单元(GRU)网络提出一种人体康复动作识别算法Pose-AMGRU。
采用OpenPose姿态估计方法从视频帧中提取骨架关节点,经过姿态数据预处理后得到表达肢体运动的关键动作特征,并利用注意力机制构建融合三层时序特征的GRU网络实现人体康复动作分类。
实验结果表明,该算法在KTH和康复动作数据集中的识别准确率分别为98.14%和100%,且在GTX1060显卡上的运行速度达到14.23frame/s,具有较高的识别准确率与实时性。
关键词：康复训练；动作识别；姿态估计；门控循环单元；注意力机制开放科学(资源服务)标志码(OSID)：中文引用格式：闫航，陈刚，佟瑶，等.基于姿态估计与GRU网络的人体康复动作识别［J］.计算机工程，2021，47（1）：12-20.英文引用格式：YAN Hang，CHEN Gang，TONG Yao，et al.Human rehabilitation action recognition based on pose estimation and GRU network［J］.Computer Engineering，2021，47（1）：12-20.Human Rehabilitation Action Recognition Based onPose Estimation and GRU NetworkYAN Hang1，2，CHEN Gang1，2，TONG Yao2，3，JI Bo1，HU Beichen1（1.College of Information Engineering，Zhengzhou University，Zhengzhou450001，China；2.Internet Medical and Health Service Collaborative Innovation Center，Zhengzhou University，Zhengzhou450001，China；3.College of Nursing and Health，Zhengzhou University，Zhengzhou450001，China）【Abstract】Rehabilitation exercise is an important treatment method for stroke patients.This paper proposes a rehabilitation action recognition algorithm，Pose-AMGRU，which combines pose estimation with Gated Recurrent Unit （GRU）in order to improve the accuracy and real-time performance of rehabilitation action recognition，and thus assist patients in in-home long-term rehabilitation training.The algorithm uses OpenPose pose estimation method to extract the skeleton joints from video frames，and the pose data is preprocessed to obtain the key action features that represent body movement.Then a GRU network with three-layer time series features is constructed by using the attention mechanism to realize rehabilitation action classification.Experimental results on KTH dataset and rehabilitation action dataset show that the proposed algorithm increases the recognition accuracy to98.14%and100%，and its running speed on GTX1060 reaches14.23frame/s，which demonstrates its excellent recognition accuracy and real-time performance.【Key words】rehabilitation training；action recognition；pose estimation；Gated Recurrent Unit（GRU）；attention mechanism DOI：10.19678/j.issn.1000-3428.00582010概述脑卒中发病人数逐年上升，已成为威胁全球居民生命健康的重大疾病，具有极高的致残率，其中重度残疾者约占40%［1］。
美国服装标准尺寸表服装尺码换算参照表女装（外衣、裙装、恤衫、上装、套装）标准尺码明细中国(cm) 160-165 / 84-86 165-170 / 88-90 167-172 / 92-96 168-173 / 98-102 170-176 / 106-110 国际XS S M L XL美国2 4-6 8-10 12-14 16-18欧洲34 34-36 38-40 42 44男装（外衣、恤衫、套装）标准尺码明细中国(cm) 165 / 88-90 170 / 96-98 175 / 108-110 180 / 118-122 185 / 126-130国际S M L XL XXL男装（衬衫）标准尺码明细中国(cm) 36 - 37 38 - 39 40 - 42 43 - 44 45 - 47国际S M L XL XXL男装（裤装）标准尺码明细尺码42 44 46 48 50腰围68 - 72 cm 71 - 76 cm 75 - 80 cm 79 - 84 cm 83 - 88 cm裤度99 cm 101.5 cm 104 cm 106.5 cm 109 cm看不懂的还可以参照下面的：服装尺寸表男仕服装尺码分类小码中码大码加大码身高165 170 175 180胸围84 90 96 102腰围75 81 87 93臀围88 90 92 100女仕服装尺码分类小码中码大码加大码身高155 160 165 170胸围80 84 88 92腰围60 64 68 72臀围84 88 92 96儿童服装尺码分类小码中码大码加大码适合年龄0-2岁2-4岁5-7岁7-10岁身高80 110-110 110-130 140-150胸围50 55 60-65 70腰围40 42 44 46臀围55 60 65-70 75美国对服装标志的有关规定美国海关边境保护局最近出版多份刊物，就服装标志规定提供指引和资料，并阐释针织成形服装、手套以及非针织或织连指手套的关税分类方法。
第1章绪论案例辨析及参考答案案例1-1某研究者的论文题目为“大学生身心健康状况及其影响因素研究”，以某地职业技术学院理、工、文、医学生（三年制）为研究对象，理、工、文、医学生分别挑选了60、38、19和46人，以问卷方式调查每位学生的一般健康状况、焦虑程度、抑郁程度等。
得出的结论是：“大学生身心健康状况不容乐观，学业问题、就业压力、身体状况差、人际交往不良、社会支持不力为主要影响因素”。
请问其结论合理吗？为什么？应该如何？案例辨析①样本不能代表总体。
总体是“大学生”，而样本仅为某地三年制职业技术学院学生；②社会学调查的样本含量显得不足；③“理、工、文、医学生分别挑选……”这种说法中隐含人为“挑选”的意思，不符合统计学要求。
正确做法应在论文的题目中明确调查的时间范围和地点，还应给“大学生”下一个明确的定义，以便确定此次调查的“总体”；对“大学生身心健康状况”可能有影响的因素很多，应结合具体问题拟定出少数最可能有影响的因素（如学科、在学年限等）进行分层随机抽样，以保证样本有较好的代表性；还应根据已知条件找到估计样本含量的计算公式，不可随意确定各学科仅调查几十人；当然，调查表中项目的设置也是十分重要的，此处从略。
案例1-2两种药用于同一种病，A药治疗5例，4例好转；B药治疗50例，36例好转。
结论是：A药优于B药。
请问其结论合理吗？为什么？应该如何？案例辨析①A药样本仅5例，样本含量太少；②得出“A药优于B药”没有交待是否采用了统计学推断方法，若用目测法得出结论，则结论没有说服力；③未明确研究目的和研究结果将被使用的范围。
正确做法①应明确研究目的和研究结果将被使用的范围，若是个别研究者或临床医生想了解这两种药的大致疗效，属于小规模的临床观察，其结论仅供少数人在今后临床实践中参考，其样本含量可能不需要很大，因为观察指标是定性的（有效、无效），一般来说，每个药物组也需要几十例（以不少于20例为宜）；若属于新药的Ⅱ期临床试验，那就要严格按有关规定，比较准确地估计出所需要的样本含量，不仅如此，还有很多严格的要求，详见本书中临床试验设计一章；②从明确定义的总体中随机抽样进行实验研究，得到的实验结果不能仅凭数据大小作出判断，应进行假设检验，以提高结论的可信度。
矮身高（矮身材）是儿科整形外科医生所普遍面对的临床疾病。
对这些儿童的诊断与治疗策略要特别关注成年身高的预测以及必要时的干涉计划。
准确的骨龄评价及其与生活年龄的差异是制订治疗计划所必不可缺的。
在许多骨龄评价方法中，GP和TW3方法为临床普遍使用。
骨成熟度的长期趋势促成了TW3方法，RUS和腕骨骨龄之间的显著差异也已经完全被接受，而且TW3方法分别建立了RUS和腕骨骨龄标准。
在亚洲和远东人群验证TW3法得到了Tanner的支持。
当与GP图谱相比较时，因为TW3法有更牢靠的数学基础，所以更为灵活，标准误差较小，但其不足是评价较为困难、费时。
在文献中已经对两种方法的优缺点进行了很好的讨论。
有许多参考文献研究了矮身高（矮身材）和各种生长异常的骨龄，但是我们未发现分析特发性矮身高（矮身材）儿童骨龄，以及家族性（familial short stature,FSS)与非家族性矮身高（non-familial short stature，non-FSS)之间骨龄延迟或提前差异的研究。
我们根据以父母身高预测的儿童最终身高而简单分类。
父母身高决定最终成年身高的作用已经得到广泛的研究。
我们设计该回顾性研究，以增加对ISS病人骨龄模式，及其两亚组、两性别之间模式变化的了解。
这些模式将有助于我们确定正确的干涉类型，而且也在一定程度上阐明ISS的自然生长史。
材料与方法转诊到我们儿科矫形外科的230名病人，身高低于韩国生长图表3rd百分位数曲线。
仔细的临床检查包括X线摄片和生长激素测定，来诊断综合症、影响骨生长和代谢性疾病和生长激素异常。
如果病人无异常，诊断为特发性矮身高（矮身材），进入本研究。
因初始诊断或是记录不完全和X线片质量差而排除的44名病人。
因此，研究包括了2003-2005年就诊的韩国血统186名病人（95男，91女）。
同时取得父母身高，如果父母身高低于韩国标准身高的3rd百分位数，确定为矮身高（矮身材）。
将病人分为两组，组A为FSS，由有矮父母（一方或双方）身高的儿童组成，共有100名病人（55男，4.8-18岁；45名女，4.1-16岁）。
浙江理工大学学报（自然科学版），第39卷，第1期，20：18年1月Journal of Zhejiang Sci-Tech University(Natural Sciences)V ol.39，No. 1，Jan.2018DQI：10. 3969/j.issn. 1673-3851 (n).2018. 01. 005基于形态参数的青年女性乳房体积预测马静8，詹诗画8，邹奉元^(浙江理工大学，a.服装学院；〕.浙江省服装工程技术研究中心，杭州310018)摘要：为建立青年女性乳房体积与形态参数的预测模型，选取了235名18〜25岁的在校青年未孕女性进行三维人体扫描，利用逆向工程软件绘制乳房轮廓线，提取乳房三维模型进行N U R B S曲面拟合，在P r o/E软件中对曲面实体化后测量体积。
通过逐步回归分析，建立了乳房体积的预测模型，提出了与乳房体积最为相关的3个形态参数，分别为横奶杯弧线长、纵奶杯弧线长和胸围差，用于指导文胸罩杯的结构设计。
随机选取10名在校青年未孕女性作为测试样本对预测模型进行验证，实验验证发现：该预测模型的调整圮系数为0. 91 7,h g.小于0. 01，预测误差在7%以内，预测效果较好。
关键词：乳房体积；乳房三维模型；形态参数；逐步回归中图分类号：TS941. 17 文献标志码：A 文章编号：1673-3851 (2018) 01-0025-060引言量身定制在服装成衣领域中广泛使用，而文胸 作为女性服装的特殊代表，其穿着的舒适性及合体 性在国内外受到越来越多的关注。
Y o u n g等[1]和Boyes等[2]在其研究中发现70 %的英国女性（尤其 是乳房较大的女性）穿着的文胸不合体，北服一爱慕 人体工学研究所曾记录有75. 8%的女性穿着文胸 号型与实际不符[3]。
文胸的舒适性与合体性是许多 女性的一个困扰，L e e等[]发现文胸穿着不合体的 原因是大多数女性并不知道她们真正的乳房大小和 形状。
1542 环球中医药2023年8月第16卷第8期 Global Traditional Chinese Medicine,August 2023,Vol.16,No.8㊃数据挖掘㊃基金项目:国家重点研发计划(2017YFC1703302)作者单位:100029 北京中医药大学第三临床医学院[李骁群(硕士研究生)㊁张皓倩(硕士研究生)㊁覃庭威(硕士研究生)];北京市丰台社区卫生服务中心中医康复科(程翰林);北京中医药大学第三附属医院治未病中心(陈婧㊁陈夷㊁赵瑞珍)作者简介:李骁群(1997-),2021级在读硕士研究生㊂研究方向:中医药防治常见病的研究㊂E⁃mail:1094241360@ 通信作者:赵瑞珍(1974-),博士,硕士生导师㊂研究方向:中医药防治代谢性疾病㊂E⁃mail:zhaoruizhen2009@不同体重指数人群的中医舌象客观参数分析李骁群 程翰林 陈婧 陈夷 张皓倩 覃庭威 赵瑞珍【摘要】 目的 分析不同体重指数(body mass index,BMI)大学生的舌象参数特征,以期为消瘦㊁超重肥胖人群的临床辨识和诊疗提供一定的舌象客观化依据㊂方法 使用TFDA⁃1型舌诊仪采集386例体检大学生的舌象图片,根据BMI 分为消瘦组㊁超重肥胖组和体重正常组,采用舌象数据采集软件提取各组的舌象参数特征,比较各组舌色及舌苔R(红色值)㊁G(绿色值)㊁B(蓝色值)㊁L(明度)㊁a(绿 红值)㊁b(蓝 黄值)㊁H(色调)㊁S(饱和度)㊁V(亮度)参数的差异,并运用Spearman 检验分析BMI 指数与舌象参数的相关性㊂结果 体重正常组舌色红色值㊁绿 红值㊁饱和度显著低于消瘦组,显著高于超重肥胖组(P <0.01),舌色亮度值显著大于其余2组(P <0.01);体重正常组苔质明度㊁绿 红值㊁亮度值显著高于其余2组(P <0.01),消瘦组舌苔绿 红值㊁亮度值高于超重肥胖组(P <0.01);3组人群苔色参数无统计学差异(P >0.05)㊂Spearman 相关性分析显示,BMI 指数与舌色红色值㊁舌色绿 红值㊁舌色饱和度㊁苔质绿 红值㊁苔质亮度值呈负相关(P <0.01)㊂结论 消瘦㊁超重肥胖人群的舌象参数有明显差异,BMI 指数与舌象参数具有一定的相关性,舌象客观参数可作为消瘦㊁超重肥胖人群的辨识标准之一,其中舌色红色值㊁绿 红值㊁饱和度,苔质绿 红值㊁亮度值可作为消瘦㊁超重肥胖人群与体重正常人群的鉴别点㊂【关键词】 舌象参数; BMI 指数; 舌诊; 消瘦; 超重肥胖【中图分类号】 R241.25 【文献标识码】 A doi:10.3969/j.issn.1674⁃1749.2023.08.007Analysis of objective parameters of tongue picture in traditional Chinese medicine for people with different body mass indexLI Xiaoqun ,CHENG Hanlin ,CHEN Jing ,CHEN Yi ,ZHANG Haoqian ,TAN Tingwei ,ZHAO Ruizhen The Third Clinical Medical College of Beijing University of Chinese Medicine ,Beijing 100029,China Corresponding author :ZHAO Ruizhen ,E⁃mail :zhaoruizhen2009@【Abstract 】 Objective To analyze the characteristics of tongue parameters of college students with different body mass index (BMI)and explore the correlation between BMI and tongue parameters,so as to provide some objective basis for clinical identification and diagnosis and treatment of lean,overweight andobese people.Methods The tongue images of 386college students were collected with TFDA⁃1tongue di⁃agnostic apparatus.They were divided into emaciated group,overweight and obesity group and normal weight group according to BMI.The tongue image parameters of each group were extracted by tongue image data acquisition software.The differences of tongue color,tongue coating RGB,Lab and HSV parameterswere compared among each group.The correlation between BMI index and tongue image parameters was analyzed by Pearson test.Results The R,a,S values of tongue color in the normal weight group were significantly lower than those in the lean group,and significantly higher than those in the overweight and环球中医药2023年8月第16卷第8期 Global Traditional Chinese Medicine,August2023,Vol.16,No.81543 obesity group(P<0.01).The V values of tongue color were significantly higher than those in the other twogroups(P<0.01);the values of L,a and V of fur in the normal weight group were significantly higherthan those in the other two groups.There was no significant difference in fur color parameters among thethree groups(P>0.05).Pearson correlation analysis showed that BMI index was negatively correlated withtongue color R,tongue color a,tongue color S,lichen a and lichen V(P<0.01).Conclusion There are significant differences in the parameters of tongue picture between the lean and overweight obese people.The objective parameters of tongue picture can be used as one of the identification criteria for the lean and overweight obese people,and provide some objective basis for the clinical identification and diagnosis and treatment of this population.【Key words】 tongue parameters; BMI; diagnosis of tongue picture; emaciation; overweightand obesity 体重指数(body mass index,BMI)是用于衡量体重是否符合正常标准的一项重要指标,正常范围为18.5~23.9kg/m2,低于或超出这个范围即表现为消瘦㊁超重肥胖㊂BMI指数异常现象在我国人群中普遍存在,会对机体产生各种负面影响,如降低身体素质机能㊁增大心脑血管及各种代谢疾病的患病风险[1⁃2],因此尽早进行体质量状态的辨识变得尤为重要㊂中医学较早认识到不同体型人群的舌象特征存在差异[3],现代研究也表明体重指数与舌象存在一定的相关性,异常BMI人群舌色㊁舌苔特征通常不同[4]㊂舌诊作为中医临床诊断和辨证的重要依据,目前正不断向客观化和标准化的方向发展,越来越多的学者已经在舌象客观研究方面取得了较大进展㊂本研究以不同BMI指数的体检大学生为切入点,通过图像数据化技术提取消瘦㊁超重肥胖人群的舌象特征参数,分析各组舌象RGB㊁Lab 及HSV参数差异,探讨BMI指数与舌象参数的相关性,以期为消瘦㊁超重肥胖人群的辨识和诊疗提供一定客观化依据[5⁃6]㊂1 资料与方法1.1 研究对象本课题的研究类型为横断面研究,采用整群抽样的方法选取2019年9月于北京中医药大学入学体检的大学生(包括研究生)作为研究对象,由经过统一培训的2名专业研究人员收集研究对象的年龄㊁性别㊁身高及体重等指标,计算BMI指数,BMI=体质量(kg)/身高(m)2㊂纳入标准:通过体检无严重器质性疾病㊁既往3个月无急慢性疾病者;年龄不低于18岁;对研究方案知情同意且能配合舌象拍摄者㊂排除标准:体检基本资料及指标不全者;因糖尿病㊁胃肠疾病㊁严重器质性病变㊁恶性肿瘤及其他急慢性代谢疾病引起的继发性消瘦和超重肥胖者;舌象拍摄过程中不配合者㊂最终纳入符合标准的体检者386例,年龄为18~22岁,平均年龄为(18.22±1.26)岁,其中男性175例,平均年龄为(18.24±1.08)岁,女性211例,平均年龄为(18.20±1.40)岁㊂本研究依托于上海中医药大学承担的国家重点研发计划 中医智能舌诊系统研发项目,为其子课题,经上海中医药大学附属曙光医院伦理委员会审查通过(批号:2018⁃626⁃55⁃01)㊂1.2 分组标准按照中国成人超重和肥胖诊断标准[7]将研究对象划分为3组:消瘦组(BMI<18.5kg/m2),体重正常组(18.5≤BMI≤23.9kg/m2),超重肥胖组(BMI≥24.0kg/m2)㊂1.3 舌象图像采集本研究使用的舌象图片采集设备为国家重点研发计划 中医智能舌诊系统研发项目提供的TFDA⁃1型舌诊仪,由相机㊁标准光源箱㊁底座㊁曲面反光罩组成㊂其中标准光源色温为5000K,显色指数为97;NIKON D40单镜头反光数码相机,手动自平衡,自动对焦,50mm定焦㊂舌象采集时研究对象采用坐位,面部正对相机镜头及标准光源,下颌放于舌诊仪固定框上,自然放松地将舌伸出口外,研究对象尽量张口,从而较大程度地暴露舌体,并保持姿势3~5秒,由1名研究人员通过相机采集照片2~3张,每位研究对象保存1张舌象图用于舌象客观参数分析及舌象判读㊂拍摄前嘱体检者禁食有色食物,排除染苔等原因对舌象识别的影响㊂1.4 舌象客观参数提取采用 舌象数据采集”软件提取各组人群的舌象参数特征,包括舌色㊁苔质㊁苔色在RGB㊁Lab及1544 环球中医药2023年8月第16卷第8期 Global Traditional Chinese Medicine,August 2023,Vol.16,No.8HSV 三种颜色空间中的9种特征参数值,每位研究对象导入1张舌象图用于舌象参数提取㊂首先点击 打开文件”将舌象图导入软件,通过图像分割得到舌体图像,然后进行舌体研究区域的选取㊂研究区域为舌尖㊁舌中㊁舌根㊁舌两边共5个区域,手动选择每个区域中心点作为研究点,舌体分区以中医诊断学舌面图为标准㊂研究点选取完毕后,点击 采集数据”通过本软件的MATLAB R2016a 智能算法对5个研究点分别进行R(红色值)㊁G(绿色值)㊁B(蓝色值)㊁L (明度)㊁a (绿 红值)㊁b (蓝 黄值)㊁H (色调)㊁S(饱和度)㊁V(亮度)参数均值提取,输出舌色参数报告并以Excel 格式保存,苔质及苔色参数的提取方式与舌色参数一致,软件操作界面见图1㊂MATLAB 是一个包含大量数学运算算法的集合,它包括从最基本的函数到矩阵㊁特征向量㊁傅里叶变换的复杂函数,并且具有完备的图像处理功能,可以通过数学表达式运算实现数值结果的可视化㊂RGB 颜色空间由红色值(Red )㊁绿色值(Green)㊁蓝色值(Blue)构成,各颜色特征取值范围为[0,255],数值越高表示相应的颜色特征越明显㊂Lab 颜色空间由明度(Luminosity)㊁绿 红值(a)㊁蓝 黄值(b)构成,明度取值范围为[0,100],数值越大表示色彩越明亮,反之越晦暗;绿 红值取值范围为[-128,127],负值为绿色,正值为红色,数值越大代表红色特征越明显;蓝 黄值取值范围为[-128,127],负值为蓝色,正值为黄色,数值越大代表黄色特征越明显㊂HSV 颜色空间由色调(Hue)㊁饱和度(Saturation)㊁亮度(Value)构成,色调取值范围为[0,1),数值越小则红色特征越明显;饱和度取值范围为[0,1],数值越高表示颜色越深,反之越浅淡;亮度取值范围为[0,1],数值越高表示越明润,数值越低表示越暗淡㊂各颜色空间模型见图2~图4㊂图1 舌象数据采集操作示意图图2 RGB颜色空间模型图3 Lab颜色空间模型图4 HSV 颜色空间模型1.5 舌象判读舌象特征由2名具有副主任及以上职称的中医师在自然光线下根据舌诊仪采集的舌象图进行判读,并将结果录入Excel 表格,若判读存有异义,则由专家组和研究人员共同讨论确定㊂舌色㊁舌形㊁苔质及苔色特征参照‘中医诊断学“中舌诊部分,具体分为淡白舌㊁淡红舌㊁红舌㊁绛舌㊁黯红舌㊁胖大舌㊁瘦舌㊁点刺舌㊁裂纹舌㊁齿痕舌㊁薄苔㊁厚苔㊁腻苔㊁剥苔㊁少苔㊁白苔㊁黄苔㊁黄白相兼苔等维度㊂环球中医药2023年8月第16卷第8期 Global Traditional Chinese Medicine,August 2023,Vol.16,No.81545 1.6 研究方法观察各BMI 分组人群的舌色㊁舌形㊁苔质及苔色特征,比较各组舌色㊁苔质及苔色RGB㊁Lab 及HSV 参数的差异,运用Spearman 检验分析BMI 指数与舌象参数的相关性㊂1.7 统计学方法所有数据运用SPSS 20.0软件统计分析㊂本研究中,性别㊁舌象特征为计数资料,以频数和构成比表示,采用c 2检验比较组间差异㊂年龄㊁舌象RGB㊁Lab㊁HSV 参数为计量资料,经检验服从正态分布且方差齐,以均数±标准差(x ±s )表示,采用单因素方差分析比较组间差异,采用LSD 检验进行两两比较㊂BMI 为计量资料,经检验不服从正态分布,使用四分位数描述,以中位数及第25㊁75百分位数M (P 25,P 75)表示,采用Kruskal⁃Wallis 检验比较组间差异,采用Bonferronni 法进行两两比较㊂相关性分析采用Spearman 检验㊂P <0.05认为差异有统计学意义㊂2 结果2.1 各BMI 分组性别㊁年龄比较本研究共纳入386例研究对象,其中消瘦组110例㊁体重正常组140例㊁超重肥胖组136例,三组研究对象性别㊁年龄相比差异无统计学意义(P >0.05),BMI 指数相比差异有统计学意义(P <0.01),见表1㊂2.2 舌象特征比较舌色方面,消瘦组以红舌为主,体重正常组以淡红舌为主,超重肥胖组各异常舌色占比大致相当,以红舌㊁淡白舌㊁黯红舌为主;舌形方面,消瘦组以瘦舌为主,体重正常组以正常舌形为主,超重肥胖组以齿痕舌为主;苔质方面,消瘦组和超重肥胖组以厚苔为主,体重正常组以薄苔为主;苔色方面,3组苔色均以白苔为主㊂综上,消瘦组多见红舌㊁瘦舌㊁白厚苔,超重肥胖者组多见红舌㊁淡白舌㊁黯红舌㊁齿痕舌㊁白厚苔,体重正常组多见淡红舌㊁正常舌形㊁薄白苔㊂各组典型舌象见图5㊂结果见表2~5㊂2.3 舌象参数比较3组间舌色R㊁a㊁S㊁V 值差异有统计学意义(P <0.01),其中体重正常组舌色R㊁a㊁S 值显著低于消瘦组㊁显著高于超重肥胖组(P <0.01),舌色V 值显著大于其余2组(P <0.01);消瘦组舌色R㊁a㊁S 值显著高于超重肥胖组(P <0.01)㊂3组间苔质L㊁a㊁V 值差异有统计学意义(P <0.01),其中体重正常组苔质L㊁a㊁V 值显著高于其余2组(P <0.01),消瘦组舌苔a㊁V 值高于超重肥胖组(P <0.01)㊂3组间苔色参数差异无统计学意义(P >0.05)㊂见表6~表8㊂表1 各组一般资料比较组别样本量男生[例(%)]女生[例(%)]年龄BMI体重过低组11041(37.3)69(62.7)18.14±0.9617.65(17.10,18.10)ab 体重正常组14063(45.0)77(55.0)18.37±1.5820.50(19.53,22.10)超重肥胖组13671(52.2)65(47.8)18.13±1.0926.00(24.93,28.28)a注:与体重正常组相比,a P <0.01;与超重肥胖组相比,b P <0.01㊂注:从左到右依次为超重肥胖组㊁体重正常组㊁消瘦组㊂图5 各组典型舌象图1546 环球中医药2023年8月第16卷第8期 Global Traditional Chinese Medicine,August2023,Vol.16,No.8表2 舌色特征比较[例(%)]组别样本量淡白舌淡红舌红舌绛舌黯红舌消瘦组1108(7.3)b23(20.9)a46(41.8)ab5(4.5)28(25.5)a 体重正常组1407(5.0)90(64.3)25(17.9)2(1.4)16(11.4)超重肥胖组13634(25.0)a29(21.3)a36(26.5)6(4.4)31(22.8)a 注:与体重正常组相比,a P<0.01;与超重肥胖组相比,b P<0.01㊂表3 舌形特征比较[例(%)]组别样本量正常舌胖大舌瘦舌点刺舌裂纹舌齿痕舌消瘦组11022(20.0)a8(7.3)a53(48.2)ab3(2.7)5(4.5)a19(17.3)b 体重正常组14092(65.7)0(0.0)17(12.1)0(0.0)0(0.0)31(22.1)超重肥胖组13619(14.0)a20(14.7)a13(9.6)4(2.9)3(2.2)77(56.6)a 注:与体重正常组相比,a P<0.01;与超重肥胖组相比,b P<0.01㊂表4 苔质特征比较[例(%)]组别样本量薄苔厚苔腻苔剥苔少苔消瘦组11039(35.5)ab46(41.8)b10(9.1)a2(1.8)13(11.8)a 体重正常组14096(68.6)40(28.6)2(1.4)0(0.0)2(1.4)超重肥胖组13625(18.4)a82(60.3)a21(15.4)a2(1.5)6(4.4)注:与体重正常组相比,a P<0.01;与超重肥胖组相比,b P<0.01㊂表5 苔色特征比较[例(%)]组别样本量白苔黄苔黄白相兼苔消瘦组11099(90.0)5(4.5)6(5.5)体重正常组140131(93.6)6(4.3)3(2.1)超重肥胖组136119(87.5)8(5.9)9(6.6)注:与体重正常组相比,a P<0.01;与超重肥胖组相比,b P<0.01㊂表6 舌色参数比较(x±s)组别样本量R G B L a消瘦组110179.32±14.89ab99.30±15.09103.39±16.0049.96±5.7930.60±3.37ab体重正常组140169.10±13.1699.42±16.71103.24±17.1149.68±6.2128.27±4.18超重肥胖组136155.50±17.57a99.19±17.69103.71±18.1949.54±6.5224.74±4.67a 组别样本量b H S V消瘦组1109.64±2.860.84±0.260.43±0.05ab0.62±0.08a体重正常组1409.55±2.420.83±0.260.41±0.030.69±0.02超重肥胖组1369.68±2.200.87±0.190.38±0.07a0.64±0.07a注:与体重正常组相比,a P<0.01;与超重肥胖组相比,b P<0.01㊂表7 苔质参数比较(x±s)组别样本量R G B L a消瘦组110167.04±15.95103.55±15.93107.70±16.8348.60±7.20a25.95±2.83ab体重正常组140165.76±16.45104.69±17.35108.64±18.1653.82±6.9728.71±2.91超重肥胖组136163.53±17.08103.90±18.99108.31±19.4849.31±7.48a20.70±2.47a 组别样本量b H S V消瘦组1108.20±2.950.83±0.280.38±0.060.66±0.02ab体重正常组1408.32±2.450.81±0.260.37±0.060.69±0.05超重肥胖组1368.17±2.540.86±0.230.37±0.070.59±0.06a注:与体重正常组相比,a P<0.01;与超重肥胖组相比,b P<0.01㊂环球中医药2023年8月第16卷第8期 Global Traditional Chinese Medicine,August2023,Vol.16,No.81547表8 苔色参数比较(x±s)组别样本量R G B L a消瘦组110167.80±15.35103.89±16.76108.29±17.7350.84±5.9923.99±4.24体重正常组140163.74±18.62105.87±19.00109.77±19.7151.13±7.1324.61±4.68超重肥胖组136162.55±18.16104.72±19.64109.09±19.9650.54±7.2424.73±4.91组别样本量b H S V消瘦组1108.20±2.790.83±0.270.38±0.060.65±0.06体重正常组1407.64±2.360.80±0.280.36±0.070.65±0.07超重肥胖组1367.76±2.750.86±0.220.36±0.070.64±0.072.4 BMI指数与舌象参数相关性分析Spearman相关性分析显示,BMI指数与舌色R㊁舌色a㊁舌色S㊁苔质a㊁苔质V值呈负相关(P<0.01),BMI越高则相应的舌象参数值越低㊂见表9㊂表9 BMI与舌象参数的相关性指标r值P值舌色R-0.518<0.01舌色a-0.569<0.01舌色S-0.366<0.01苔质a-0.442<0.01苔质V-0.335<0.013 讨论体重指数又称身体质量指数㊁体质指数,中国成人超重和肥胖诊断标准将BMI<18.5kg/m2定义为消瘦,将BMI≥24.0kg/m2定义为超重肥胖,二者均属于BMI异常状态,对机体健康均会造成不良影响[7]㊂研究表明,消瘦容易诱发免疫力低下㊁贫血㊁营养不良㊁月经紊乱等疾病[8];超重肥胖在我国的发病率急剧上升并逐步呈现出年轻化趋势,在大学生中的检出率高达31%[9],不仅会导致体型异常,同时也是高血压㊁代谢综合征㊁Ⅱ型糖尿病㊁心血管疾病㊁慢性肾脏病及抑郁症等疾病的重要危险因素[10]㊂舌诊属于中医望诊的范畴,具有反应灵敏㊁客观准确㊁辨识方便的优势,为临床辨病辨证提供了重要的诊断依据,‘临证验舌法“言: 凡内外杂证,亦无一不呈其形,著其色于舌 据舌以分脏腑㊁配主方 危急疑难之顷,往往症无可参,脉无可按,而惟以舌为凭㊂”随着人工智能技术的发展[11],图像数据化分析技术广泛应用在中医舌诊领域,促进了舌诊的客观化㊁标准化和规范化㊂3.1 舌象特征结果分析本研究观察了各BMI分组人群的舌象特征分布情况,发现体重正常组多见淡红舌㊁正常舌形㊁薄白苔,有较多临床研究[12⁃13]与本文结论一致㊂超重肥胖组舌象以红舌㊁淡白舌㊁黯红舌㊁齿痕舌㊁白厚苔为主[14]㊂消瘦组以红舌㊁瘦舌[15]㊁白厚苔居多㊂有研究显示超重肥胖人群黯红舌㊁齿痕舌㊁厚腻苔占比显著高于体重正常人群,消瘦人群以红绛舌㊁厚苔为主[16⁃17],与本文研究结论相似㊂3.2 舌象参数结果分析本研究应用图像数据化技术分析了不同BMI指数大学生的舌色㊁苔质㊁苔色参数,结果提示舌象参数显示的结果与本文舌象主观判读的结论基本一致,这表明将舌象客观参数应用于消瘦㊁超重肥胖人群的舌象特征研究中是可行的㊂体重正常组舌色R㊁a㊁S值介于其余两组之间,提示该组舌色呈现为淡红色,舌色V值高于其余两组,提示体重正常组舌色更加明亮且润泽;苔质L㊁V值明显高于其余两组,提示体重正常组苔质明亮度高;a为绿 红值,而舌苔中无此颜色特征值,此处反映的应是舌面的颜色特征,a值的差异考虑受舌苔厚薄程度影响,舌苔越厚则舌面a值越低,因此苔质a值可间接反映舌苔厚薄,体重正常组苔质a值最高提示该组苔质薄㊂三组苔色参数相比显示无统计学意义,表明苔色差异不大,均以白苔为主㊂分析舌象参数结果可知体重正常组舌象多呈现为舌质淡红㊁明亮润泽㊁苔质薄㊁苔色白,这与中医学理论相契合㊂中医认为体重正常者气血调和,胃气充盛,徐灵胎在‘舌鉴总论“中曰 舌乃心苗 舌故当舌地淡红,舌胎微白,而红必红润内充,白必胎微不厚 不滑不燥,斯为无病之舌”,傅松元在‘舌胎统志“中亦言 舌为心之苗,其色当红,红不娇艳 必得淡红上有薄白之胎气”㊂超重肥胖组舌色R㊁a㊁S值以及苔质L㊁a㊁V值较低,说明其舌质淡白㊁晦暗少泽㊁苔质厚,结合舌象主观判读的结果,超重肥胖组红舌㊁黯红舌和齿1548 环球中医药2023年8月第16卷第8期 Global Traditional Chinese Medicine,August2023,Vol.16,No.8痕舌的出现频率也较高㊂超重肥胖为本虚标实之证,脾气亏虚为本,胃热㊁痰湿㊁瘀血为标㊂胃强脾弱,脾虚无力生化气血,血不荣舌致舌色浅淡而呈淡白舌,这与舌色R㊁a㊁S值偏低相符[18];脾胃运化失司,膏脂痰湿内停,故可见齿痕舌,白厚苔是由胃气兼夹痰湿秽浊上蒸于舌面所致,反映于苔质参数方面则L㊁a㊁V值较低;红舌多主热证,超重肥胖者胃热炽盛,体内热象已显故可见红舌;日久痰湿壅滞阻碍气机运行,气不行血,形成血瘀,可见舌色黯红㊂Hsu PC等[19]提取了2型糖尿病患者的BMI及舌象,并使用自动舌诊系统(ATDS)分析舌象特征,发现BMI越高则黯红舌㊁厚苔出现比例越高,舌体润泽程度越低㊂王露等[20]运用ZBOX⁃I型中医舌象数字化分析仪采集了2型糖尿病患者舌象,发现超重肥胖组舌苔燥㊁厚及腐腻指标显著高于正常组㊂消瘦组舌色R㊁a㊁S值在三组中最高,苔质L㊁a㊁V值较体重正常组低,提示该组舌色红,苔质厚㊂元代朱丹溪提出 瘦人火多” 瘦人阴虚火热”的观点,认为消瘦人群多阴虚内热;叶天士指出: 形瘦肤干畏冷,由阴气走乎阳位,益气以培生阳,温摄以固下真”,认为消瘦人群也会出现阳气亏虚㊁气血不足㊂现代研究发现消瘦人群多为阴虚体质和阳虚体质,这与古代医家有关瘦人的论述相吻合[21⁃22]㊂消瘦人群阴虚内热,虚火上炎,故舌色鲜红,可见舌色红色特征R㊁a值及饱和度S值最高;阴虚日久,损及阳气,阳虚无力温化水液,痰浊内停,上蒸于舌面而致白厚苔,故舌苔L㊁a㊁V值较低㊂有研究发现,消瘦人群中红绛舌和舌色平均a值最高,认为消瘦组舌红色度最深,可与本研究的结果相印证[18]㊂也有学者使用舌脉象数字化采集分析仪提取消瘦儿童的舌象参数,发现消瘦儿童舌尖舌色R㊁G㊁B值显著高于正常儿童,可进一步印证本研究的结果[23]㊂3.3 舌象客观化指标与BMI相关性结果分析本研究表明,BMI指数与舌色R㊁舌色a㊁舌色S㊁苔质a㊁苔质V值均呈负相关,即BMI指数越大各舌象参数值越小,二者具有显著的相关性,这与金昕等[24]的研究结果大致相符㊂陈小愚[25]使用便携式舌诊仪采集并分析了超重肥胖糖尿病患者的舌象参数特征,认为舌象参数与BMI具有相关性,全舌质及舌边左L㊁b值,舌苔及舌边右b值均与BMI 指数呈正相关㊂近年来,舌诊客观化技术广泛应用于中医临床诊疗过程,通过量化中医舌诊信息,能够准确地描述舌象特征,提高舌诊的精确性和客观标准性,为中医临床辨证诊疗提供客观依据㊂本研究基于图像数据化技术分析了不同BMI指数大学生的舌象特征,发现消瘦㊁体重正常及超重肥胖人群的舌象参数存在明显差异,BMI指数与舌象参数具有显著相关性,其中舌色R㊁a㊁S值以及苔质a㊁V值可作为消瘦㊁超重肥胖人群与体重正常人群的鉴别点,这提示舌象参数用于消瘦㊁超重肥胖人群的辨识评价中是可行的,能为该人群的临床辨识和诊疗提供一定的客观化依据㊂本研究是消瘦及超重肥胖等异常BMI指数人群评估方法的一次客观化探索和尝试,尚存在一些不足,例如研究对象主要是以大学生为主,缺乏其他年龄段人群的舌象特征分析;同时并不是所有的舌象参数都存在差异性,有些舌象参数反映的结果并不能与临床舌象相对应㊂故在今后的研究中,需进一步增加样本量,纳入不同年龄段的研究对象从而构建出不同年龄结构的舌象库,进行大样本舌象特征分析,并进一步优化舌象客观参数,使其更加准确地与临床舌象相对应,以期将图像数据化分析技术更好地应用于中医舌诊中,为临床辨证与诊断提供更加准确的客观化依据㊂参考文献[1] 满青青,庞邵杰,王慧,等.2010 2012年中国普通农村45岁及以上居民低体重率及影响因素[J].卫生研究,2018,47(1):32⁃36,50.[2] Puzhko S,Aboushawareb SAE,Kudrina I,et al.Excess bodyweight as a predictor of response to treatment with antidepressantsin patients with depressive disorder[J].J Affect Disord,2020,267:153⁃170.[3] 王河宝,胡芳,喻松仁.舌诊在肥胖症健康风险评估中的作用研究[J].江西中医药大学学报,2020,32(4):113⁃115. [4] 李丹溪,梁嵘,陈东宁,等.体质指数与舌象特征的相关性研究[J].国际中医中药杂志,2016,38(1):20⁃24. [5] 蔡轶珩,胡绍斌,关静,等.中医舌诊客观化技术发展分析及应用探讨[J].世界科学技术 中医药现代化,2021,23(7):2447⁃2453.[6] 刘梦,王曦廷,周璐,等.基于深度学习与迁移学习的中医舌象提取识别研究[J].中医杂志,2019,60(10):835⁃840. [7] 中华人民共和国卫生部疾病控制司.中国成人超重和肥胖症预防控制指南[M].北京:人民卫生出版社,2006. [8] 杨梦利,彭玉林,娄晓民,等.河南大学生体质量指数与身体素质的关系[J].中国学校卫生,2018,39(7):1101⁃1103. [9] 尤继富.吉林省大学生超重肥胖现状及其影响因素[J].中国卫生工程学,2021,20(5):737⁃740.[10] LAI S H,TSAI Y W,CHEN Y C,et al.Obesity,hyperhomocys⁃teinaemia and risk of chronic kidney disease:a population⁃based环球中医药2023年8月第16卷第8期 Global Traditional Chinese Medicine,August2023,Vol.16,No.81549study[J].Fam Pract,2018,35(3):259⁃265.[11] 钱鹏,燕海霞,李福凤.中医舌诊客观化研究的临床应用进展[J].中华中医药杂志,2021,36(5):2839⁃2842. [12] 方枫琪,庄育培,刘锦鸿,等.在校大学生舌象与健康情况的相关性研究[J].按摩与康复医学,2017,8(17):8⁃11. [13] 邓露露,孙悦,丁成华,等.在校本科生舌象和体质关系调查分析[J].江西中医药,2016,47(2):41⁃42,59. [14] 付培涛.肥胖症的因症态系统分类与干预研究[D].南昌:江西中医药大学,2020.[15] 张军峰,潘敏,费晓军,等.在校大学生舌象和中医体质关系调查分析[J].中华中医药杂志,2014,29(2):358⁃361. [16] 杨文国,徐建云,石莹,等.简单对应分析在大学生舌象研究中的应用[J].时珍国医国药,2014,25(12):2998⁃3001. [17] 李丹溪.体重与舌象的相关性研究[D].北京:北京中医药大学,2015.[18] 梁嵘,李丹溪,陈东宁,等.不同体重人群的舌色色度值研究[C]//.第九次全国中西医结合诊断学术研讨会论文集,2015:93⁃97.[19] HSU P C,WU H K,HUANG Y C,et al.The tongue featuresassociated with type2diabetes mellitus[J].Medicine(Baltimore),2019,98(19):e15567.[20] 王露,高键,王忆勤,等.数字化舌诊对2型糖尿病患者血糖水平㊁营养状况及膳食结构的评估作用[J].上海中医药杂志,2011,45(6):25⁃27.[21] 朱丽冰,王济,朱燕波,等.体重指数与中医体质类型的相关性探析[J].环球中医药,2017,10(2):164⁃169.[22] 郑小丰.中医阴虚质BMI状况及其身体成分分析的临床研究[D].广州:广州中医药大学,2017.[23] 孙源,刘娜娜,陈佳,等.消瘦儿童104例中医体质及舌象特征参数分析[J].中华中医药杂志,2020,35(1):353⁃355. [24] 金昕,陈思,徐杰,等.单纯性肥胖患者的客观化舌象与体重㊁体脂分布的关联性研究[J].世界中西医结合杂志,2016,11(5):660⁃666.[25] 陈小愚.超重/肥胖2型糖尿病患者的舌象研究[D].北京:北京中医药大学,2020.(收稿日期:2022⁃10⁃29)(本文编辑:王馨瑶)。
Duchenne 肌营养不良症（Duchenne musculardystrophy ，DMD ）是儿童最常见的X -连锁隐性遗传性肌肉疾病，其临床特点为四肢骨骼肌进行性、对称性肌无力和肌萎缩［1］。
早期受累肌肉表现为弥漫性或局限性水肿，晚期肌肉及肌间隙内可见脂肪浸润，肌纤维变细、减少，最终肌肉相应收缩功能丧失。
患儿多在2～5岁时因血清肌酸磷酸激酶升高而被发现，12岁左右丧失独立行走能力，20～30岁因心肺功能衰竭而死亡［2］。
DOI ：10.3969/j.issn.1672-0512.2024.01.019 ［基金项目］ 深圳市医疗卫生三名工程项目（SZSM202011005）；广东省深圳市科创委基金资助项目（JCYJ20230807093815031）；深圳市儿童医院疑难疾病精准诊治攻关项目（LCYJ2022092）。
［通信作者］ 李志勇，Email ：*************。
DTI 评估Duchenne 型肌营养不良症严重程度的初步研究陈太雅1，2，胡颖熠1，2，黄 杨2，3，方雪琳2，3，王景刚4，方佃刚2，路新国5，李志勇21.中国医科大学，辽宁 沈阳 110000；2.广东省深圳市儿童医院放射科，广东 深圳 518038；3.汕头大学医学院，广东 汕头 515041；4.广东省深圳市儿童医院康复科，广东 深圳 518038；5.广东省深圳市儿童医院神经内科，广东 深圳 518038［摘要］ 目的：探讨DTI 评估Duchenne 肌营养不良症（DMD ）患儿病情严重程度的临床价值。
方法：28例DMD 患儿均行大腿肌肉常规MRI 和DTI 检查，测量右侧大腿14块肌肉（臀大肌、阔筋膜张肌、股外侧肌、股中间肌、股内侧肌、股直肌、缝匠肌、长收肌、大收肌、股薄肌、半膜肌、半腱肌、股二头肌长头、股二头肌短头）的各向异性分数（FA ）值、ADC 值。
根据运动功能评估量表（MFM ）评价患儿运动功能，并分析各肌肉FA 值、ADC 值和14块肌肉平均FA 值、ADC 值与MFM 总评分的相关性。
不同最大摄氧量者的Heath-Carter法体型特征分析隋月林;刘媛媛;尹帅;路兰红;丁文锋【摘要】目的:探讨高水平最大摄氧量(VO2 max)的Heath-Carter法体型分类.方法:对某全训部队416名男战士,按GJB1337-92规定进行VO2 max测定,按国际通用的Heath-Carter方法测量体型.结果:416名男战士VO2 max均值为(53.6±6.4)ml·kg-1·min-1,总体评价为优秀;良好以上者276人,占66.4％,VO2 max均值为(57.1±4.8)ml·kg-1·min-1,体型均值为2.21±0.67-4.86±3.40-2.63±0.95,体型位置均数为1.7,良好以上者的体型分类中有79.3％集中于中胚型.结论:高水平VO2 max的体型分类高度集中于中胚型.【期刊名称】《解剖学杂志》【年(卷),期】2018(041)006【总页数】3页(P687-689)【关键词】最大摄氧量;Heath-Carter法;体型【作者】隋月林;刘媛媛;尹帅;路兰红;丁文锋【作者单位】沧州医学高等专科学校,沧州 061001;沧州医学高等专科学校,沧州061001;沧州中西医结合医院,沧州 061001;沧州医学高等专科学校,沧州 061001;沧州医学高等专科学校,沧州 061001【正文语种】中文最大摄氧量(maximal oxygen uptake，VO2max)是人体在进行有大量肌肉群参加的长时间剧烈运动中，当心肺功能和肌肉利用氧的能力达到本人极限时，单位时间(通常以分钟为单位)所能摄取的氧量。
目前，评价有氧工作能力的最常用的指标是VO2max。
它集中反映了个体的最大摄氧水平以及氧利用率，能够综合、客观地反映个体的有氧能力和体质状况[1]。
Heath-Carter法被“国际生物发展规划”推荐为目前世界上最为流行的体型方法[2]。
大学生自我控制量表的修订谭树华，郭永玉(华中师范大学心理学院，湖北武汉430079)【摘要】目的：修订自我控制量表(SCS)，考察其心理测量学指标。
方法：对799名武汉市大学生进行测查，对量表进行验证性因素分析和信、效度检验。
结果：验证性因素分析的结果显示，SCS的五因素结构拟合较好。
SCS的内部一致性信度为0.862，重测信度为0.850。
以被试的平均学分绩、人际关系满意感、生活满意感、心理健康水平为效标，与SCS的相关分别为0.146；0.280；0.163；0.317。
结论：SCS符合心理测量学的要求；可作为测量我国大学生自我控制能力的工具。
【关键词】自我控制量表；信度；效度中图分类号:R395.1文献标识码:A文章编号:1005-3611(2008)05-0468-03Revision of Self-Control Scale for Chinese College StudentsTAN Shu-hua，GUO Yong-yuSchool of Psychology，Central China Normal University，Wuhan430079，China【Abstract】Objective：To revise the Self-Control Scale(SCS).Methods：Data were collected from a sample of799col-lege students of Wuhan and analyzed by Confirmatory Factor Analysis,reliability test and validity test.Results：The re-sults of Confirmatory Factor Analysis showed that the revised SCS was five-factor construct and had good construct validi-ties.The Cronbach’sαcoefficient of the SCS scale was0.862,and the reliability coefficient of the test-retest stability co-efficient was0.850.The correlation between total score of SCS and that of Grade Point Average(GPA)was0.146;the cor-relation between total score of SCS and that of Interpersonal Satisfaction Scale(ISS)was0.280;the correlation between total score of SCS and that of Life Satisfaction Scale(LSS)was0.163,and the correlation between total score of SCS and that of General Health Questionnaire(CHQ)was0.317.Conclusion：The revised scale of SCS has good psychometric quality and can be used in Chinese college students.【Key words】Self-control Scale；Reliability；Validity人们最健康、最幸福的时候就是自我和环境完全匹配的时候，只是日常生活中个体和环境很难完全匹配，但匹配的程度可以通过调整自我来得到最大化地提升[1]。
两种婴幼儿肺功能仪检测结果一致性评价姜欣玫姜欣玫，，李硕李硕，，刘传合刘传合，，宋欣宋欣，，张一凡张一凡，，侯馨悦侯馨悦，，张艳涛首都儿科研究所附属儿童医院变态反应科哮喘防治中心与肺功能室，北京 100020摘要 目的 评价国产呼吸家B6婴幼儿肺功能仪和德国耶格公司婴幼儿肺功能仪检测结果的一致性。
方法 方便选择2022年7—10月在首都儿科研究所附属儿童医院就诊需要进行婴幼儿潮气功能测定的98例患儿，根据年龄段将患儿分为0~1岁组24例、>1~2岁组41例、>2~3岁组33例，各组均应用两种设备检测一次，分年龄段比较两种设备各检测指标差异，运用组内相关系数（intra-class correlation coefficient ， ICC ）评价两种婴幼儿检测仪各项指标一致性。
结果 在不同年龄组患儿中，两种婴幼儿肺功能仪所测结果的患儿的潮气量、呼吸频率、吸气时间、呼气时间、吸呼比、达峰时间比、达峰容积比、潮气呼吸峰流量比较，差异均无统计学意义（P 均>0.05）。
达峰容积比ICC 组内相关系数0.782，其他参数如潮气量、呼吸频率、吸气时间、呼气时间、吸呼比、达峰时间比、潮气呼气峰流量等的ICC 组内相关系数分别是0.985、0.939、0.925、0.952、0.839、0.846、0.816，一致性强。
结论 两种婴幼儿肺功能仪测定的各项参数具有良好的一致性。
关键词 潮气呼吸肺功能；婴幼儿；一致性中图分类号 R 4 文献标志码 Adoi10.11966/j.issn.2095-994X.2023.09.12.22Evaluation of the Consistency of Test Results of Two Infant and Young Children's Lung Function MetersJIANG Xinmei, LI Shuo, LIU Chuanhe, SONG Xin, ZHANG Yifan, HOU Xinyue, ZHANG Yantao Asthma Prevention and Control Center and Lung Function Unit, Department of Allergy Treatment, Beijing Children's Hospital CapitalMedical University, Beijing, 100020 ChinaAbstract Objective To evaluate the consistency of the test results between the China BreathHome B6 infant and young children's lung func⁃tion meter and the infant and young children's lung function meter of the German JAEGER company. Methods Ninety-eight children who needed tidal function measurement in infants and young children attending the Children's Hospital affiliated to the Capital Institute of Pediat⁃rics from July to October 2022 were conveniently selected, and the children were divided into 24 cases in the 0-1 year old group, 41 cases in the >1-2 year old group, and 33 cases in the >2-3 year old group according to the age groups, and each group was tested by the two equip⁃ments for one time, and differences of the two equipments were compared with the detection indexes of the two equipments in different agegroups, and the consistency of the two kinds of infant and young children's testers was evaluated by using the intra-class correlation coeffi⁃cient (ICC). Results Among children in different age groups, the tidal volume, respiratory rate, inspiratory time, expiratory time, inspiratory-to-expiratory ratio, the ratio of time taken to reach peak expiratony flow to total expiratory time, the ratio of peak expiratory volume to total ex⁃piratory volume, and tidal peak respiratory flow rate were compared between children with the results measured by the two types of infant spi⁃rometers, and the differences were not statistically significant (all P >0.05). The intra-ICC group correlation coefficient for the ratio of peak ex⁃piratory volume to total expiratory volume was 0.782, and the intra-ICC group correlation coefficients for other parameters such as tidal vol⁃ume, respiratory rate, inspiratory time, expiratory time, inspiratory to expiratory ratio, the ratio of time taken to reach peak expiratony flow to total expiratory time, and peak tidal expiratory flow were 0.985, 0.939, 0.925, 0.952, 0.839, 0.846, and 0.816, respectively, which were in strong agreement. Conclusion The parameters measured by the two infant lung function meters had good agreement.Key words Tidal breathing pulmonary function; Infants and young children; Consistency呼吸系统的疾病占儿童所有疾病的首位，其病死率处5岁以下儿童的第一位[1]。
⽤⾝⾼和体重数据进⾏性别分类的实验报告⽤⾝⾼和体重数据进⾏性别分类的实验报告⼀：基本要求1、利⽤K-L 变换进⾏特征提取。
2、在正态分布假设下估计概率密度，建⽴最⼩错误率Bayes 分类器。
3、试验直接设计线性分类器的⽅法，与基于概率密度估计的贝叶斯分类器进⾏⽐较。
⼆、实验数据训练样本：FAMALE.TXT （50个⼥同学的⾝⾼与体重数据） MALE.TXT （50个男同学的⾝⾼与体重数据）测试样本：Text1.TXT （35个同学的⾝⾼与体重数据，其中20个男同学，15个⼥同学） Text2.TXT （300个同学的⾝⾼与体重数据，其中250个男同学，50个⼥同学）三、具体做法1、不考虑类别信息对整个样本集进⾏K-L 变换（即PCA ），并将计算出的新特征⽅向表⽰在⼆维平⾯上，考察投影到特征值最⼤的⽅向后男⼥样本的分布情况并⽤该主成分进⾏分类。
2、利⽤类平均向量提取判别信息，选取最好的投影⽅向，考察投影后样本的分布情况并⽤该投影⽅向进⾏分类。
3、采⽤⾝⾼和体重数据作为特征，在正态分布假设下估计概率密度，建⽴最⼩错误率Bayes 分类器，写出得到的决策规则，将该分类器应⽤到训练/测试样本，考察训练/测试错误情况。
在分类器设计时可以考察采⽤不同先验概率（如0.5 vs. 0.5, 0.75 vs. 0.25, 0.9 vs. 0.1等）进⾏实验，考察对决策和错误率的影响。
4、⽤Fisher 线性判别⽅法求分类器，将该分类器应⽤到训练和测试样本，考察训练和测试错误情况。
将训练样本和求得的决策边界画到图上，同时把以往⽤Bayes ⽅法求得的分类器也画到图上，⽐较结果的异同。
四、原理简述及程序框图1.不考虑类别信息对整个样本集进⾏K-L 变换（1）读⼊female.txt 和male.txt 两组数据，组成⼀个样本集。
计算样本均值向量u E x=和协⽅差()()Tx u x u c E ??--??= （2）计算协⽅差阵特征值和特征向量（3）选取特征值最⼤的特征向量作为投影⽅向（4）选取阈值进⾏判断计算样本均值向量和协⽅差协⽅差阵特征值和特征向量选取特征值最⼤的特征向量作为投影⽅向选取阈值进⾏判断2.利⽤类平均向量提取判别信息来进⾏K-L 变换（1）读⼊female.txt 和male.txt 两组数据，组成⼀个样本集。
The Importance of Height Measurement in Physical Examination Physical examination, a routine practice in healthcare, plays a pivotal role in assessing the overall well-being of individuals. Among the various parameters measured during a physical, height stands out as a crucial indicator of health status. This essay delves into the significance of height measurement in physical examination, exploring its impact on health monitoring, disease diagnosis, and personal development.Firstly, height measurement serves as a baseline for tracking growth and development. In children and adolescents, height is a sensitive indicator of nutritional status, hormonal balance, and general health. Regular monitoring of height allows healthcare providers to detect any deviations from the normal growth pattern, which could be indicative of underlying health issues. For instance, a significant deviation in height growth could suggest the presence of growth hormone deficiencies or excessive hormone secretion, both of which require prompt medical attention.Moreover, height measurement is crucial in the diagnosis of certain diseases. Certain medical conditions, such as dwarfism and gigantism, are directly associated with abnormal growth patterns. By comparing an individual's height to reference ranges, doctors can identify potential health issues and initiate appropriate diagnostic procedures. Additionally, height measurement can also aid in the diagnosis of chronic diseases such as diabetes and cardiovascular diseases, as these conditions can often affect body proportions and height.Furthermore, height measurement plays a role in assessing the risk of certain health conditions. Studies have shown that height is associated with various health outcomes, including cardiovascular disease, cancer, and even mortality. While the exact mechanisms underlying these associations are still being investigated, understanding therelationship between height and health risks can help individuals make informed decisions about their lifestyle and healthcare.Apart from its medical significance, height measurement also has social and psychological implications. In many cultures, height is often associated with attractiveness, confidence, and success. While these associations may not always be scientifically accurate, they can have a profound impact on individuals' self-esteem and social interactions. Therefore, accurate height measurement can help individuals gain a better understanding of their physical attributes and foster a positive body image.However, it is important to note that height measurement alone is not sufficient for a comprehensive health assessment. It should be combined with other parameters such as weight, body mass index (BMI), and blood pressure to provide a more holistic picture of an individual's health status. Additionally, healthcare providers should interpret height measurements with caution, taking into account factors such as ethnicity, age, and gender, which can influence height variations.In conclusion, height measurement is a crucial component of physical examination, with far-reaching implications for health monitoring, disease diagnosis, and personal development. By regularly measuring and interpreting height data accurately, healthcare providers can gain valuable insights into an individual's health status and provide tailored healthcare advice. Moreover, individuals should also embrace height measurement as a means to understand their own bodies better and make informed decisions about their health and well-being.Moreover, the accuracy of height measurement is paramount for effective healthcare management. Technical advancements in measurement devices, such as the use of stadiometers, have significantly improved the precision of height measurements. It is crucial for healthcareprofessionals to be trained in the proper use of these devices to ensure accurate and consistent results. Additionally, regular calibration of measurement equipment is essential to maintain its accuracy over time.The significance of height measurement extends beyond the individual level to population health studies. Large-scale epidemiological studies often rely on height data to identify trends and patterns in health outcomes. For instance, comparing the average heights of different populations can provide insights into nutritional status and living conditions. Such studies contribute to the understanding of health disparities and the development of effective public health policies.In conclusion, height measurement is a fundamental aspect of physical examination that offers valuable insights into individual and population health. It is crucial for healthcare providers to appreciate its importance and ensure accurate and consistent measurements. By leveraging height data effectively, we can improve health outcomes, diagnose diseases early, and foster a healthier society overall.。
HEALTH EXAMINATION GUIDELINESFOR ENTRY INTOMALAYSIAN HIGHER EDUCATIONAL INSTITUTIONS1. PLEASE READ THE INSTRUCTIONS CAREFULLY BEFORE FILLING IN THE FORM.2. PLEASE FILL IN THE FORM IN ENGLISH LANGUAGE.3. PLEASE WRITE IN CAPITAL LETTERS.4. THIS FORM HAS 4 SECTIONS:a) SECTION 1 (PART A AND B) TO BE FILLED BY THE APPLICANT; ANDb) SECTION 2,3 AND 4 TO BE FILLED BY THE EXAMINING DOCTOR5. PLEASE COMPLETE THE ENTIRE TEST REQUIRED IN THIS FORM.6. THE UNIVERSITY / COLLEGE ONLY ACCEPT MEDICAL EXAMINATION DONE WITHIN90 DAYS BEFORE ARRIVAL IN MALAYSIA.THEORIGINAL LABORATORY RESULTS.ALLATTACH7. PLEASEALONGCHEST X-RAY FILM (OR DIGITAL IMAGES) AND REPORT BRING8. PLEASEFOR REGISTRATION, FOR THE PURPOSE OF VERIFICATION, IF NECESSARY.9. PLEASE ENSURE THE X-RAY FILMS OR DIGITAL IMAGES ARE LABELLED WITHYOUR NAME AND DATE TAKEN (IN ENGLISH).10. CHEST X-RAY DONE WITHIN 6 MONTHS PRIOR TO REGISTRATION CAN BE ACCEPTED.11. THE UNIVERSITY / COLLEGE RESERVES THE RIGHT TO REPEAT FULL MEDICALCHECK UP OR ANY SPECIFIC LABORATORY TESTS SHOULD THERE BE ANY DOUBT IN THE MEDICAL REPORT SUBMITTED, ALL COSTS INVOLVED SHALL BE BORNE BY THE CANDIDATES.12. T H E U N I V E R S I T Y /C O L L E G E R E S E RV E S T H E R I G H T TO R E J E C T A N YAPPLICATION:a) BASED ON THE RESULTS OF THE HEALTH EXAMINATION; ORb) SHOULD THERE BE ANY EVIDENCE THAT THE APPLICANT HAS GIVENFALSE INFORMATION IN THE HEALTH EXAMINATION REPORT OR ANYDOCUMENTS.SUPPORTINGSECTION 1 (PART A)FULL NAME (AS IN PASSPORT)INTERNATIONAL PASSPORT NUMBER NATIONALITY CONTACT NUMBER IN MALAYSIABLOOD GROUP (RHESUS)ACADEMIC YEAR STUDENT ID PROGRAMME OF STUDYPROGRAMME CODEH T R I B F O E T A D AGE NEXT OF KINNEXT OF KIN’S ADDRESS NEXT OF KIN’S CONTACT NUMBER SEX MARITAL STATUSThe details of the blood type recorded here are as reported by the patient and have not been tested or verified to be correct by the medical practitioner completing this online medical screening questionnaire. The medical practitioner completing this form disclaims any and all liability to the fullest extent permitted by law for any personal injury, suffering or loss caused by any reliance on this information by any other party.SECTION 1 (PART B)Declaration of self and family illness. Explain in full if you or your immediate* family has any of the following illnesses. * Immediate family refers to mother, brothers / sisters.Current medication (Long Term)Notes :1. *A valid Yellow Fever vaccination certificate is required from all travellers coming from or transited more than 12 hours through countries with risk of Yellow Fever transmission.2.All students are required to take vaccines as listed in numbers 2-7 above.3.The students are required to bring along the International Certificate of Vaccination or Prophylaxis with them for verification of information.SECTION 2 - PHYSICAL EXAMINATION1. BASIC MEASUREMENTVISION TESTNORMAL DEFECTIVE FULL NAME (AS IN PASSPORT)INTERNATIONAL PASSPORT NUMBER TYPE OF APPLICATIONEMGS REFERENCE NUMBER DATE OF MEDICAL SCREENING WEIGHT (kg)SYSTOLIC (mmHg)DIASTOLIC (mmHg)UNAIDED (L)UNAIDED (R)HEIGHT (m) :BLOOD PRESSURE:PULSE RATE (PER MINUTE)BMI(kg/m²)AIDED (L)AIDED (R)COLOR VISION TEST COMMENT 2. GENERAL EXAMINATIONHEARING ABILITYNORMAL DEFECTIVE COMMENTLEFTRIGHTSECTION 2A - PHYSICAL EXAMINATION - EBOLAHave you in the last 30 days travelled to or from the following Ebola affected countries:FULL NAME (AS IN PASSPORT)INTERNATIONAL PASSPORT NUMBER TYPE OF APPLICATIONEMGS REFERENCE NUMBER DATE OF MEDICAL SCREENINGHave you in the last 30 days come into contact with someone, who has in the last 30 days, traveled to or from thefollowing Ebola affected countries:Do you have any of the following Ebola virus symptoms?Have you in the last 30 days come into contact with Ebola infected persons or animals?SECTION 3 - LABORATORY RESULTSFULL NAME (AS IN PASSPORT)INTERNATIONAL PASSPORT NUMBER EMGS REFERENCE NUMBER DATE OF LAB TEST NAME OF LAB* TPHA is done if VDRL is reactive** all test results / reports is valid for 6 monthsSECTION 4 - CHEST X-RAY FINDINGSFULL NAME (AS IN PASSPORT)INTERNATIONAL PASSPORT NUMBER EMGS REFERENCE NUMBERPLACE OF CHEST X-RAY DATE OF CHEST X-RAYCHEST X-RAY NO.COMMENTFULL NAME (AS IN PASSPORT)INTERNATIONAL PASSPORT NUMBER EMGS REFERENCE NUMBER TYPE OF APPLICATION DATE OF CERTIFICATIONCOMMENTQUALIFICATION OF EXAMINING DOCTOR NAME OF EXAMINING DOCTOR HOSPITAL/CLINIC REGISTRATION NUMBERSECTION 5 - CERTIFICATION BY THE EXAMINING DOCTORHEREBY THE STUDENT IS CERTIFIED ASFOR STUDY IN MALAYSIA.SUITABLE UNSUITABLE。
