Exploring the effect of the structural model of active aging on the self-assessment of quality of life among older people: A cross-sectional and analytical study

ABSTRACT BACKGROUND: Although studies have examined the relationship between variables associated with active aging and quality of life (QoL), no studies have been identified to have investigated the effect of a structural model of active aging on QoL in a representative sample of older people in the community. OBJECTIVE: To measure the domains and facets of QoL in older people and identify the effect of the structural model of active aging on the self-assessment of QoL. DESIGN AND SETTING: This cross-sectional analytical study included 957 older people living in urban areas. Data were collected from households using validated instruments between March and June 2018. Descriptive, confirmatory factor, and structural equation modeling analyses were performed. RESULTS: Most older people self-rated their QoL as good (58.7%), and the highest mean scores were for the social relationships domain (70.12 ± 15.4) and the death and dying facet (75.43 ± 26.7). In contrast, the lowest mean scores were for the physical domains (64.41 ± 17.1) and social participation (67.20 ± 16.2) facets. It was found that active aging explained 50% of the variation in self-assessed QoL and directly and positively affected this outcome (λ = 0.70; P < 0.001). CONCLUSION: Active aging had a direct and positive effect on the self-assessment of QoL, indicating that the more individuals actively aged, the better the self-assessment of QoL.

Modelos de variables latentes en patrones de alimentación y actividad física en niños/adolescentes: una revisión sistemática / Models of latent variables used for eating and physical activity patterns in children/ adolescents: a systematic review

Introducción. Debido a la poca evidencia sobre el modelamiento de los patrones de alimentación y actividad física (AF), basado en variables latentes, el presente estudio de revisión pretende describir las técnicas estadísticas aplicadas para modelar estos patrones en niños y adolescentes y valorar su calidad metodológica. Materiales y métodos. La búsqueda se realizó en bases de datos electrónicas (Science Direct, PubMed, SCOPUS, Web of science y Cochrane) con las palabras "diet", 'physical activity', children y 'latent variable'. Se incluyeron artículos que utilizaron modelos estadísticos basados en variables latentes para analizar patrones de alimentación y AF en niños y adolescentes sanos, publicados entre 2014­2019, en inglés o español. Resultados. Entre los 27 artículos seleccionados, el Modelo de Ecuaciones Estructurales (MEE) fue el más utilizado (77,78%); seguido del Modelo de Perfil Latente (7,41%), mientras, el restante, 14,81% aplican el Modelo del Factor Común, Modelo Ecológico y el Modelo de Regresión Logística Multinivel. El MEE fue aplicado a 12 de los 16 artículos con enfoque de AF, y en 7 de los 9 artículos con enfoque de Alimentación. El 48,15% de los estudios sí justificaba el uso del modelo, y el 37,04% poseen una calidad "Excelente" (cumplen el 85% o más de los ítems de STROBE). Conclusiones. El MEE fue el más utilizado para abstraer los patrones de AF y alimentación en niños y adolescentes, sin embargo, solo la mitad de los artículos justifica su pertinencia. Las guías de reporte de estudios deberían evaluar la calidad metodológica de los modelos estadísticos aplicados(AU)

Introduction. Due to the limited evidence on the modeling of eating and physical activity (PA) patterns based on latent variables, the present review study aims to describe the statistical techniques applied to model these patterns in children and adolescents and to assess their methodological quality. Materials and methods. The search was performed in electronic databases (Science Direct, PubMed, SCOPUS, Web of science and Cochrane) with the words 'diet', 'physical activity', children and 'latent variable'. We included articles that used statistical models based on latent variables to analyze diet and PA patterns in healthy children and adolescents, published between 2014-2019, in English or Spanish. Results. Among the 27 selected articles, the Structural Equation Model (SEM) was the most used (77.78%); followed by the Latent Profile Model (7.41%), while, the remaining 14.81% applied the Common Factor Model, Ecological Model and Multilevel Logistic Regression Model. The SEM was applied to 12 of the 16 articles with PA approach, and in 7 of the 9 articles with eating approach. The 48.15% of studies did justify the use of the model, and 37.04% were classified as "Excellent" quality (meet 85% or more of the STROBE items). Conclusions. The SEM was the most commonly used to model the PA and eating patterns in children and adolescents, however, only half of the articles justify their relevance. Study reporting guidelines should evaluate the methodological quality of the statistical models applied(AU)

Modeling Methods and Influencing Factors for Age Estimation Based on DNA Methylation / 法医学杂志

Age estimation based on tissues or body fluids is an important task in forensic science. The changes of DNA methylation status with age have certain rules, which can be used to estimate the age of the individuals. Therefore, it is of great significance to discover specific DNA methylation sites and develop new age estimation models. At present, statistical models for age estimation have been developed based on the rule that DNA methylation status changes with age. The commonly used models include multiple linear regression model, multiple quantile regression model, support vector machine model, artificial neural network model, random forest model, etc. In addition, there are many factors that affect the level of DNA methylation, such as the tissue specificity of methylation. This paper reviews these modeling methods and influencing factors for age estimation based on DNA methylation, with a view to provide reference for the establishment of age estimation models.

Imputation method for dropout in single-cell transcriptome data / 生物医学工程学杂志

Single-cell transcriptome sequencing (scRNA-seq) can resolve the expression characteristics of cells in tissues with single-cell precision, enabling researchers to quantify cellular heterogeneity within populations with higher resolution, revealing potentially heterogeneous cell populations and the dynamics of complex tissues. However, the presence of a large number of technical zeros in scRNA-seq data will have an impact on downstream analysis of cell clustering, differential genes, cell annotation, and pseudotime, hindering the discovery of meaningful biological signals. The main idea to solve this problem is to make use of the potential correlation between cells and genes, and to impute the technical zeros through the observed data. Based on this, this paper reviewed the basic methods of imputing technical zeros in the scRNA-seq data and discussed the advantages and disadvantages of the existing methods. Finally, recommendations and perspectives on the use and development of the method were provided.

Research Advances in Musculoskeletal Biomechanical Modeling in 2021 / 医用生物力学

Biomechanical model of musculoskeletal system has accurate human anatomy and good biological fidelity. It can accurately and effectively reveal the biomechanical state and predict the internal mechanical response of musculoskeletal system. Therefore, it has been widely used in biomechanical study of musculoskeletal system, diagnosis and treatment of bone diseases, implant optimization design and preoperative planning. In 2021, the latest advances in biomechanical modeling method of musculoskeletal system mainly included three aspects, i.e., individualized finite element modeling, statistical model modeling and musculoskeletal system modeling. On this basis, the latest relevant literatures were summarized in this review to illustrate the progress and main applications of the above modeling method, and the future development direction of musculoskeletal modeling was discussed.

Advances in the study of biological age in healthy aging / 中华老年医学杂志

Population aging increases the demand for human aging research and its clinical applications.Traditionally, the chronological age(CA), that is, the age based on the calendar, is used to describe the state of aging.However, the aging process and speed among individuals are not consistent and often show clear individual differences in biological aging.Therefore, CA cannot truly reflect people's conditions of body structure and function, has drawbacks leading to unreliable and wrong assessment, and is unable to accurately describe the human body's state of aging.In recent years, it has been proposed that the biological age(BA)should be used to more comprehensively and accurately describe the stage of human aging.Combining mathematical algorithms with a variety of biomarkers, predictive models can be constructed to quantify BA.These approaches have been increasingly appreciated for their improved accuracy and received further investigation.This article reviews the value of BA, currently commonly used calculation methods and their progress and prospects in healthy aging.

Review on the study of spreading of the COVID-19 based on dynamic models / 中华医学科研管理杂志

Objective@#To Summarize mathematical and statistical models used in the area of infectious diseases modelling, to provide ideas and suggestions for the model-based analysis and decision-making of COVID-19.@*Methods@#By reviewing the commonly used mathematical and statistical models in the analysis of infectious diseases, with a focus on the mathematical models of the severe acute respiratory syndrome coronavirus 2(SARS-CoV-2) that have been published and their practical effects.@*Results@#Infectious diseases modelling based on multi-source information has become the main research trend, and the published mathematical models of COVID-19 epidemic need to be improved in terms of accuracy and scalability.@*Conclusions@#It is recommended to build a more advanced mathematical/statistical model by allowing for the characteristics of new coronaviruses and to use more informative data to improve the analysis and decision-making of the new epidemic.

Factors Associated with a Prolonged Hospital Length of Stay in a Population of Patients with Exacerbation of Chronic Obstructive Pulmonary Disease in a Reference Hospital in Northeastern Colombia / Factores asociados a estancia prolongada en una población de pacientes con exacerbación de Enfermedad Pulmonar Obstructiva Crónica en un hospital de referencia del Nororiente Colombiano

A prolonged hospital length of stay during an episode of exacerbation of chronic obstructive pulmonary disease is a condition that increases the risk of suffering associated medical complications. Objective: The objective of this study was to determine the factors associated with a prolonged hospital length of stay in exacerbations of chronic obstructive pulmonary disease through a prediction model. Materials and Methods: In a cross-sectional study we gathered the data of the medical records of a hospital located in the Eastern region of Colombia, between years 2012 and 2014. We carried out a descriptive, bivariate and multivariate analysis. Results: A total of 212 patients were included in this study. 61.32% showed a prolonged hospital length of stay. We found a significant statistical association between the prolonged hospital stay and the independent variables of the bivariate analysis: dyspnea (OR [Odds Ratio]: 2.87 p = 0.04), fever (OR: 2; p = 0.02), inpatient oxygen (OR: 2.34, p = 0.003), inpatient anticholinergics (OR: 2.91, p = 0.002), inpatient antibiotic (OR: 2.25, p= 0.004), segs (OR: 1.02, p= 0.01) and lymphocytes (OR: 0.95, p = 0.003). The predictive model had a p value of 0.4950 in the analysis of goodness (Pearson Test) and a p value of 0.2689 in the goodness of fit test (Hosmer-Lemeshow Test), indicating an adequate fit. Also, the model showed an area under the curve of 0.6588. Conclusions: Our prediction model included the following variables: age, anticholinergics and segs, for their significant association. It has an adequate fit and a good pattern of prediction.

Modelagem estatística: perguntas que você sempre quis fazer, mas nunca teve coragem / Statistical modeling: questions you have always wanted to ask but never had the courage to

Dando continuidade aos artigos da série "Perguntas que você sempre quis fazer, mas nunca teve coragem", que tem como objetivo responder e sugerir referências para o melhor entendimento das principais dúvidas dos pesquisadores do Hospital de Clínicas de Porto Alegre sobre estatística, este quarto artigo se propõe a responder às principais dúvidas levantadas sobre modelagem estatística. São discutidas questões referentes à classificação de variáveis em independentes e dependentes, diferenças entre correlação, associação e regressão, os principais tipos de regressão e quais etapas são necessárias na construção de modelos. Os conceitos são abordados numa linguagem acessível ao público leigo e diversas referências são sugeridas para os curiosos em relação ao tema. (AU)

Continuing the series of articles "Questions you have always wanted to ask but never had the courage to," which aims to answer the most common questions of researchers at Hospital de Clínicas de Porto Alegre regarding statistics and to suggest references for a better understanding, this forth article addresses the topic of statistical modeling. Questions about classification of variables as dependent or independent, differences between correlation, association and regression, types of regression and steps for statistical modeling are discussed. The concepts are explained in plain language for lay readers and several references are suggested for those curious about the topic. (AU)

Social determinants of tuberculosis via a zero-inflated model in small areas of a city in Southeastern Brazil

Abstract INTRODUCTION: This study aimed to analyze social factors involved in the spatial distribution and under-reporting of tuberculosis (TB) in the city of Vitória, Espírito Santo State, Brazil. METHODS: This was an ecological study of the reported cases of TB between 2009 and 2011, according to census tracts. The outcome was TB incidence for the study period and the variables of exposure were proportions of literacy, inhabitants with an income of up to half the minimum monthly wage (MMW), and inhabitants associated with sewer mains or with access to safe drinking water. We used a zero-inflated process, zero-inflated negative binomial regression (ZINB), and selected an explanatory model based on the Akaike Information Criterion (AIC). RESULTS: A total of 588 cases of tuberculosis were reported in Vitória during the study period, distributed among 223 census tracts (38.6%), with 354 (61.4%) tracts presenting zero cases. In the ZINB model, the mean value of p i was 0.93, indicating that there is a 93% chance that an observed false zero could be due to sub-notification. CONCLUSIONS: It is important to prioritize areas exhibiting determinants that influence the occurrence of TB in the municipality of Vitória. The zero-inflated model can be useful to the public health sector since it identifies the percentage of false zeros, generating an estimate of the real epidemiological condition of TB in Vitória.

Public Awareness of Early and Late Complications of Type 2 Diabetes - Application of Latent Profile Analysis in Determining Questionnaire Cut-Off Points

OBJECTIVES: A questionnaire was designed to determine public understanding of early and late complications of Type 2 diabetes mellitus (T2DM). METHODS: A cross-sectional study was performed in participants who were selected using a multi-stage sampling method and a standard questionnaire of 67 questions was proposed. An expert panel selected 53 closed-ended questions for content validity to be included in the questionnaire. The reliability of the questionnaire was tested using Cronbach’s alpha coefficient giving a score of 0.84. RESULTS: Of the 825 participants, 443 (57.6%) were male, and 322 (41.87%) were 40 years or more. The proportion of low-, moderate- and high- awareness about T2DM and its complications was 29.26%, 62.68%, and 8.06%, respectively. Friends (56.31%) and internet and social networks (20.55%) were the 2 major sources of awareness, respectively. The medical staff (e.g., physicians) had the lowest share in the level of public awareness (3.64%) compared to other sources. CONCLUSION: These results present data that shows the general population awareness of T2DM is low. Healthcare policymakers need to be effective at raising awarenes of diabetes and it should be through improved education.

Effects of human and organizational deficiencies on workers' safety behavior at a mining site in Iran / 한국역학회지

OBJECTIVES: Throughout the world, mines are dangerous workplaces with high accident rates. According to the Statistical Center of Iran, the number of occupational accidents in Iranian mines has increased in recent years. This study investigated and analyzed the human and organizational deficiencies that influenced Iranian mining accidents. METHODS: In this study, the data associated with 305 mining accidents were analyzed using a systems analysis approach to identify critical deficiencies in organizational influences, unsafe supervision, preconditions for unsafe acts, and workers' unsafe acts. Partial least square structural equation modeling (PLS-SEM) was utilized to model the interactions among these deficiencies. RESULTS: Organizational deficiencies had a direct positive effect on workers' violations (path coefficient, 0.16) and workers' errors (path coefficient, 0.23). The effect of unsafe supervision on workers' violations and workers' errors was also significant, with path coefficients of 0.14 and 0.20, respectively. Likewise, preconditions for unsafe acts had a significant effect on both workers' violations (path coefficient, 0.16) and workers' errors (path coefficient, 0.21). Moreover, organizational deficiencies had an indirect positive effect on workers' unsafe acts, mediated by unsafe supervision and preconditions for unsafe acts. Among the variables examined in the current study, organizational influences had the strongest impact on workers' unsafe acts. CONCLUSIONS: Organizational deficiencies were found to be the main cause of accidents in the mining sector, as they affected all other aspects of system safety. In order to prevent occupational accidents, organizational deficiencies should be modified first.

Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors / 한국역학회지

OBJECTIVES: To identify the most important demographic risk factors for a diagnosis of type 2 diabetes mellitus (T2DM) using a neural network model. METHODS: This study was conducted on a sample of 234 individuals, in whom T2DM was diagnosed using hemoglobin A1c levels. A multilayer perceptron artificial neural network was used to identify demographic risk factors for T2DM and their importance. The DeLong method was used to compare the models by fitting in sequential steps. RESULTS: Variables found to be significant at a level of p < 0.2 in a univariate logistic regression analysis (age, hypertension, waist circumference, body mass index [BMI], sedentary lifestyle, smoking, vegetable consumption, family history of T2DM, stress, walking, fruit consumption, and sex) were entered into the model. After 7 stages of neural network modeling, only waist circumference (100.0%), age (78.5%), BMI (78.2%), hypertension (69.4%), stress (54.2%), smoking (49.3%), and a family history of T2DM (37.2%) were identified as predictors of the diagnosis of T2DM. CONCLUSIONS: In this study, waist circumference and age were the most important predictors of T2DM. Due to the sensitivity, specificity, and accuracy of the final model, it is suggested that these variables should be used for T2DM risk assessment in screening tests.

Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors / 한국역학회지

OBJECTIVES: To identify the most important demographic risk factors for a diagnosis of type 2 diabetes mellitus (T2DM) using a neural network model.METHODS: This study was conducted on a sample of 234 individuals, in whom T2DM was diagnosed using hemoglobin A1c levels. A multilayer perceptron artificial neural network was used to identify demographic risk factors for T2DM and their importance. The DeLong method was used to compare the models by fitting in sequential steps.RESULTS: Variables found to be significant at a level of p < 0.2 in a univariate logistic regression analysis (age, hypertension, waist circumference, body mass index [BMI], sedentary lifestyle, smoking, vegetable consumption, family history of T2DM, stress, walking, fruit consumption, and sex) were entered into the model. After 7 stages of neural network modeling, only waist circumference (100.0%), age (78.5%), BMI (78.2%), hypertension (69.4%), stress (54.2%), smoking (49.3%), and a family history of T2DM (37.2%) were identified as predictors of the diagnosis of T2DM.CONCLUSIONS: In this study, waist circumference and age were the most important predictors of T2DM. Due to the sensitivity, specificity, and accuracy of the final model, it is suggested that these variables should be used for T2DM risk assessment in screening tests.

Effects of human and organizational deficiencies on workers' safety behavior at a mining site in Iran / 한국역학회지

OBJECTIVES: Throughout the world, mines are dangerous workplaces with high accident rates. According to the Statistical Center of Iran, the number of occupational accidents in Iranian mines has increased in recent years. This study investigated and analyzed the human and organizational deficiencies that influenced Iranian mining accidents.METHODS: In this study, the data associated with 305 mining accidents were analyzed using a systems analysis approach to identify critical deficiencies in organizational influences, unsafe supervision, preconditions for unsafe acts, and workers' unsafe acts. Partial least square structural equation modeling (PLS-SEM) was utilized to model the interactions among these deficiencies.RESULTS: Organizational deficiencies had a direct positive effect on workers' violations (path coefficient, 0.16) and workers' errors (path coefficient, 0.23). The effect of unsafe supervision on workers' violations and workers' errors was also significant, with path coefficients of 0.14 and 0.20, respectively. Likewise, preconditions for unsafe acts had a significant effect on both workers' violations (path coefficient, 0.16) and workers' errors (path coefficient, 0.21). Moreover, organizational deficiencies had an indirect positive effect on workers' unsafe acts, mediated by unsafe supervision and preconditions for unsafe acts. Among the variables examined in the current study, organizational influences had the strongest impact on workers' unsafe acts.CONCLUSIONS: Organizational deficiencies were found to be the main cause of accidents in the mining sector, as they affected all other aspects of system safety. In order to prevent occupational accidents, organizational deficiencies should be modified first.

Multilevel model for influencing factors of the selection of first diagnosed agencies among residents reporting illness within two weeks in Hubei Province / 重庆医学

Objective To explore the selection of medical unit and the major influencing factors among residents in Hubei province,to allocate reasonably the health resources and provide reference for developing medical policy.Methods With the method of multi-stage stratified cluster sampling,household survey were done.The multilevel statistical model was used to analyze the influencing factors of the first diagnosed agencies.Results The proportions of residents who chose primary medical institutions as the first diagnosed agencies were 64.5％ in urban areas and 84.3％ in rural areas,and the visiting rate decreased as the level of health care institutions increased.The selection of first diagnosed agencies among patients were related to district (city or village,OR=0.463,95％CI..0.254-0.842),age (OR=1.023,95％CI:1.010-1.036),the educational attainment (OR＞1.000),illness duration in days (OR=0.945,95％CI:0.917-0.973) and number of days in bed (OR=0.854,95 ％ CI:0.825-0.884).Conclusion The residents who chose primary medical institutions as the first diagnosed agencies took a large proportion.District,age,the educational attainment and the illness duration in days had influence on the selection of the first diagnosed agencies among residents.

Comparison of neurological clinical manifestation in patients with hemorrhagic and ischemic stroke / 世界急诊医学杂志（英文）

@#BACKGROUND: Cerebrovascular accident (CVA) is the third leading cause of death and disability in developed countries. Anyone suspected of having a stroke should be taken immediately to a medical facility for diagnosis and treatment. The symptoms that follow a stroke aren't significant and depend on the area of the brain that has been affected and the amount of tissue damaged. Parameters for predicting long-term outcome in such patients have not been clearly delineated, therefore the aim of this study was to investigate this possibility and to test a system that might practicably be used routinely to aid management and predict outcomes of individual stroke patients. METHODS: A descriptive hospital-based study of the neurological symptoms and signs of 503 patients with ischemic stroke, including severe headache, seizure, eye movement disorder, pupil size, Glasgow Coma Scale (GCS), agitation were analyzed in this study. RESULTS: In the current study, dilated pupils, agitation, acute onset headache, lower GCS score, seizure, and eye gaze impairment had significantly higher prevalence in hemorrhagic stroke patients (P<0.001). However, the rate of gradual progressive headache is significantly higher in ischemic stroke patients (P<0.001). CONCLUSION: Although this result provides reliable indicators for discrimination of stroke types, imaging studies are still the gold standard modality for diagnosis.

Relationship between anatomical factors and dosimetric sparing of the bladder in IMRT plans for rectal cancer / 中华放射肿瘤学杂志

Objective To establish a statistical model that can quantitatively analyze the dosimetric sparing of the bladder based on individual patient’ s anatomy in the static intensity-modulated radiotherapy (IMRT) plans for rectal cancer.Methods Static IMRT plans (7 AP fields) for 100 rectal cancer patients were used to train the model from 2012 to 2013.The anatomical features were quantitatively analyzed by the sizes of overlap regions of bladder-planning target volume (PTV) and bladder-PTV+0.5(0.5 cm margin around the PTV) .The mathematic relationship between anatomical features and dosimetric sparing of the bladder was evaluated after the bladder sparing dose was analyzed using dose-volume histogram.The established model was verified in the IMRT plans for additional 20 rectal cancer patients.Results Bladder V50 was linearly correlated with the ratio of bladder-PTV overlap size to bladder volume ( denoted as x%) , with an equation of V50=0.89x-0.99.Bladder V40 showed an approximately linear correlation with the ratio of bladder-PTV+0.5 overlap size to bladder volume (denoted as y%).The mean dose depended on both x%and y%.For the additional 20 plans, the absolute deviation between predicted and actual values for V50 and V40 were (-3.13%-3.78%) and (-5.30%-5.66%) , respectively, and the relative deviation for the mean dose was (-3.94%-3.76%) .Conclusions The model obtained in this work provides an effective method for quantitatively estimating the bladder sparing dose in IMRT plans for rectal cancer.

Non-destructive linear model for leaf area estimation in Vernonia ferruginea Less / Modelo linear não-destrutivo para estimativa de área foliar de Vernonia ferruginea Less

Leaf area estimation is an important biometrical trait for evaluating leaf development and plant growth in field and pot experiments. We developed a non-destructive model to estimate the leaf area (LA) of Vernonia ferruginea using the length (L) and width (W) leaf dimensions. Different combinations of linear equations were obtained from L, L2, W, W2, LW and L2W2. The linear regressions using the product of LW dimensions were more efficient to estimate the LA of V. ferruginea than models based on a single dimension (L, W, L2 or W2). Therefore, the linear regression “LA=0.463+0.676WL” provided the most accurate estimate of V. ferruginea leaf area. Validation of the selected model showed that the correlation between real measured leaf area and estimated leaf area was very high.

A estimativa de área foliar é um importante traço biométrico para avaliação do desenvolvimento foliar e do crescimento vegetal em experimentos de campo e casa-de-vegetação. Foi desenvolvido um modelo linear não destrutivo capaz de estimar a área foliar (AF) de Vernonia ferruginea usando o comprimento (C) e a largura (L) foliar. Diferentes combinações de equações lineares foram obtidas a partir de C, C2, L, L2, CL e C2L2. As regressões lineares usando o produto de dimensões CL foram mais eficientes para estimar a AF de V. ferruginea do que os modelos baseados em uma única dimensão (C, L, C2 ou L2). O modelo linear "AF = 0,463+0,676 CL" forneceu com maior precisão a AF de V. ferruginea em relação aos demais modelos testados. A validação do modelo selecionado revelou elevada correlação entre a área foliar real e a área foliar estimada pelo modelo.

Discussion on Statistical Pattern Recognition Model Related to Herbal Property and Lipid Components of Chinese Materia Medica / 世界科学技术-中医药现代化

This study was aimed to explore recognition models and to establish statistical pattern recognition methods of cold-hot property markers based on lipid components GC-MS chromatogram of Chinese materia medica (CMM). GC-MS fingerprints of lipid components contained in 60 kinds of cold or hot property of CMM were used as the research object. The database was established. Five types of model establishment strategies were compared. Optimal modeling patterns were screened for the identification of herbal property markers of lipid components GC-MS chromatogram. The results showed that support vector machine (SVM) was the best model to discriminate cold or hot property among 60 types of CMM, which were able to effectively mark the characteristic area. The strongest markers tending to cold property was at the retention time of 61.550 min, while the strongest markers tending to hot property was at the retention time of 31.395 min. It was concluded that cold or hot property of CMM had close relationship with lipid components. The lipid component was one of the material bases of CMM. The mathematical statistical model based on material base and herbal property can be used to identify and predict the cold and hot property of CMM.