Randomized Controlled Trial Design Based on Patient Cohorts： Methods and Applications of Trials Within Cohorts / 中国实验方剂学杂志

Trials within cohorts （TwiCs） are design methods derived from randomized controlled trials （RCTS）. They have been widely used in chronic disease areas such as tumors and cardiovascular diseases. The basis of the TwiCs design is a prospective cohort of specific diseases. When RCTS need to be implemented， some patients meeting the inclusion and exclusion criteria are randomly sampled from the cohort to receive "trial interventions"， while the remaining patients in the cohort who meet the inclusion and exclusion criteria continue to receive conventional treatment as control groups. By comparing the efficacy differences between the intervention measures of the trial group and the control group， the efficacy of intervention measures was evaluated. Within the cohort， the same process could be repeated to carry out multiple RCTS， so as to evaluate different intervention measures or compare the efficacy of different doses or timing of interventions. Compared with classical RCTS， TwiCs make it easier to recruit patients from the cohort and have higher external validity， providing a new research paradigm for improving the efficiency and applicability of RCTS in clinical practice. However， TwiCs may also face the challenge of poor compliance of patients in the cohort. Researchers need to take effective measures to control these patients in the design and operation of TwiCs. This article focused on the methodological key points during the implementation of TwiCs， including multi-stage informed consent （patients are informed of consent at three stages： entering the cohort， entering the trial group， and after the trial）， randomization procedures （only random sampling of patients from the cohort to receive "trial interventions"）， sample size calculation， and statistical analysis methods. The article also compared the differences between TwiCs and traditional RCTS and illustrated TwiCs research design and analysis with examples， so as to provide new research ideas and methods for clinical researchers.

Prediction of declarative memory profile in panic disorder patients: a machine learning-based approach

Objective: To develop a classification framework based on random forest (RF) modeling to outline the declarative memory profile of patients with panic disorder (PD) compared to a healthy control sample. Methods: We developed RF models to classify the declarative memory profile of PD patients in comparison to a healthy control sample using the Rey Auditory Verbal Learning Test (RAVLT). For this study, a total of 299 patients with PD living in the city of Rio de Janeiro (70.9% females, age 39.9 ± 7.3 years old) were recruited through clinician referrals or self/family referrals. Results: Our RF models successfully predicted declarative memory profiles in patients with PD based on RAVLT scores (lowest area under the curve [AUC] of 0.979, for classification; highest root mean squared percentage [RMSPE] of 17.2%, for regression) using relatively bias-free clinical data, such as sex, age, and body mass index (BMI). Conclusions: Our findings also suggested that BMI, used as a proxy for diet and exercises habits, plays an important role in declarative memory. Our framework can be extended and used as a prospective tool to classify and examine associations between clinical features and declarative memory in PD patients.

CONTEXTUAL LEARNING APPROACH FOR SYNDROME DISEASE NAMED ENTITY RECOGNITION

The groundwork for extracting a significant amount of biomedical information from unstructured texts into structured formats is the difficult research area of biological entity recognition from medical documents. The existing work implemented the named entity recognition for diseases using the sequence labelling framework. The performance of this strategy, however, is not always adequate, and it frequently cannot fully exploit the semantic information in the dataset. The Syndrome Diseases Named Entity problem is presented in this work as a sequence labelling with multi-context learning. By using well-designed text/queries, this formulation may incorporate more previous information and to decode it using decoding techniques such conditional random fields (CRF). We performed experiments on three biomedical datasets, and the outcomes show how effective our methodology is on the BC5CDR-Disease, JNLPBA and NCBI-Disease, compared with other techniques our methodology performs with accuracy levels of 96.70%,98.65 and 96.72% respectively.

Ensayos aleatorizados publicados por instituciones ubicadas en Perú: estudio bibliométrico del periodo 2000 a 2022

Objetivo: Caracterizar la producción científica de ensayos aleatorizados por instituciones ubicadas en Perú del 01 de enero de 2000 al 30 de diciembre de 2022. Materiales y Métodos: Estudio bibliométrico, se revisaron dos bases de datos (MEDLINE y SciELO). Se incluyeron artículos originales en la que al menos un autor consignó como filiación una institución ubicada en Perú. Se caracterizó la producción científica según: área temática, participación de instituciones ubicadas en Perú, revistas científicas en las que se publicaron los artículos, y aprobación de los estudios por un Comité de Ética en Investigación (CEI). Resultados: Se analizó un total de 402 artículos, se evidenció una tendencia creciente de la producción científica, pasando de seis en el año 2000 a 39 en el año 2021, el área temática predominante es la infecciosa, las dos primeras instituciones con mayor número de ensayos aleatorizados pertenecen al sector educación, 189 (47,0%) artículos fueron publicados en revistas de Estados Unidos, en 37 (9,2%) artículos no se consigna información de aspectos éticos o no se declara explícitamente si fue o no aprobado por un CEI. Conclusión: Hay una tendencia creciente de la producción científica sobre este diseño de estudio, el área temática predominante es la infecciosa, las instituciones peruanas más productivas pertenecen al sector educación, cerca de la mitad de los artículos fueron publicados en revistas de Estados Unidos, y en una décima parte de los artículos no se señala explícitamente si el estudio fue o no aprobado por un CEI.

Objetive: To characterize the scientific production of randomized trials by institutions located in Peru from January 1, 2000 to December 30, 2022. Materials and methods: Bibliometric study, two databases (MEDLINE and SciELO) were reviewed. Original articles were included in which at least one author stated an institution located in Peru as affiliation. Scientific production was characterized according to: thematic area, participation of institutions located in Peru, scientific journals in which the articles were published, and approval of the studies by a Research Ethics Committee (REC). Results: A total of 402 articles were analyzed, a growing trend in scientific production was evidenced, going from six in the year 2000 to 39 in the year 2021, the predominant thematic area is infectious, the first two institutions with the highest number of randomized trials belong to the education sector, 189 (47.0%) articles were published in journals in the United States, in 37 (9.2%) articles there is no information on ethical aspects or it is not explicitly stated whether or not it was approved by a CEI. Conclusions: There is a growing trend of scientific production on this study design, the predominant thematic area is infectious, the most productive Peruvian institutions belong to the education sector, about half of the articles were published in journals in the United States, and in a tenth part of the articles do not explicitly state whether or not the study was approved by an REC.

National health surveys: overview of sampling techniques and data collected using complex designs / Encuestas nacionales de salud: descripción general de las técnicas de muestreo y los datos recopilados por diseños complejos / Inquéritos nacionais de saúde: visão geral sobre técnicas de amostragem e dados coletados por desenhos complexos

Abstract This article aimed to present an overview of national health surveys, sampling techniques, and components of statistical analysis of data collected using complex sampling designs. Briefly, surveys aimed at assessing the nutritional status of Brazilians and maternal and child health care were described. Surveys aimed at investigating access to and use of health services and funding, those aimed at surveillance of chronic noncommunicable diseases and associated behaviors, and those focused on risk practices regarding sexually transmitted infections were also addressed. Health surveys through social networks, including online networks, deserved specific attention in the study. The conclusion is that the development of health surveys in Brazil, in different areas and using different sampling methodologies, has contributed enormously to the advancement of knowledge and to the formulation of public policies aimed at the health and well-being of the Brazilian population.

Resumen Este estudio tuvo como objetivo presentar una descripción de las encuestas nacionales de salud, las técnicas de muestreo y los componentes del análisis estadístico de diseños de muestreo complejos. Brevemente, se describieron encuestas destinadas a evaluar el estado nutricional y la atención a la salud materno-infantil. También se abordaron las encuestas dirigidas a investigar el acceso y uso de los servicios de salud y el financiamiento, las dirigidas a la vigilancia de las enfermedades crónicas no transmisibles y comportamientos asociados, y las enfocadas a las prácticas de riesgo para las infecciones de transmisión sexual. Las encuestas de salud a través de las redes sociales, incluidas las virtuales, merecieron atención específica en el estudio. Se concluye que el desarrollo de encuestas de salud en Brasil ha contribuido enormemente para el avance del conocimiento y para la formulación de políticas públicas dirigidas a la salud y el bienestar de la población brasileña.

Resumo O artigo teve por finalidade apresentar um panorama dos inquéritos nacionais de saúde, técnicas de amostragem e componentes da análise estatística de dados coletados por desenhos complexos de amostragem. Foram descritos, brevemente, os inquéritos dirigidos à avaliação do estado nutricional dos brasileiros e da atenção à saúde materno-infantil. Inquéritos voltados à investigação do acesso, utilização dos serviços e financiamento da saúde, aqueles dedicados à vigilância das doenças crônicas não transmissíveis e comportamentos associados e os focados nas práticas de risco às infecções sexualmente transmissíveis foram também abordados. As pesquisas de saúde por redes sociais, incluindo as virtuais, mereceram atenção específica. Conclui-se que o desenvolvimento de inquéritos de saúde no Brasil, em diferentes áreas e por distintas metodologias de amostragem, contribuiu enormemente para o avanço do conhecimento e a formulação de políticas públicas dirigidas à saúde e bem-estar da população brasileira.

Estudo da mortalidade infantil nos estados brasileiros utilizando aplicação de dados em painel / Study of child mortality in brazilian states using panel data application / Estudio de mortalidad infantil en estados brasileños mediante aplicación de datos de panel

Este trabalho tem como objetivo determinar uma relação linear entre a Taxa de Mortalidade Infantil (TMI) e um conjunto de variáveis socioeconômicas observadas por unidades federativas no período de 2005 à 2010 utilizando o modelo de dados em painel de efeitos fixo e aleatório. Metodologia: trata-se de um estudo descritivo com abordagem quantitativa, com utilização dos Sistema de Informação sobre Mortalidade (SIM) e o Sistema de Informações sobre Nascidos Vivos (SINASC) e em seguida utilizou-se o software R para realizar esta análise de dados com a função plm. Resultados: os estudos mostram que o modelo mais adequado é o de efeito fixo com transformação logarítmica nas variáveis independentes e na variável dependente que foram as seguintes: TMI, taxa de analfabetismo, PIB per capita, proporção pessoas com baixa renda, percentual da população servida por rede de abastecimento de água e a proporção da população servida por coleta de lixo. Conclusão: As variáveis independentes que causam impacto significativo na TMI são taxa de analfabetismo, PIB per capita e proporção de pessoas com baixa renda.

This work aims to determine a linear relationship between the Infant Mortality Rate (IMR) and a set of socioeconomic variables observed by federative units in the period from 2005 to 2010 using the fixed and random effects panel data model. Methodology: this is a descriptive study with a quantitative approach, using the Mortality Information System (SIM) and the Live Birth Information System (SINASC) and then using the R software to perform this data analysis with the plm function. Results: studies show that the most appropriate model is the fixed effect model with logarithmic transformation in the independent variables and the dependent variable, which were as follows: IMR, illiteracy rate, GDP per capita, proportion of people with low income, percentage of the population served by water supply network and the proportion of the population served by garbage collection. Conclusion: The independent variables that have a significant impact on IMR are the illiteracy rate, GDP per capita and the proportion of people with low income.

Este trabajo tiene como objetivo determinar una relación lineal entre la Tasa de Mortalidad Infantil (TMI) y un conjunto de variables socioeconómicas observadas por las unidades federativas en el período 2005 a 2010 utilizando el modelo de datos de panel de efectos fijos y aleatorios. Metodología: se trata de un estudio descriptivo con enfoque cuantitativo, utilizando el Sistema de Información de Mortalidad (SIM) y el Sistema de Información de Nacidos Vivos (SINASC) y luego utilizando el software R para realizar este análisis de datos con la función plm. Resultados: los estudios muestran que el modelo más adecuado es el modelo de efectos fijos con transformación logarítmica en las variables independientes y la variable dependiente, las cuales fueron las siguientes: TMI, tasa de analfabetismo, PIB per cápita, proporción de personas con bajos ingresos, porcentaje de la población atendida por red de suministro de agua y la proporción de la población atendida por recolección de basura. Conclusión: Las variables independientes que tienen un impacto significativo en la TMI son la tasa de analfabetismo, el PIB per cápita y la proporción de personas con bajos ingresos.

Recognition models of cigarette smoking behavior by real-time indoor PM2.5 concentrations in public places / 环境与职业医学

Background Public places are frequently polluted by cigarette smoking, and there is a lack of accurate, real-time, and intelligent monitoring technology to identify smoking behavior. It is necessary to develop a tool to identify cigarette smoking behavior in public places for more efficient control of cigarette smoking and better indoor air quality. Objective To construct a model for recognizing cigarette smoking behavior based on real-time indoor concentrations of PM2.5 in public places. Methods Real-time indoor PM2.5 concentrations were measured for at least 7 continuous days in 10 arbitrarily selected places (6 public service providers and and 4 office or other places) from Oct. to Nov. 2022 in Pudong New Area, Shanghai. Indoor nicotine concentrations were monitored with passive samplers simultaneously. Outdoor PM2.5 concentration data were obtained from three municipal environmental monitoring stations which were nearest to each monitoring point during the same period. Mann-Whitney U test was used to compare indoor and outdoor means of PM2.5 concentrations, and Spearman rank correlation was used to analyze indoor PM2.5 and nicotine concentrations. An interactive plot and a random forest model was applied to examine the association between video observation validated indoor smoking behavior and real-time indoor PM2.5 concentrations in an Internet cafe. Results The average indoor PM2.5 concentration in the places providing public services [(97.5±149.3) µg·m−3] was significantly higher than that in office and other places [(19.8±12.2) µg·m−3] (P=0.011). The indoor/outdoor ratio (I/O ratio) of PM2.5 concentration in the public service providers ranged from 1.1 to 19.0. Furthermore, the indoor PM2.5 concentrations in the 10 public places were significantly correlated with the nicotine concentrations (rs=0.969, P<0.001). Among them, the top 3 highly polluted places were Internet cafes, chess and card rooms, and KTV. The results of random forest modeling showed that, for synchronous real-time PM2.5 concentration, the area under the curve (AUC) was 0.66, while for PM2.5 concentration at a lag of 4 min after the incidence of smoking behavior, the AUC increased to 0.72. Conclusion The indoor PM2.5 concentrations in public places are highly correlated with smoking behavior. Based on real-time indoor PM2.5 monitoring, a preliminary recognition model for smoking behavior is constructed with acceptable accuracy, indicating its potential values applied in smoking control and management in public places.

Establishment of random survival forest model of the prognosis of anaplastic thyroid cancer and its predictive efficacy analysis / 肿瘤研究与临床

Objective:To investigate the factors influencing the prognosis of anaplastic thyroid cancer (ATC) and to evaluate the application value of established random survival forest (RSF) model in the prognosis prediction of ATC.Methods:A total of 707 ATC patients diagnosed by histopathology in the Surveillance, Epidemiology and End Results (SEER) database of the National Cancer Institute from 2004 to 2015 were selected and randomly divided into the training set (495 cases) and the validation set (212 cases). Univariate Cox regression risk model was used to analyze the related factors affecting overall survival (OS) of patients in the training set. The multivariate Cox proportional risk model based on the minimum Akaike information criterion (AIC) was used to analyze the above variables and then the variables were screened out. The traditional Cox model for predicting OS was constructed based on the screened variables. The RSF algorithm was used to analyze the variables with P < 0.05 in the univariate Cox regression analysis, and 5 important features were selected. Multivariate Cox proportional risk model was selected based on the minimum AIC. Then the RSF-Cox model for predicting OS was constructed by using screened variables. The time-dependent receiver operating characteristic (tROC) curve and the area under the curve (AUC), calibration curve, decision curve and integrated Brier score (IBS) in the training set and the validation set were used to evaluate the prediction performance of the models. Results:Univariate Cox regression analysis showed that age, chemotherapy, lymph node metastasis, radiotherapy, surgical method, tumor infiltration degree, tumor number, tumor diameter and diagnosis time were factors affecting the prognosis of ATC (all P < 0.05). Multivariate Cox regression analysis based on minimal AIC (4 855.8) showed that younger age (61-70 years vs. > 80 years: HR = 0.732, 95% CI 0.56-0.957, P = 0.023; ≤ 50 years vs. > 80 years: HR = 0.561, 95% CI 0.362-0.87, P = 0.010), receiving chemotherapy (receiving or not: HR = 0.623, 95% CI 0.502-0.773, P < 0.001), receiving radiotherapy (receiving or not: HR = 0.695, 95% CI 0.559-0.866, P = 0.001), receiving surgery (lobectomy, no surgery or unknown: HR = 0.712, 95% CI 0.541-0.939, P = 0.016; total resection or subtotal resection vs. no surgery or unknown: HR = 0.535, 95% CI 0.436-0.701, P < 0.001), and tumor diameter (≤ 2 cm vs. > 6 cm: HR = 0.495, 95% CI 0.262-0.938, P = 0.031; > 2 cm and ≤ 4 cm vs. > 6 cm: HR = 0.714, 95% CI 0.520-0.980, P = 0.037; > 4 cm and ≤ 6 cm vs. > 6 cm: HR = 0.699, 95 % CI 0.545-0.897, P = 0.005) were independent protective factors for OS of ATC patients. Lymph node metastasis (N 1 unknown vs. N 0: HR = 1.664, 95% CI 1.158-2.390, P = 0.006; N 1b: HR = 1.312, 95% CI 1.029-1.673, P = 0.028), more aggressive tumor infiltration degree (group 3 vs. group 1: HR = 1.492, 95% CI 1.062-2.096, P = 0.021; group 4 vs. group 1: HR = 1.636, 95% CI 1.194 - 2.241, P = 0.002) were independent risk factors for OS of ATC patients. Although diagnosis time was not statistically significant (2010-2015 vs.2004-2009: HR = 1.166, 95% CI 0.962-1.413, P = 0.118), the inclusion of it could improve the efficacy of the traditional Cox model. RFS algorithm was used to select out 5 important variables: surgical method, tumor diameter, age group, chemotherapy, and tumor number. Multivariate Cox regression analysis based on minimum AIC (4 884.6) showed that chemotherapy (receiving or not: HR = 0.574, 95% CI 0.476-0.693, P < 0.001), surgical method (lobectomy, no surgery or unknown: HR = 0.730, 95% CI 0.567-0.940, P = 0.015; total resection or subtotal resection vs. no surgery or unknown: HR = 0.527, 95% CI 0.423-0.658, P < 0.001), tumor diameter (≤ 2 cm vs. > 6 cm: HR = 0.428, 95% CI 0.231-0.793, P = 0.007; > 2 cm and ≤ 4 cm vs. > 6 cm: HR = 0.701, 95% CI 0.513-0.958, P = 0.026; > 4 cm and ≤ 6 cm vs. > 6 cm: HR = 0.681, 95% CI 0.536-0.866, P = 0.002) were independent factors for OS of ATC patients. RSF-Cox model was constructed based on 3 variables. The tAUC curve analysis showed that RSF-Cox model for predicting the 6-month, 12-month, and 18-month OS rates were 93.56, 92.62, and 90.80, respectively in the training set, and 93.05, 92.47, and 90.20, respectively in the validation set; in the traditional Cox model, the corresponding OS rates were 89.00, 87.76, 85.24, respectively in the training set, and 86.22, 83.68, 82.86, respectively in the validation set. When predicting OS rate at 6-month, 12-month and 18-month, the calibration curve of RSF-Cox model was closer to 45° compared with that of traditional Cox model, and the clinical net benefit of decision curve in RSF-Cox model was higher than that in traditional Cox model. The IBS of RSF-Cox model (0.089) was lower than that of traditional Cox model (0.111). Conclusions:The RSF model based on chemotherapy, surgical method and tumor diameter can effectively predict the OS of ATC patients.

Analysis of indicators related to visceral fat index based on the random forest model / 中华健康管理学杂志

Objective:To explore indicators related to visceral fat index by constructing a random forest model.Methods:In this cross-sectional study, the laboratory measures and body composition analysis records of 617 hospital employees (in-service and retired) who underwent physical examination in Heilongjiang Provincial Hospital Health Management Center from March to September 2021 were selected. The subjects were divided into a training set ( n=411) and a test set ( n=206) with the ratio of 2∶1. A total of 110 predictors were included in the model. The model was constructed with the training set and was evaluated with the test set. The optimal number of nodes and decision trees were selected to evaluate the prediction performance of the optimal model. And the top 10 relatively important factors were selected for further investigation. The 617 participants were further divided in to groups according to the visceral fat index: the normal or high visceral fat index group, and the differences of the top 10 relatively important factors were further compared between the two groups. Results:The optimal number of nodes of the final random forest model was 39 and the number of decision trees was 300. The accuracy, precision, sensitivity and specificity of the model was 83.3%, 73.9%, 89.4% and 78.7%, respectively. The area under the receiver operating characteristic curve and 95% confidence interval of the model was 0.881 (0.832-0.931). The top 10 relatively important factors in the model were body mass index, gender, age, serum uric acid, red blood cell count, monocyte cell count, C-peptide, carcinoembryonic antigen, glycosylated hemoglobin and glutamyl transpeptidase. There were significant differences in the up-mentioned 10 indicators between the subjects with normal and high visceral fat index (all P<0.05). Conclusions:The random forest model built in this study has good performance in predicting visceral fat index, and visceral fat is related with changes in liver function, pancreas function and immune function.

Feasibility study of predicting lung tumor target movement based on body surface motion monitoring / 中华放射肿瘤学杂志

Objective:To evaluate the feasibility of predicting lung cancer target position by online optical surface motion monitoring.Methods:CT images obtained in different ways of stereotactic body radiotherapy (SBRT) plans from 16 lung cancer cases were selected for experimental simulation. The planned CT and the original target position were taken as the reference, and the 10 phases of CT in four dimension CT and each cone beam (CBCT) were taken as the floating objects, on which the floating target location was delineated. The binocular visual surface imaging method was used to obtain point cloud data of reference and floating image body surface, while the point cloud feature information was extracted for comparison. Based on the random forest algorithm, the feature information difference and the corresponding target area position difference were fitted, and an online prediction model of the target area position was constructed.Results:The model had a high prediction success rate for the target position. The variance explainded and root mean squared error ( RMSE) of left-right, superior-inferior, anterior-posterior directions were 99.76%, 99.25%, 99.58%, and 0.0447 mm, 0.0837 mm, 0.0616 mm, respectively. Conclusion:The online monitoring of lung SBRT target position proposed in this study is feasible, which can provide reference for online monitoring and verification of target position and dose evaluation in clinical radiotherapy.

Construction of a hypoglycemia prediction model for older adults with type 2 diabetes based on random forest algorithm / 中国实用护理杂志

Objective:To construct a hypoglycemia random forest prediction model for older adults with type 2 diabetes, and assess the model′s prognostication performance through internal and external verification.Methods:From August 2022 to January 2023, 300 older adults with type 2 diabetes in Beijing Hospital were selected. The demographic characteristics, medical history, laboratory tests, and other data of the patients were collected, and the data set was randomly divided into the training set and verification set in a ratio of 7∶3. The hypoglycemia prediction model for older adults with type 2 diabetes was constructed and optimized based on the random forest algorithm. The calibration curve was used to evaluate the model′s calibration, and the ROC was used to evaluate the model′s discrimination. The clinical applicability of the model was assessed by the decision curve analysis. The risk factors for hypoglycemia in the older adults were explored by prioritizing the contributions of variables in prediction. The Bootstrap method was used for internal validation, and the validation set was used for external validation.Results:Among the 300 older adults with type 2 diabetes, 128 cases (42.67%) experienced hypoglycemia within one week. The predictive contributions of risk factors in the model were ranked as follows: the number of episodes of hypoglycemia in one month, HDL-C, heart disease, diabetes knowledge and education, combination therapy, age, duration of diabetes, staple food restriction, glycosylated hemoglobin, and gender. The internal and external calibration curves of the hypoglycemia random forest model for the older adults with type 2 diabetes fluctuated around the diagonal, indicating that the calibration degree of the predictive model is good. The AUROC of internal verification was 0.823 (95% CI 0.752-0.894), the sensitivity and specificity were 0.867 and 0.698, respectively. The external verification was 0.859 (95% CI 0.817 - 0.902), and sensitivity and specificity were 0.789 and 0.804, respectively, showing that the overall discrimination of the prediction model was good. The DCA curves were far from the all-positive line and all-negative line, which indicated that the prediction model had good clinical applicability. Conclusions:The predictive effect of this model is good, and it is suitable for predicting the risk of hypoglycemia in older adults with type 2 diabetes, and it provides a reference for early hypoglycemia screening and predictive intervention for this kind of patients.

Grey correlation analysis on influencing factors of Yang deficiency constitution / 数字中医药（英文）

@#【Objective 】 To explore the influencing factors of Yang deficiency constitution in traditional Chinese medicine (TCM) from the perspective of mathematics with the use of calculation formulas, so as to protect patients from getting diseases caused by Yang deficiency constitution and provide suggestions for TCM disease prevention. 【Methods】  Based on the classification and determination criteria of TCM constitution implemented by China Association of Chinese Medicine, data for 24 solar terms from May 5, 2020(Start of Summer) to April 20, 2021 (Grain Rain) for the identification of Yang deficiency were collected by mobile constitution identification system. The grey correlation analysis method was used to determine the grey correlation degree of the factors influencing Yang deficiency constitution. In addition, a random forest model was constructed for the verification of the results from the grey correlation analysis, and for the evaluation of correlation degree between Yang deficiency constitution and its influencing factors. 【Results】  A total of 16 259 sets of data were collected from healthy or sub-healthy individuals aged from 18 to 60 years living in the central and northeastern parts of Sichuan Province(China) for the identification of TCM constitutions. After screening and preprocessing, a total of 544 sets of data for the identification of Yang deficiency constitution, involving 18 aspects of factors influencing Yang deficiency constitution. The results of the grey correlation analysis showed that there were 12 influencing factors whose grey correlation degree with Yang deficiency constitution was greater than 0.6. The accuracy of these 12 influencing factors with the training set and validation set of the Yang deficiency constitution random forest model were 98.39% and 93.12%, respectively. 【Conclusion】  In the sample data selected in this paper, grey correlation analysis is the appropriate technology to analyze the influencing factors of Yang deficiency constitution. It provides a new idea and a new methodological reference for the research and analysis of the influencing factors of TCM constitution.

Study on prediction model of mosquito breeding in small containers based on random forest / 环境与职业医学

Background Aedes albopictus is the dominant mosquito species in residential areas in Shanghai. There are many types of small containers with accumulated water in residential areas, providing a large number of breeding environments for Aedes alpopicuts and leading to an increasing transmission risk of mosquito-borne diseases. Objective To use random forest to predict breeding of Aedes mosquitoes in small aquatic container habitat in two concentrated reconstruction communities of rural areas in Shanghai, and to understand associated influence of environmental factors on the breeding of Aedes mosquitoes in the process of urbanization.Methods Small-scale habitat surveys of Aedes mosquitoes were carried out in two suburb concentrated reconstruction communities (Community A and B) in Shanghai, and the environment where the habitat was located was recorded and analyzed in both communities. The habitat where eggs, larvae, or pupae were found was recorded as positive. Spatial weight matrix was applied on a household basis, and global Moran's I index was used to carry out spatial autocorrelation analysis on the small-scale habitat and positive habitat in the environment of the two communities. When Moran's I is greater than 0, it means that the data present a positive spatial correlation; when Moran's I is less than 0, it means that the data are spatially negatively correlated; when Moran's I is 0, the spatial distribution is random. Combining the results of P and Z values, we explored the spatial distribution characteristics of small-scale habitat and positive habitat in the community environment. Random forest algorithm in machine learning was used to classify and sort environmental-related factors, and predict the breeding of Aedes mosquitoes in small aquatic habitat; receiver operating characteristic (ROC) curve was used to carry out model fitting evaluation. Results The environmental factors including building location (χ2=23.35, P<0.001), open space (χ2=8.83, P=0.003), and having trees (χ2=11.02, P=0.001) had a significant impact on the positive rate of small-scale habitat. The results of spatial characteristics analysis showed that the global Moran's I index of small-scale habitat was −0.092 (Z=−1.09, P=0.274) in Community A and 0.034 (Z=0.52, P=0.602) in Community B, and the global Moran's I index of positive habitat was −0.092 (Z=−1.14, P=0.255) in Community A and 0.070 (Z=0.95, P=0.342) in Community B. Since the P values of Community A and B were greater than 0.1 and the Z values were between −1.65 and 1.65, for both small-scale habitat and positive habitat the spatial characteristics were randomly distributed and no significant spatial aggregation was found. In the fitted random forest algorithm classification prediction model with the top 10 characteristic factors of importance, the area under curve (AUC) value was 0.95, and the prediction fitting effect was satisfactory. The results of classification and sorting indicated that counts of household small-scale habitat and positive habitat were the most important factors for breeding. Conclusion The random forest model constructed by environmental factor indicators can be used to predict the breeding situation of Aedes mosquitoes in small-scale aquatic habitat, and provide a basis for scientific prevention and control of mosquito breeding for the target area.

A Ventricular Fibrillation Recognition Method Based on Random Forest and BP Neural Network / 中国医疗器械杂志

Ventricular fibrillation is the most common pathophysiological mechanism leading to cardiac arrest. If cardiac arrest can be rescued in time, the survival rate of patients can be greatly improved. Therefore, rapid and accurate identification of ventricular fibrillation is extremely important. This paper proposes an automatic detection algorithm for ventricular fibrillation based on random forest and BP (back propagation) neural network. Pass the ECG signal through a 6 s moving window, calculate 6 kinds of characteristic parameters according to the time-frequency domain information of the signal, use these 6 kinds of characteristic parameters as the input of the classifier, carry out classification and test, and give the authoritative experts in the database. A total of 44 cases of related data were used to evaluate the method. The results show that using the ten-fold cross-validation method, the accuracy of classification of ventricular fibrillation in the CU database (Creighton University Ventricular Tachyarrhythmia Database) and the AHA database (the American Heart Association Database) has reached 96.38% and 99.45%, which has certain applicability.

Application andevaluation of random quality control sampling in donor blood detetion by ELISA / 中国输血杂志

【Objective】 To evaluate the effectiveness of random quality control sampling in blood sample detetion by ELISA. 【Methods】 Blood samples of 5 mL specification of blood donors from our blood station from May to July 2022 were selected for routine operation on a fully automated sampler. J standard substances(3 mL specification) as daily samples were added to A1 well, H12 well and random wells of HBsAg, anti-HCV, anti-HIV, and -TP, and then placed in a fully automated enzyme immunoassay analyzer for testing. With random well quality control as the internal quality control judgment standard, 20 consecutive tests were conducted and were divided into A1 (well) group, H12 (well) group and random (well) group according to different well positions. Quality control maps were drawn using Levey-Jennings quality control chart with random group as the framework, and were compared with the quality control map of A1 well and H12 well results in the same day. 【Results】 The mean quality control levels of infectious indicators of blood transfusion in blood donors by ELISA were: HBsAg 3.87±0.28, anti-HCV 3.79±0.38, anti-HIV 3.64±0.30 and anti-TP 4.53±0.51. 【Comparison】 of HBsAg, anti-HCV, anti-HIV and anti-TP, between random group, A1 group and H12 group were HBsAg 3.87± 0.28 vs 4.09±0.30 vs 3.64±0.26, anti-HCV 3.78±0.37 vs 3.96±0.38 vs 3.63±0.38, anti-HIV 3.63±0.31 vs 3.82±0.32 vs 3.48±0.28 and anti-TP 4.51±0.51 vs 4.71±0.52 vs 4.36±0.51, The S/CO value of each indicator were H12 group<random group<Al group (P<0.05), and the mean quality control levels of random group were similar to each detection indicator (P>0.05) . Using random group as the quality control framework standard, 5 points in group A1 fell outside of +2s, and 1 point in group H12 fell outside of -2s, resulting in a total of 6 alarms. With the quality control substance placed in A1 well of the ELISA plate, the judgment of detection results of the entire ELISA plate could be inevitably affected, especially the last row of low concentration virus marker samples on the ELISA plate. 【Conclusion】 The application of random quality control sampling method in donor blood by ELISA is scientific and reasonable, which can reduce the systematic error caused by artificial setting of ELISA plate fixed well positions and can also discover edge effects that affect the detection results.

Automatic sleep staging based on power spectral density and random forest / 生物医学工程学杂志

The method of using deep learning technology to realize automatic sleep staging needs a lot of data support, and its computational complexity is also high. In this paper, an automatic sleep staging method based on power spectral density (PSD) and random forest is proposed. Firstly, the PSDs of six characteristic waves (K complex wave, δ wave, θ wave, α wave, spindle wave, β wave) in electroencephalogram (EEG) signals were extracted as the classification features, and then five sleep states (W, N1, N2, N3, REM) were automatically classified by random forest classifier. The whole night sleep EEG data of healthy subjects in the Sleep-EDF database were used as experimental data. The effects of using different EEG signals (Fpz-Cz single channel, Pz-Oz single channel, Fpz-Cz + Pz-Oz dual channel), different classifiers (random forest, adaptive boost, gradient boost, Gaussian naïve Bayes, decision tree, K-nearest neighbor), and different training and test set divisions (2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, single subject) on the classification effect were compared. The experimental results showed that the effect was the best when the input was Pz-Oz single-channel EEG signal and the random forest classifier was used, no matter how the training set and test set were transformed, the classification accuracy was above 90.79%. The overall classification accuracy, macro average F1 value, and Kappa coefficient could reach 91.94%, 73.2% and 0.845 respectively at the highest, which proved that this method was effective and not susceptible to data volume, and had good stability. Compared with the existing research, our method is more accurate and simpler, and is suitable for automation.

Genotyping Characteristics of Human Fecal Escherichia coli and Their Association with Multidrug Resistance in Miyun District, Beijing / 生物医学与环境科学（英文）

OBJECTIVE@#To explore the genotyping characteristics of human fecal Escherichia coli( E. coli) and the relationships between antibiotic resistance genes (ARGs) and multidrug resistance (MDR) of E. coli in Miyun District, Beijing, an area with high incidence of infectious diarrheal cases but no related data.@*METHODS@#Over a period of 3 years, 94 E. coli strains were isolated from fecal samples collected from Miyun District Hospital, a surveillance hospital of the National Pathogen Identification Network. The antibiotic susceptibility of the isolates was determined by the broth microdilution method. ARGs, multilocus sequence typing (MLST), and polymorphism trees were analyzed using whole-genome sequencing data (WGS).@*RESULTS@#This study revealed that 68.09% of the isolates had MDR, prevalent and distributed in different clades, with a relatively high rate and low pathogenicity. There was no difference in MDR between the diarrheal (49/70) and healthy groups (15/24).@*CONCLUSION@#We developed a random forest (RF) prediction model of TEM.1 + baeR + mphA + mphB + QnrS1 + AAC.3-IId to identify MDR status, highlighting its potential for early resistance identification. The causes of MDR are likely mobile units transmitting the ARGs. In the future, we will continue to strengthen the monitoring of ARGs and MDR, and increase the number of strains to further verify the accuracy of the MDR markers.

Screening of natural drug molecules against Fusarium oxysporum of ginseng root rot based on machine learning / 药学学报

italic>Fusarium oxysporum widely exists in farmland soil and is one of the main pathogenic fungi of root rot, which seriously affects the growth and development of plants and often causes serious losses of cash crops. In order to screen out natural compounds that inhibit the activity of Fusarium oxysporum more economically and efficiently, random forest, support vector machine and artificial neural network based on machine learning algorithms were constructed using the information of known inhibitory compounds in ChEMBL database in this study. And the antibacterial activity of the screened drugs was verified thereafter. The results showed that the prediction accuracy of the three models reached 77.58%, 83.03% and 81.21%, respectively. Based on the inhibition experiment, the best inhibition effect (MIC = 0.312 5 mg·mL-1) of ononin was verified. The virtual screening method proposed in this study provides ideas for the development and creation of new pesticides derived from natural products, and the screened ononin is expected to be a potential lead compound for the development of novel inhibitors of Fusarium oxysporum.

Association between phenolic compound exposure and dyslipidemia in the population / 环境与职业医学

Background Phenolic compounds may adversely affect human health, but the current relevant studies are mostly limited to the impact of single phenolic compound exposure on human health, and there is still a lack of studies on the population-based association between combined exposure to multiple common phenolic compounds and dyslipidemia. Objective To explore the association of phenolic compound combined exposure and dyslipidemia based on principal component analysis-random forest (PCA-RF) strategy. Methods The data were from the National Health and Nutrition Examination Survey (2013–2016). A total of 1301 adult residents aged ≥ 20 years with complete information on demographics and lifestyle, urine phenol concentrations (bisphenol A, bisphenol F, bisphenol S, triclocarban, benzophenone, and triclosan), and serum concentrations of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were included in this study. The concentrations of six urinary phenolic compounds were determined by solid phase extraction coupled with high performance liquid chromatography and tandem mass spectrometry, and the lipid indicators were determined by enzymatic methods. Principal component analysis combined with random forest model was used for model construction. First, principal component analysis was performed on 18 original variables including 6 phenolic compounds and 12 basic characteristic indicators, and then random forest model was established with dyslipidemia and its four evaluation indicators as dependent variables and the extracted principal components as independent variables, respectively. Results The PCA-RF analysis showed that bisphenol A, bisphenol F, and benzophenone may be important factors for dyslipidemia in the study subjects; bisphenol A, bisphenol F, and triclosan may be important factors for TC level in the study subjects; bisphenol A, bisphenol F, triclocarban, and benzophenone may be important factors for TG level in the study subjects; bisphenol A may be an important factor for LDL-C level in the study subjects; bisphenol F and benzophenone may be important factors for HDL-C level in the study subjects. Conclusion Phenolic compound exposure may be an important risk factor for the development of dyslipidemia. PCA-RF strategy can be effectively used to explore the association between phenolic compound exposure and dyslipidemia in the population.

Diagnosis and Treatment Reasoning of Integrated Traditional Chinese and Western Medicine Against Acute Abdomen Based on Knowledge Graph / 中国实验方剂学杂志

ObjectiveIn view of the standardization of clinical diagnosis and treatment of the acute abdomen and the inheritance of diagnosis and treatment experience of prestigious veteran traditional Chinese medicine（TCM） doctors， a diagnosis and treatment reasoning algorithm based on association rule mining under incomplete evidence（AMIE）+ random walk was proposed to provide information services and technical support for primary doctors by recommending personalized diagnosis and treatment plans based on medical records. MethodThe experience of diagnosis and treatment of acute abdomen of prestigious veteran TCM doctors and the text data of clinical diagnosis and treatment guidelines of integrated TCM and western medicine were collected to complete the task of knowledge extraction and construct acute abdomen knowledge graph based on Neo4j. On the basis of ontology-supported rule-based reasoning， the rule reasoning based on similar syndromes was used to expand the syndrome combinations whose Jaccard similarity was greater than the threshold in the syndrome recommendation results. The semantic path coverage algorithm was used to calculate the semantic similarity between the symptom nodes. The symptom nodes were divided into 10 categories， and the symptom nodes in the same category were extended. The random walk algorithm was used to search the symptom nodes connected with the syndrome， and the connection rules between the syndrome and symptom nodes were extended to realize the knowledge reasoning of AMIE+ random walk. ResultThe acute abdomen knowledge graph included 1 320 nodes and 2 464 relationships. According to the link prediction evaluation index of knowledge reasoning， the reasoning results of the three algorithms in the auxiliary diagnosis and treatment of acute abdomen were compared. The AMIE+ random walk algorithm complemented the knowledge graph by extending the similar syndrome connection rules and the syndrome-symptom connection rules. Compared with the knowledge reasoning algorithm based on ontology rules， the area under the curve （AUC） was 15.18% higher and the accuracy was 30.36% higher， which achieved more accurate and effective knowledge inference. ConclusionThis study used knowledge graph technology to visualize the diagnosis and treatment of acute abdomen with TCM and western medicine， assisting primary clinicians in intuitively viewing the diagnosis and treatment process and data relationship. The proposed diagnosis and treatment reasoning algorithm can realize the personalized diagnosis and treatment plan recommendation at the level of "disease-syndrome-diagnosis-treatment-prescription"， which can assist primary doctors in disease diagnosis and treatment and clinical decision-making， contribute to the knowledge sharing and application of diagnosis and treatment experience and clinical guidelines of prestigious veteran TCM doctors， improve the level of primary clinical diagnosis and treatment， and promote the normalization and standardization of the diagnosis and treatment process of acute abdomen with integrated TCM and western medicine.