Conductas de salud en estudiantes universitarios: un análisis bayesiano / Health behaviors in college students: a Bayesian analysis / Comportamentos de saúde em estudantes universitários: uma análise bayesiana

En la actualidad, diversos estudios han explorado las diferencias de las conductas de salud en los estudiantes universitarios de acuerdo con características como edad, sexo y ciclo de estudio, no obstante, estos se han centrado en un enfoque frecuentista basado en la prueba de significancia de la hipótesis nula (NHST). Objetivo. Explorar las diferencias de las conductas de salud de acuerdo con el sexo, edad y ciclo académico, así como establecer la relación entre estas y la percepción de salud general en estudiantes universitarios peruanos, desde un enfoque bayesiano. Materiales y métodos. Se ejecutó un estudio cuantitativo, comparativo, correlacional y transversal, en una muestra de 708 universitarios seleccionados de manera intencional. Se utilizó el cuestionario de conductas de salud (CEJUV-R) y una ficha de datos sociodemográficos. Resultados. Los hallazgos muestran que los hombres tienen mejores hábitos de actividad y condición física y organización del sueño, con respecto a las mujeres. Asimismo, se observa una evidencia moderada a favor de la hipótesis alternativa del autocuidado en función de la edad y el ciclo académico. Finalmente, la actividad física, la organización del descanso, el autocuidado y la organización del sueño presentan evidencias muy fuertes (BF>100) de su relación con la percepción general de salud. Conclusión. El análisis bayesiano mostró evidencia a favor de la hipótesis alterna en algunas de las conductas de salud en función del sexo, edad y ciclo académico, lo que resaltan la importancia de promover conductas más saludables entre los estudiantes universitarios peruanos atendiendo a sus características personales.

Currently, several studies have explored the differences in health behaviors in university students according to characteristics such as age, sex and study cycle; however, these have focused on a frequentist approach based on the null hypothesis significance test (NHST). Objective. To explore the differences in health behaviors according to sex, age and academic cycle, as well as to establish the relationship between these and the perception of general health in Peruvian university students, from a Bayesian approach. Materials and methods. A quantitative, comparative, correlational and cross-sectional study was carried out in a sample of 708 intentionally selected university students. The health behaviors questionnaire (CEJUV-R) and a sociodemographic data sheet were used. Results. The findings show that men have better habits of activity and physical condition and sleep organization, with respect to women. Likewise, there is moderate evidence in favor of the alternative hypothesis of self-care as a function of age and academic cycle. Finally, physical activity, rest organization, self-care and sleep organization present very strong evidence (BF>100) of their relationship with the general perception of health. Conclusion. The Bayesian analysis showed evidence in favor of the alternative hypothesis in some of the health behaviors as a function of sex, age and academic cycle, which highlights the importance of promoting healthier behaviors among Peruvian university students according to their personal characteristics.

Atualmente, vários estudos exploraram as diferenças nos comportamentos de saúde em estudantes universitários de acordo com características como idade, gênero e ciclo de estudos; no entanto, eles se concentraram em uma abordagem frequentista baseada no teste de significância da hipótese nula (NHST). Objetivo. Explorar as diferenças nos comportamentos de saúde de acordo com o sexo, a idade e o ciclo acadêmico, bem como estabelecer a relação entre eles e a percepção da saúde geral em estudantes universitários peruanos, a partir de uma abordagem bayesiana. Materiais e métodos. Foi realizado um estudo quantitativo, comparativo, correlacional e transversal em uma amostra de 708 estudantes universitários selecionados intencionalmente. Foram utilizados o questionário de comportamento de saúde (CEJUV-R) e uma planilha de dados sociodemográficos. Resultados. Os achados mostram que os homens têm melhores hábitos de atividade física, condicionamento físico e organização do sono do que as mulheres. Também há evidências moderadas a favor da hipótese alternativa de autocuidado em função da idade e do ciclo acadêmico. Por fim, a atividade física, a organização do descanso, o autocuidado e a organização do sono mostram evidências muito fortes (BF>100) de sua relação com a percepção geral da saúde. Conclusão. A análise bayesiana mostrou evidências a favor da hipótese alternativa em alguns dos comportamentos de saúde em função do sexo, da idade e do ciclo acadêmico, destacando a importância de promover comportamentos mais saudáveis entre os estudantes universitários peruanos de acordo com suas características pessoais.

Research on operation risk assessment methodology of hospital orthopedic equipment based on dynamic Bayesian network / 中国医学装备

Objective:To construct a risk identification model based on dynamic Bayesian network(DBN),and to explore its application value in the operation management of orthopedic equipment in hospital.Methods:Risk factors in orthopedic equipment management were identified based on DBN model,and risk evaluation index set was established to provide early warning and prevention for possible risk factors.12 pieces of orthopedic medical equipment in clinical use in The Second Affiliated Hospital of Air Force Medical University from January 2020 to February 2022 were selected,the traditional orthopedic equipment quality operation management method(referred to as traditional mode)and the DBN-based risk identification mode(referred to as DBN mode)were adopted for equipment management respectively.The equipment operation effects,risk incidence rates and treatment efficiency of the two modes were compared.Results:The start-up operation efficiency and equipment quality qualification rate of the equipment in DBN mode were(93.54±4.05)%and(97.51±6.68)%,respectively,which were higher than those in the traditional mode;the troubleshooting time and the equipment component damage rate were(7.14±1.64)hours and(0.48±0.11)%,respectively,which were lower than those in the traditional mode,the difference was statistically significant(t=8.862,8.228,32.994,73.047,P<0.05).The function failure rate,parts damage rate,unqualified cleaning and disinfection rate and improper management rate of equipment in 479 equipment usage data,897 operations,300 equipment disinfection records and 500 equipment daily inspection records in the DBN mode were 0.21%(1/479),0.33%(3/897),1.33%(4/30)and 2.0%(10/500),respectively,which were lower than those in the traditional mode,the difference was statistically significant(x2=21.527,12.964,3.485,6.914,P<0.05).The effective rate of 500 cases of orthopedic medical equipment treatment of DBN mode was 97.8%(489/500),which was significantly higher than that of traditional mode,the difference was statistically significant(x2=12.617,P<0.05).Conclusion:The application of risk identification model based on DBN to the management of orthopedic medical equipment in hospital can strengthen the quality of orthopedic equipment management,improve the efficiency of equipment operation and treatment,and prevent and avoid equipment risks.

Establishment and validation of Bayesian network model: for predicting the risk of severe bleeding after microchannel percutaneous nephrolithotripsy in the treatment of staghorn renal calculi / 现代泌尿外科杂志

【Objective】 To explore the risk factors of severe postoperative hemorrhage in patients with staghorn renal calculi treated with mini-percutaneous nephrolithotomy (M-PCNL), and to construct a Bayesian network model to predict postoperative hemorrhage. 【Methods】 A retrospective analysis was conducted on 160 patients with staghorn renal calculi who were treated with M-PCNL by surgeons with equivalent qualifications at the First Affiliated Hospital of Xinxiang Medical College during Jan. 2020 and Jan. 2022.A computer-generated random number method was used to divide them into a modeling group (120 cases) and a validation group (40 cases).Patients in the modeling group were divided into severe bleeding group (38 cases) and non-severe bleeding group (82 cases).The general information of the two groups was compared, and the independent risk factors of severe postoperative hemorrhage were analyzed.A Bayesian network model was constructed using R software, the inference prediction was conducted using Netica software, and the performance of the model was evaluated with receiver operating characteristic (ROC) curve. 【Results】 Multivariate logistic regression analysis showed that renal insufficiency (OR: 2.845, 95%CI: 1.563-6.515), mixmum diameter of stones ≥2 cm (OR: 2.063, 95%CI: 1.824-4.555), operation time ≥90 minutes (OR: 3.632, 95%CI: 2.365-7.11), one-stage operation (OR: 2.321, 95%CI: 1.874-6.332), and multi-channel stone removal (OR: 1.842, 95%CI: 1.366-3.687) were independent risk factors of postoperative severe hemorrhage (P<0.05).Based on the above parameters, a Bayesian network model was established, which was then evaluated with the modeling and validation groups internally and externally.The AUC of the modeling group was 0.879 (95%CI: 0.804-0.931, P<0.001), with sensitivity and specificity being 87.68% and 89.63%, respectively.The AUC of the validation group was 0.875(95%CI: 0.818-0.908, P<0.001), with sensitivity and specificity being 87.55% and 89.40%, respectively.The model showed good discrimination. 【Conclusion】 Renal dysfunction, mixmum diameter of stones ≥2 cm, operation time ≥90 minutes, one-stage operation, and multi-channel stone removal are risk factors of severe hemorrhage in patients after M-PCNL.The prediction model has good predictive ability and can effectively describe the complex mechanism between diseases and risk factors.

Association between urinary metal levels and kidney stones in metal smelter workers / 环境与职业医学

Background Arsenic, cobalt, barium, and other individual metal exposure have been confirmed to be associated with the incidence of kidney stones. However, there are few studies on the association between mixed metal exposure and kidney stones, especially in occupational groups. Objective To investigate the association between mixed metal exposure and kidney stones in an occupational population from a metal smelting plant. Methods A questionnaire survey was conducted to collect sociodemographic characteristics, medical history, and lifestyle information of 1158 mixed metal-exposed workers in a metal smelting plant in Guangdong Province from July 2021 to January 2022. Midstream morning urine samples were collected from the workers, the concentrations of 18 metals including lithium, vanadium, chromium, manganese, cobalt, nickel, copper, zinc, arsenic, selenium, strontium, molybdenum, cadmium, cesium, barium, tungsten, titanium, and lead were measured by inductively coupled plasma mass spectrometry, and the urinary mercury levels were measured by cold atomic absorption spectroscopy. Based on predetermined inclusion criteria, a total of 919 mixed metal-exposed workers were included in the study, including 117 workers in the kidney stone group and 802 workers in the non-kidney stone group. With a detection rate of urinary metals greater than 80% as entry criterion, 16 eligible metals were finally included for further analysis. Parametric or non-parametric methods were used to compare the differences between continuous or categorical variables of the non-kidney stone group and the kidney stone group. Logistic regression models were constructed to explore the association between individual metal exposures and kidney stones. Weighted quantile sum (WQS) regression models were used to evaluate the association between mixed metal exposure and kidney stones, as well as the weights of each metal on kidney stones. Then Bayesian kernel machine regression (BKMR) models were used to explore the overall effect of mixed metal exposure on renal calculi and the potential interactions between metals. Results We found that there were significant differences in sex, age, length of service, and body mass Index (BMI) between the non-kidney stone group and the kidney stone group (P<0.05). The urinary concentrations of molybdenum and barium in the kidney stone group were higher than those in the non-kidney stone group, and the differences were statistically significant (P<0.05). The logistic regression models demonstrated that urinary cobalt, arsenic, molybdenum, and barium were positively correlated with the risk of kidney stones (Ptrend<0.05). The WQS regression models showed that the mixed exposure to vanadium, cobalt, arsenic, molybdenum, and barium was positively associated with the risk of kidney stones (P<0.05). Among them, molybdenum, arsenic, and barium accounted for 0.391, 0.337, and 0.154, respectively. The BKMR results revealed a positive association between metal mixture exposure and the risk of kidney stones (P<0.05). When other metals were fixed at the 25th, 50th, or 75th percentile, arsenic, molybdenum, cobalt, and barium exhibited significant positive effects on the risk of kidney stones (P<0.05), while vanadium showed a significant negative effect (P<0.05). The interaction analysis demonstrated interactions between barium and cobalt, as well as between vanadium and cobalt (P<0.05). Conclusion In the occupational population of this smelter, occupational mixed metal exposure could increase the risk of kidney stones, and the main metals are molybdenum, arsenic, barium, and cobalt.

Traditional Chinese Medicine Syndromes in Depressive Episodes of Bipolar Disorder Based on Cluster Analysis and Bayesian Network：A Cross-sectional Study / 中医杂志

ObjectiveTo explore the elements， distribution and characteristics of traditional Chinese medicine （TCM） syndromes in depressive episodes of bipolar disorder （BD）. MethodsBasic information， along with the four examination information， the Hamilton Depression Scale and Young Mania Rating Scale scores， were collected from 293 outpatients with BD at Beijing Anding Hospital， Capital Medical University. The four examination information with an occurrence rate greater than 12% were retained. The R language “dist” function was used to calculate the distances between samples using the Euclidean distance method. The hierarchical clustering of the four examination information was performed using the “hclust” function and the squared Euclidean distance method. A team of five researchers was formed to determine the nature and location of the essential elements of TCM syndrome in BD based on the clustering results. The PC algorithm was used to construct a Bayesian network model of the essential elements. The working group combined the essential elements of TCM syndromes in the Bayesian network according to the reference model results， and then extracted common TCM syndromes. The score of each patient based on the essential elements was matched with the common TCM syndromes to determine the syndrome type of each patient. The working group then performs conformity and revision based on this， obtaining the final distribution of TCM syndromes for the patients. ResultsThere were 77 common TCM symptoms in BD with a frequency greater than 12%. The top 15 symptoms with higher frequencies were slippery pulse， mental fatigue and lack of strength， wiry pulse， excessive rumination， preference for solitude， vexation， agitation and irritability， dry mouth， palpitations， profuse dreaming， unwarranted worries， chest oppression， thin white coating， amnesia， frequent sighing， and poor appetite. TCM syndrome elements of BD can be grouped into 11 categories. The nature of disease-related essential elements included fire， qi deficiency， blood deficiency， qi counterflow， yin deficiency， dampness， heat， fire from constraint， and phlegm. The location of disease-related essential elements included heart， liver， spleen， stomach， kidney， bladder channel， and gallbladder. By constructing a Bayesian network model and considering the opinions from the experts， six common syndromes of BD were identified， among which the highest proportion was heart-stomach heat accumulation， accounting for 27.99% （82 cases）， followed by heart-spleen deficiency （55 cases， 18.77%）， non-interaction between the heart and the kidney （49 cases， 16.72%）， liver constraint and blood deficiency （42 cases， 14.33%）， heart qi deficiency （37 cases， 12.63%）， and damp-heat in the liver and gallbladder （28 cases， 9.56%）. ConclusionsThe nature of disease-related elements of BD are predominantly fire and heat， while the location of disease-related essential elements are primarily associated with the heart， liver， and spleen. The most common TCM syndromes are heart-stomach heat accumulation and heart-spleen deficiency.

Application of Bayesian probabilistic linkage model in birth and death data linking / 上海预防医学

ObjectiveTo elucidate the principles and methods of the Bayesian probabilistic linkage model， and to demonstrate the effect of applying the model in linking birth and death data. MethodsThrough the Shanghai birth and death registration system， data of 199 025 infants born in 2017 and 1 512 infants who died in 2017 and 2018 were collected. After cleaning the data， the data were divided into monthly blocks and fully linked. The Jaro-Winkler algorithm and Euclidean distance were employed to measure the similarity of fields for matching. A Bayesian probabilistic linkage model was constructed and the linking effect was evaluated using a confusion matrix. ResultsUsing the Bayesian probabilistic linkage model， the birth and death data of infants were effectively linked， revealing that 36.71% of infants who died in Shanghai were born outside the city， and the probability of infant death was 2.6‰. The confusion matrix of the test set showed a recall rate of 0.86， precision of 0.76， and an F-score of 0.81. ConclusionThe practical application of Bayesian probabilistic linkage demonstrates a good model performance， enabling the establishment of birth-death cohorts that more accurately reflect the true levels of infant mortality. Utilizing this technique to integrate data from different departments can effectively improve research efficiency in the field of public health.

Association between exposure to multiple metals and lung function in welders by multi-pollutant statistical models / 环境与职业医学

Background Welders' exposure to welding fumes with multiple metals leads to decreased pulmonary function. Previous studies have focused on single metal exposure, while giving little attention to the impact of metal mixtures. Objective To assess the association between metal levels in urine and blood of welders and pulmonary function indicators, and to identify key metals for occupational health risk assessment. Methods Questionnaire surveys, lung function tests, urine and blood sampling were conducted among welders and control workers in a shipyard in Shanghai. Inductively coupled plasma mass spectrometry (ICP-MS) was used to detect the concentrations of 12 metals such as vanadium, chromium, and manganese in urine and blood. Spearman correlation was applied to analyze the correlations between the metals in urine and blood. Multiple linear regression, weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR) were used to analyze the relationships between mixed metal exposure and pulmonary function parameters, such as forced vital capacity (FVC), forced vital capacity as a percentage of predicted value (FVC%), forced expiratory volume in the first second (FEV1), forced expiratory volume in the first second as a percentage of predicted value (FEV1%), and forced expiratory volume in the first second/forced vital capacity (FEV1/FVC). Results This study enrolled 445 subjects, including 322 welders (72.36%) and 123 controls (27.64%). The mean age of the 445 participants was (37.64±8.80) years, and 87.19% participants were male. The welders had significantly higher levels of urinary cadmium (0.88 vs 0.58 μg·L−1), blood chromium (5.86 vs 5.06 μg·L−1), and blood manganese (24.24 vs 21.38 μg·L−1) than the controls (P<0.05). The Spearman correlation coefficients between the metals in urine and blood ranged from −0.46 to 0.68. After adjustment for confounders, the multiple linear regression indicted that the urine molybdenum of the welders was negatively correlated with FVC and FEV1. There were also negative correlations between the molybdenum in blood and FVC, FVC%, FEV1, and FEV1%, and between the copper in blood and FEV1/FVC. The WQS model showed that FEV1 and FVC decreased by 0.112 L and 0.353 L with each quartile increase of metal mixture concentrations in urine and blood among the welders respectively, and the leading contributors were copper, zinc, vanadium, and antimony. The BKMR model showed a negative overall effect of metal mixtures in urine and blood among the welders on FVC, FVC%, FEV1, and FEV1%, and the univariate exposure response-relationship between the molybdenum concentration in urine or blood and FVC, FVC%, FEV1, or FEV1% had an approximately linear decreasing trend. Meanwhile, there may be an interaction of cadmium with manganese, nickel, or vanadium, and an interaction of vanadium with iron, molybdenum, zinc, or copper, when different metals in urine among the welders interacted with FEV1%. Conclusion Exposure to multiple metals in welders leads to a decline in lung function, with molybdenum, antimony, copper, and zinc as the leading contributors.

Association of polycyclic aromatic hydrocarbon metabolite concentration with sleep quality in workers / 环境与职业医学

Background Sleep is a crucial physiological activity for the human body, and research has shown that air pollution can affect sleep quality. However, the association between polycyclic aromatic hydrocarbons (PAHs) exposure, neurotoxic compounds in air pollutants, and sleep quality remains uncertain. Objective To evaluate the association of PAHs exposure with sleep quality, and to provide evidence for improving sleep quality. Methods This study used a cross-sectional design. We selected 632 workers from a coking plant of a large state-owned enterprise as the exposure group, and 477 workers from the energy and power plant of the same enterprise as the control group. All workers worked in three shifts. A questionnaire survey was conducted to collect basic information including gender, years of service, age, educational level, smoking, alcohol consumption, consumption of fried foods, cooking frequency, types of cooking fuels. Worker's post-shift morning midstream urine was sampled to determine the concentrations of eight PAHs metabolites (OH-PAHs) using gas chromatography-tandem mass spectrometry (GC-MS). Worker's sleep quality was assessed using Pittsburgh Sleep Quality Index (PSQI). A higher PSQI score indicated a lower sleep quality. Associations of urinary OH-PAHs levels with sleep quality in the workers were analyzed using linear regression, Bayesian kernel-machine regression (BKMR), and quantile g-computation. Results The median (P25, P75) concentration of total OH-PAHs in the exposure group [88.84 (46.27, 151.96) μg·L−1] was higher than that in the control group [54.33 (24.86, 97.97) μg·L−1]. Additionally, the PSQI score (\begin{document}$ \overline{x}\pm {s} $\end{document}) in the exposure group (5.16±3.84) was higher than that in the control group (4.60±3.17). The multiple linear regression revealed that an increase in the sum of the concentrations of eight OH-PAHs after natural logarithmic transformation (lnΣ8OH-PAHs) was associated with an increase of 0.3646 (95%CI: 0.1337, 0.5955) in PSQI score, and an increase in lnΣlow-ring OH-PAHs was associated with an increase of 0.2954 (95%CI: 0.0941, 0.4967) in PSQI score. The BKMR analysis demonstrated that PSQI score was gradually increased as the increasing of lnΣ8OH-PAHs concentration. The quantile g-computation analysis indicated that a quantile increase in lnΣ8OH-PAHs concentration was associated with an increase of 0.4062% (95%CI: 0.1176%, 0.6949%) in PSQI score. Conclusion Compared to the controls, the coking workers show a higher concentration of urinary OH-PAHs and report worse sleep quality. The concentration of OH-PAHs is significantly negatively associated with sleep quality.

Correlation between Combined Urinary Metal Exposure and Grip Strength under Three Statistical Models: A Cross-sectional Study in Rural Guangxi / 生物医学与环境科学（英文）

OBJECTIVE@#This study aimed to investigate the potential relationship between urinary metals copper (Cu), arsenic (As), strontium (Sr), barium (Ba), iron (Fe), lead (Pb) and manganese (Mn) and grip strength.@*METHODS@#We used linear regression models, quantile g-computation and Bayesian kernel machine regression (BKMR) to assess the relationship between metals and grip strength.@*RESULTS@#In the multimetal linear regression, Cu (β = -2.119), As (β = -1.318), Sr (β = -2.480), Ba (β = 0.781), Fe (β = 1.130) and Mn (β = -0.404) were significantly correlated with grip strength ( P < 0.05). The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was -1.007 (95% confidence interval: -1.362, -0.652; P < 0.001) when each quartile of the mixture of the seven metals was increased. Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength, with Cu, As and Sr being negatively associated with grip strength levels. In the total population, potential interactions were observed between As and Mn and between Cu and Mn ( P interactions of 0.003 and 0.018, respectively).@*CONCLUSION@#In summary, this study suggests that combined exposure to metal mixtures is negatively associated with grip strength. Cu, Sr and As were negatively correlated with grip strength levels, and there were potential interactions between As and Mn and between Cu and Mn.

Nonparametric item response models: a comparison on recovering true scores / Modelos de resposta ao item não-paramétricos: comparando a recuperação de escores verdadeiros / Modelos de respuesta al item no-paramétricos: comparando la recuperación de puntuaciones verdaderas

Nonparametric procedures are used to add flexibility to models. Three nonparametric item response models have been proposed, but not directly compared: the Kernel smoothing (KS-IRT); the Davidian-Curve (DC-IRT); and the Bayesian semiparametric Rasch model (SP-Rasch). The main aim of the present study is to compare the performance of these procedures in recovering simulated true scores, using sum scores as benchmarks. The secondary aim is to compare their performances in terms of practical equivalence with real data. Overall, the results show that, apart from the DC-IRT, which is the model that performs the worse, all the other models give results quite similar to those when sum scores are used. These results are followed by a discussion with practical implications and recommendations for future studies.(AU)

Procedimentos não paramétricos são usados para adicionar flexibilidade aos modelos. Três modelos não paramétricos de resposta ao item foram propostos, mas não comparados diretamente: o Kernel smoothing (KS-IRT); a Curva Davidiana (DC-IRT); e o modelo semiparamétrico Rasch Bayesiano (SP-Rasch). O objetivo principal do presente estudo é comparar o desempenho desses procedimentos na recuperação de escores verdadeiros simulados, utilizando escores de soma como benchmarks. O objetivo secundário é comparar seus desempenhos em termos de equivalência prática com dados reais. De forma geral, os resultados mostram que, além do DC-IRT, que é o modelo que apresenta o pior desempenho, todos os outros modelos apresentam resultados bastante semelhantes aos de quando se usam somatórios. Esses resultados são seguidos de uma discussão com implicações práticas e recomendações para estudos futuros.(AU)

Se utilizan procedimientos no paramétricos para agregar flexibilidad a los modelos. Se propusieron tres modelos de respuesta al ítem no paramétricos, pero no se compararon directamente: Kernel smoothing (KS-IRT); la curva davidiana (DC-IRT); y el modelo bayesiano de Rasch semiparamétrico (SP-Rasch). El objetivo principal del presente estudio es comparar el desempeño de estos procedimientos en la recuperación de puntajes verdaderos simulados, utilizando puntajes de suma como puntos de referencia. El objetivo secundario es comparar su desempeño en términos de equivalencia práctica con datos reales. En general, los resultados muestran que, a excepción de DC-IRT, que es el modelo con peor desempeño, todos los otros modelos presentan resultados bastante similares a los obtenidos cuando se utilizan sumatorios. Estos resultados son seguidos por una discusión con implicaciones prácticas y recomendaciones para estudios futuros.(AU)

Network Meta-analysis of Chinese patent medicine containing Hirudo in treatment of atherosclerosis / 中国中药杂志

This study aimed to evaluate the efficacy and safety of Chinese patent medicines containing Hirudo in the treatment of atherosclerosis(AS) by network Meta-analysis, and to provide evidence-based reference for clinical treatment of AS. The clinical randomized controlled trial(RCT) on the treatment of atherosclerosis with Chinese patent medicines containing Hirudo were searched in CNKI, Wanfang, VIP, SinoMed, PubMed and EMbase from the establishment of the databases to July 1, 2022. And data extraction and quality assessment of the included RCT was performed according to the Cochrane standards. Stata 17 and ADDIS 1.16.5 were then used for Bayesian model network Meta-analysis. Finally, 67 RCTs with a total sample size of 6 826 cases were included, 3 569 cases in the experimental group and 3 257 cases in the control group, involving three oral Chinese patent medicines. Network Meta-analysis showed that in terms of reducing intima-media thickness(IMT), the top three Chinese patent medicines were Tongxinluo Capsules+sta-tins>Maixuekang Capsules+statins>Maixuekang Capsules. In terms of reducing plaque area, the top one was Maixuekang Capsules+sta-tins, and the other Chinese patent medicines had similar efficacy. For lowering AS Crouse scores, the top three were Maixuekang Capsules>Tongxinluo Capsules+statins>Naoxintong Capsules. For decreasing plaque number, the top three were Naoxintong Capsules+sta-tins>Tongxinluo Capsules+statins>Tongxinluo Capsules. With regard to adverse reactions/events, Naoxintong Capsules+statins had the lo-west incidence. In conclusion, in Chinese patent medicines containing Hirudo for the treatment of AS, Tongxinluo Capsules+statins, Maixuekang Capsules, Maixuekang Capsules+statins, and Naoxintong Capsules+statins were the primary choices to reduce IMT, AS Crouse scores, plaque area, and plaque number, respectively. The efficacy of Chinese patent medicines containing Hirudo with or without statins was more significant than that of statins alone in the four outcome indexes. Additionally, the treatment of AS should be evaluated comprehensively, and attention should be paid to Chinese patent medicines or their combination with western medicine, to optimize the treatment effect and minimize adverse reactions as the benchmark.

Lean strategy for data mining and continuous improvement of Chinese pharmaceutical process: a case study of sporoderm-removal Ganoderma lucidum spore powder / 中国中药杂志

In the digital transformation of Chinese pharmaceutical industry, how to efficiently govern and analyze industrial data and excavate the valuable information contained therein to guide the production of drug products has always been a research hotspot and application difficulty. Generally, the Chinese pharmaceutical technique is relatively extensive, and the consistency of drug quality needs to be improved. To address this problem, we proposed an optimization method combining advanced calculation tools(e.g., Bayesian network, convolutional neural network, and Pareto multi-objective optimization algorithm) with lean six sigma tools(e.g., Shewhart control chart and process performance index) to dig deeply into historical industrial data and guide the continuous improvement of pharmaceutical processes. Further, we employed this strategy to optimize the manufacturing process of sporoderm-removal Ganoderma lucidum spore powder. After optimization, we preliminarily obtained the possible interval combination of critical parameters to ensure the P_(pk) values of the critical quality properties including moisture, fineness, crude polysaccharide, and total triterpenes of the sporoderm-removal G. lucidum spore powder to be no less than 1.33. The results indicate that the proposed strategy has an industrial application value.

Research on mild cognitive impairment diagnosis based on Bayesian optimized long-short-term neural network model / 生物医学工程学杂志

The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.

Associations between urinary paraben levels and obesity of 10-year-old children / 环境与职业医学

Background Parabens, a widely used class of preservatives, are suspected to be potential obesogens as emerging endocrine disrupting chemicals with reproductive and developmental toxicity. Objective To analyze five urinary parabens (PBs) and estimate the associations of exposure to PBs with adiposity measures in 10-year-old school-age children. Methods A total of 471 school-age children aged 10 years from the Sheyang Mini Birth Cohort were enrolled in this study. A questionnaire survey was conducted to collect socio-demographic information, physical activity, and dietary intake. Weight, height, and waist circumference of children were measured, and age- and sex-adjusted body mass index (BMI-Z score) was calculated. Spot urine samples were collected during the follow-up visits. Urinary concentrations of five PBs including methyl-paraben (MeP), ethyl-paraben (EtP), propyl-paraben (PrP), butyl-paraben (BuP), and benzyl-paraben (BzP) were detected by gas chromatography-tandem mass spectrometry (GC-MS/MS). Generalized linear models (GLMs) and Bayesian kernel machine regression (BKMR) models were applied to estimate associations of individual/overall urinary PBs concentrations with BMI Z-score and waist circumference. Results The positive rates of selected five urinary PBs were in the range from 78.98% to 98.94%. The urinary PBs concentrations (geometric mean) were in the range of 0.31-5.43 μg·L−1. The children's BMI Z-score and waist circumference (mean ± standard deviation) were (0.56±1.40) and (67.62±10.07) cm respectively. The GLMs results showed that the urinary BzP concentration was negatively associated with waist circumference (b=−0.08, 95%CI: −0.14, −0.02; P=0.01). In sex-stratified analysis, the urinary concentration of BzP was negatively associated with BMI-Z score (b=−0.59, 95%CI: −0.88, −0.30; P<0.001) and waist circumference (b=−0.80, 95%CI: −1.23, −0.37; P<0.001) in boys, but not in girls. The BKMR results also found significant negative correlations of urinary BzP concentrations with BMI-Z score and waist circumference, which were consistent with the GLM results. Conclusion The selected 10-year-old children are extensively exposed to PBs in the study area. Furthermore, childhood PBs exposure may have potential impacts on childhood adiposity measures with sex-specific effects.

Influencing factors of death in patients with MDR-TB based on Bayesian Cox regression model / 中南大学学报(医学版)

Objective:Multidrug-resistant tuberculosis(MDR-TB)has a high mortality and is always one of the major challenges in global TB prevention and control.Analyzing the factors that may impact the adverse outcomes of MDR-TB patients is helpful for improving the systematic management and optimizing the treatment strategies for MDR-TB patients.For follow-up data,the Cox proportional hazards regression model is an important multifactor analysis method.However,the method has significant limitations in its application,such as the fact that it is difficult to deal with the impacts of small sample sizes and other practical issues on the model.Therefore,Bayesian and conventional Cox regression models were both used in this study to analyze the influencing factors of death in MDR-TB patients during the anti-TB therapy,and compare the differences between these 2 methods in their application. Methods:Data were obtained from 388 MDR-TB patients treated at Lanzhou Pulmonary Hospital from November 1,2017 to March 31,2021.Survival analysis was employed to analyze the death of MDR-TB patients during the therapy and its influencing factors.Conventional and Bayesian Cox regression models were established to estimate the hazard ratios(HR)and their 95% confidence interval(95% CI)for the factors affecting the death of MDR-TB patients.The reliability of parameter estimation in these 2 models was assessed by comparing the parameter standard deviation and 95% CI of each variable.The smaller parameter standard deviation and narrower 95% CI range indicated the more reliable parameter estimation. Results:The median survival time(1st quartile,3rd quartile)of the 388 MDR-TB patients included in the study was 10.18(4.26,18.13)months,with the longest survival time of 31.90 months.Among these patients,a total of 12 individuals died of MDR-TB and the mortality was 3.1%.The median survival time(1st quartile,3rd quartile)for the deceased patients was 4.78(2.63,6.93)months.The majority of deceased patients,accounting for 50%,experienced death within the first 5 months of anti-TB therapy,with the last mortality case occurring within the 13th month of therapy.The results of the conventional Cox regression model showed that the risk of death in MDR-TB patients with comorbidities was approximately 6.96 times higher than that of patients without complications(HR=6.96,95% CI 2.00 to 24.24,P=0.002)and patients who received regular follow-up had a decrease in the risk of death by approximately 81% compared to those who did not receive regular follow-up(HR=0.19,95% CI 0.05 to 0.77,P=0.020).In the results of Bayesian Cox regression model,the iterative history plot and Blue/Green/Red(BGR)plot for each parameter showed the good model convergence,and parameter estimation indicated that the risk of death in patients with a positive first sputum culture was lower than that of patients with a negative first sputum culture(HR=0.33,95% CI 0.08 to 0.87).Additionally,compared to patients without complications,those with comorbidities had an approximately 6.80-fold increase in the risk of death(HR=7.80,95% CI 1.90 to 21.91).Patients who received regular follow-up had a 90% reduction in the risk of death compared to those who did not receive regular follow-up(HR=0.10,95% CI 0.01 to 0.30).The comparison between these 2 models showed that the parameter standard deviations and corresponding 95% CI ranges of other variables in the Bayesian Cox model were significantly smaller than those in the conventional model,except for parameter standard deviations of receiving regular follow-up(Bayesian model was 0.77;conventional model was 0.72)and pulmonary cavities(Bayesian model was 0.73;conventional model was 0.73). Conclusion:The first year of anti-TB therapy is a high-risk period for mortality in MDR-TB patients.Complications are the main risk factors of death in MDR-TB patients,while patients who received regular follow-up and had positive first sputum culture presented a lower risk of death.For data with a small sample size and low incidence of outcome,the Bayesian Cox regression model provides more reliable parameter estimation than the conventional Cox model.

Construction of a risk prediction model of delirium during general anesthesia recovery based on Bayesian network / 中国实用护理杂志

Objective:To construct a Bayesian network risk prediction model for delirium during recovery from general anesthesia. To explore the network relationship between awakening delirium of general anesthesia and its related factors, and to reflect the influence intensity of each factor on awakening delirium of general anesthesia through network reasoning.Methods:This is a cross-sectional study. From February to May 2022, the Chinese version of the four rapid delirium diagnosis protocols for general anesthesia patients admitted to the department of Anesthesia, the First Hospital of Shanxi Medical University were adopted as research subjects through convenience sampling method to carry out the delirium screening program during awakening, and general information and blood sample laboratory test results of the subjects were collected. The single factor analysis was used to screen the correlative factors of awakening delirium and a Bayesian network model based on the maximum minimum climb method (MMHC) was constructed.Results:A total of 480 patients were included in the study, and the delirium rate during the recovery period of general anesthesia was 12.9%(62/480). The Bayesian network of awakening delirium consisted of 11 nodes and 18 directed edges. The Bayesian network showed that age, sodium, cerebral infarction and hypoproteinemia were the direct factors related to awakening delirium, while ASA grade, hematocele and hemoglobin were the indirect factors related to awakening delirium. The area under its ROC curve was 0.80(0.78-0.83).Conclusions:Bayesian networks can well reveal the complex network connections between awakening delirium and its related factors, and then prevent and control awakening delirium accordingly.

Estimation of Average Treatment Effect Using Bayesian Additive Regression Tree in Observational Study / 中国卫生统计

Objective To explore the statistical performance and applicable conditions of Bayesian additive regression tree(BART)for estimating average treatment effect in observational studies.Methods The difference of estimates between BART and multivariate regression,propensity score matching,and inverse probability weighting through simulations and actual epidemiological data was compared.Results The results of these simulations showed that under the linear assumption,the performance of BART was close to that of the commonly used methods;when the relationship among variables in the data was complex and non-linear,BART performed markedly better than the others.When the ignorability assumption was not satisfied and there was unobserved confounding,all methods performed worse,but BART was still significantly better than the others and relatively robust.In the actual epidemiological data,this method was used to estimate the average treatment effect of smoking cessation on weight change.Conclusion In most observational studies,outcomes are influenced by multiple factors,making it difficult for researchers to properly specify relationships between variables.It is difficult to identify all these variables or determine the relationship between them.In terms of model fitting and result accuracy,BART is worth recommending.

Probing into Possible Factors and Gender Differences of Anemia in Patients with Diabetes Nephropathy based on Bayesian Network Model / 中国卫生统计

Objective Probing into possible factors and gender differences of anemia in patients with diabetes nephropathy based on Bayesian network model.Methods 224 patients with diabetes nephropathy who were treated in Ningde City hospital from June 2020 to June 2023 were taken as the research objects,patients were divided into an anemia group(98 cases)and a non anemia group(126 cases)based on whether they were anemic.Comparison of anemia related indicators in diabetes nephropathy patients of different sexes.Comparison of clinical data between two groups of patients with diabetes nephropathy.Multivariate logistic regression was used to analyze the influencing factors of anemia in patients with diabetes nephropathy.Build a Bayesian network model using R software and perform inference and prediction of the model.The effectiveness of the model was verified by receiver operating characteristic and calibration curve.Results Male patients showed significantly higher levels of Hb,RBC,and HCT compared to female patients;MCV,MCH,and MCHC were significantly lower than those in female patients,with statistically significant differences(P<0.05).Sex,course of diabetes,stage of kidney disease,ALB,CHE,eGFR and HbA1c were independent risk factors for anemia in patients with diabetes nephropathy(P<0.05).The results of receiver operating characteristic and calibration curve of training set and verification set show that the Bayesian network prediction model has good discrimination and accuracy.Conclusion Sex,course of diabetes,stage of kidney disease,ALB,CHE,eGFR and HbA1c are independent risk factors for anemia in patients with diabetes nephropathy.There are certain gender differences in anemia related indicators among patients.

Bayesian Network Meta-analysis of Therapeutic Effect of Oral Chinese Patent Medicines on Coronary Artery Disease Complicated with Carotid Atherosclerosis / 中国实验方剂学杂志

ObjectiveTo compare the therapeutic effects of oral Chinese medicines （including Chinese patent medicines） on coronary artery disease （CAD） by the Bayesian network Meta-analysis. MethodThe randomized controlled trials of treating CAD with oral Chinese medicines were retrieved from the China National Knowledge Infrastructure （CNKI）， Wanfang Data， VIP， PubMed， Web of Science， Embase， and Cochrane Library from the inception to December 1， 2022. The Cochrane risk of bias assessment tool was used to evaluate the quality of the included articles. The direct meta-analysis was performed to compare the performance of oral Chinese medicines alone and in combination with Western medicine in the treatment of CAD in terms of intima-media thickness （IMT）， vascular endothelial function， plaque score， hypersensitive C-reactive protein （hs-CRP）， total cholesterol （TC）， triglyceride （TG）， high-density lipoprotein cholesterol （HDL-C）， low-density lipoprotein cholesterol （LDL-C）， and total response rate. Furthermore， the Bayesian network Meta-analysis was performed to compare the therapeutic effects of different Chinese medicines. ResultA total of 41 articles were included. The direct meta-analysis results showed that Chinese medicines combined with Western medicine outperformed Western medicine alone in recovering all the indicators of CAD. The Bayesian network meta-analysis yielded the following results. In terms of the total response rate， modified Huangqi Guizhi Wuwutang and Sanqi Huayu pills had obvious advantages over other Chinese medicines. In terms of IMT and plaque score， Xiaoban Huazhuo decoction， Yiqi Tongluo formula， Ruangan Jiangzhi capsules， and Guanxin Shutong capsules had obvious advantages over other Chinese medicines. In terms of blood lipid indicators， Shenqi Roumai mixture， Ruangan Jiangzhi capsules， Xiaoban Huazhuo decoction， Qiwei Sanxiong decoction， and Sanqi Huayu pills were superior to other Chinese medicines. The Chinese medicines above mainly had the functions of activating blood， resolving stasis， resolving phlegm， and dredging vessels. ConclusionThe combination of oral Chinese medicines and Western medicine is effective in treating CAD. Clinicians can use the drugs targeting abnormal indicators according to the results of this Bayesian network meta-analysis combined with the actual situation of patients to achieve better therapeutic effects.

Relationship between mixed exposure to heavy metals and seminal fructose in men of childbearing age / 环境与职业医学

Background The human body is usually exposed to a variety of heavy metals at the same time, and different types and concentrations of heavy metals may have complex interactions during their absorption and metabolism in the human body. Seminal fructose is an important energy source for sperm movement. A large number of studies have shown that metal exposure may impair semen quality, and seminal fructose is an important factor affecting male reproduction, so it is necessary to investigate the relationship between mixed heavy metal exposure and seminal fructose to explore the mechanism of semen quality damage caused by metal exposure. Objective To understand the status of common heavy metal exposure in men of childbearing age in Puyang City, Henan Province, and to study the relationship between mixed exposure to heavy metals and seminal fructose, as well as potential interactions among heavy metals. Methods Volunteers were recruited from the Puyang Maternal and Child Health Hospital Reproductive Center for a cross-sectional survey on general demographic characteristics, smoking, alcohol consumption, and other information. Semen samples were collected to detect 12 metals such as vanadium (V), manganese (Mn), cobalt (Co), nickel (Ni), zinc (Zn), selenium (Se), silver (Ag), cadmium (Cd), barium (Ba), thallium (Tl), iron (Fe), and lead (Pb) in seminal plasma and seminal fructose. After correcting for selected confounding factors, a Bayesian kernel machine regression (BKMR) model was used to evaluate the impact of seminal plasma heavy metal mixed exposure and its interactions on seminal fructose. Results A total of 825 adult males were enrolled. The concentrations in M (P25, P75) of V, Mn, Co, Ni, Zn, Se, Ag, Cd, Ba, Tl, Fe, and Pb in seminal plasma were 0.39 (0.28, 0.54), 12.31 (8.92, 17.52), 0.26 (0.18, 0.38), 5.15 (3.32, 8.64), 182159.80 (121847.80, 199144.50), 13.61 (10.55, 17.68), 0.03 (0.02, 0.04), 0.34 (0.27, 0.46), 8.64 (5.94, 13.43), 0.06 (0.05, 0.08), 168.74 (114.17, 259.45), and 1.69 (1.15, 2.36) μg·L−1 respectively. The Spearman correlation results indicated that there was a negative correlation between V, Mn, Co, Zn, Se, Ba, Tl, or Fe in seminal plasma and seminal fructose (P<0.05), and the values of r (95%CI) were −0.044 (−0.087, −0.001), −0.129 (−0.171, −0.087), −0.055 (−0.099, −0.012), −0.099 (−0.143, −0.056), −0.053 (−0.097, −0.010), −0.068 (−0.111, −0.025), −0.095 (−0.138, −0.052), and −0.082 (−0.125, −0.039), respectively. The results of multiple linear regression indicated that there was a negative correlation between the exposure level of Cd, Mn, Zn, Ag, Ba, Tl, or Fe in seminal plasma and seminal fructose (P<0.05), the values of associated β (95%CI) were −0.551 (−0.956, −0.147), −0.315 (−0.419, −0.212), −0.187 (−0.272, −0.103), −0.161 (−0.301, −0.021), −0.188 (−0.314, −0.062), −1.159 (−2.170, −0.147), and −0.153 (−0.230, −0.076), respectively. The BKMR model analysis showed that seminal fructose level decreased with the increase of plasma metal mixed exposure concentration. Compared with all metal exposure at P50, the seminal fructose level decreased by 0.2374 units when all metal exposure was at P75. Seminal plasma Zn [posterior inclusion probabilities (PIPs)=1.0000] had the strongest effect on seminal fructose, followed by Mn (PIPs=0.5872), Se (PIPs=0.5656), and Ba (PIPs=0.5398). The univariate exposure-response curve showed a negative approximate linear correlations between Ba or Mn and seminal fructose, a positive linear correlation between Se and seminal fructose, and an approximate inverted U-shaped association between Zn and seminal fructose. No significant interaction between studied metals was found. Conclusion Mixed metal exposure may lead to decrease of seminal fructose, in which Zn, Mn, Se, and Ba may play an important role. Mn and Zn exposure may reduce the level of seminal fructose, Se may increase the level of seminal fructose, and there may be a threshold effect between Zn exposure and seminal fructose level. No interaction between different metals on seminal fructose is found.