Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates.

BACKGROUND: Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research. RESULTS: The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575. CONCLUSIONS: We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.

OEDL: an optimized ensemble deep learning method for the prediction of acute ischemic stroke prognoses using union features.

Background: Early stroke prognosis assessments are critical for decision-making regarding therapeutic intervention. We introduced the concepts of data combination, method integration, and algorithm parallelization, aiming to build an integrated deep learning model based on a combination of clinical and radiomics features and analyze its application value in prognosis prediction. Methods: The research steps in this study include data source and feature extraction, data processing and feature fusion, model building and optimization, model training, and so on. Using data from 441 stroke patients, clinical and radiomics features were extracted, and feature selection was performed. Clinical, radiomics, and combined features were included to construct predictive models. We applied the concept of deep integration to the joint analysis of multiple deep learning methods, used a metaheuristic algorithm to improve the parameter search efficiency, and finally, developed an acute ischemic stroke (AIS) prognosis prediction method, namely, the optimized ensemble of deep learning (OEDL) method. Results: Among the clinical features, 17 features passed the correlation check. Among the radiomics features, 19 features were selected. In the comparison of the prediction performance of each method, the OEDL method based on the concept of ensemble optimization had the best classification performance. In the comparison to the predictive performance of each feature, the inclusion of the combined features resulted in better classification performance than that of the clinical and radiomics features. In the comparison to the prediction performance of each balanced method, SMOTEENN, which is based on a hybrid sampling method, achieved the best classification performance than that of the unbalanced, oversampled, and undersampled methods. The OEDL method with combined features and mixed sampling achieved the best classification performance, with 97.89, 95.74, 94.75, 94.03, and 94.35% for Macro-AUC, ACC, Macro-R, Macro-P, and Macro-F1, respectively, and achieved advanced performance in comparison with that of methods in previous studies. Conclusion: The OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.

The effect of health literacy, self-efficacy, social support and fear of disease progression on the health-related quality of life of patients with cancer in China: a structural equation model.

BACKGROUND: Health literacy (HL), self-efficacy (SE), social support (SS) and fear of disease progression (FOP) are all important factors affecting health-related quality of life (HRQoL) in cancer patients. However, their synergistic effects and underlying mechanisms on HRQoL in cancer patients remain unclear. Therefore, the purpose of this study was to construct a structural equation model (SEM) to explore the underlying mechanism of factors affecting HRQoL. It is hoped that this study will provide a theoretical basis for future interventions. METHODS: A cross-sectional design and convenience sampling method were used to investigate cancer inpatients in two general hospitals in Chongqing and Chengdu. Data were collected using structured scales, including HL, SE, SS, FOP and HRQoL. Finally, the SEM was constructed, and P ≤ 0.05 was considered significant. RESULTS: There were 1749 participants included in this study. Correlation analysis showed that all variables were significantly correlated with one another except for symptoms, physical health (PD) and social family (SF) (p < 0.01). The SEM of the HRQoL had a good overall fit (GFI = 0.943, AGFI = 0.917, NFI = 0.950, RFI = 0.936, CFI = 0.955, IFI = 0.955, RMSEA = 0.072). The model indicated that HL had the strongest correlation with HRQoL (ß = 0.398, p < 0.01), followed by FOP (ß = -0.364, p < 0.01), SE (ß = 0.347, p < 0.01) and SS (ß = 0.184, p < 0.01). CONCLUSIONS: The HRQoL of cancer patients is correlated with HL, SS, SE and FOP. HL can directly affect HRQoL and mediate HRQoL through SS and SE. Future programs should consider HL promotion, SE improvement and SS expansion as the breakthrough point when designing targeted intervention strategies. At the same time, the importance of the impact of FOP on the HRQoL of patients with cancer should not be ignored.

Topological abnormality of structural covariance network in MRI-negative frontal lobe epilepsy.

Background: Frontal lobe epilepsy (FLE) is the second most common type of focal epilepsy, however, imaging studies of FLE have been far less than Temporal lobe epilepsy (TLE) and the structural findings were not consistent in previous literature. Object: Investigate the changes in cortical thickness in patients with FLE and the alteration of the structural covariance networks (SCNs) of cortical thickness with graph-theory. Method: Thirty patients with FLE (18 males/12 females; 28.33 ± 11.81 years) and 27 demographically matched controls (15 males/12 females; 29.22 ± 9.73 years) were included in this study with high-resolution structural brain MRI scans. The cortical thickness was calculated, and structural covariance network (SCN) of cortical thickness were reconstructed using 68 × 68 matrix and analyzed with graph-theory approach. Result: Cortical thickness was not significantly different between two groups, but path length and node betweenness were significantly increased in patients with FLE, and the regional network alterations were significantly changed in right precentral gyrus and right temporal pole (FDR corrected, p < 0.05). Comparing to HC group, network hubs were decreased and shifted away from frontal lobe. Conclusion: The topological properties of cortical thickness covariance network were significantly altered in patients with FLE, even without obvious surface-based morphological damage. Graph-theory based SCN analysis may provide sensitive neuroanatomical biomarkers for FLE.

Investigation of health literacy status and related influencing factors in military health providers of Chinese People's liberation Army, a cross-sectional study.

OBJECTIVE: The aim of this study was to investigate health literacy and analyze its influencing factors in military health providers of the Chinese People's Liberation Army (PLA Army). METHODS: From November to December 2018, cluster sampling was used to select 1512 military health providers from the Army Medical University. Health literacy was measured by using the Chinese Citizen Health Literacy Questionnaire (HLQ) (2015 edition). Influencing factors that may affect health literacy were assessed using the chi-square test and multivariate logistic regression models. RESULTS: The knowledge rate of health literacy was relatively low (21.6%). The knowledge rate of health-related skills (HRS, 18.7%) was the lowest of the three aspects of health literacy, and the knowledge rate of chronic diseases (CD, 19.6%) was the lowest of the six dimensions of health literacy. Participants who were older, were female, were of Han ethnicity, were the only child in their families, came from urban areas, never used tobacco, and had higher household income were likely to have higher health literacy. CONCLUSION: The health literacy levels of military health providers of the PLA Army are relatively low. Further research and health education are necessary to improve health literacy.

Maternal rodent exposure to di-(2-ethylhexyl) phthalate decreases muscle mass in the offspring by increasing myostatin.

BACKGROUND: Di-(2-ethylhexyl) phthalate (DEHP) and its metabolites can cross the placenta and may cause birth defects and developmental disorders. However, whether maternal DEHP exposure affects skeletal muscle development in the offspring and the pathways involved are unknown. This study investigated the effects of maternal DEHP exposure and the contribution of myostatin (MSTN) to skeletal muscle development in the offspring. METHODS: Pregnant wild-type and muscle-specific myostatin knockout (MSTN KO) C57BL/6 mice were randomized to receive vehicle (corn oil) or 250 mg/kg DEHP by gavage every other day until their pups were weaned (postnatal day 21 [PND21]). Body weights of the offspring mice were measured longitudinally, and their hindleg muscles were harvested at PD21. Also, C2C12 cells were treated with mono-2-ethylhexyl phthalate (MEHP), the primary metabolite of DEHP, and proteolysis, protein synthesis, and myogenesis markers were measured. The contribution of myostatin to maternal DEHP exposure-induced muscle wasting in the offspring was determined. RESULTS: Maternal DEHP exposure reduced body weight growth, myofibre size, and muscle mass in the offspring compared to controls (Quad: 2.70 ± 0.1 vs. 3.38 ± 0.23, Gastroc: 2.29 ± 0.09 vs. 2.81 ± 0.14, Tibialis: 1.01 ± 0.07 vs. 1.25 ± 0.11, mg/tibial length in mm, all P < 0.01, n = 35). Maternal DEHP exposure significantly increased Myostatin expression (2.45 ± 0.41 vs. 0.03 ± 0.00 DEHP vs. controls, P < 0.01, n = 5), Atrogin-1(2.68 ± 0.65 vs. 0.63 ± 0.01, P < 0.05, n = 5), MuRF1 (1.56 ± 0.51 vs. 0.31 ± 0.01, P < 0.05, n = 5), and Smad2/3 phosphorylation (4.12 ± 0.35 vs. 0.49 ± 0.18, P < 0.05), and decreased MyoD (0.27 ± 0.01 vs. 1.52 ± 0.01, P < 0.05, n = 5), Myogenin (0.25 ± 0.03 vs. 1.95 ± 0.56, P < 0.05, n = 5), and AKT phosphorylation (4.12 ± 0.35 vs. 1.00 ± 0.06, P < 0.05, n = 5), in skeletal muscle of the offspring in MSTNflox/flox , but not in MSTN KO mice. Maternal DEHP exposure resulted in up-regulation of CCAAT/enhancer-binding protein δ (C/EBPδ, 4.12 ± 0.35 vs. 1.00 ± 0.19, P < 0.05, n = 5) in skeletal muscle of the offspring in MSTNflox/flox and MSTN KO mice (4.12 ± 0.35 vs. 4.35 ± 0.28, P > 0.05, n = 5). In vitro, C/EBPδ silencing abrogated the MEHP-induced increases in Myostatin, MuRF-1, and Atrogin-1 and decreases in MyoD and Myogenin expression. CONCLUSIONS: Maternal DEHP exposure impairs skeletal muscle development in the offspring by enhancing the C/EBPδ-myostatin pathway in mice.

COVID-19 epidemic and public health interventions in Shanghai, China: Statistical analysis of transmission, correlation and conversion.

Background: The Shanghai COVID-19 epidemic is an important example of a local outbreak and of the implementation of normalized prevention and disease control strategies. The precise impact of public health interventions on epidemic prevention and control is unknown. Methods: We collected information on COVID-19 patients reported in Shanghai, China, from January 30 to May 31, 2022. These newly added cases were classified as local confirmed cases, local asymptomatic infections, imported confirmed cases and imported asymptomatic infections. We used polynomial fitting correlation analysis and illustrated the time lag plot in the correlation analysis of local and imported cases. Analyzing the conversion of asymptomatic infections to confirmed cases, we proposed a new measure of the conversion rate (C r ). In the evolution of epidemic transmission and the analysis of intervention effects, we calculated the effective reproduction number (R t ). Additionally, we used simulated predictions of public health interventions in transmission, correlation, and conversion analyses. Results: (1) The overall level of R t in the first three stages was higher than the epidemic threshold. After the implementation of public health intervention measures in the third stage, R t decreased rapidly, and the overall R t level in the last three stages was lower than the epidemic threshold. The longer the public health interventions were delayed, the more cases that were expected and the later the epidemic was expected to end. (2) In the correlation analysis, the outbreak in Shanghai was characterized by double peaks. (3) In the conversion analysis, when the incubation period was short (3 or 7 days), the conversion rate fluctuated smoothly and did not reflect the effect of the intervention. When the incubation period was extended (10 and 14 days), the conversion rate fluctuated in each period, being higher in the first five stages and lower in the sixth stage. Conclusion: Effective public health interventions helped slow the spread of COVID-19 in Shanghai, shorten the outbreak duration, and protect the healthcare system from stress. Our research can serve as a positive guideline for addressing infectious disease prevention and control in China and other countries and regions.

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19.

OBJECTIVES: To develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19. METHODS: We included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning-based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients. RESULTS: There was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936-0.976) and 0.953 (95% CI: 0.891-0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness. CONCLUSIONS: We presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation. KEY POINTS: • Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources. • We proposed a deep learning-based framework for accurate lung involvement quantification on chest CT images. • Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19.

An immunoscore nomogram for predicting survival in patients with oesophageal cancer.

This study aimed to construct and validate an immunoscore nomogram that may be used to predict the prognosis of oesophageal cancer. With the gene expression data of oesophageal cancer in a public database, we used CIBERSORT to estimate the fractions of 22 infiltrating immune cell types. We then built an immunoscore signature based on 12 types of infiltrating immune cells using the least absolute shrinkage and selection operator (LASSO) model. This immunoscore was used as an independent predictor in the prognostic model (training cohort: [hazard ratio (HR), 4.78; 95% confidence interval (CI), 2.64-8.67; P < 0.001], validation cohort: [HR, 2.15; 95% CI, 1.04-4.45; P = 0.040]). Subgroup analysis by clinical features showed that overall survival was significantly different between the high-immunoscore group and the low-immunoscore group. The predictors that constituted the individualized prediction nomogram were immunoscore, age, and tumour stage. The nomogram had good discrimination and calibration. Decision curve analysis showed that the immunoscore nomogram was clinically useful. Therefore, the novel immunoscore signature based on infiltrating immune cells can be used as a reliable predictor of the prognosis of oesophageal cancer, and the immunoscore nomogram is a convenient tool for predicting the survival of individual patients.

Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics.

The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread to become a worldwide emergency. Early identification of patients at risk of progression may facilitate more individually aligned treatment plans and optimized utilization of medical resource. Here we conducted a multicenter retrospective study involving patients with moderate COVID-19 pneumonia to investigate the utility of chest computed tomography (CT) and clinical characteristics to risk-stratify the patients. Our results show that CT severity score is associated with inflammatory levels and that older age, higher neutrophil-to-lymphocyte ratio (NLR), and CT severity score on admission are independent risk factors for short-term progression. The nomogram based on these risk factors shows good calibration and discrimination in the derivation and validation cohorts. These findings have implications for predicting the progression risk of COVID-19 pneumonia patients at the time of admission. CT examination may help risk-stratification and guide the timing of admission.

Cumulative Evidence for Associations between Genetic Variants and Risk of Esophageal Cancer.

BACKGROUND: A large number of studies have been conducted to investigate associations between genetic variants and esophageal cancer risk in the past several decades. However, findings from these studies have been generally inconsistent. We aimed to provide a summary of the current understanding of the genetic architecture of esophageal cancer susceptibility. METHODS: We performed a comprehensive field synopsis and meta-analysis to evaluate associations between 95 variants in 70 genes or loci and esophageal cancer risk using data from 304 eligible publications, including 104,904 cases and 159,797 controls, through screening a total of 21,328 citations. We graded levels of cumulative epidemiologic evidence of a significant association with esophageal cancer using the Venice criteria and false-positive report probability tests. We constructed functional annotations for these variants using data from the Encyclopedia of DNA Elements Project and other databases. RESULTS: Thirty variants were nominally significantly associated with esophageal cancer risk. Cumulative epidemiologic evidence of a significant association with overall esophageal cancer, esophageal squamous cell carcinoma, or esophageal adenocarcinoma was strong for 13 variants in or near 13 genes (ADH1B, BARX1, CDKN1A, CHEK2, CLPTM1L, CRTC1, CYP1A1, EGF, LTA, MIR34BC, PLCE1, PTEN, and PTGS2). Bioinformatics analysis suggested that these variants and others correlated with them might fall in putative functional regions. CONCLUSIONS: Our study summarizes the current literature on the genetic architecture of esophageal cancer susceptibility and identifies several potential polymorphisms that could be involved in esophageal cancer susceptibility. IMPACT: These findings provide direction for future studies to identify new genetic factors for esophageal cancer.

Association between green tea intake and risk of gastric cancer: a systematic review and dose-response meta-analysis of observational studies.

OBJECTIVE: To examine and quantify the potential dose-response relationship between green tea intake and the risk of gastric cancer. DESIGN: We searched PubMed, EMBASE, Web of Science, CBM, CNKI and VIP up to December 2015 without language restrictions. SETTING: A systematic review and dose-response meta-analysis of observational studies. SUBJECTS: Five cohort studies and eight case-control studies. RESULTS: Compared with the lowest level of green tea intake, the pooled relative risk (95 % CI) of gastric cancer was 1·05 (0·90, 1·21, I 2=20·3 %) for the cohort studies and the pooled OR (95 % CI) was 0·84 (0·74, 0·95, I 2=48·3 %) for the case-control studies. The pooled relative risk of gastric cancer was 0·79 (0·63, 0·97, I 2=63·8 %) for intake of 6 cups green tea/d, 0·59 (0·42, 0·82, I 2=1·0 %) for 25 years of green tea intake and 7·60 (1·67, 34·60, I 2=86·5 %) for drinking very hot green tea. CONCLUSIONS: Drinking green tea has a certain preventive effect on reducing the risk of gastric cancer, particularly for long-term and high-dose consumption. Drinking too high-temperature green tea may increase the risk of gastric cancer, but it is still unclear whether high-temperature green tea is a risk factor for gastric cancer. Further studies should be performed to obtain more detailed results, including other gastric cancer risk factors such as smoking and alcohol consumption and the dose of the effective components in green tea, to provide more reliable evidence-based medical references for the relationship between green tea and gastric cancer.

Statistical Use in Clinical Studies: Is There Evidence of a Methodological Shift?

BACKGROUND: Several studies indicate that the statistical education model and level in medical training fails to meet the demands of clinicians, especially when they want to understand published clinical research. We investigated how study designs and statistical methods in clinical studies have changed in the last twenty years, and we identified the current trends in study designs and statistical methods in clinical studies. METHODS: We reviewed 838 eligible clinical study articles that were published in 1990, 2000, and 2010 in four journals New England Journal of Medicine, Lancet, Journal of the American Medical Association and Nature Medicine. The study types, study designs, sample designs, data quality controls, statistical methods and statistical software were examined. RESULTS: Substantial changes occurred in the past twenty years. The majority of the studies focused on drug trials (61.6%, n = 516). In 1990, 2000, and 2010, there was an incremental increase in RCT studies (74.4%, 82.8%, and 84.0%, respectively, p = 0.013). Over time, there was increased attention on the details of selecting a sample and controlling bias, and there was a higher frequency of utilizing complex statistical methods. In 2010, the most common statistical methods were confidence interval for superiority and non-inferiority comparison (41.6%), survival analysis (28.5%), correction analysis for covariates (18.8%) and Logistic regression (15.3%). CONCLUSIONS: These findings indicate that statistical measures in clinical studies are continuously developing and that the credibility of clinical study results is increasing. These findings provide information for future changes in statistical training in medical education.

Cognition of and Demand for Education and Teaching in Medical Statistics in China: A Systematic Review and Meta-Analysis.

BACKGROUND: Although a substantial number of studies focus on the teaching and application of medical statistics in China, few studies comprehensively evaluate the recognition of and demand for medical statistics. In addition, the results of these various studies differ and are insufficiently comprehensive and systematic. OBJECTIVES: This investigation aimed to evaluate the general cognition of and demand for medical statistics by undergraduates, graduates, and medical staff in China. METHODS: We performed a comprehensive database search related to the cognition of and demand for medical statistics from January 2007 to July 2014 and conducted a meta-analysis of non-controlled studies with sub-group analysis for undergraduates, graduates, and medical staff. RESULTS: There are substantial differences with respect to the cognition of theory in medical statistics among undergraduates (73.5%), graduates (60.7%), and medical staff (39.6%). The demand for theory in medical statistics is high among graduates (94.6%), undergraduates (86.1%), and medical staff (88.3%). Regarding specific statistical methods, the cognition of basic statistical methods is higher than of advanced statistical methods. The demand for certain advanced statistical methods, including (but not limited to) multiple analysis of variance (ANOVA), multiple linear regression, and logistic regression, is higher than that for basic statistical methods. The use rates of the Statistical Package for the Social Sciences (SPSS) software and statistical analysis software (SAS) are only 55% and 15%, respectively. CONCLUSION: The overall statistical competence of undergraduates, graduates, and medical staff is insufficient, and their ability to practically apply their statistical knowledge is limited, which constitutes an unsatisfactory state of affairs for medical statistics education. Because the demand for skills in this area is increasing, the need to reform medical statistics education in China has become urgent.

The effectiveness of the problem-based learning teaching model for use in introductory Chinese undergraduate medical courses: a systematic review and meta-analysis.

BACKGROUND: Although the problem-based learning (PBL) emerged in 1969 and was soon widely applied internationally, the rapid development in China only occurred in the last 10 years. This study aims to compare the effect of PBL and lecture-based learning (LBL) on student course examination results for introductory Chinese undergraduate medical courses. METHODS: Randomized and nonrandomized controlled trial studies on PBL use in Chinese undergraduate medical education were retrieved through PubMed, the Excerpta Medica Database (EMBASE), Chinese National Knowledge Infrastructure (CNKI) and VIP China Science and Technology Journal Database (VIP-CSTJ) with publication dates from 1st January 1966 till 31 August 2014. The pass rate, excellence rate and examination scores of course examination were collected. Methodological quality was evaluated based on the modified Jadad scale. The I-square statistic and Chi-square test of heterogeneity were used to assess the statistical heterogeneity. Overall RRs or SMDs with their 95% CIs were calculated in meta-analysis. Meta-regression and subgroup meta-analyses were also performed based on comparators and other confounding factors. Funnel plots and Egger's tests were performed to assess degrees of publication bias. RESULTS: The meta-analysis included 31studies and 4,699 subjects. Fourteen studies were of high quality with modified Jadad scores of 4 to 6, and 17 studies were of low quality with scores of 1 to 3. Relative to the LBL model, the PBL model yielded higher course examination pass rates [RR = 1.09, 95%CI (1.03, 1.17)], excellence rates [RR = 1.66, 95%CI (1.33, 2.06)] and examination scores [SMD = 0.82, 95%CI (0.63, 1.01)]. The meta-regression results show that course type was the significant confounding factor that caused heterogeneity in the examination-score meta-analysis (t = 0.410, P<0.001). The examination score SMD in "laboratory course" subgroup [SMD = 2.01, 95% CI: (1.50, 2.52)] was higher than that in "theory course" subgroup [SMD = 0.72, 95% CI: (0.56, 0.89)]. CONCLUSIONS: PBL teaching model application in introductory undergraduate medical courses can increase course examination excellence rates and scores in Chinese medical education system. It is more effective when applied to laboratory courses than to theory-based courses.

The AIDS epidemic and economic input impact factors in Chongqing, China, from 2006 to 2012: a spatial-temporal analysis.

OBJECTIVE: To analyse the spatial-temporal clustering of the HIV/AIDS epidemic in Chongqing and to explore its association with the economic indices of AIDS prevention and treatment. METHODS: Data on the HIV/AIDS epidemic and economic indices of AIDS prevention and treatment were obtained from the annual reports of the Chongqing Municipal Center for Disease Control for 2006-2012. Spatial clustering analysis, temporal-spatial clustering analysis, and spatial regression were used to conduct statistical analysis. RESULTS: The annual average new HIV infection rate, incidence rate for new AIDS cases, and rate of people living with HIV in Chongqing were 5.97, 2.42 and 28.12 per 100,000, respectively, for 2006-2012. The HIV/AIDS epidemic showed a non-random spatial distribution (Moran's I≥0.310; p<0.05). The epidemic hotspots were distributed in the 15 mid-western counties. The most likely clusters were primarily located in the central region and southwest of Chongqing and occurred in 2010-2012. The regression coefficients of the total amount of special funds allocated to AIDS and to the public awareness unit for the numbers of new HIV cases, new AIDS cases, and people living with HIV were 0.775, 0.976 and 0.816, and -0.188, -0.259 and -0.215 (p<0.002), respectively. CONCLUSIONS: The Chongqing HIV/AIDS epidemic showed temporal-spatial clustering and was mainly clustered in the mid-western and south-western counties, showing an upward trend over time. The amount of special funds dedicated to AIDS and to the public awareness unit showed positive and negative relationships with HIV/AIDS spatial clustering, respectively.

Risk factors of stroke in Western and Asian countries: a systematic review and meta-analysis of prospective cohort studies.

BACKGROUND: There has been an increasing trend in the incidence of stroke worldwide in recent years, and the number of studies focusing on the risk factors for stroke has also increased every year. To comprehensively evaluate the risk factors of stroke identified in prospective Western and Asian cohort studies. METHODS: Population-based cohort studies on stroke were searched in databases (PubMed, EMBASE, Web of Science, Google Scholar, etc.), and the library of the Third Military Medical University was manually searched for relevant information. A meta-analysis of Western and Asian studies on risk factors was performed. The pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated to assess the final group of cohort studies. RESULTS: After screening, 22 prospective cohort studies were included in the analyses of this investigation. Two factors, smoking and alcohol consumption, showed statistically significant differences between Western and Asian populations, and the results were as follows (W/A): 2.05 (95% CI, 1.68 ~ 2.49)/1.27 (95% CI, 1.04 ~ 1.55) and 0.89 (95% CI, 0.76 ~ 1.04)/1.28 (95% CI, 1.07 ~ 1.53). The factor BMI = 18.5-21.9 kg/m2 showed statistically significant differences only in Western populations, 0.96 (95% CI, 0.93 ~ 0.99); the factor SBP = 120-139 mm Hg showed statistically significant differences only in Asian populations, 2.29 (95% CI, 1.04 ~ 5.09). CONCLUSIONS: The prevalences of risk factors affect the stroke morbidity in Western and Asian populations, which may be biased by race. The meta-analysis of population-based studies suggests that different preventive measures should be adopted for Western and Asian population groups that are at high risk for stroke.