Objective: To understand the microclimate in primary and secondary school classrooms for the study period during the winter heating season, so as to provide a reference for the revision and improvement of relevant health standards. Methods: In December 2024, stratified random sampling was used to select 30 primary and secondary schools and 180 classrooms from the northern regions with centralized heating (Liaoning Province, Tianjin City) and the southern regions without centralized heating (Shanghai City, Anhui Province, and Jiangxi Province). Indoor temperature, relative humidity, wind speed, CO 2 and other indicators were measured on site. Variance analysis, t-test, Mann-Whitney U test and Kruskal-Wallis H test were used to analyze the differences in the microclimate of classrooms among regions and urban and rural differences. Results: The average temperature in the middle of the classrooms tested on site was (16.47±4.72)℃, and the variance analysis showed that the difference between the regions was statistically significant ( F=27.80, P <0.01). Among them, Tianjin had the highest average temperature of (20.43± 2.12 )℃, followed by Liaoning (19.03±2.23)℃, Shanghai (15.33±5.32)℃, Anhui (12.79±1.74)℃, and Jiangxi (11.69± 1.68 )℃. Horizontal temperature difference was 0.90 (0.50, 1.60)℃, the vertical temperature difference was 0.20 (0.10,0.60)℃, the average relative humidity was (44.39±16.16)%, the wind speed was 0.03(0.01,0.11)m/s, and the differences among different provinces and cities were statistically significant ( H/F =40.62, 82.69, 95.06, 55.28, all P <0.01). The average CO 2 volume concentration in urban areas of Tianjin, Liaoning, and Shanghai was 0.21(0.16,0.30)%, and there was no statistically significant difference ( H=4.65, P =0.10). There were grade differences in relative humidity ( F =3.71, 6.21) and CO 2 ( H =14.72, 12.92) in the north and the south (all P <0.05). In addition, the temperature, relative humidity, wind speed and CO 2 in the middle of the classroom were 42.8%, 67.8%, 100.0% and 22.2% respectively. Conclusions The temperature in the middle of the classroom in the non centralized heating area is lower than the standard, the relative humidity of classroom in the centralized heating area is lower than the standard，and the CO 2 in the classroom in winter is lower than the standard. It is recommended to install heating facilities in schools with low temperatures to increase the temperature and increase the frequency of ventilation in classrooms or adopt mechanical ventilation strategies to reduce CO 2 volume concentration.

[Objective] To establish a new scoring system to predict the perioperative outcomes (operation time, intraoperative blood loss, and trifecta achievement) in patients undergoing robot-assisted partial nephrectomy (RAPN) by integrating the RENAL and Mayo adhesive probability (MAP) scores. [Methods] Clinical data of 178 patients with renal cell carcinoma who underwent RAPN performed by the same surgeon in our hospital during Jan.2015 and Jan.2022 were retrospectively analyzed.The RENAL and MAP scores of all patients were calculated.Linear regression and logistic regression were used to evaluate the associations between the components of the RENAL and MAP scores (a total of 6 variables) and perioperative outcomes.The factors with significant associations were then included into logistic regression analysis to identify independent predictors for constructing an assessment system for perioperative outcomes, and the receiver operating characteristic (ROC) curve was plotted to calculate the area under the curve (AUC) to predict its efficacy. [Results] Multivariate linear regression analysis showed that tumor size (β=6.14, 95%CI: 1.93—10.34, P=0.004), exophytic rate (β=10.60, 95%CI: 3.44—17.76, P=0.004), and perinephric fat thickness (β=16.48, 95%CI: 8.52—24.45, P<0.001) were significantly associated with operation time.Tumor size (β=10.55 95%CI: 5.60—15.49, P<0.001) was associated with both intraoperative blood loss and trifecta achievement (OR=1.73, 95%CI: 1.26—2.36, P=0.001). Multivariate logistic regression analysis of these 3 factors identified tumor size (OR=9.07, 95% CI: 1.18—69.45, P=0.03) and perinephric fat thickness (OR=2.28, 95%CI: 1.86—6.04, P=0.01) as independent predictors of perioperative outcomes.Based on these findings, the tumor size and perinephric fat thickness (RP) scoring was constructed, which demonstrated better predictive ability than RENAL score or MAP score alone (RP vs.RENAL vs.MAP: 0.766 vs.0.548 vs.0.684). [Conclusion] The RP score includes fewer variables than the RENAL and MAP scores but outperforms them.

Objective:To compare the application of Micronutritional Risk Assessment(MNA),Universal Screening Tool for Malnutrition(MUST)and Nutritional Risk Screening 2002(NRS2002)in nutritional risk assessment among patients with digestive tract tumors during perichemotherapy,based on the Global Leadership Initiative on Malnutrition(GLIM)standard.Methods:A prospective cross-section study was conducted,including 114 patients with digestive tract tumors hospitalized by Department of General Surgery,Huangshan Shoukang Hospital from January 2020 to December 2021.All patients were evaluated by GLIM assessment,the correlation between GLIM and MNA,MUST and NRS 2002 screening results was compared,and the consistency among different methods was compared.Patients were divided into malnourished group(nutritional risk group)or normal nourished group according to the results of the three tools.The differences in single anthropometric or test indicators between the groups were compared.Results:According to GLIM,the proportion of malnutrition was 36.8%.The proportion of malnutrition evaluated by MNA,MUST,and NRS2002 were 63.2%,47.4%,and 32.5%,respectively.The sensitivity and negative predictive value of MNA in assessing nutrition-related risks were the highest,while the specificity,Jorden index,Kappa value and positive predictive value of NRS2002 were the highest.There were statistical differences in levels of body mass index,hemoglobin(Hb),triglyceride,total cholesterol,albumin,prealbumin(P-ALB),blood creatinine,lymphocyte counts,and hospitalization costs between two groups assessed by three different tools(P<0.05).Levels of Hb and P-ALB were statistically different between the two groups of the three screening tools.Conclusion:Based on GLIM evaluation results,MNA and other nutritional screening tools are applicable to the assessment of nutritional risks of patients with gastrointestinal cancer during perichemotherapy due to the joint evaluation of measurement indicators.The MNA is more recommended with the highest detection rate and sensitivity for nutritional risks assessment.

Poly(lactic-co-glycolide)(PLGA)is a key excipient in long-acting sustained-release preparations,and its degradation properties directly affect the drug release behavior.In this study,PLGA microspheres were prepared by microfluidic techniques,and the morphology changes of the microspheres were observed by scanning electron microscopy(SEM).In alkaline environment,due to the accelerated hydrolysis of ester bonds,the surface of the microspheres was rapidly dissolved and eroded,and the degradation rate was significantly higher than that in acidic environment.High temperature accelerated the degradation of PLGA microspheres.Under neutral and alkaline conditions,the microspheres showed aggregation and adhesion.Under acidic conditions,the microspheres gradually decomposed into irregular fragments.The high ionic strength further promoted the surface corrosion of the microspheres,especially under extreme pH conditions.Simultaneously,PLGA microspheres encapsulating coumarin were prepared to simulate the microsphere formulation.The release rate of coumarin after degradation of the microspheres under different conditions was observed by measuring the absorbance with ultraviolet-visible spectrophotometry.The results were consistent with those of the blank microspheres.This study revealed that the degradation of PLGA microspheres was significantly pH-dependent,temperature sensitive and ion strength responsive.These findings not only helped to understand and optimize the long-term stability and controlled release performance of drug-carrying microspheres,but also provided a theoretical basis for further improvement of PLGA-based drug carrier design.

Objective:To examine frailty in residents aged over 50 in the Chengdu community, investigate the risk factors linked to frailty, and analyze the impact of exercise habits on frailty.Methods:This retrospective study utilized a stratified whole-sample method to select Chengdu residents aged over 50 from 6 communities.Relevant information was collected through face-to-face interactions using a self-developed frailty questionnaire between May 2022 and May 2023.Exercise habits were defined as engaging in physical activity more than three times a week for at least 30 minutes per session, consistently maintained for a minimum of five years prior to the survey.The frailty index(FI)was established based on the multiple-deficit cumulative model.Respondents were classified into categories of no frailty(FI<0.1), mild frailty(0.1 ≤ FI<0.2), and moderate-severe frailty(FI≥0.2).Univariate and multivariate logistic regression analyses were conducted to investigate the factors influencing frailty and to compare the prevalence and progression of frailty among individuals with and without exercise habits across different age groups.Results:A total of 999 participants, comprising 483(48.3%)males and 516(51.7%)females, were enrolled in the study.The average age of the participants was 65.70±9.61 years.Among them, 346(34.6%)were classified as having mild frailty, while 107(10.7%)had moderate-severe frailty.Univariate analysis revealed that age, gender, marital status, education, smoking, alcohol consumption, and exercise habits were potential factors influencing frailty( P<0.05).Multifactorial logistic regression models indicated that individuals aged 70-79 and 80-85 were predictors of mild and moderate-severe frailty, respectively.Being female was identified as an independent risk factor for both mild frailty( OR: 1.196; 95% CI: 1.262-3.073)and moderate-severe frailty( OR: 2.721; 95% CI: 1.282-5.774).Additionally, not engaging in regular exercise was associated with an increased risk of mild frailty( OR: 1.588; 95% CI: 1.149-2.193)and moderate-severe frailty( OR: 3.186; 95% CI: 1.819-5.583).Statistical analysis revealed significant differences in frailty distribution and frailty index changes between individuals with and without exercise habits over the age of 60( P<0.05). Conclusions:Age and gender were found to be closely associated with frailty, with the impact of exercise habits on frailty becoming more pronounced after the age of 60.Encouraging middle-aged and older individuals to adopt regular exercise routines can play a significant role in promoting healthy aging in China.

Objective To investigate the relationship between plasma metalloproteinase-tissue inhibitor 1 (TIMP-1),vascular endothelial growth factor (VEGF) and potentially transforming growth factor binding protein 2 (LTBP-2) levels and risk stratification and death in patients with acute pulmonary embolism (APE). Methods A toral of 110 APE patients admitted to the Third People's Hospital of Haikou from January 2022 to January 2024 were selected for risk stratification,and they were divided into low-risk group(n=28),medium-risk group(n=43) and high-risk groups(n=39). According to the occurrence of death in APE patients,they were divided into a survival group (n=79)and a death group(n=31). Enzyme-linked immunosorbent assay (ELISA) was used to measure the levels of plasma TIMP-1,VEGF and LTBP-2. Applying multiple Logistic regression analysis to identify risk factors affecting the mortality of APE patients and plotting ROC curve to analyze the predictive value of plasma TIMP-1,VEGF and LTBP-2 levels for APE patients mortality and. Pearson correlation analysis was used to analyze the correlation between plasma levels of TIMP-1,VEGF and LTBP-2 and clinical indexes in APE patients. Results The heart rate,B-type brain natriuretic peptide (BNP),D-dimer,TIMP-1(207.15±62.84pg/ml vs 152.48±41.62pg/ml),VEGF (726.35±190.46pg/ml vs 419.27±115.28pg/ml) and LTBP-2(29.17±6.38ng/ml vs 13.26±3.70ng/ml) levels in the death group were significantly higher than those in the survival group,the difference were statistically significant (t=5.386～21.194,all P<0.05). The levels of plasma TIMP-1(204.15±60.17 pg/ml,178.18±51.30pg/ml vs 148.20±34.80pg/ml),VEGF(720.83±204.18pg/ml,580.16±158.37pg/ml vs 412.15±109.26pg/ml) and LTBP-2(28.40±6.41ng/ml,21.37±5.26ng/ml vs 12.84±3.12ng/ml) in high-risk and medium-risk groups were significantly higher than those in the low-risk group(t=8.417～19.850),and those in the high-risk group were higher than those in the medium-risk group(t=7.964,9.381,11.470),the differences were statistically significant (all P<0.05),respectively. Multiple Logistic regression analysis showed that increased plasma BNP,D-dimer,TIMP-1,VEGF and LTBP-2 levels were risk factors for death in APE patients (all P<0.05). ROC curve analysis showed that TIMP-1,VEGF and LTBP-2 combined predicted the highest AUC(95％CI) of death in APE patients[0.938(0.881～0.997)],with an accuracy of 88.2％. Pearson correlation analysis showed that the levels of plasma TIMP-1,VEGF and LTBP-2 in APE patients were positively correlated with BNP and D-dimer (r=0.416～0.753,all P<0.05). Conclusion Elevated levels of plasma TIMP-1,VEGF and LTBP-2 are associated with high risk and mortality in APE,and the combination of these three factors has good predictive value for mortality in APE patients.

BACKGROUND: Biomarkers-based prediction of long-term risk of acute coronary syndrome (ACS) is scarce. We aim to develop a risk score integrating clinical routine information (C) and plasma biomarkers (B) for predicting long-term risk of ACS patients. METHODS: We included 2729 ACS patients from the OCEA (Observation of cardiovascular events in ACS patients). The earlier admitted 1910 patients were enrolled as development cohort; and the subsequently admitted 819 subjects were treated as validation cohort. We investigated 10-year risk of cardiovascular (CV) death, myocardial infarction (MI) and all cause death in these patients. Potential variables contributing to risk of clinical events were assessed using Cox regression models and a score was derived using main part of these variables. RESULTS: During 16,110 person-years of follow-up, there were 238 CV death/MI in the development cohort. The 7 most important predictors including in the final model were NT-proBNP, D-dimer, GDF-15, peripheral artery disease (PAD), Fibrinogen, ST-segment elevated MI (STEMI), left ventricular ejection fraction (LVEF), termed as CB-ACS score. C-index of the score for predication of cardiovascular events was 0.79 (95% CI: 0.76-0.82) in development cohort and 0.77 (95% CI: 0.76-0.78) in the validation cohort (5832 person-years of follow-up), which outperformed GRACE 2.0 and ABC-ACS risk score. The CB-ACS score was also well calibrated in development and validation cohort (Greenwood-Nam-D'Agostino: P = 0.70 and P = 0.07, respectively). CONCLUSIONS CB-ACS risk score provides a useful tool for long-term prediction of CV events in patients with ACS. This model outperforms GRACE 2.0 and ABC-ACS ischemic risk score.

The glucagon receptor (GCGR) is a critical target for the treatment of metabolic disorders such as Type 2 Diabetes Mellitus (T2DM) and obesity. Activation of GCGR enhances systemic insulin sensitivity through paracrine stimulation of insulin secretion, presenting a promising avenue for treatment. However, the discovery of effective GCGR agonists remains a challenging and resource-intensive process, often requiring time-consuming wet-lab experiments to synthesize and screen potential compounds. Recent advances in artificial intelligence technologies have demonstrated great potential in accelerating drug discovery by streamlining screening and efficiently predicting bioactivity. In the present work, we propose DeepGCGR, a two-layer deep learning model that leverages graph convolutional networks (GCN) integrated with a multiple attention mechanism to expedite the identification of GCGR agonists. In the first layer, the model predicts the bioactivity of various compounds against GCGR, efficiently filtering large chemical libraries to identify promising candidates. In the second layer, DeepGCGR classifies high bioactive compounds based on their functional effects on GCGR signaling, identifying those with potential agonistic or antagonistic effects. Moreover, DeepGCGR was specifically applied to identify novel GCGR-regulating compounds for the treatment of T2DM from natural products derived from traditional Chinese medicine (TCM). The proposed method will not only offer an effective strategy for discovering GCGR-targeting compounds with functional activation properties but also provide new insights into the development of T2DM therapeutics.
Deep Learning ; Drug Discovery/methods* ; Humans ; Diabetes Mellitus, Type 2/metabolism* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/pharmacology*