In the context of rapid global aging, the number of vulnerable elderly individuals who are sensitive to climate change and air pollution is increasing rapidly, potentially augmenting the burden of related diseases. The intrinsic capacity (IC) of the elderly refers to the comprehensive ability of individuals in physical, cognitive, and mental health aspects, typically encompassing 5 dimensions: cognitive status, motor ability, mental health, sensory function, and vitality. This article reviewed the advancements in epidemiological research on the effects of air pollution and climate change (including meteorological factors) on the overall intrinsic capacity of the elderly and its various dimensions. The results indicated that pollutants such as fine particulate matter (PM_2.5) and ozone (O₃) are most significantly associated with the decline in the cognitive function and vitality dimensions, and extreme meteorological events like high temperatures are also related to the functional deterioration of each dimension of IC. Nevertheless, the current studies mostly focus on the impact of atmospheric environmental factors on a specific dimension of IC rather than on overall IC, and research on the combined exposure to multiple atmospheric factors is even rarer, and the exploration of associated mechanisms is insufficient. Future research should enhance the investigation of the influence and mechanism of the combined exposure to air pollution and climate change on the dynamic changes of IC, and promote multi-center research and transnational cooperation. This review is conducive to clarifying the potential impact of atmospheric environmental factors on the IC of the elderly, providing a scientific basis for formulating health intervention policies to address climate change and air pollution.

Objective:To evaluate the association between heatwaves and fall-related mortality.Methods:A total of 61 421 fall-related mortality from 2013 to 2022 in 7 provinces of China were included in a time-stratified case-crossover design, with daily meteorological data derived from the fifth generation European Reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts. Conditional logistic regression chimeric distributed lag nonlinear model was used to analyze the association between heatwaves and fall-related mortality and stratified analysis was conducted according to gender and age.Results:Heatwaves were associated with an increased risk of fall-related morality. The risk of fall-related mortality during heatwaves was higher than during non-heatwave periods ( OR=1.11, 95% CI: 1.05-1.18). The attributable fraction of fall-related motality due to heatwaves was 10.25% (95% CI: 4.49%-15.36%). For each 1 ℃ increase above the heatwave threshold, the risk of fall-related mortality increased by 34% ( OR=1.34, 95% CI: 1.02-1.76). The effect of heatwave duration on fall-related mortality was not statistically significant. Stratified analyses indicated that women experienced a higher risk of fall-related mortality during heatwaves ( OR=1.13, 95% CI: 1.04-1.22) compared to man ( OR=1.10, 95% CI: 1.04-1.17). Conclusions:Heatwave increases the risk of fall-related mortality, and the intensity of heatwaves modify this risk. Women are vulnerable populations.

Objective To investigate the value of combined prediction of serum 25-hydroxyvita-min D[25-(OH)-D],homocysteine(Hey)and brain natriuretic peptide(BNP)for pregnancy out-comes in patients with hypertensive disorders of pregnancy(HDP).Methods A total of 200 patients with HDP were selected as research subjects and divided into gestational hypertension(GH)group(n=84),mild preeclampsia(PE)group(n=67)and severe PE group(n=49)according to the severity of the disease.Additionally,50 healthy pregnant women were selected as control group.The serum levels of 25-(OH)-D,Hey and BNP were compared among the groups,and their relationships with the severity of HDP were analyzed.According to the pregnancy outcomes of HDP patients,they were divided into poor pregnancy outcome group(n=82)and good pregnancy outcome group(n=118).The serum levels of 25-(OH)-D,Hey and BNP were compared between the poor and good pregnancy outcome groups.Logistic regression analysis was used to screen for the influencing factors of poor pregnancy outcomes in HDP patients.Results Compared with the control group,the severe PE group had the lowest serum 25-(OH)-D level,followed by the mild PE group and the GH group,with statistically significant differences(P＜0.001).Compared with the control group,the severe PE group had the highest serum Hcy and BNP levels,followed by the mild PE group and the GH group,with statistically significant differences(P＜0.001).Serum 25-(OH)-D was negatively cor-related with the severity of HDP in patients(r=-0.427,P＜0.001),while serum Hey and BNP were positively correlated with the severity of HDP in patients(r=0.585 and 0.682;P＜0.001).The serum 25-(OH)-D level in the poor pregnancy outcome group was significantly lower than that in the good pregnancy outcome group,while the serum Hcy and BNP levels were significantly higher than those in the good pregnancy outcome group(P＜0.05).Logistic regression analysis results showed that elevated serum Hey and BNP levels,severe PE and elevated systolic blood pressure were independent risk factors for poor pregnancy outcomes in HDP patients,while high 25-(OH)-D level was a protective factor(P＜0.05).The area under the curve(AUC)of the prediction model constructed based on the Logistic regression analysis results was 0.911(95％CI,0.863 to 0.947),with asensitivity of 91.46％and specificity of 75.42％.Conclusion As the severity of HDP in pa-tients increases,their serum 25-(OH)-D level decreases,while serum Hey and BNP levels in-crease.The combined prediction of serum 25-(OH)-D,Hey and BNP has a high value for predic-ting poor pregnancy outcomes in HDP patients.

Objective:To evaluate the association between heatwaves and fall-related mortality.Methods:A total of 61 421 fall-related mortality from 2013 to 2022 in 7 provinces of China were included in a time-stratified case-crossover design, with daily meteorological data derived from the fifth generation European Reanalysis dataset produced by the European Centre for Medium-Range Weather Forecasts. Conditional logistic regression chimeric distributed lag nonlinear model was used to analyze the association between heatwaves and fall-related mortality and stratified analysis was conducted according to gender and age.Results:Heatwaves were associated with an increased risk of fall-related morality. The risk of fall-related mortality during heatwaves was higher than during non-heatwave periods ( OR=1.11, 95% CI: 1.05-1.18). The attributable fraction of fall-related motality due to heatwaves was 10.25% (95% CI: 4.49%-15.36%). For each 1 ℃ increase above the heatwave threshold, the risk of fall-related mortality increased by 34% ( OR=1.34, 95% CI: 1.02-1.76). The effect of heatwave duration on fall-related mortality was not statistically significant. Stratified analyses indicated that women experienced a higher risk of fall-related mortality during heatwaves ( OR=1.13, 95% CI: 1.04-1.22) compared to man ( OR=1.10, 95% CI: 1.04-1.17). Conclusions:Heatwave increases the risk of fall-related mortality, and the intensity of heatwaves modify this risk. Women are vulnerable populations.

OBJECTIVE: To analyze the risk factors of in-hospital death in patients with sepsis in the intensive care unit (ICU) based on machine learning, and to construct a predictive model, and to explore the predictive value of the predictive model. METHODS: The clinical data of patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to April 2021 were retrospectively analyzed,including demographic information, vital signs, complications, laboratory examination indicators, diagnosis, treatment, etc. Patients were divided into death group and survival group according to whether in-hospital death occurred. The cases in the dataset (70%) were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. Based on seven machine learning models including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), a prediction model for in-hospital mortality of sepsis patients was constructed. The receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the seven models from the aspects of identification, calibration and clinical application, respectively. In addition, the predictive model based on machine learning was compared with the sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) models. RESULTS: A total of 741 patients with sepsis were included, of which 390 were discharged after improvement, 351 died in hospital, and the in-hospital mortality was 47.4%. There were significant differences in gender, age, APACHE II score, SOFA score, Glasgow coma score (GCS), heart rate, oxygen index (PaO₂/FiO₂), mechanical ventilation ratio, mechanical ventilation time, proportion of norepinephrine (NE) used, maximum NE, lactic acid (Lac), activated partial thromboplastin time (APTT), albumin (ALB), serum creatinine (SCr), blood urea nitrogen (BUN), blood uric acid (BUA), pH value, base excess (BE), and K⁺ between the death group and the survival group. ROC curve analysis showed that the area under the curve (AUC) of RF, XGBoost, LR, ANN, DT, SVM, KNN models, SOFA score, and APACHE II score for predicting in-hospital mortality of sepsis patients were 0.871, 0.846, 0.751, 0.747, 0.677, 0.657, 0.555, 0.749 and 0.760, respectively. Among all the models, the RF model had the highest precision (0.750), accuracy (0.785), recall (0.773), and F1 score (0.761), and best discrimination. The calibration curve showed that the RF model performed best among the seven machine learning models. DCA curve showed that the RF model exhibited greater net benefit as well as threshold probability compared to other models, indicating that the RF model was the best model with good clinical utility. CONCLUSIONS The machine learning model can be used as a reliable tool for predicting in-hospital mortality in sepsis patients. RF models has the best predictive performance, which is helpful for clinicians to identify high-risk patients and implement early intervention to reduce mortality.
Humans ; Hospital Mortality ; Retrospective Studies ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Intensive Care Units

ObjectiveThis study aimed to investigate the HIV genotypic subtypes and molecular transmission clusters among men who have sex with men （MSM） with newly reported HIV infections in Dehong Dai and Jingpo Autonomous Prefecture （Dehong Prefecture）， Yunnan Province， China， between 2010 and 2019. The study aimed to identify potential high-risk transmitters and provide reference data for screening， management， and intervention of infection sources. MethodsPlasma samples from newly reported HIV-positive MSM individuals in Dehong Prefecture between 2010 and 2019 were collected. The viral pol gene fragments were amplified， sequenced， and genotyped. Genetic distances （GD） between pairwise sequences were analyzed and calculated. MEGA 7.0 and Gephi were used for phylogenetic and molecular transmission network analysis. ResultsA total of 159 newly reported HIV infections among MSM were included in the study， with successful genotyping of 100 cases. Nine HIV-1 subtypes were identified， with the most prevalent being CRF01_AE subtype （52%）， followed by CRF07_BC subtype （31%）， CRF55_01B subtype （10%）， and others （7%）. Cluster analysis revealed a total network access rate of 67%， forming three transmission clusters. CRF01_AE subtype formed two transmission clusters with 38 and 3 infected individuals， while CRF07_BC subtypes formed one transmission cluster with 26 infected individuals. The transmission network within the CRF01_AE clusters exhibited a more complex relationship. Significant differences in educational level were observed between the two main transmission clusters. ConclusionThe predominant HIV subtypes among newly reported MSM cases in Dehong Prefecture between 2010 and 2019 were CRF01_AE and CRF07_BC. Significant cultural differences are observed between the main transmission clusters. Continued monitoring of genotypic subtypes and targeted interventions within transmission clusters are warranted.

Objective:To explore the predictive value of serum 25-hydroxyvitamin D [25 (OH) D] and interleukin-6 (IL-6) levels in the development of osteoporosis (OP) in gestational diabetes mellitus (GDM) during early pregnancy.Methods:A total of 178 pregnant women with GDM admitted to Shanxi Provincial Children’s Hospital from Sep. 2018 to May. 2022 were selected as the research subjects and divided into OP group and non-OP group based on bone density. General information of patients was collected, and biochemical indicators related to bone metabolism were measured, including calcium (Ca), β-collagen special sequence ( β-CTX), type 1 procollagen amino-terminal peptide (PINP), n-terminal bone calciuim (N-MID), osteocalcin (OC), and bone alkaline phosphatase (BAP). The levels of 25 (OH) D and IL-6 in the serum of the two groups were measured. The predictive value of 25 (OH) D and IL-6 levels for GDM complicated with OP was analyzed. Results:Compared with the non-OP group (29.95±6.88), the serum 25 (OH) D level was significantly lower in the OP group (20.78±6.02) ( t=5.56, P<0.001), and the IL-6 level was significantly higher in the OP group (75.19±9.15) than in the non-OP group (50.23±10.08) ( t=10.26, P<0.001). There was no significant difference in the levels of Ca, P, β-CTX, PINP, or N-MID between the two groups (all P>0.05). The levels of BAP (13.25±3.12) and OC (6.62±1.24) in the OP group were significantly lower than those in the non-OP group (BAP: 18.23±3.78, t=5.52, P<0.001; OC: 8.75±1.52, t=5.87, P<0.001). Pearson correlation analysis showed a positive correlation between 25 (OH) D level and bone density, BAP, and OC ( r=0.26, 0.21, 0.23, all P<0.05), while IL-6 level showed a negative correlation with bone density, BAP, and OC ( r=-0.39, -0.21, -0.27, all P<0.05). ROC curve analysis showed that serum 25 (OH) D and IL-6 could serve as effective diagnostic markers for GDM complicated with OP (AUC=0.853, P<0.0001; AUC=0.869, P<0.0001) during early pregnancy. Conclusions:The serum 25 (OH) D level is decreased and IL-6 level is increased in GDM patients with OP during early pregnancy. The levels of serum 25 (OH) D and IL-6 have predictive value for the development of GDM complicated with OP during early pregnancy.

ObjectiveTo determine the trend and influencing factors of HIV subtypes in newly reported HIV/AIDS cases in Dehong Dai and Jingpo Autonomous Prefecture （Dehong Prefecture） from 2017 to 2019. MethodsRNA extraction was conducted among newly reported HIV/AIDS cases in Dehong Prefecture from 2017 to 2019 whose plasma volume was more than 200 μL. The gag， env and pol genes were amplified by using RT-PCR and then sequenced to determine the subtypes. ResultsA total of 3 287 HIV-infected cases were newly reported in Dehong from 2017 to 2019. The HIV gag， env and pol genotypes were determined in 1 813 cases. The major subtypes were subtype C （28.4%，515/1 813）， recombination form BC （22.0%，398/1 813） and CRF_01AE （18.1%，329/1 813）. Furthermore， the proportion of subtype B， subtype C and CRF01_AE decreased over years， whereas 01/BC， CRF07_BC and CRF08_BC increased over years in both Chinese and Burmese patients （χ²=75.212，P<0.001）. There were significant differences in gender， age， marital status， ethnicity， educational level and transmission route among Chinese and Burmese HIV-infected cases with diverse HIV genotypes （all P<0.05）. ConclusionHIV subtypes in Dehong change over time， which demonstrates that the proportion of BC recombinant subtypes and unique recombinant subtypes increased significantly.

Objective:To investigate the distributions of human immunodeficiency virus (HIV) and hepatitis C virus (HCV) genotypes among newly reported HIV/HCV co-infected Burmese patients in Dehong Dai and Jingpo Autonomous Prefecture, Yunnan Province from 2016 to 2019.Methods:A total of 1 289 newly reported HIV/HCV co-infected Burmese patients in Dehong Dai and Jingpo Autonomous Prefecture were collected through the National Acquired Immunodeficiency Syndrome Comprehensive Prevention and Control Data Information System From January 2016 to December 2019. Among them, 996 subjects with a plasma volume of ≥200 μL were selected to perform HIV and HCV genotyping. The HIV pol gene, the HCV core protein-binding envelope protein ( CE1) gene and non-structural protein 5B ( NS5 B) gene were amplified using the nested polymerase chain reaction.The phylogenetic tree was constructed by MEGA 7.0 software to classify the genotypes. Chi-square test was used for statistical analysis. Trend chi-square test was used to analyze the trend of HIV and HCV genotypes. Results:Among the 996 cases with HIV/HCV co-infection, HIV and HCV sequences from a total of 554 subjects (55.6%, 554/996) were successfully obtained, and the genotypes of HIV and HCV were diverse. HIV genotype C (40.3%, 223/554) and BC recombinant (33.6%, 186/554) were the most prevalent, followed by genotype B (6.5%, 36/554) and circulating recombinant form (CRF)01_AE (3.6%, 20/554). HCV genotype 3b was the most prevalent (31.2%, 173/554), followed by genotype 6u (19.5%, 108/554), 1a (17.5%, 97/554), 6n (11.4%, 63/554), 3a (8.7%, 48/554) and 6xg (6.3%, 35/554). The prevalence of HIV genotype C showed a downward trend ( χtrend2=7.23, P<0.001), while the prevalence of BC recombinant showed an upward trend ( χtrend2=5.97, P<0.001), and the proportion of BC recombinant was higher than genotype C in 2019 (54.9%(101/184) vs 21.7%(40/184)). However, there were no statistically significant differences in the proportions of genotype 3b, 6u and 1a from 2016 to 2019 ( χtrend2=1.43, 1.79 and 0.39, respectively, P=0.152, 0.074 and 0.695, respectively). The HIV genotype distribution among patients with different ethnic groups were significantly different ( χ2=22.06, P=0.037). Conclusions:The diversity of HIV and HCV genotypes is high and complex among HIV/HCV co-infected Burmese patients in Dehong Dai and Jingpo Autonomous Prefecture. BC recombinant shows a trend of becoming the predominant HIV genotype among these co-infected patients. Therefore, surveillance of the prevalence of HCV and HIV genotypes in Burmese population needs to be further strengthened.

Objective:To study the prevalence and correlates of plasma cytomegalovirus (CMV) viremia among newly reported antiretroviral therapy (ART)-naive HIV/AIDS patients in Taizhou during 2017-2018.Methods:CMV DNA was measured in plasma specimens of newly reported ART-naive HIV/AIDS patients by quantitative PCR. Both univariable and multivariable logistic regression analyses were carried out to evaluate CMV viremia correlations among the individuals.Results:Of 612 HIV/AIDS patients, 480 (78.4%) were male, 125 (20.4%) were over 60 years old, 177 (28.9%) were infected via homosexual transmission, and 430 (70.3%) via heterosexual transmission. The prevalence of CMV viremia among HIV/AIDS patients was 13.4% (82/612). Multivariable logistic regression analysis showed that the risk of CMV viremia in CD 4+ lymphocyte cells counts (CD 4+) ≤200 cells/μl group was higher than CD 4 counts >500 cells/μl ( OR=5.10, 95% CI:1.74-14.96, P=0.003); The median CMV DNA level (log 10) of 82 viremic patients was 1.57 ( P 25, P 75:1.04,2.13); Viremic patients with CD 4 counts ≤200 cells/μl had the highest CMV viral load ( P<0.01). Conclusions:Among ART-naive HIV/AIDS patients, the prevalence of CMV viremia was significantly associated with immunodeficiency status. Further research is needed to evaluate the association between CMV viremia and the course of HIV infection.