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 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.

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 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

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.

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 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.

Objective:To explore the HIV-1 drug resistance and subtypes in newly reported HIV/AIDS patients before antiretroviral therapy (ART) in Taizhou city.Methods:A cross-sectional study of HIV-1 drug resistance was conducted among newly reported HIV/AIDS patients before ART in Taizhou from January 2016 to December 2018. HIV-1 pol gene sequences were obtained by RT-PCR. The sequences were submitted to the Stanford University drug resistance database. The drug resistance mutation and the sensitivity of HIV-1 strains to antiretroviral drugs were determined according to WHO Guidelines on HIV drug resistance surveillance of 2014. Results:A total of 806 HIV-1 pol gene sequences were obtained successfully. The overall HIV-1 drug resistance rate was 2.9% (23/806), 1.9% for non-nucleoside reverse transcriptase inhibitors (NNRTIs), 0.6% for nucleoside reverse transcriptase inhibitors (NRTIs), and 0.0% for protease inhibitors (PIs), respectively. From 2016 to 2018, the HIV-1 drug resistance rate was 1.6%, 1.8%, and 4.8%, respectively. The resistance mutations of NNRTIs and NRTIs were mainly K103 N (0.7%) and M184I/V (0.5%). HIV-1 subtypes were mostly CRF01_AE (42.7%,344/806),CRF07_BC (28.9%,233/806) and CRF08_BC (11.2%,90/806).HIV-1 subtypes among homosexually transmitted infections were mostly CRF01_AE (53.3%, 136/255) and CRF07_BC (32.2%, 82/255), and HIV-1 subtypes among heterosexually transmitted infections were mainly CRF01_AE (37.7%, 203/539), CRF07_BC (27.5%, 148/539) and CRF08_BC (16.1%, 87/539). Conclusion:HIV-1 drug resistance rate among newly reported HIV/AIDS patients before ART remained low in Taizhou during 2016 to 2018, an increasing trend seemed to be notifiable and warrants continuous surveillance of HIV-1 drug resistance.

Professor 's experience of acupuncture combined with medication for epilepsy is summarized, which is explained from epilepsy's etiology and pathogenesis, diagnosis and treatment of acupuncture and medication, respectively. Besides, the theoretical foundation and use instruction of acupuncture technique "-" for epilepsy are introduced. Professor highly values the adherence to etiology and pathogenesis, pays attention to syndrome differentiation and searches for the primary disease cause. He proposes the wind, phlegm, stasis and deficiency are the pathogenesis of epilepsy, and points out acupuncture could be applied during attack stage and remittent stage, but electroacupuncture should be used with caution. Regulating spirit is the key for treating epilepsy. The combination of acupuncture and medication could regulate the governor vessel and guide to the origin, which have significant curative effect.
Acupuncture Therapy ; Combined Modality Therapy ; Electroacupuncture ; Epilepsy ; therapy ; Humans

Comprehensive geriatric assessment (CGA) is widely used for creating tailored individual care plans for seniors who usually are suffering from multiple diseases,frailty,disability and cognition decline.Skilled communicators are of importance for high-quality clinical data collection.Communication skills used in CGA are summarized in this article by reviewing related references and combining practical experiences in the process of applying international resident assessment instruments (interRAI).