OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

Objective To design an intelligent airborne soldier physical training system based on human body composition analysis to solve the problems in diversity of training mode,targeted training plan and high incidence of military training-related injuries.Methods The intelligent airborne military physical training system was designed with B/S architecture and developed with Python language,which was composed of four functional modules for airborne soldier information acquisition,trainee physical fitness state assessment,physical fitness training program recommendation and airborne soldier physical fitness training program evaluation.The airborne soldier information acquisition module collected and analyzed the trainee physiological parameter information with a human body composition analyzer,clarified the parameter characteristics related to physical training with considerations on military physical training requirements and constructed a trainee physical fitness assessment parameter model;the trainee physical fitness state assessment module established an evaluation model based on machine learning to realize stage-by-stage physical fitness evaluation for airborne soldiers;the physical fitness training program recommendation module was constructed based on the physical training feature similarity algorithm and graph embedding theory to provide decision making assistance for program development of airborne military physical training;the airborne soldier physical fitness training program evaluation module compared the physical fitness and evaluation results before and after training by means of list and chart,and updated the training program based on the evaluation results by calling the physical training program recommendation module.Results The intelligent airborne soldier physical training system contributed to forming an individualized physical fitness training recommendation mechanism after trainee body evaluation,modifying training program based on comparison and feedback for stage-by-stage training evaluation,so as to decrease the incidence of military training-related injuries while increasing the training efficiency.Conclusion The system developed improves airborne soldier physical training in rationality and reliability,and provides references for intelligent military training of the PLA.[Chinese Medical Equipment Journal,2025,46(2):16-23]

Encephalomyocarditis virus(EMCV)is a zoonotic pathogen that causes encephalitis and myocarditis as its primary clinical manifestations.To explore effective preventive measures,this study utilized a Bac-to-Bac expression system to insert the EMCV P12A and 3C genes into the pFastBacDual shuttle vector,resulting in the generation of the recombinant baculovirus Ac-P12A-3C.This facilitated the large-scale expression and purification of EMCV virus-like particles(VLPs),which were correctly assembled into particles of approximately 30 nm in diameter,as ob-served by electron microscopy.Immunization and challenge experiments in mice demonstrated that these VLPs could effectively protect against EMCV infection,achieving a protection rate of 100％.Histopathological sections indicated that,compared to the PBS control group,the VLP immuniza-tion group exhibited significantly reduced tissue damage,along with a marked decrease in viral load within the tissues.In piglets,immunization with the VLPs elicited a robust humoral response,with neutralizing antibody titers reaching 1∶320 to 1∶640 after a second immunization,and no signifi-cant adverse reactions were observed throughout the immunization process.This study preliminarily explores the immunogenicity and safety of the VLP vaccine,laying the foundation for the development of a subunit vaccine based on EMCV VLPs and offering a new strategy for the prevention and control of encephalomyocarditis.

Objective:To study the influencing factors of postpartum post-traumatic stress disorder(PTSD)in pregnant women in the third trimester of pregnancy based on propensity score matching(PSM).Methods:A retrospective analysis was conducted on clinical data of 220 pregnant women in the third trimester who visited our hospital from October 2022 to October 2024,they were divided into PTSD group(n=52)and non-PTSD group(n=168)based on whether PTSD occurred.PSM was adopted to match the age,body mass index(BMI),educational level,average monthly income per capita,natural conception,premature birth,pregnancy complications,first delivery,insomnia,the progress of labor,parenting methods,infant health status and postpartum depression of the two groups at a 1∶1 ratio.After matching,Logistic regression was used to analyze the influencing factors of postpartum PTSD in pregnant women in the third trimester of pregnancy.Result:When comparing the educational level,natural conception,premature birth,pregnancy complications,progress of labor and postpartum depression of the two groups before matching,the differences were statistically significant(P＜0.05).When comparing the comparisons of educational level,natural conception,premature birth,pregnancy complications,progress of labor and postpartum depression of the two groups after matching,the differences were statistically significant(P＜0.05).Logistic regression analysis showed that junior high school education level or below,non-natural conception,premature birth,pregnancy complications,abnormal labor process and postpartum depression were risk factors for PTSD(P＜0.05).Conclusion:Based on PCM,it was found that low educational level,non-natural conception,premature birth,pregnancy complications,abnormal labor process and postpartum depression are risk factors for postpartum PTSD in pregnant women in the third trimester of pregnancy,and reasonable preventive interventions could be taken as early as possible in pregnant women with high risk of PTSD.

Encephalomyocarditis virus(EMCV)is a zoonotic pathogen that causes encephalitis and myocarditis as its primary clinical manifestations.To explore effective preventive measures,this study utilized a Bac-to-Bac expression system to insert the EMCV P12A and 3C genes into the pFastBacDual shuttle vector,resulting in the generation of the recombinant baculovirus Ac-P12A-3C.This facilitated the large-scale expression and purification of EMCV virus-like particles(VLPs),which were correctly assembled into particles of approximately 30 nm in diameter,as ob-served by electron microscopy.Immunization and challenge experiments in mice demonstrated that these VLPs could effectively protect against EMCV infection,achieving a protection rate of 100％.Histopathological sections indicated that,compared to the PBS control group,the VLP immuniza-tion group exhibited significantly reduced tissue damage,along with a marked decrease in viral load within the tissues.In piglets,immunization with the VLPs elicited a robust humoral response,with neutralizing antibody titers reaching 1∶320 to 1∶640 after a second immunization,and no signifi-cant adverse reactions were observed throughout the immunization process.This study preliminarily explores the immunogenicity and safety of the VLP vaccine,laying the foundation for the development of a subunit vaccine based on EMCV VLPs and offering a new strategy for the prevention and control of encephalomyocarditis.

Objective To design an intelligent airborne soldier physical training system based on human body composition analysis to solve the problems in diversity of training mode,targeted training plan and high incidence of military training-related injuries.Methods The intelligent airborne military physical training system was designed with B/S architecture and developed with Python language,which was composed of four functional modules for airborne soldier information acquisition,trainee physical fitness state assessment,physical fitness training program recommendation and airborne soldier physical fitness training program evaluation.The airborne soldier information acquisition module collected and analyzed the trainee physiological parameter information with a human body composition analyzer,clarified the parameter characteristics related to physical training with considerations on military physical training requirements and constructed a trainee physical fitness assessment parameter model;the trainee physical fitness state assessment module established an evaluation model based on machine learning to realize stage-by-stage physical fitness evaluation for airborne soldiers;the physical fitness training program recommendation module was constructed based on the physical training feature similarity algorithm and graph embedding theory to provide decision making assistance for program development of airborne military physical training;the airborne soldier physical fitness training program evaluation module compared the physical fitness and evaluation results before and after training by means of list and chart,and updated the training program based on the evaluation results by calling the physical training program recommendation module.Results The intelligent airborne soldier physical training system contributed to forming an individualized physical fitness training recommendation mechanism after trainee body evaluation,modifying training program based on comparison and feedback for stage-by-stage training evaluation,so as to decrease the incidence of military training-related injuries while increasing the training efficiency.Conclusion The system developed improves airborne soldier physical training in rationality and reliability,and provides references for intelligent military training of the PLA.[Chinese Medical Equipment Journal,2025,46(2):16-23]

Objective:To study the influencing factors of postpartum post-traumatic stress disorder(PTSD)in pregnant women in the third trimester of pregnancy based on propensity score matching(PSM).Methods:A retrospective analysis was conducted on clinical data of 220 pregnant women in the third trimester who visited our hospital from October 2022 to October 2024,they were divided into PTSD group(n=52)and non-PTSD group(n=168)based on whether PTSD occurred.PSM was adopted to match the age,body mass index(BMI),educational level,average monthly income per capita,natural conception,premature birth,pregnancy complications,first delivery,insomnia,the progress of labor,parenting methods,infant health status and postpartum depression of the two groups at a 1∶1 ratio.After matching,Logistic regression was used to analyze the influencing factors of postpartum PTSD in pregnant women in the third trimester of pregnancy.Result:When comparing the educational level,natural conception,premature birth,pregnancy complications,progress of labor and postpartum depression of the two groups before matching,the differences were statistically significant(P＜0.05).When comparing the comparisons of educational level,natural conception,premature birth,pregnancy complications,progress of labor and postpartum depression of the two groups after matching,the differences were statistically significant(P＜0.05).Logistic regression analysis showed that junior high school education level or below,non-natural conception,premature birth,pregnancy complications,abnormal labor process and postpartum depression were risk factors for PTSD(P＜0.05).Conclusion:Based on PCM,it was found that low educational level,non-natural conception,premature birth,pregnancy complications,abnormal labor process and postpartum depression are risk factors for postpartum PTSD in pregnant women in the third trimester of pregnancy,and reasonable preventive interventions could be taken as early as possible in pregnant women with high risk of PTSD.

AIM:To investigate the molecular mechanisms of KG-1 cell proliferation by profiling its responses to various protein kinase inhibitors.METHODS:CCK-8 assay,real time quantitative PCR(qRT-PCR)and Western-blot were used to detect the effect of various protein kinase inhibitors on KG-1 cell proliferation,the expression levels of mRNA and phosphorylation level of signaling pro-teins in the FGFR1 downstream pathways.RE-SULTS:NVP-BGJ398 and PD173074 effectively in-hibited the proliferation of KG-1 cells,indicative of a crucial role of FGFR downstream signaling.After treatment with FGFR inhibitors,the levels of p-FG-FR1OP2-FGFR1 and p-STAT5 decreased significantly(P<0.001),p-AKT decreased slightly(P<0.05),with-out affecting the p-ERK level(P>0.05).CONCLU-SION:FGFR1OP2-FGFR1 mainly acts on the down-stream STAT5 signaling pathway to promote cell proliferation.Comprehensive protein kinase inhibi-tion analysis is a reliable and direct approach to identify functional drivers of cancer cell prolifera-tion.

Objective To investigate the risk factors associated with necrosis in interstitial oedematous pancreatitis(IOP)and to develop a nomogram model for the early prediction of necrosis in IOP.Methods A retrospective analysis was conducted on 306 patients diagnosed with IOP.Patients were stratified into necrosis and edema groups based on the presence or absence of pancreatic necrosis through follow-up CT-enhanced examinations.Logistic regression analysis was employed to identify independent predictive factors for necrosis in IOP.Subsequently,a nomogram model was developed,and its discriminative ability,accuracy,and practicality were assessed through receiver operating characteristic(ROC)curve,calibration curve,and decision curve analysis(DCA).Results Balthazar computed tomography severity index(CTSI),gender,lactate dehydrogenase(LDH),and triglyceride(TG)were finally identified as four independent predictors for constructing the nomogram model.The area under the curve(AUC)of the nomogram model was 0.800[95％confidence interval(CI)0.731-0.869].The calibration curve indicated good consistency between the predicted probabil-ity and the actual probability of necrosis in IOP(P=0.737).DCA suggested high practicality of the nomogram model within the threshold probability range of 3％to 66％and 75％to 96％.Conclusion The nomogram model based on Balthazar CTSI,gender,LDH,and TG demonstrates good efficacy in early prediction of necrosis in IOP.

Objective To investigate the factors influencing the pregnancy outcomes during frozen-thawed embryo transfer(FET)cycles in patients with polycystic ovary syndrome(PCOS).Methods A retrospective analysis was conducted on patients'data from 882 FET cycles.According to the pregnancy outcome,the patients were divided into non-implantation group(Group A),abortion group(Group B1)and live birth group(Group B2).Clinical data and laboratory parameters were compared among the three groups,and ordered Logistic regression analysis was used to study the factors influencing pregnancy outcomes after FET.Patients were also divided into four groups(C1-C4)based on the number of high-quality embryos obtained(0-3,4-6,7-10,≥11),and their clinical data and laboratory parameters were compared.Results The clinical pregnancy rate,live birth rate,and miscar-riage rate in the 882 treatment cycles were 71.09%(627/882),61.68%(544/882),and 13.24%(83/627),respectively.Single-factor analysis showed significant differences in body mass index(BMI),infertility type,hu-man chorionic gonadotropin(hCG)day estradiol(E2)level,number of retrieved oocytes,and number of high-quality embryos among Groups A,B1,and B2(P<0.05).Further multiple Logistic regression analysis revealed that BMI(OR=1.046,95%CI:1.001-1.093,P=0.044)and a history of previous pregnancy(OR=1.417,95%CI:1.030-1.950,P=0.032)were independent risk factors for successful FET in PCOS patients,while an in-creased number of high-quality embryos was an independent protective factor for successful pregnancy.Based on the results of Group B2,compared to Group A,OR=0.920,95%CI:0.880-0.962,P=0.000;compared to Group B1,OR=0.923,95%CI:0.862-0.988,P=0.022.Compared with the other three groups(C1-C3),the total amount of gonadotropin(Gn)in the C4 group was the lowest and the number of oocytes obtained was the high-est(P<0.05).Multiple comparisons showed that Group C4 had lower BMI,follicle-stimulating hormone(FSH),very low-density lipoprotein(vLDL)levels,a higher luteinizing hormone and follicle-stimulating hormone(LH/FSH)ratio compared to Group C1(P<0.05).Group C4 had lower fasting insulin(FINS)and homeostasis model assessment of insulin resistance(HOMA-IR)levels compared to Group C3,and higher high-density lipoprotein-cholesterol(HDL-C)and apolipoprotein A1(Apo A1)levels compared to Groups C2 and C3(P<0.05).Con-clusion BMI,the history of previous pregnancy and the number of high-quality embryos were both independent factors for predicting pregnancy outcomes in PCOS patients undergoing FET cycles.Patients with a higher number of high-quality embryos have a higher clinical pregnancy rate during FET cycles.