This study explored the effects and underlying mechanism of Raf kinase inhibitory protein(RKIP)on apoptosis in mast cells sensitized by Echinococcus granulosus cyst fluid.Bone marrow-derived mast cells(BMMCs)were isolated and cultured from RKIP knockout(KO)and wild-type(WT)C57BL/6 mice.Cells were divided into control and sensitized groups.The sensitized group was incubated for 24 h in RPMI1640 medium containing 10%serum from mice infected with E.granulosus,then activated for 3 h or 6 h with E.granulosus cyst fluid.The control group was incubated for 24 h in RPMI1640 medium,and then received an equal vol-ume of PBS.Cells and supernatants were collected for analysis.Flow cytometry was used to detect the expression of CD117 and FcεRⅠα on BMMCs.The levels of β-hexosaminidase,IL-4,and TNF-α in the supernatant were quantified with ELISA.Western blot analy-sis was used to assess expression changes in RKIP,apoptosis-related proteins,and pathway proteins in BMMC before and after sensi-tization.Flow cytometry analysis revealed that after 4 weeks of induction,the CD117 and FcεRⅠα double-positivity rates on both WT and KO BMMC exceeded 90%.ELISA indicated that the E.granulosus cyst fluid resulted in significantly greater β-hexosaminidase re-lease(F=16.88,P<0.05),and levels of IL-4(F=16.51,P<0.05)and TNF-α(F=9.78,P<0.05)in the KO sensitized group than the WT sensitized group.With respect to the WT control group,the WT sensitized group showed significantly down-regulated pro-tein expression levels of RKIP(F=8.20,P<0.05)and Bcl-2(F=101.40,P<0.01)after 3 h,but significantly up-regulated levels of p-PI3K(F=8.04,P<0.05),p-Akt(F=32.52,P<0.01),p-P65(F=13.29,P<0.05),and cleaved-caspase-3(F=46.34,P<0.01).With respect to the WT sensitized group,the KO sensitized group showed significantly up-regulated protein expression of p-PI3K(F=8.45,P<0.05),p-Akt(F=8.58,P<0.05),p-P65(F=11.02,P<0.05),and Bcl-2(F=84.50,P<0.001)after 3 h,but significantly down-regulated expression of cleaved-caspase-3(F=15.66,P<0.05).In conclusion,RKIP may inhibit the PI3K/Akt/NF-κB pathway,thereby inducing apoptosis in mast cells sensitized by E.granulosus cyst fluid.This process may help ease aller-gic reactions caused by mast cells in echinococcosis,thus offering a promising new approach for preventing and treating such reactions.

This study analyzed the epidemiological characteristics of imported malaria in Yunnan Province from 2020 to 2023,to provide scientific evidence for formulating measures to decrease imported malaria and prevent re-establishment of malaria transmission.Malaria data reported by the China Disease Prevention and Control Information System were analyzed to determine parasite species;sources of infection;temporal,spatial,and population distributions;and importation routes.A total of 828 malaria cases were reported in the province.Plasmodium vivax and Plasmodium falciparum accounted for 89.98％and 8.33％of cases,respectively.A total of 47.58％of cases were imported from Myanmar,and all P.falciparum malaria ca-ses were from Africa.Thirteen(81.25％)prefectures or municipalities reported malaria,among which Dehong,Baoshan,Kunming,and Lincang reported 94.32％of cases.A total of 52.54％of cases were in young men.The proportion of cross-bor-der personnel flow,land input,and aircraft input were 88.89％and 11.11％respectively.A total of 98.19％of patients sought medical care within 7 days after fever onset,and 82.85％initiated diagnosis for malaria,and 84.90％of diagnoses were con-firmed by health facilities at or below the county level.Imported malaria is a major challenge in preventing re-establishment of transmission in Yunnan.Most imported cases involved cross-border malaria transmission of mainly Plasmodium vivax between China and Myanmar.To achieve malaria elimination,vigilance of health staff in malaria diagnosis and treatment should be pro-moted,and intensive malaria health education should be provided to people traveling to malaria endemic territories,to enable individual protection,and timely diagnosis and treatment after return from endemic countries.

This study analyzed the epidemiological characteristics of imported malaria in Yunnan Province from 2020 to 2023,to provide scientific evidence for formulating measures to decrease imported malaria and prevent re-establishment of malaria transmission.Malaria data reported by the China Disease Prevention and Control Information System were analyzed to determine parasite species;sources of infection;temporal,spatial,and population distributions;and importation routes.A total of 828 malaria cases were reported in the province.Plasmodium vivax and Plasmodium falciparum accounted for 89.98％and 8.33％of cases,respectively.A total of 47.58％of cases were imported from Myanmar,and all P.falciparum malaria ca-ses were from Africa.Thirteen(81.25％)prefectures or municipalities reported malaria,among which Dehong,Baoshan,Kunming,and Lincang reported 94.32％of cases.A total of 52.54％of cases were in young men.The proportion of cross-bor-der personnel flow,land input,and aircraft input were 88.89％and 11.11％respectively.A total of 98.19％of patients sought medical care within 7 days after fever onset,and 82.85％initiated diagnosis for malaria,and 84.90％of diagnoses were con-firmed by health facilities at or below the county level.Imported malaria is a major challenge in preventing re-establishment of transmission in Yunnan.Most imported cases involved cross-border malaria transmission of mainly Plasmodium vivax between China and Myanmar.To achieve malaria elimination,vigilance of health staff in malaria diagnosis and treatment should be pro-moted,and intensive malaria health education should be provided to people traveling to malaria endemic territories,to enable individual protection,and timely diagnosis and treatment after return from endemic countries.

This study explored the effects and underlying mechanism of Raf kinase inhibitory protein(RKIP)on apoptosis in mast cells sensitized by Echinococcus granulosus cyst fluid.Bone marrow-derived mast cells(BMMCs)were isolated and cultured from RKIP knockout(KO)and wild-type(WT)C57BL/6 mice.Cells were divided into control and sensitized groups.The sensitized group was incubated for 24 h in RPMI1640 medium containing 10%serum from mice infected with E.granulosus,then activated for 3 h or 6 h with E.granulosus cyst fluid.The control group was incubated for 24 h in RPMI1640 medium,and then received an equal vol-ume of PBS.Cells and supernatants were collected for analysis.Flow cytometry was used to detect the expression of CD117 and FcεRⅠα on BMMCs.The levels of β-hexosaminidase,IL-4,and TNF-α in the supernatant were quantified with ELISA.Western blot analy-sis was used to assess expression changes in RKIP,apoptosis-related proteins,and pathway proteins in BMMC before and after sensi-tization.Flow cytometry analysis revealed that after 4 weeks of induction,the CD117 and FcεRⅠα double-positivity rates on both WT and KO BMMC exceeded 90%.ELISA indicated that the E.granulosus cyst fluid resulted in significantly greater β-hexosaminidase re-lease(F=16.88,P<0.05),and levels of IL-4(F=16.51,P<0.05)and TNF-α(F=9.78,P<0.05)in the KO sensitized group than the WT sensitized group.With respect to the WT control group,the WT sensitized group showed significantly down-regulated pro-tein expression levels of RKIP(F=8.20,P<0.05)and Bcl-2(F=101.40,P<0.01)after 3 h,but significantly up-regulated levels of p-PI3K(F=8.04,P<0.05),p-Akt(F=32.52,P<0.01),p-P65(F=13.29,P<0.05),and cleaved-caspase-3(F=46.34,P<0.01).With respect to the WT sensitized group,the KO sensitized group showed significantly up-regulated protein expression of p-PI3K(F=8.45,P<0.05),p-Akt(F=8.58,P<0.05),p-P65(F=11.02,P<0.05),and Bcl-2(F=84.50,P<0.001)after 3 h,but significantly down-regulated expression of cleaved-caspase-3(F=15.66,P<0.05).In conclusion,RKIP may inhibit the PI3K/Akt/NF-κB pathway,thereby inducing apoptosis in mast cells sensitized by E.granulosus cyst fluid.This process may help ease aller-gic reactions caused by mast cells in echinococcosis,thus offering a promising new approach for preventing and treating such reactions.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

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*