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.

Objective To investigate the clinical efficacy and safety of facilitated percutaneous coronary intervention(PCI)with half-dose recombinant staphylokinase(r-SAK)in patients with ST-segment elevation myocardial infarction(STEMI)who are expected to undergo PCI within 120 minutes.Methods From October 2021 to August 2022,a total of 200 STEMI patients in eight centers were included and randomly assigned in a 1﹕1 ratio to either r-SAK group or control group.Patients received loading doses of aspirin and ticagrelor and intravenous heparin and were randomized to receive an intravenous bolus of either 5 mg r-SAK or normal saline prior to PCI.The outcomes were set as ST-segment resolution(STR)at 60-90 minutes after PCI,the proportion and transition of pathological Q waves on the 5th day after PCI,and the proportion of high-sensitivity cardiac troponin T(hs-cTnT)peaking within 12 hours of onset.The safety outcome was major bleeding events defined as Bleeding Academic Research Consortium(BARC)≥type 3 bleeding during hospitalization.Results Compared with the control group,the r-SAK group had a higher proportion of STR≥70%within 60-90 minutes after PCI(58.3%vs.40.3%,P=0.009);a lower proportion of pathological Q waves(59.1%vs.74.1%,P=0.040);a lower rate of Q wave progression(14.8%vs.43.2%,P＜0.001);a higher rate of Q wave disappearance(12.5%vs.3.7%,P=0.027);and a higher proportion of hs-cTnT peaking within 12 hours of symptom onset[31/40(77.5%)vs.17/33(51.5%),P=0.027].Regarding the safety outcome,no significant difference in BARC≥type 3 bleeding was found between the two groups during hospitalization(P＞0.05).Conclusions For STEMI patients who were expected to undergo primary PCI within 120 minutes of symptom onset,the facilitated PCI with half-dose r-SAK significantly increased the proportion of STR≥70%at 60-90 minutes after PCI,reduced the formation of pathological Q waves,and shortened the time to peak hs-cTnT,without increasing the risk of bleeding,which should be an alternative reperfusion strategy worthy of further study.

Objective To investigate the clinical efficacy and safety of facilitated percutaneous coronary intervention(PCI)with half-dose recombinant staphylokinase(r-SAK)in patients with ST-segment elevation myocardial infarction(STEMI)who are expected to undergo PCI within 120 minutes.Methods From October 2021 to August 2022,a total of 200 STEMI patients in eight centers were included and randomly assigned in a 1﹕1 ratio to either r-SAK group or control group.Patients received loading doses of aspirin and ticagrelor and intravenous heparin and were randomized to receive an intravenous bolus of either 5 mg r-SAK or normal saline prior to PCI.The outcomes were set as ST-segment resolution(STR)at 60-90 minutes after PCI,the proportion and transition of pathological Q waves on the 5th day after PCI,and the proportion of high-sensitivity cardiac troponin T(hs-cTnT)peaking within 12 hours of onset.The safety outcome was major bleeding events defined as Bleeding Academic Research Consortium(BARC)≥type 3 bleeding during hospitalization.Results Compared with the control group,the r-SAK group had a higher proportion of STR≥70%within 60-90 minutes after PCI(58.3%vs.40.3%,P=0.009);a lower proportion of pathological Q waves(59.1%vs.74.1%,P=0.040);a lower rate of Q wave progression(14.8%vs.43.2%,P＜0.001);a higher rate of Q wave disappearance(12.5%vs.3.7%,P=0.027);and a higher proportion of hs-cTnT peaking within 12 hours of symptom onset[31/40(77.5%)vs.17/33(51.5%),P=0.027].Regarding the safety outcome,no significant difference in BARC≥type 3 bleeding was found between the two groups during hospitalization(P＞0.05).Conclusions For STEMI patients who were expected to undergo primary PCI within 120 minutes of symptom onset,the facilitated PCI with half-dose r-SAK significantly increased the proportion of STR≥70%at 60-90 minutes after PCI,reduced the formation of pathological Q waves,and shortened the time to peak hs-cTnT,without increasing the risk of bleeding,which should be an alternative reperfusion strategy worthy of further study.

Objective:To investigate the predictive value of color Doppler ultrasound combined with electrocardio-gram for right heart dys function in patients with pulmonary heart disease(PHD).Methods:A total of 100 PHD patients admitted in Dongcheng Branch of First Affiliated Hospital of Anhui Medical University between January 2020 and December 2023 were retrospectively analyzed.According to results of 6min walking test(6MWT),pa-tients were divided into good right heart function group(n=64,≥350m)and right heart dysfunction group(n=36,＜350m).The indexes of cardiac color ultrasound[isovolumic relaxation time(IVRT),isovolumetric contraction time(IVCT)and right ventricular Tei index],ECG[24h mean R-R interval standard deviation(SDNN),normal R-R interval standard deviation per 5min(SDANN)and the ratio of low frequency components to high frequency components(LF/HF)]were compared between two groups.Receiver operating characteristic(ROC)curve was drawn to analyze the diagnostic value of color Doppler ultrasound,ECG and their combination for right heart dys-function in PHD patients.Spearman correlation coefficient was used to analyze the association of color Doppler ul-trasound,ECG and their combination with right heart dysfunction in PHD patients.Results:Compared with those in good right heart function group,patients in right heart dysfunction group had significant higher IVRT[(120.64±14.08)ms vs.(97.87±10.93)ms],IVCT[(84.28±12.33)ms vs.(71.92±10.61)ms]and Tei index[(0.85±0.11)vs.(0.63±0.07)](P＜0.001 all),and significant lower SDNN[(75.52±12.58)ms vs.(85.58±11.75)ms],SDANN[(63.86±10.92)ms vs.(76.75±11.71)ms]and LF/HF[(1.33±0.19)vs.(1.84±0.27)](P＜0.001 all).ROC curve indicated that the AUC of color Doppler ultrasound combined ECG in diagnosing right heart dysfunction in PHD patients was 0.911(95％CI 0.838～0.959),which was significantly higher than those of color Doppler ultrasound[0.775(95％CI 0.681～0.853),Z=2.404,P=0.016]and ECG[0.688(95％CI 0.588～0.777),Z=3.968,P=0.001]alone.Spearman correlation analysis indicated that there was a significant positive correlation of color Doppler ultrasound(r=0.547),ECG(r=0.375)and their combination(r=0.810)with right heart dysfunction in PHD patients(P＜0.001 all),and the correlation between combined detection and right heart dysfunction in PHD patients was significantly higher.Conclusion:Color Doppler ultrasound combined with ECG possesses high diagnostic performance for right heart dysfunction in PHD patients.

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:To investigate the predictive value of color Doppler ultrasound combined with electrocardio-gram for right heart dys function in patients with pulmonary heart disease(PHD).Methods:A total of 100 PHD patients admitted in Dongcheng Branch of First Affiliated Hospital of Anhui Medical University between January 2020 and December 2023 were retrospectively analyzed.According to results of 6min walking test(6MWT),pa-tients were divided into good right heart function group(n=64,≥350m)and right heart dysfunction group(n=36,＜350m).The indexes of cardiac color ultrasound[isovolumic relaxation time(IVRT),isovolumetric contraction time(IVCT)and right ventricular Tei index],ECG[24h mean R-R interval standard deviation(SDNN),normal R-R interval standard deviation per 5min(SDANN)and the ratio of low frequency components to high frequency components(LF/HF)]were compared between two groups.Receiver operating characteristic(ROC)curve was drawn to analyze the diagnostic value of color Doppler ultrasound,ECG and their combination for right heart dys-function in PHD patients.Spearman correlation coefficient was used to analyze the association of color Doppler ul-trasound,ECG and their combination with right heart dysfunction in PHD patients.Results:Compared with those in good right heart function group,patients in right heart dysfunction group had significant higher IVRT[(120.64±14.08)ms vs.(97.87±10.93)ms],IVCT[(84.28±12.33)ms vs.(71.92±10.61)ms]and Tei index[(0.85±0.11)vs.(0.63±0.07)](P＜0.001 all),and significant lower SDNN[(75.52±12.58)ms vs.(85.58±11.75)ms],SDANN[(63.86±10.92)ms vs.(76.75±11.71)ms]and LF/HF[(1.33±0.19)vs.(1.84±0.27)](P＜0.001 all).ROC curve indicated that the AUC of color Doppler ultrasound combined ECG in diagnosing right heart dysfunction in PHD patients was 0.911(95％CI 0.838～0.959),which was significantly higher than those of color Doppler ultrasound[0.775(95％CI 0.681～0.853),Z=2.404,P=0.016]and ECG[0.688(95％CI 0.588～0.777),Z=3.968,P=0.001]alone.Spearman correlation analysis indicated that there was a significant positive correlation of color Doppler ultrasound(r=0.547),ECG(r=0.375)and their combination(r=0.810)with right heart dysfunction in PHD patients(P＜0.001 all),and the correlation between combined detection and right heart dysfunction in PHD patients was significantly higher.Conclusion:Color Doppler ultrasound combined with ECG possesses high diagnostic performance for right heart dysfunction in PHD patients.