OBJECTIVE To explore the risk factors for severe influenza patients in Fuzhou,and to provide reference for the prevention and control of severe influenza.METHODS Information on 412 patients with severe influenza in Fuzhou City with onset dates from Jan.2023 to Jan.2025 was collected from the China Influenza Surveillance In-formation System and the China Disease Prevention and Control Information System.The severe patients were matched 1∶1 with non-severe patients based on gender and age±3 years of the severe patients.Basic infor-mation,vaccination history,clinical symptoms,types of infecting viruses and medical history of both groups of patients were collected to summarize the risk factors for severe illness progression in influenza patients.RESULTS The proportions of obesity,retired personnel,children or students,unemployed individuals and smokers in the se-vere group were all higher than those in the non-severe group,while the vaccination rate(6.07％)was lower than that of the non-severe group(16.50％)(P＜0.001).The proportions of typical symptoms of shortness of breath/dyspnea and altered mental status/convulsions in the severe group were 23.30％and 21.60％,respectively,which were higher than those in the non-severe group(P＜0.001).The proportions of individuals with a history of chro-nic respiratory diseases and cancer/tumors in the severe group were 26.70％and 19.90％,respectively,which were higher than those in the non-severe group(P＜0.001).The proportion of influenza A(H1 N1)in the severe group(58.50％)was higher than that in the non-severe group(39.32％)(P＜0.001).Retired personnel,children or students and unemployed individuals were high-risk groups for the severe influenza,while farmers were a low-risk group.Obesity(OR=1.966),unvaccination(OR=3.738),smoking(OR=1.787),typical symptoms of shortness of breath/dyspnea(OR=3.305),altered mental status/convulsions(OR=4.099),history of chronic respiratory diseases(OR=4.820)and history of cancer/tumors(OR=3.269)and infection with influenza A(H1N1)(OR=6.422)and influenza A(H3N2)(OR=4.441)were risk factors for the severe influenza(P＜0.05).The recovery time in the severe group was 21(6,33)days,which was longer than that in the non-severe group(P＜0.001).CONCLUSIONS Obesity,unvaccination,smoking,typical symptoms of shortness of breath/dyspnea,altered mental status/convulsions,history of chronic respiratory diseases and history of cancer/tumors and infection with influenza A(H1N1)and influenza A(H3N2)are risk factors for severe influenza patients.It is necessary to strengthen influenza prevention and control among the elderly and children,enhance health edu-cation,and continuously promote influenza vaccination among key populations.

Objective To establish a method for the simultaneous determination of peimine,peiminine,and hupehenine in Juhong pills,to investigate the raw material usage of Fritillaria thunbergii Miq.,and to preliminarily develop a detection approach for identifying adulteration with Fritillaria hupehensis Hsiao et K.C.Hsia.Methods The high-performance liquid chromatography-triple quadruple mass spectrometry(HPLC-MS/MS)was performed on an Agilent Poroshell 120 SB C18 column(2.1 mm×100 mm,2.7 μm)with a mobile phase consisting of acetonitrile-methanol(1∶1)and 0.1%formic acid under gradient elution.The flow rate was set at 0.3 mL·min-1.The column temperature was maintained at 35℃,and the injection volume was 2 μL.Electrospray ionization(ESI)in positive ion mode and multiple reaction monitoring were employed to quantify the three components in 170 batches of Juhong pills.Simulated positive samples with varying adulteration ratios of Fritillaria hupehensis Hsiao et K.C.Hsia were prepared to establish a simple yet efficient detection limit for adulteration.Results Three components showed good linear correlation within their ranges(r≥0.997 5),and the averaged recoveries ranged from 92.0%-106.2%.A total of 61 batches were suspected of Fritillaria thunbergii Miq.adulteration with Fritillaria hupehensis Hsiao et K.C.Hsia in raw material inputs,with the peimine to peiminine ratio below 1.15.Among these samples,27 batches were large honey-bound pills with hupehenine levels of 2.636-9.939 μg·g-1;34 batches were water-honeyed pills showing significantly higher hupehenine contamination at 6.752-48.137 μg·g-1.Conclusion The established method is simple,reliable,and accurate for the quality control of Fritillaria thunbergii Miq.adulteration in Juhong pills without imposing significant additional costs.

OBJECTIVE To explore the risk factors for severe influenza patients in Fuzhou,and to provide reference for the prevention and control of severe influenza.METHODS Information on 412 patients with severe influenza in Fuzhou City with onset dates from Jan.2023 to Jan.2025 was collected from the China Influenza Surveillance In-formation System and the China Disease Prevention and Control Information System.The severe patients were matched 1∶1 with non-severe patients based on gender and age±3 years of the severe patients.Basic infor-mation,vaccination history,clinical symptoms,types of infecting viruses and medical history of both groups of patients were collected to summarize the risk factors for severe illness progression in influenza patients.RESULTS The proportions of obesity,retired personnel,children or students,unemployed individuals and smokers in the se-vere group were all higher than those in the non-severe group,while the vaccination rate(6.07％)was lower than that of the non-severe group(16.50％)(P＜0.001).The proportions of typical symptoms of shortness of breath/dyspnea and altered mental status/convulsions in the severe group were 23.30％and 21.60％,respectively,which were higher than those in the non-severe group(P＜0.001).The proportions of individuals with a history of chro-nic respiratory diseases and cancer/tumors in the severe group were 26.70％and 19.90％,respectively,which were higher than those in the non-severe group(P＜0.001).The proportion of influenza A(H1 N1)in the severe group(58.50％)was higher than that in the non-severe group(39.32％)(P＜0.001).Retired personnel,children or students and unemployed individuals were high-risk groups for the severe influenza,while farmers were a low-risk group.Obesity(OR=1.966),unvaccination(OR=3.738),smoking(OR=1.787),typical symptoms of shortness of breath/dyspnea(OR=3.305),altered mental status/convulsions(OR=4.099),history of chronic respiratory diseases(OR=4.820)and history of cancer/tumors(OR=3.269)and infection with influenza A(H1N1)(OR=6.422)and influenza A(H3N2)(OR=4.441)were risk factors for the severe influenza(P＜0.05).The recovery time in the severe group was 21(6,33)days,which was longer than that in the non-severe group(P＜0.001).CONCLUSIONS Obesity,unvaccination,smoking,typical symptoms of shortness of breath/dyspnea,altered mental status/convulsions,history of chronic respiratory diseases and history of cancer/tumors and infection with influenza A(H1N1)and influenza A(H3N2)are risk factors for severe influenza patients.It is necessary to strengthen influenza prevention and control among the elderly and children,enhance health edu-cation,and continuously promote influenza vaccination among key populations.

Objective To establish a method for the simultaneous determination of peimine,peiminine,and hupehenine in Juhong pills,to investigate the raw material usage of Fritillaria thunbergii Miq.,and to preliminarily develop a detection approach for identifying adulteration with Fritillaria hupehensis Hsiao et K.C.Hsia.Methods The high-performance liquid chromatography-triple quadruple mass spectrometry(HPLC-MS/MS)was performed on an Agilent Poroshell 120 SB C18 column(2.1 mm×100 mm,2.7 μm)with a mobile phase consisting of acetonitrile-methanol(1∶1)and 0.1%formic acid under gradient elution.The flow rate was set at 0.3 mL·min-1.The column temperature was maintained at 35℃,and the injection volume was 2 μL.Electrospray ionization(ESI)in positive ion mode and multiple reaction monitoring were employed to quantify the three components in 170 batches of Juhong pills.Simulated positive samples with varying adulteration ratios of Fritillaria hupehensis Hsiao et K.C.Hsia were prepared to establish a simple yet efficient detection limit for adulteration.Results Three components showed good linear correlation within their ranges(r≥0.997 5),and the averaged recoveries ranged from 92.0%-106.2%.A total of 61 batches were suspected of Fritillaria thunbergii Miq.adulteration with Fritillaria hupehensis Hsiao et K.C.Hsia in raw material inputs,with the peimine to peiminine ratio below 1.15.Among these samples,27 batches were large honey-bound pills with hupehenine levels of 2.636-9.939 μg·g-1;34 batches were water-honeyed pills showing significantly higher hupehenine contamination at 6.752-48.137 μg·g-1.Conclusion The established method is simple,reliable,and accurate for the quality control of Fritillaria thunbergii Miq.adulteration in Juhong pills without imposing significant additional costs.

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.

Objective:To establish a predictive model of the first cesarean delivery in multiparous women based on the situation of two consecutive pregnancies.Methods:The data of patients with two consecutive deliv-eries of single live birth and the previous delivery was vaginal delivery in the First Affiliated Hospital of Soochow U-niversity during the second delivery time range from January 1,2018 to December 31,2021 were retrospectively analyzed.According to whether the second pregnancy occurred cesarean section,the patients were divided into two groups(vaginal delivery group and cesarean section group).Univariate,stepwise,and multiple Logistic re-gression analyses were used to screen the influencing factors of multipara's first cesarean section delivery,and the prediction model was established.R language was used to build the model's nomogram and calibration curve.The bootstrap resampling method was used for internal verification.After establishing the model,clinical data of patients with two consecutive births of single live birth between January 1,2022 and April 1,2023 were retrospec-tively collected for external verification of the model.Results:①A total of 2709 patients were included in this study for modeling,of which 6.31％(171/2709)underwent cesarean section for the first time.603 cases were included for external verification.②According to univariate,stepwise and multivariate Logistic regression analysis,all the variables affecting the first delivery by cesarean section were screened out,including:abnormal labor in previous labor,age of current delivery,assisted reproductive technology,hypertension disorder complicating pregnancy,pregnancy with thrombocytopenia,oligohydramnios,excessive amniotic fluid,macrosomia,fetal growth restriction,abnormal fetal position,fetal distress,all of the above variables P＜0.05 and incorporated into the final prediction model.③The AUC of this model was 0.949(95％CI 0.928-0.969),and the calibration curve showed that the model intercept was 0 and the slope was 1.Hosmer-Lemeshow test had a P＞0.05,indicating that the model had a high accuracy.④The AUC of external validation was 0.958,the slope of the calibration curve was 0.972,and the Hosmer-Lemeshow test had a P of 0.49.Conclusions:The prediction model of the first delivery by cesarean section during the second pregnancy has been established.The prediction efficiency of the model is good,and it can provide a tool for the individualized evaluation of menstrual women in clinical work.