Objective To analyze the trends in the disease burden of schistosomiasis in China from 1992 to 2021, and to project the disease burden of schistosomiasis in China from 2022 to 2030, so as to provide insights into the elimination of schistosomiasis in China. Methods The prevalence, age-standardized prevalence, disability-adjusted life year (DALYs) rate and age-standardized DALYs rate of schistosomiasis, as well as the years lost due to disability (YLDs) rate and age-standardized YLDs rate of anemia attributable to Schistosoma infections in China, the world and different socio-demographic index (SDI) regions were captured from the Global Burden of Disease Study 2021 (GBD 2021) data resources, and the trends in the disease burden due to schistosomiasis were evaluated with estimated annual percentage change (EAPC) and its 95% confidence interval (CI). In addition, the age, period and cohort effects on the prevalence of schistosomiasis were examined in China using an age-period-cohort (APC) model, and the disease burden of schistosomiasis was predicted in China from 2022 to 2030 using a Bayesian age-period-cohort (BAPC) model. Results The age-standardized prevalence and DALYs rate of schistosomiasis, and the age-standardized YLDs rate of anemia attributable to Schistosoma infections were 761.32/10⁵, 5.55/10⁵ and 0.38/10⁵ in China in 2021. These rates were all lower than the global levels (1 914.30/10⁵, 21.90/10⁵ and 3.36/10⁵, respectively), as well as those in the medium SDI regions (1 413.61/10⁵, 12.10/10⁵ and 1.93/10⁵, respectively), low-medium SDI regions (2 461.03/10⁵, 26.81/10⁵ and 4.48/10⁵, respectively), and low SDI regions (5 832.77/10⁵, 94.48/10⁵ and 10.65/10⁵, respectively), but higher than those in the high SDI regions (59.47/10⁵, 0.49/10⁵ and 0.05/10⁵, respectively) and high-medium SDI regions (123.11/10⁵, 1.20/10⁵ and 0.12/10⁵, respectively). The prevalence and DALYs rate of schistosomiasis were higher among men (820.79/10⁵ and 5.86/10⁵, respectively) than among women (697.96/10⁵ and 5.23/10⁵, respectively) in China in 2021, while the YLDs rate of anemia attributable to Schistosoma infections was higher among women (0.66/10⁵) than among men (0.12/10⁵). The prevalence of schistosomiasis peaked at ages of 30 to 34 years among both men and women, while the DALYs rate of schistosomiasis peaked among men at ages of 15 to 19 years and among women at ages of 20 to 24 years. The age-standardized prevalence of schistosomiasis showed a moderate decline in China from 1992 to 2021 relative to different SDI regions [EAPC = -1.51%, 95% CI: (-1.65%, -1.38%)], while the age-standardized DALYs rate [EAPC = -3.61%, 95% CI: (-3.90%, -3.33%)] and age-standardized YLDs rate of anemia attributable to Schistosoma infections [EAPC = -4.16%, 95% CI: (-4.38%, -3.94%)] appeared the fastest decline in China from1992 to 2021 relative to different SDI regions. APC modeling showed age, period, and cohort effects on the trends in the prevalence of schistosomiasis in China from 1992 to 2021, and the prevalence of schistosomiasis appeared a rise followed by decline with age, and reduced with period and cohort. BAPC modeling revealed that the age-standardized prevalence and age-standardized DALYs rate of schistosomiasis, and age-standardized YLDs rate of anemia attributable to Schistosoma infections all appeared a tendency towards a decline in China from 2022 to 2030, which reduced to 722.72/10⁵ [95% CI: (538.74/10⁵, 906.68/10⁵)], 5.19/10⁵ [95% CI: (3.54/10⁵, 6.84/10⁵)] and 0.30/10⁵ [95% CI: (0.21/10⁵, 0.39/10⁵)] in 2030, respectively. Conclusions The disease burden of schistosomiasis appeared a tendency towards a decline in China from 1992 to 2021, and is projected to appear a tendency towards a decline from 2022 to 2030. There are age, period and cohort effects on the prevalence of schistosomiasis in China. Precision schistosomiasis control is required with adaptations to current prevalence and elimination needs.

Objective: To investigate the effects of "liver-smoothing and spirit-regulating" acupuncture on the intestinal flora, lipopolysaccharide (LPS) and the hippocampal toll-like receptor 4 (TLR4)/ transcription factor (NF)-κB signaling pathway in depressive disorder mouse model, and to explore its underlying mechanisms. Methods: Eighteen male SPF-grade C57BL/6J mice were randomly assigned to the control, model, and acupuncture groups using a random number method, with six mice in each group. The depression disorder model was induced in mice from both the acupuncture and model groups using CUMS. The mice in the acupuncture group were treated with acupuncture at the acupoints of "Baihui" (DU20), "Yintang" (DU29), "Hegu" (LI4), and "Taichong" (LR3) on the 15th day of modeling, with a duration of 20 min per session, once per day, for 2 consecutive weeks. Behavioral differences were assessed using the sucrose preference test, open field test, and forced swim test. Hematoxylin-eosin staining was used to observe pathological changes in the hippocampus and colon. The levels of the inflammatory factors interleukin (IL)-1β, IL-6, tumor necrosis factor (TNF)-α, and LPS in the hippocampus and colon were measured using Enzyme-linked Immunosorbent Assay. Western blotting was used to detect the expression of TLR4 and NF-κB protein in the hippocampus. Changes in gut microbiota structure and abundance were analyzed by 16 S rDNA sequencing. Results: Compared to the control group, the model group showed reduced sucrose preference rate, time in the center area, and total distance, with an increase in immobility time (P<0.01). Inflammatory pathological changes were observed in the hippocampal CA1 region and colon. The contents of IL-1β, IL-6, TNF-α, and LPS in the hippocampus and colon increased (P<0.01). The protein expression levels of hippocampal TLR4 and NF-κB were increased (P<0.01). The Chao1 index was increased (P<0.01). The relative abundances of Pseudomonadales, Acinetobacter, Moraxellaceae, Solibacillus, Escherichia_shigella, Enterobacteriaceae, Enterobacterales, Dubosiella, and Erysipelottichales were decreased, while the relative abundances of Alloprevotella and gram_negative_bacteriurh_cTPY_13 were increased (P<0.05). The pathways of lipopolysaccharide biosynthesis and pathogenic Escherichia coli infection were upregulated, and the pathway of terpenoid backbone biosynthesis was downregulated (P<0.01). Compared to the model group, the acupuncture group showed increased sucrose preference, time in the center area, and total distance, with a decrease in immobility time (P<0.01). The inflammatory pathological changes in the hippocampal CA1 region and colon were alleviated. The contents of IL-1β, IL-6, TNF-α, and LPS in the hippocampus and colon were reduced(P<0.01). The protein expression levels of hippocampal TLR4 and NF-κB were reduced (P<0.01). The Chao1 index was decreased (P<0.05), and the relative abundances of Dubosiella and Erysipelotrichaceae were increased, while the relative abundance of Rikenellaceae, Alloprevotella, and gram_negative_bacteriuch_cTPY_13 were decreased(P<0.05). The pathways of lipopolysaccharide biosynthesis and pathogenic Escherichia coli infection were significantly downregulated, and the pathway of terpenoid backbone biosynthesis was upregulated (P<0.01). Conclusion " Liver-smoothing and spirit-regulating" acupuncture can improve depressive symptoms in depressive disorder mice, potentially through regulating the LPS and TLR4/NF-κB signaling pathway mediated by intestinal flora, reducing the inflammatory response of the hippocampus, and improving the pathological injury of the hippocampus.

BACKGROUND:Olfactory dysfunction is an early biological marker of various diseases.However,the neuroimaging mechanism by which olfactory dysfunction occurs following cerebral small vessel disease is unclear. OBJECTIVE:To explore the different neuroimaging mechanisms of olfactory function regulation in patients with cerebral small vessel disease and Parkinson's disease,and explore the potential application value of olfactory function assessment in patients with cerebral small vessel disease. METHODS:Neuropsychological and olfactory tests,high-resolution structural magnetic resonance and resting-state functional magnetic resonance data were collected in 80 patients with cerebral small vessel disease,44 healthy controls and 29 patients with Parkinson's disease.DPABI,SPM12 and SPSS were used to analyze and compare the amplitude of low frequency fluctuation,regional homogeneity and functional connectivity values between the cerebral small vessel disease,control and Parkinson's disease groups.Correlations between the significantly altered resting-state functional magnetic resonance imaging measures and olfactory and cognitive scores were evaluated. RESULTS AND CONCLUSION:Compared with the control group,low-frequency fluctuation amplitude of the right dorsolateral superior frontal gyrus and the regional homogeneity of the left wedge leaf were significantly reduced in the cerebral small vessel disease and Parkinson's disease groups.The right dorsolateral superior frontal gyrus and the left cuneiform lobe are the seed points.Compared with the Parkinson's disease group,the functional connectivity values of the right anterior cunei,inferior temporal gyrus,anterior central gyrus and dorsolateral superior frontal gyrus,left posterior central gyrus and inferior temporal gyrus were significantly enhanced in the control and cerebral small vessel disease groups.The left cuneiform lobe was the seed point.Compared with the control group,the functional connectivity of the left lingual gyrus was significantly weakened in the cerebral small vessel disease and Parkinson's disease groups.The functional connectivity values of the left middle temporal gyrus and the right posterior central gyrus were enhanced in the control group compared with the cerebral small vessel disease and Parkinson's disease group,and that was enhanced in the cerebral small vessel disease group compared with the Parkinson's disease group.Correlation analysis showed that the olfactory score and cognitive score were positively correlated in the cerebral small vessel disease group,and the regional homogeneity of the left wedge lobe was negatively correlated with the Montreal Cognitive Assessment Scale score,while the functional connectivity of left wedge lobe-left middle temporal gyrus in the Parkinson's disease group was positively correlated with the olfactory recognition score,and the functional connectivity values of the left wedge lobe-left posterior central gyrus and left wedge lobe-left lingual gyrus were positively correlated with the olfactory identification score and the total olfactory score,respectively.The regulation of olfactory function in patients with cerebral small vessel disease has a different neuroimaging mechanism from that of olfactory dysfunction in patients with Parkinson's disease.The olfactory function of patients with cerebral small vessel disease is related to cognitive function.It is speculated that the olfactory function following cerebral small vessel disease is a secondary change of brain dysfunction,while olfactory dysfunction following Parkinson's disease is directly caused by abnormal function of olfactory-related brain areas.Olfactory function assessment in patients with cerebral small vessel disease has potential application in predicting cognitive function.

The guidelines for curriculum ideological and political of nursing ethics explored the ideological and political elements of Chinese nursing,and proposed the curriculum's ideological and political goals.The development framework and basic ideas of guidelines were formed from the aspects of the integration path of curriculum ideological and political,and professional teaching,searching for the entry point of curriculum ideological and political,reforming the teaching methodology,enriching the form of teaching,and constructing the case base of curriculum ideological and political.It promoted the deep integration of nursing professional knowledge transmission and ideological value guidance,created a distinctive education system of curriculum ideological and political for nursing ethics,and provided a reference for the curriculum ideological and political construction of national nursing ethics.

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.

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.