To analyze the causal relationships between mental disorders,insomnia,and irritable bowel syndrome(IBS) using a bidirectional two-sample Mendelian randomization(MR) approach.

The primary analysis was conducted using the random-effects inverse variance weighted(IVW) method on genome-wide association study summary data.Heterogeneity was assessed using Cochran's Q statistic.The horizontal pleiotropy was tested using the intercept from MR-Egger regression.

Forward MR analysis revealed that major depressive disorder,schizophrenia,and insomnia were positively associated with the increased risk of IBS(all P<0.05).Reverse MR analysis indicated that IBS was positively associated with the increased risk of major depressive disorder and insomnia (all P<0.05).Cochran's Q test in the forward MR analysis showed heterogeneity in the causal estimates for the genetic instruments of schizophrenia,insomnia,and bipolar disorder,while no heterogeneity was observed for other outcomes.MR-Egger regression indicated no significant horizontal pleiotropy for any of the results(P>0.05).

Major depressive disorder,schizophrenia, and insomnia have causal associations with IBS.

To predict the risk of postoperative delirium in patients undergoing total hip arthroplasty using machine learning models.

A total of 622 patients who underwent total hip arthroplasty in Ganzhou People's Hospital were selected as research subjects from January 2020 to December 2024.The Confusion Assessment Method(CAM) was used to assess postoperative delirium. The Boruta algorithm was employed to screen for important feature variables associated with postoperative delirium risk.Patients were randomly divided into training set(442 cases) and testing set(180 cases) at 7∶3 ratio.Nine machine learning models were constructed,trained,and validated using ten-fold cross-validation.The area under the curve(AUC) of receiver operator characteristic was used to evaluate model performance and identify the best machine learning model.Decision curve analysis was used to assess the clinical utility of the model.The SHapley additive explanations(SHAP) method,including bar plots,summary plots,dependence plots,and force plots,was used to interpret and visualize the machine learning models.

The incidence of postoperative delirium among the 622 patients undergoing total hip arthroplasty was 30.87%.The Boruta algorithm identified nine important postoperative delirium risk feature variables.Based on the feature importance scores(Z-values),the ranking from highest to lowest was C-reactive protein(CRP),anesthesia duration,albumin (ALB),age,total bilirubin(TB),blood glucose,intraoperative blood loss(IBL),history of diabetes,and cerebrovascular disease(CSD).Multivariate Logistic regression analysis showed that age,ALB,TB,blood glucose,CRP,and anesthesia duration were independent influencing factors for postoperative delirium in patients undergoing total hip arthroplasty(all P<0.05).The XGBoost model demonstrated excellent performance in both the training and test sets,exhibiting the strongest robustness and predictive efficacy for estimating the risk of postoperative delirium in patients undergoing total hip arthroplasty.Interpretation and visualization of the XGBoost model using SHAP revealed that the model could predict postoperative delirium risk in patients undergoing total hip arthroplasty with high accuracy.

Age,ALB,TB,blood glucose,CRP,anesthesia duration are independent influencing factors for postoperative delirium in patients undergoing total hip arthroplasty.The XGBoost model demonstrated high predictive value for postoperative delirium in patients undergoing total hip arthroplasty.

To construct a network model of symptom clusters and quality of life in postoperative breast cancer patients undergoing chemotherapy.To identify the core symptom clusters and the core domains of quality of life,and to determine the predictive relationships between them,thereby providing scientific guidance for improving patients' quality of life.

A total of 260 breast cancer patients receiving chemotherapy in the oncology department of a tertiary grade A hospital in Yinchuan city were selected as the survey subjects using convenience sampling from May to December 2024.A longitudinal survey was conducted to track their symptoms and quality of life before,during,and after chemotherapy.Data were analyzed using exploratory factor analysis,partial correlation networks,and cross-lagged panel networks.

Exploratory factor analysis and partial correlation network analysis revealed four stable symptom clusters in postoperative breast cancer patients undergoing chemotherapy,namely the sickness behavior symptom cluster,the psychosocial symptom cluster,the body image symptom cluster,and the gastrointestinal symptom cluster.The centrality of fatigue symptoms remained the top five across all three periods(r_s1=3.02,r_s2=4.21,r_s3=4.10).Cross-lagged panel network analysis further indicated that the body image symptom cluster was the core symptom cluster in postoperative breast cancer patients undergoing chemotherapy,and physical well-being was the core domain of quality of life.The body image symptom cluster at the previous time point positively predicted physical well-being at the subsequent time point across the three periods(β=1.24 and β=1.49,respectively).

The core symptom cluster in postoperative breast cancer patients undergoing chemotherapy is the body image symptom cluster.Physical well-being is the core domain of quality of life.The body image symptom cluster positively predicts improvements in quality of life.Healthcare professionals should develop interventions based on core symptom clusters during treatment to reduce the transmission effect of the body image symptom cluster within the symptom network,indirectly promote the improvement of physical well-being,and thereby enhance quality of life.

To analyze the categories and influencing factors of anxiety of the existential meaninglessness in elderly patients with chronic diseases.

A total of 493 elderly patients with chronic diseases from six tertiary grade A hospitals in Hunan province were selected as research subjects by convenience sampling from July to September 2024.A questionnaire survey was conducted using the General Information Questionnaire,Anxiety Measure of the Existential Meaninglessness Scale,Chinese Self-Transcendence Scale,and Health empowerment scale for elderly patients with chronic disease.Latent profile analysis and multinomial Logistic regression were used to explore the categories and influencing factors of anxiety of the existential meaninglessness in elderly patients with chronic diseases.

Anxiety of the existential meaninglessness in elderly patients with chronic diseases could be divided into three categories,namely low existential meaninglessness anxiety group(48.48%),moderate existential meaninglessness anxiety mixed group(43.41%),and high existential meaninglessness anxiety group(8.11%).Logistic regression analysis showed that economic resources,monthly family income,attitude towards life,type of medical insurance,and health empowerment were influencing factors for the categories of anxiety of the existential meaninglessness in elderly patients with chronic diseases(all P<0.05).

Anxiety of the existential meaninglessness in elderly patients with chronic diseases exhibits group heterogeneity.Targeted interventions based on different categories could be implemented in clinical nursing to reduce the anxiety of existential meaninglessness in elderly patients with chronic diseases.

To analyze the symptom transition patterns and influencing factors in patients with acute pancreatitis(AP) before and after discharge.

A total of 204 AP patients admitted to the First Affiliated Hospital of Naval Medical University were selected as study subjects by a convenience sampling method from October 2022 to October 2024.Data were collected using a general information questionnaire and Memorial Symptom Assessment Scale-Chinese Version.Latent transition analysis was performed to evaluate symptoms on the 1st to 7th day after admission and the 3rd month after discharge.

The symptom profiles of the 204 patients before and after discharge were both categorized into two groups,namely low symptom burden and high symptom burden.The probability of transitioning from the low symptom burden group before discharge to the high symptom burden group after discharge was 61.8%.The probability of transitioning from the high symptom burden group to the low symptom burden group was 52.9%.Logistic regression analysis showed that urban employee medical insurance(OR=0.076) and mild disease severity(OR=0.044) were associated with a decreased probability of transitioning from low to high symptom burden,while the monthly household income per capita <3 000 yuan(OR=2.648) increased this probability.Additionally,age<60 years(OR=2.074) increased the likelihood of transitioning from high to low symptom burden,while the monthly household income per capita<3 000 yuan(OR=0.286) decreased this probability.

Symptom profiles among AP patients exhibit group heterogeneity before and after discharge.Healthcare professionals should implement individualized management strategies based on symptom transition patterns.

To explore the dynamic change characteristics of the incidence of hospital infections before and after the intervention of antimicrobial stewardship measures,and to quantitatively evaluate the short-term and long-term impacts of antimicrobial stewardship measures.

Data on the infection rate of Klebsiella pneumoniae(KPN) among inpatients from July 2017 to December 2018 were collected as pre-intervention data.Data on the KPN infection rate among inpatients from January 2019 to June 2020 were collected as post-intervention data.The Bayesian interrupted time series method was used to evaluate the changing trend of KPN infection rate before and after the implementation of antimicrobial stewardship measures.

The average monthly KPN infection rate from July 2017 to June 2020 was 0.941%.The average monthly KPN infection rate before the implementation of antimicrobial stewardship measures was 0.947%.The average monthly KPN infection rate after the implementation of antimicrobial stewardship measures was 0.934%.The parameter estimation results of the model with a lag period of 2 months showed that {\widehat{\beta }}_{\mathrm{2}} was statistically significant(P<0.05),with a risk ratio of 0.818.{\widehat{\beta }}_{\mathrm{3}} was statistically significant(P<0.05),{\widehat{\beta }}_{\mathrm{1}}+{\widehat{\beta }}_{\mathrm{3}}=0.021,with a risk ratio of 1.021.{\widehat{\beta }}_{\mathrm{4}} was statistically significant(P<0.05),with a risk ratio of 1.661.The results of the relative changes between the predictive values and counterfactual values showed that the changes from March to May 2019 all decreased (all P<0.05),while the changes from April to June 2020 all increased (all P<0.05).

The Bayesian interrupted time series model could effectively evaluate the short-term and long-term effects of intervention measures.It could also evaluate whether the intervention had a lag effect or not.The implementation of hospital antimicrobial stewardship measures could reduce the infection risk in the short term,with a 2-month lag period in the intervention effect,while the long-term effect was not ideal.

To explore the latent categories and influencing factors of complication risk perception in young and middle-aged patients with type 2 diabetes.

A total of 364 young and middle-aged patients with type 2 diabetes who visited two tertiary grade A hospitals in Henan Province were selected as the study subjects by convenience sampling method from February to August 2024.Surveys were conducted using a general information questionnaire,the Chinese version of Risk Perception Survey-Diabetes Mellitus(RPS-DM),Health Literacy Scale(HLS),Family APGAR Index(APGAR),and Perceived Social Support Scale(PSSS).Latent profile analysis was performed to assess complication risk perception.Logistic regression analysis was used to explore the influencing factors of the latent categories.

The complication risk perception of the young and middle-aged patients with type 2 diabetes could be classified into three latent categories,namely low risk perception-high optimistic bias(40%),moderate risk perception-low personal control(44%),and high risk perception-comprehensive type(16%).Logistic regression analysis showed that place of residence,per-capita monthly family income,family history of diabetes,duration of diabetes,BMI,diabetes health literacy,family caring,and perceived social support were influencing factors of the latent categories(P<0.05).

There is heterogeneity in the characteristics of complication risk perception in young and middle-aged patients with type 2 diabetes.Healthcare providers could implement targeted interventions based on the features of different patient categories to improve their level of complication risk perception.

To explore the dynamic sleep quality trajectories and analyze the influencing factors in postoperative patients with cervical spondylotic myelopathy.

A total of 244 postoperative patients with cervical spondylotic myelopathy in our hospital were selected as the study subjects by convenience sampling from August 2023 to August 2024.The Chinese version of the Pittsburgh Sleep Quality Index(PSQI) was used to assess sleep quality of patients at baseline,1 week,1 month,and 3 months after operation.Latent class growth modeling was employed to analyze the classification of sleep quality trajectories.Univariate analysis and Logistic regression analysis were used to explore the influencing factors of the trajectories.

The sleep quality trajectories of the 244 postoperative patients with cervical spondylotic myelopathy could be categorized into 3 categories,namely high quality-improving type(65 cases,26.6%),low quality-improving type(109 cases,44.7%),and low quality-declining type(70 cases,28.7%).Logistic regression analysis showed that age,monthly per-capita household income,preoperative disease duration,number of involved spinal segments,preoperative Chinese version of Japanese Orthopaedic Association(JOA) score,preoperative Visual Analogue Scale(VAS) score,preoperative Self-Rating Depression Scale(SDS) score,and preoperative Self-Rating Anxiety Scale(SAS) score were all influencing factors for the sleep quality trajectories in postoperative patients wtih cervical spondylotic myelopathy(P<0.05).

There is certain heterogeneity in the sleep quality trajectories in postoperative patients with cervical spondylotic myelopathy,which are influenced by multiple factors.It suggests that healthcare professionals can predict postoperative sleep quality trajectories of patients based on these influencing factors and develop personalized nursing plans to improve patient's sleep quality.