Development and validation of a nomogram for assessing hepatocellular carcinoma risk after SVR in hepatitis C patients with advanced fibrosis and cirrhosis.

BACKGROUND: Hepatitis C patients with advanced fibrosis or cirrhosis are at high risk of developing hepatocellular carcinoma (HCC), even after sustained virological response (SVR). Clinical recommendations impose a significant burden on patients by recommending lifelong screening for HCC every six months. The goals of this study were to develop a nomogram that accurately stratifies risk of HCC and improve the screening approach that is currently in use. METHOD: Risk factors for HCC were identified using univariate and multivariate analyses in this prospective study. We developed and validated a nomogram for assessing hepatocellular carcinoma risk after SVR in patients with advanced fibrosis and cirrhosis. RESULTS: During the median follow-up period of 61.00 (57.00-66.00) months in the derivation cohort, 37 patients (9.61%) developed HCC. Older age (HR = 1.08, 95% CI 1.02-1.14, p = 0.009), male gender (HR = 2.38, 95% CI 1.10-5.13, p = 0.027), low serum albumin levels (HR = 0.92, 95% CI 0.86-1.00, p = 0.037), and high liver stiffness measurement (LSM) (HR = 1.03, 95% CI 1.01-1.06, p = 0.001) were found to be independent predictors of HCC development. Harrell's C-index for the derivation cohort was 0.81. The nomogram's 3-, 5- and 7-years time-dependent AUROCSs were 0.84 (95% CI 0.80-0.88), 0.83 (95% CI 0.79-0.87), and 0.81 (95% CI 0.77-0.85), respectively (all p > 0.05). According to the nomogram, patients are categorized as having low, intermediate, or high risk. The annual incidence rates of HCC in the three groups were 0.18%, 1.29%, and 4.45%, respectively (all p < 0.05). CONCLUSIONS: Older age, male gender, low serum albumin levels, and high LSM were risk factors for HCC after SVR in hepatitis C patients with advanced fibrosis and cirrhosis. We used these risk factors to establish a nomogram. The nomogram can identify a suitable screening plan by classifying hepatitis C patients according to their risk of HCC.

Efficacy and Safety of Type III Collagen Lyophilized Fibers Using Mid-to-Deep Dermal Facial Injections for the Correction of Dynamic Facial Wrinkles.

BACKGROUND: This study aimed to evaluate the therapeutic efficacy and safety of injecting Type III collagen lyophilized fibers into the mid-to-deep layers of the facial dermis to ameliorate dynamic facial wrinkles. METHODS: In this retrospective analysis, clinical data were collected from patients exhibiting dynamic facial wrinkles (encompassing frown lines, forehead lines, and crow's feet) with a wrinkle severity rating scale (WSRS) score of 3 or higher. In the control group, 75 participants received collagen implant injections into the mid-to-deep facial dermal layers, whereas 76 participants in the experimental group received injections of Type III collagen lyophilized fibers in similar layers. The study analyzed and compared clinical efficacy, WSRS score alterations, patient satisfaction, and safety profiles between the groups over the 30-day and 90-day treatment periods. RESULTS: At the 30-day mark, the therapeutic efficacy was not significantly different between the two groups (P > 0.05). However, at 90 days, the treatment efficacy in the experimental group surpassed that in the control group, showing a statistically significant difference (P < 0.05). After 30 days of treatment, the WSRS score improvement in the experimental group was significantly superior to that in the control group (P < 0.05). Conversely, at the 90-day mark, the results revealed no significant variation in WSRS score improvements between the two groups (P > 0.05). Regarding treatment satisfaction among researchers and participants post-30 and 90-day treatment in both groups, no statistically significant differences were observed (P > 0.05). Similarly, the incidence of adverse reactions between the groups was not statistically significant (P > 0.05). CONCLUSIONS: Injections of lyophilized type III collagen fibers into the mid-to-deep layers of the facial dermis have a definitive therapeutic effect on dynamic facial wrinkles. This treatment not only substantially diminishes wrinkle severity but also has a commendable safety profile. LEVEL OF EVIDENCE I: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

An explainable artificial intelligence framework for risk prediction of COPD in smokers.

BACKGROUND: Since the inconspicuous nature of early signs associated with Chronic Obstructive Pulmonary Disease (COPD), individuals often remain unidentified, leading to suboptimal opportunities for timely prevention and treatment. The purpose of this study was to create an explainable artificial intelligence framework combining data preprocessing methods, machine learning methods, and model interpretability methods to identify people at high risk of COPD in the smoking population and to provide a reasonable interpretation of model predictions. METHODS: The data comprised questionnaire information, physical examination data and results of pulmonary function tests before and after bronchodilatation. First, the factorial analysis for mixed data (FAMD), Boruta and NRSBoundary-SMOTE resampling methods were used to solve the missing data, high dimensionality and category imbalance problems. Then, seven classification models (CatBoost, NGBoost, XGBoost, LightGBM, random forest, SVM and logistic regression) were applied to model the risk level, and the best machine learning (ML) model's decisions were explained using the Shapley additive explanations (SHAP) method and partial dependence plot (PDP). RESULTS: In the smoking population, age and 14 other variables were significant factors for predicting COPD. The CatBoost, random forest, and logistic regression models performed reasonably well in unbalanced datasets. CatBoost with NRSBoundary-SMOTE had the best classification performance in balanced datasets when composite indicators (the AUC, F1-score, and G-mean) were used as model comparison criteria. Age, COPD Assessment Test (CAT) score, gross annual income, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), anhelation, respiratory disease, central obesity, use of polluting fuel for household heating, region, use of polluting fuel for household cooking, and wheezing were important factors for predicting COPD in the smoking population. CONCLUSION: This study combined feature screening methods, unbalanced data processing methods, and advanced machine learning methods to enable early identification of COPD risk groups in the smoking population. COPD risk factors in the smoking population were identified using SHAP and PDP, with the goal of providing theoretical support for targeted screening strategies and smoking population self-management strategies.

A hybrid model for tuberculosis forecasting based on empirical mode decomposition in China.

BACKGROUND: Pulmonary Tuberculosis is a major public health problem endangering people's health, a scientifically accurate predictive model is of great practical significance for the prevention and treatment of pulmonary tuberculosis. METHODS: The reported incidence data of pulmonary tuberculosis were from the National Public Health Science Data Center ( https://www.phsciencedata.cn/ ). The ARIMA, LSTM, EMD-SARIMA, EMD-LSTM, EMD-ARMA-LSTM models were established using the reported monthly incidence of tuberculosis reported in China from January 2008 to December 2018. The MSE, MAE, RMSE and MAPE were used to evaluate the performance of the models to determine the best model. RESULTS: Comparing decomposition-based single model with undecomposed single model, it was found that: when predicting the incidence trend in the next year, compared with SARIMA model, the MSE, MAE, RMSE and MAPE of EMD-SARIMA decreased by 39.3%, 19.0%, 22.1% and 19.8%, respectively. The MSE, MAE, RMSE and MAPE of EMD-LSTM were reduced by 40.5%, 12.8%, 22.9% and 12.7%, respectively, compared with the LSTM model; Comparing the decomposition-based hybrid model with the decomposition-based single model, it was found that: when predicting the incidence trend in the next year, compared with EMD-SARIMA model, the MSE, MAE, RMSE and MAPE of EMD-ARMA-LSTM model decreased by 21.7%, 10.6%, 11.5% and 11.2%, respectively. The MSE, MAE, RMSE and MAPE of EMD-ARMA-LSTM were reduced by 16.7%, 9.6%, 8.7% and 12.3%, respectively, compared with EMD-LSTM model. Furthermore, the performance of the model were consistent when predicting the incidence trend in the next 3 months, 6 months and 9 months. CONCLUSION: The prediction performance of the decomposition-based single model is better than that of the undecomposed single model, and the prediction performance of the combined model using the advantages of different models is better than that of the decomposition-based single model, so the EMD-ARMA-LSTM combination model can improve the prediction accuracy better than other models, which can provide a theoretical basis for predicting the epidemic trend of pulmonary tuberculosis and formulating prevention and control policies.

A study on the preparation conditions of lidocaine microemulsion based on multi-objective genetic algorithm.

Background: Topical lidocaine microemulsion preparations with low toxicity, low irritation, strong transdermal capability and convenient administration are urgently needed. Methods: Box-Behnken design was performed for three preparation conditions of 5% lidocaine microemulsions: mass ratio of the mass ratio of surfactant/(oil phase + surfactant) (X1), the mass ratio of olive oil/(α-linolenic acid + linoleic acid) (X2) and the water content W% (X3). Then, five multi-objective genetic algorithms were used to optimize the three evaluation indices to optimize the effects of lidocaine microemulsion preparations. Finally, the ideal optimization scheme was experimentally verified. Results: Non-dominated Sorting Genetic Algorithm-II was used for 30 random searches. Among these, Scheme 2: X1 = 0.75, X2 = 0.35, X3 = 75%, which resulted in Y1 = 0.17 µg/(cm2·s) and Y2 = 0.74 mg/cm2; and the Scheme 19: X1 = 0.68, X2 = 1.42, X3 = 75% which resulted in Y1 = 0.14 µg/(cm2·s) and Y2 = 0.80 mg/cm2, provided the best matches for the objective function requirements. The maximum and average fitness of the method have reached stability after 3 generations of evolution. Experimental verification of the above two schemes showed that there were no statistically significant differences between the measured values of Y1 and Y2 and the predicted values obtained by optimization (p > 0.05) and are close to the target value. Conclusion: Two lidocaine microemulsion preparation protocols were proposed in this study. These preparations resulted in good transdermal performance or long anesthesia duration, respectively.

The relationship between social capital and health from a configuration perspective: an evidence from China.

BACKGROUND: The debate on the relationship between social capital and health is still ongoing. To enhance previous research, this study uses data drawn from China to analyse the situations in which social capital is related to good health and the various configurations that result in good health outcomes. METHODS: Using the data of China Family Panel Studies, the conditions of age, gender, marriage, education, income, structural social capital and cognitive social capital were included to analyse the sufficient and necessary conditions for achieving good general health and their different configurations using the fsQCA method. RESULTS: None of the listed conditions were prerequisites for excellent general health in terms of either their presence or their absence. The sufficiency analysis found 11 configurations with an average of 3-4 conditions per configuration; in no configuration was the condition of social capital present alone. Structured social capital and cognitive social capital exhibited negative states in configurations 1 and 2, respectively. The most prevalent factor in all configurations was the condition of age. CONCLUSIONS: The relationship between social capital and health is both positive and negative, with cognitive social capital playing a larger role in the positive relationship than structural social capital. Social capital is neither a necessary nor a sufficient condition for health, and it must be combined with a variety of other factors to promote health. A variety of methods can be used to promote an individual's health, as different populations require different approaches to good general health, and no single pathway applies to all populations. In the Chinese population, an individual's age is a significant determinant of their health status.

Diabetes mellitus early warning and factor analysis using ensemble Bayesian networks with SMOTE-ENN and Boruta.

Diabetes mellitus (DM) has become the third chronic non-infectious disease affecting patients after tumor, cardiovascular and cerebrovascular diseases, becoming one of the major public health issues worldwide. Detection of early warning risk factors for DM is key to the prevention of DM, which has been the focus of some previous studies. Therefore, from the perspective of residents' self-management and prevention, this study constructed Bayesian networks (BNs) combining feature screening and multiple resampling techniques for DM monitoring data with a class imbalance in Shanxi Province, China, to detect risk factors in chronic disease monitoring programs and predict the risk of DM. First, univariate analysis and Boruta feature selection algorithm were employed to conduct the preliminary screening of all included risk factors. Then, three resampling techniques, SMOTE, Borderline-SMOTE (BL-SMOTE) and SMOTE-ENN, were adopted to deal with data imbalance. Finally, BNs developed by three algorithms (Tabu, Hill-climbing and MMHC) were constructed using the processed data to find the warning factors that strongly correlate with DM. The results showed that the accuracy of DM classification is significantly improved by the BNs constructed by processed data. In particular, the BNs combined with the SMOTE-ENN resampling improved the most, and the BNs constructed by the Tabu algorithm obtained the best classification performance compared with the hill-climbing and MMHC algorithms. The best-performing joint Boruta-SMOTE-ENN-Tabu model showed that the risk factors of DM included family history, age, central obesity, hyperlipidemia, salt reduction, occupation, heart rate, and BMI.

The prognostic index PRIMA-PI combined with Ki67 as a better predictor of progression of disease within 24 months in follicular lymphoma.

Background: Progression of disease within 24 months (POD24) is a risk factor for poor survival in follicular lymphoma (FL), and there is currently no optimal prognostic model to accurately predict patients with early disease progression. How to combine traditional prognostic models with new indicators to establish a new prediction system, to predict the early progression of FL patients more accurately is a future research direction. Methods: This study retrospectively analyzed patients with newly diagnosed FL patients in Shanxi Provincial Cancer Hospital from January 2015 to December 2020. Data from patients undergoing immunohistochemical detection (IHC) were analyzed using χ2 test and multivariate Logistic regression. Also, we built a nomogram model based on the results of LASSO regression analysis of POD24, which was validated in both the training set and validation set, and additional external validation was performed using a dataset (n = 74) from another center, Tianjin Cancer Hospital. Results: The multivariate Logistic regression results suggest that high-risk PRIMA-PI group, Ki-67 high expression represent risk factors for POD24 (P<0.05). Next, PRIMA-PI and Ki67 were combined to build a new model, namely, PRIMA-PIC to reclassify high and low-risk groups. The result showed that the new clinical prediction model constructed by PRIMA-PI with ki67 has a high sensitivity to the prediction of POD24. Compared to PRIMA-PI, PRIMA-PIC also has better discrimination in predicting patient's progression-free survival (PFS) and overall survival (OS). In addition, we built nomogram models based on the results of LASSO regression (histological grading, NK cell percentage, PRIMA-PIC risk group) in the training set, which were validated using internal validation set and external validation set, we found that C-index and calibration curve showed good performance. Conclusion: As such, the new predictive model-based nomogram established by PRIMA-PI and Ki67 could well predict the risk of POD24 in FL patients, which boasts clinical practical value.

MBOAT7 rs641738 (C>T) is associated with NAFLD progression in men and decreased ASCVD risk in elder Chinese population.

Background and aim: The MBOAT7 rs641738 (C>T) variant has demonstrated an association with non-alcoholic fatty liver disease (NAFLD) in both adult and pediatric patients, while few studies have been conducted in elderly populations. Hence, a case-control study was undertaken to assess their correlation in elderly residents in a Beijing community. Materials and methods: A total of 1,287 participants were included. Medical history, abdominal ultrasound, and laboratory tests were recorded. Liver fat content and fibrosis stage were detected by Fibroscan. Genotyping of genomic DNA was performed using the 96.96 genotyping integrated fluidics circuit. Results: Of the recruited subjects, 638 subjects (56.60%) had NAFLD, and 398 subjects (35.28%) had atherosclerotic cardiovascular disease (ASCVD). T allele carriage was associated with higher ALT (p=0.005) and significant fibrosis in male NAFLD patients (p=0.005) compared to CC genotype. TT genotype was associated with reduced risk of metabolic syndrome (OR=0.589, 95%CI: 0.114-0.683, p=0.005) and type 2 diabetes (OR=0.804, 95%CI: 0.277-0.296, p=0.048) in NAFLD population when compared to the CC genotype. In addition, TT genotype was also associated with reduced risk of ASCVD (OR=0.570, 95%CI:0.340-0.953, p=0.032) and less obesity (OR=0.545, 95%CI: 0.346-0.856, p=0.008) in the whole population. Conclusion: MBOAT7 rs641738 (C>T) variant was associated with fibrosis in male NAFLD patients. The variant also reduced risk of metabolic traits and type 2 diabetes in NAFLD and ASCVD risk in Chinese elders.

Development and validation of a novel model to predict liver-related mortality in patients with idiosyncratic drug-induced liver injury.

BACKGROUND: Early identification of patients with high mortality risk is critical for optimizing the clinical management of drug-induced liver injury (DILI). We aimed to develop and validate a new prognostic model to predict death within 6 months in DILI patients. METHODS: This multicenter study retrospectively reviewed the medical records of DILI patients admitted to three hospitals. A DILI mortality predictive score was developed using multivariate logistic regression and was validated with area under the receiver operating characteristic curve (AUC). A high-mortality-risk subgroup was identified according to the score. RESULTS: Three independent DILI cohorts, including one derivation cohort (n = 741) and two validation cohorts (n = 650, n = 617) were recruited. The DILI mortality predictive (DMP) score was calculated using parameters at disease onset as follows: 1.913 × international normalized ratio + 0.060 × total bilirubin (mg/dL) + 0.439 × aspartate aminotransferase/alanine aminotransferase - 1.579 × albumin (g/dL) - 0.006 × platelet count (109/L) + 9.662. The predictive performance for 6-month mortality of DMP score was desirable, with an AUC of 0.941 (95% CI: 0.922-0.957), 0.931 (0.908-0.949) and 0.960 (0.942-0.974) in the derivation, validation cohorts 1 and 2, respectively. DILI patients with a DMP score ≥ 8.5 were stratified into high-risk group, whose mortality rates were 23-, 36-, and 45-fold higher than those of other patients in the three cohorts. CONCLUSIONS: The novel model based on common laboratory findings can accurately predict mortality within 6 months in DILI patients, which should serve as an effective guidance for management of DILI in clinical practice.

Comparison of acknowledged hepatocellular carcinoma risk scores in high-risk hepatitis C patients with sustained virological response.

Hepatitis C patients with advanced fibrosis or cirrhosis are at high risk of developing hepatocellular carcinoma (HCC) even after sustained virological response (SVR). Several HCC risk scores have been developed but which one is most suitable for this population is unclear. In this study, we compared the prediction ability of the aMAP model, THRI model, PAGE-B model and Models of HCV in a prospective hepatitis C cohort in order to propose better model(s) to clinical practice. Adult hepatitis C patients with baseline advanced fibrosis (141 cases), compensated cirrhosis (330 cases) and decompensated cirrhosis (80 cases) were included and followed up every 6 months for about 7 years or until HCC development. Demographic data, medical history and laboratory results were recorded. HCCs were diagnosed by radiography, AFP or liver histology. The median follow-up period was 69.93(60.99-74.93) months, during which 53 (9.62%) patients developed HCC. The areas under the receiver operating characteristic curve of aMAP, THRI, PAGE-B and Models of HCV scores were 0.74, 0.72, 0.70 and 0.63 respectively. The predictive power of the aMAP model score was comparable to that of THRI, PAGE-Band higher than that of Models of HCV (p < 0.05). Dividing patients into non-high-risk and high-risk groups, the cumulative incidence rates of HCC based on aMAP, THRI, PAGE-B and Models of HCV was 5.57% vs. 24.17%, 1.10% vs. 13.90%, 5.80% vs. 15.90% and 6.41% vs. 13.81% (all p < 0.05). The AUC of the four models were all below 0.7 in male while all were higher than 0.7 in female. The performance of all the models was not influenced by fibrosis stage. aMAP, THRI model and PAGE-B model were all performed well while THRI model and PAGE-B model were easier to calculate. There was no need to select score according to fibrosis stage but should be caution when explain the results in male patients.

Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China.

BACKGROUND: Influenza is an acute respiratory infectious disease that is highly infectious and seriously damages human health. Reasonable prediction is of great significance to control the epidemic of influenza. METHODS: Our Influenza data were extracted from Shanxi Provincial Center for Disease Control and Prevention. Seasonal-trend decomposition using Loess (STL) was adopted to analyze the season characteristics of the influenza in Shanxi Province, China, from the 1st week in 2010 to the 52nd week in 2019. To handle the insufficient prediction performance of the seasonal autoregressive integrated moving average (SARIMA) model in predicting the nonlinear parts and the poor accuracy of directly predicting the original sequence, this study established the SARIMA model, the combination model of SARIMA and Long-Short Term Memory neural network (SARIMA-LSTM) and the combination model of SARIMA-LSTM based on Singular spectrum analysis (SSA-SARIMA-LSTM) to make predictions and identify the best model. Additionally, the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to evaluate the performance of the models. RESULTS: The influenza time series in Shanxi Province from the 1st week in 2010 to the 52nd week in 2019 showed a year-by-year decrease with obvious seasonal characteristics. The peak period of the disease mainly concentrated from the end of the year to the beginning of the next year. The best fitting and prediction performance was the SSA-SARIMA-LSTM model. Compared with the SARIMA model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 38.12, 17.39 and 21.34%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 42.41, 18.69 and 24.11%, respectively, in prediction performances. Furthermore, compared with the SARIMA-LSTM model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 28.26, 14.61 and 15.30%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 36.99, 7.22 and 20.62%, respectively, in prediction performances. CONCLUSIONS: The fitting and prediction performances of the SSA-SARIMA-LSTM model were better than those of the SARIMA and the SARIMA-LSTM models. Generally speaking, we can apply the SSA-SARIMA-LSTM model to the prediction of influenza, and offer a leg-up for public policy.

Machine learning-enabled risk prediction of chronic obstructive pulmonary disease with unbalanced data.

BACKGROUND AND OBJECTIVE: Since the early symptoms of chronic obstructive pulmonary disease (COPD) are not obvious, patients are not easily identified, causing improper time for prevention and treatment. In present study, machine learning (ML) methods were employed to construct a risk prediction model for COPD to improve its prediction efficiency. METHODS: We collected data from a sample of 5807 cases with a complete COPD diagnosis from the 2019 COPD Surveillance Program in Shanxi Province and extracted 34 potentially relevant variables from the dataset. Firstly, we used feature selection methods (i.e., Generalized elastic net, Lasso and Adaptive lasso) to select ten variables. Afterwards, we employed supervised classifiers for class imbalanced data by combining the cost-sensitive learning and SMOTE resampling methods with the ML methods (Logistic Regression, SVM, Random Forest, XGBoost, LightGBM, NGBoost and Stacking), respectively. Last, we assessed their performance. RESULTS: The cough frequently at age 14 and before and other 9 variables are significant parameters for COPD. The Stacking heterogeneous ensemble model showed relatively good performance in the unbalanced datasets. The Logistic Regression with class weighting enjoyed the best classification performance in the balancing data when these composite indicators (AUC, F1-Score and G-mean) were used as criteria for model comparison. The values of F1-Score and G-mean for the top three ML models were 0.290/0.660 for Logistic Regression with class weighting, 0.288/0.649 for Stacking with synthetic minority oversampling technique (SMOTE), and 0.285/0.648 for LightGBM with SMOTE. CONCLUSIONS: This paper combining feature selection methods, unbalanced data processing methods and machine learning methods with data from disease surveillance questionnaires and physical measurements to identify people at risk of COPD, concluded that machine learning models based on survey questionnaires could provide an automated identification for patients at risk of COPD, and provide a simple and scientific aid for early identification of COPD.

Effectiveness Evaluation of Repetitive Transcranial Magnetic Stimulation Therapy Combined with Mindfulness-Based Stress Reduction for People with Post-Stroke Depression: A Randomized Controlled Trial.

Background: Post-stroke depression (PSD) is most prevalent during the rehabilitative period following a stroke. Recent studies verified the effects of repetitive transcranial magnetic stimulation therapy (rTMS) and mindfulness-based stress reduction (MBSR) in patients with depression. However, the effectiveness and prospect of application in PSD patients remain unclear. This study sought to evaluate the effectiveness of a combined intervention based on rTMS and MBSR for the physical and mental state of PSD patients. Methods: A randomized, double-blind, sham-controlled study design was employed. Participants were recruited from the Rehabilitation Medicine Centre and randomly assigned to receive either MBSR combined with active or sham rTMS or sham rTMS combined with general psychological care. We used a 17-item Hamilton Depression Rating Scale (HAMD-17), a mini-mental state examination (MMSE), the Modified Barthel Index (MBI), and the Pittsburgh Sleep Quality Index (PSQI) to evaluate depressed symptoms, cognitive function, activities of daily living (ADL), and sleep quality at baseline, post-intervention, and the 8-week follow-up. A two-factor analysis of variance was used to compare differences between groups, and Pearson's linear correlation was used to analyze the possible relationship between variables and potential predictors of depression improvement. Results: Seventy-two participants were randomized to rTMS−MBSR (n = 24), sham rTMS−MBSR (n = 24), or sham rTMS−general psychological care (n = 24). A total of 71 patients completed the questionnaire, a 99% response rate. There were significant time and group interaction effects in HAMD-17, MMSE, MBI, and PSQI scores (p < 0.001). The repeated-measure ANOVA showed a significant improvement of all variables in rTMS−MBSR compared to sham rTMS−MBSR and sham rTMS combined with general psychological care (p < 0.05). Additional results demonstrated that cognitive function, sleep quality, and activities of daily living are associated with depressive symptoms, and cognitive function is a potential variable for improved depression. Conclusion: Depressive symptoms can be identified early by assessing cognitive function, and rTMS−MBSR might be considered a potentially helpful treatment for PSD.

Efficacy and Safety of Sofosbuvir-based Regimens in Hepatitis C Patients With Decompensated Cirrhosis: A Systematic Review and Meta-analysis.

Background and Aims: Decompensated cirrhotic patients with hepatitis C (HCV) are often under-represented in clinical trials. We aimed to evaluate pooled data on the efficacy and safety of sofosbuvir (SOF)-based regimens in these patients. Methods: We conducted a systemic review and meta-analysis by searching multiple databases for studies published from October 2010 to October 2020. Outcomes of interest were sustained virologic response (SVR) and safety of SOF-based regimens in decompensated HCV patients. Two reviewers independently performed the study selection and data extraction. Results: We included 33 studies that enrolled 5,302 HCV patients. The pooled SVR rate in decompensated patients with SOF-based regimens was 85.1% (95% CI: 82.8-87.3). Patients on SOF/velpatasvir±ribavirin achieved a significantly higher SVR (91.0%, 95% CI: 87.7-93.9) than that of SOF/ledipasvir±ribavirin [(86.3%, 95% CI: 84.6-87.8); p=0.004)], or on SOF/daclatasvir±ribavirin (82.4%, 95% CI: 78.2-86.2%; p<0.001). Adding ribavirin to SOF-based regimens (pooled SVR 84.9%, 95% CI: 81.7-87.9) did not significantly increase the SVR [(83.8% (95% CI: 76.8-89.8%; p=0.76)] in decompensated patients, which was also true in subgroup analyses for each regimen within the same treatment duration. However, adding ribavirin significantly increased the frequency of adverse events from 52.9% (95% CI: 28.0-77.1) to 89.2% (95% CI: 68.1-99.9) and frequency of severe events. The pooled incidence of hepatocellular carcinoma and case-fatality of decompensated patients were 3.1% (95% CI: 1.5-5.0) and 4.6% (95% CI: 3.1-6.3), respectively. The overall heterogeneity was high. There was no publication bias. Conclusions: The analysis found that 12 weeks of SOF/velpatasvir without ribavirin is the preferred therapy, with a significantly higher SVR compared with other SOF-based regimens in decompensated HCV patients.

Human papillomavirus vaccination behaviors among gynecological outpatients based on extended unified theory of acceptance and use of technology / 预防医学

Objective@#To investigate the factors affecting human papillomavirus (HPV) vaccination behaviors among gynecological outpatients based on extended unified theory of acceptance and use of technology (UTAUT2), so as to provide insights into the development of HPV vaccination behavioral interventions.@*Methods@# Patients at ages of 45 years and younger that were admitted to the outpatient department of gynecological of Shanxi Provincial People's Hospital from October 2021 to August 2022 were recruited, and the factors affecting HPV vaccination behaviors were identified using UTAUT2.@*Results@#A total of 431 female outpatients were enrolled, including 163 patients at ages of 36 to 45 years (37.82%), 272 cases with an educational level of college degree and above (63.11%) and 253 patients with per capita monthly household income of more than 3 000 Yuan (58.70%). The coverage of HPV vaccination was 24.36%, and the main cause of non-vaccination was difficulty in high-valent HPV vaccine appointment. Price value, social impact and efficacy expectation posed a positive impact on HPV vaccination behaviors via intention of vaccination (β=0.11, 0.08, 0.07, all P<0.05) and intention of vaccination and effort expectancy (β=0.10, 0.07, 0.06, all P<0.05), and effort expectancy played a mediating effect between intention of vaccination and vaccination behaviors (β=0.28, P<0.05).@*Conclusion@#Efficacy expectation, social impact, price value, intention of vaccination and effort expectancy may positively affect HPV vaccination behaviors.

[Epidemiological characteristics and Spatial-temporal clustering of hand, foot and mouth disease in Shanxi province, 2009-2020].

Objective: To analyze the epidemiology and spatial-temporal distribution characteristics of hand, foot and mouth disease (HFMD) in Shanxi province. Methods: The data of HFMD in Shanxi province from 2009 to 2020 were collected from notifiable disease management information system of Chinese information system for disease control and prevention and analyzed by descriptive epidemiology, Joinpoint regression, spatial autocorrelation analysis and spatio- temporal scanning analysis. Results: A total of 293 477 HFMD cases were reported in Shanxi province from 2009 to 2020, with an average annual incidence of 67.64/100 000 (293 477/433 867 454), severe disease rate of 5.36/100 000 (2 326/433 867 454), severe disease ratio of 0.79%(2 326/293 477), mortality of 0.015/100 000 (66/433 867 454), and fatality rate of 22.49/100 000 (66/293 477). The reported incidence rate, severe disease rate, mortality rate and fatality rate of HFMD showed decreasing trends. The main high-risk groups were scattered children and kindergarten children aged 0-5. The incidence of HFMD had obvious seasonal variation, with two peaks every year: the main peak was during June-July, the secondary peak was during September-October and the peak period is from April to November. A total of 13 942 laboratory cases were confirmed, with a diagnosis rate of 4.75% (13 942/293 477), including 4 438 (35.11%, 4 438/293 477) Enterovirus A71 (EV-A71) positive cases, 4 609 (33.06%, 4 609/293 477) Coxsackievirus A16 (CV-A16) positive cases, and 4 895 (31.83%, 4 895/293 477) other enterovirus positive cases. There was a spatial positive correlation (Moran's I ranged from 0.12 to 0.58, all P<0.05) and the spatial clustering was obvious. High-risk regions were mainly distributed in Taiyuan in central Shanxi province, Linfen and Yuncheng in southern Shanxi province, and Changzhi in southeastern Shanxi province. Spatial-temporal scanning analysis revealed 1 the most likely cluster and 8 secondary likely clusters, of which the most likely cluster (RR=2.65, LLR=22 387.42, P<0.001) located in Taiyuan and Jinzhong city, Shanxi province, including 12 counties (districts), and accumulated from April 1, 2009 to November 30, 2018. Conclusions: There was obvious spatial-temporal clustering of HFMD in Shanxi province, and the epidemic situation was in decline. The key areas were the districts in urban areas and the counties adjacent to it. Meanwhile, the monitoring and classification of other enterovirus types of HFMD should be strengthened.

Comparative transcriptome analysis of longissimus dorsi tissues with different intramuscular fat contents from Guangling donkeys.

BACKGROUND: Donkey meat has low fat and high protein contents and is rich in various unsaturated fatty acids and trace elements that are beneficial to human digestion and absorption. IMF (intramuscular fat), also known as marbling, is an important indicator of the lean meat to fat ratio, which directly affects the tenderness and juiciness of the meat. At present, the underlying molecular variations affecting IMF content among donkey breeds are unclear. The Guangling donkey is an indigenous species in China. This study explored candidate regulatory genes that affect IMF content in Guangling donkeys. The IMF content of the longissimus dorsi muscle in 30 Guangling donkeys was measured. Six donkeys of similar age were selected according to age factors and divided into two groups, the high (H) and low (L) fat groups, according to their IMF content. RESULTS: RNA-seq technology was used to compare the muscle transcriptome between the two groups. More than 75.0% of alternative splicing (AS) events were of the skipped exon (SE) type. A total of 887 novel genes were identified; only 386 novel genes were aligned to the annotation information of various databases. Transcriptomics analysis revealed 167 differentially expressed genes (DEGs), of which 64 were upregulated and 103 were downregulated between the H and L groups. Gene ontology analysis showed that the DEGs were enriched in multiple biological processes and pathways that are related to adipocyte differentiation, lipid synthesis, and neutral lipid metabolism. KEGG pathway analysis suggested that arachidonic acid metabolism, the HIF-1 signalling pathway, fructose and mannose metabolism, glycerophospholipid metabolism, and the AMPK signalling pathway were involved in lipid deposition. In addition, a gene-gene interaction network was constructed that revealed that the DEGs, including SCD, LEPR, CIDEA, DLK1, DGAT2, ITGAL, HMOX1, WNT10B, and DGKA, had significant roles in adipocyte differentiation and adipogenesis. The selected DEGs were further validated by qRT-PCR. CONCLUSION: This study improves the in-depth understanding of gene regulation and protein expression regarding IMF deposition and lays a basis for subsequent molecular breeding studies in Guangling donkeys.

Using Bayesian networks with Max-Min Hill-Climbing algorithm to detect factors related to multimorbidity.

Objectives: Multimorbidity (MMD) is a medical condition that is linked with high prevalence and closely related to many adverse health outcomes and expensive medical costs. The present study aimed to construct Bayesian networks (BNs) with Max-Min Hill-Climbing algorithm (MMHC) algorithm to explore the network relationship between MMD and its related factors. We also aimed to compare the performance of BNs with traditional multivariate logistic regression model. Methods: The data was downloaded from the Online Open Database of CHARLS 2018, a population-based longitudinal survey. In this study, we included 10 variables from data on demographic background, health status and functioning, and lifestyle. Missing value imputation was first performed using Random Forest. Afterward, the variables were included into logistic regression model construction and BNs model construction. The structural learning of BNs was achieved using MMHC algorithm and the parameter learning was conducted using maximum likelihood estimation. Results: Among 19,752 individuals (9,313 men and 10,439 women) aged 64.73 ± 10.32 years, there are 9,129 ones without MMD (46.2%) and 10,623 ones with MMD (53.8%). Logistic regression model suggests that physical activity, sex, age, sleep duration, nap, smoking, and alcohol consumption are associated with MMD (P < 0.05). BNs, by establishing a complicated network relationship, reveals that age, sleep duration, and physical activity have a direct connection with MMD. It also shows that education levels are indirectly connected to MMD through sleep duration and residence is indirectly linked to MMD through sleep duration. Conclusion: BNs could graphically reveal the complex network relationship between MMD and its related factors, outperforming traditional logistic regression model. Besides, BNs allows for risk reasoning for MMD through Bayesian reasoning, which is more consistent with clinical practice and thus holds some application prospects.