ObjectiveTo observe the clinical effect of modified Huanglian Wendantang on cardiovascular risk factors in patients with metabolic syndrome under the guidance of treating disease before its onset. MethodsA total of 82 patients with metabolic syndrome treated in the First Affiliated Hospital of Heilongjiang University of Chinese Medicine from July 2023 to July 2024 were selected and allocated into an observation group （41 cases） and a control group （41 cases） by the random number table method. The control group received routine treatment， and the observation group was treated with modified Huanglian Wendantang on the basis of routine treatment. Both groups were treated for 8 weeks. The therapeutic effects on TCM symptoms after treatment in the two groups were evaluated. The levels of obesity degree indicators， blood pressure indicators， glucose and lipid metabolism indicators， inflammatory factors， and vascular endothelial function indicators before and after treatment in the two groups were measured， and the treatment safety was evaluated. ResultsAfter treatment， the total response rate of TCM symptoms in the observation group was 97.56% （40/41）， which was higher than that （87.80%， 36/41） in the control group （χ²=5.205， P<0.05）. After treatment， both groups showed declines （P<0.05） in systolic blood pressure （SBD）， diastolic blood pressure （DBP）， triglyceride （TG）， total cholesterol （TC）， low density lipoprotein cholesterol （LDL-C）， fasting blood glucose， 2-hour postprandial blood glucose （2 h PG）， glycosylated hemoglobin （HbA1c）， fasting insulin （FINS）， Homeostatic Model Assessment for Insulin Resistance （HOMA-IR）， body mass index （BMI）， waist circumference （WC）， waist-to-hip ratio （WHR）， leptin （LEP）， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， endothelin-1 （ET-1）， and inducible nitric oxide synthase （iNOS）. Moreover， the declines in the observation group were more obvious than those in the control group （P<0.05， P<0.01）. After treatment， both groups showed elevated levels of high density lipoprotein cholesterol （HDL-C）， adiponectin （ADP）， nitric oxide （NO）， and endothelial nitric oxide synthase （eNOS） （P<0.05）， and the above indexes in the observation group were higher than those in the control group （P<0.01）. ConclusionUnder the guidance of the thought of treating disease before its onset， modified Huanglian Wendantang was used to treat patients with metabolic syndrome. The decoction improved the clinical efficacy by ameliorating IR to improve insulin sensitivity， reducing inflammation， and protecting the vascular endothelial function. It inhibits cardiovascular risk factors without inducing adverse reactions， being worthy of clinical application and promotion.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Hepatocellular carcinoma (HCC) is an aggressive primary liver malignancy often diagnosed at an advanced stage, resulting in a poor prognosis. Accurate risk stratification and early detection of HCC are critical unmet needs for improving outcomes. Several blood-based biomarkers and imaging tests are available for early detection, prediction, and monitoring of HCC. However, serum protein biomarkers such as alpha-fetoprotein have shown relatively low sensitivity, leading to inaccurate performance. Imaging studies also face limitations related to suboptimal accuracy, high cost, and limited implementation. Recently, liquid biopsy techniques have gained attention for addressing these unmet needs. Liquid biopsy is non-invasive and provides more objective readouts, requiring less reliance on healthcare professional’s skills compared to imaging. Circulating tumor cells, cell-free DNA, and extracellular vesicles are targeted in liquid biopsies as novel biomarkers for HCC. Despite their potential, there are debates regarding the role of these novel biomarkers in the HCC care continuum. This review article aims to discuss the technical challenges, recent technical advancements, advantages and disadvantages of these liquid biopsies, as well as their current clinical application and future directions of liquid biopsy in HCC.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Hepatocellular carcinoma (HCC) is an aggressive primary liver malignancy often diagnosed at an advanced stage, resulting in a poor prognosis. Accurate risk stratification and early detection of HCC are critical unmet needs for improving outcomes. Several blood-based biomarkers and imaging tests are available for early detection, prediction, and monitoring of HCC. However, serum protein biomarkers such as alpha-fetoprotein have shown relatively low sensitivity, leading to inaccurate performance. Imaging studies also face limitations related to suboptimal accuracy, high cost, and limited implementation. Recently, liquid biopsy techniques have gained attention for addressing these unmet needs. Liquid biopsy is non-invasive and provides more objective readouts, requiring less reliance on healthcare professional’s skills compared to imaging. Circulating tumor cells, cell-free DNA, and extracellular vesicles are targeted in liquid biopsies as novel biomarkers for HCC. Despite their potential, there are debates regarding the role of these novel biomarkers in the HCC care continuum. This review article aims to discuss the technical challenges, recent technical advancements, advantages and disadvantages of these liquid biopsies, as well as their current clinical application and future directions of liquid biopsy in HCC.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Hepatocellular carcinoma (HCC) is an aggressive primary liver malignancy often diagnosed at an advanced stage, resulting in a poor prognosis. Accurate risk stratification and early detection of HCC are critical unmet needs for improving outcomes. Several blood-based biomarkers and imaging tests are available for early detection, prediction, and monitoring of HCC. However, serum protein biomarkers such as alpha-fetoprotein have shown relatively low sensitivity, leading to inaccurate performance. Imaging studies also face limitations related to suboptimal accuracy, high cost, and limited implementation. Recently, liquid biopsy techniques have gained attention for addressing these unmet needs. Liquid biopsy is non-invasive and provides more objective readouts, requiring less reliance on healthcare professional’s skills compared to imaging. Circulating tumor cells, cell-free DNA, and extracellular vesicles are targeted in liquid biopsies as novel biomarkers for HCC. Despite their potential, there are debates regarding the role of these novel biomarkers in the HCC care continuum. This review article aims to discuss the technical challenges, recent technical advancements, advantages and disadvantages of these liquid biopsies, as well as their current clinical application and future directions of liquid biopsy in HCC.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

ObjectiveTo analyze the protective effect of different types of influenza vaccines (InfV) against influenza A among the individuals aged between 3‒17 years old, and to provide a scientific basis for the prevention and control of influenza in the future. MethodsA retrospective cohort study was conducted to collect data on the incidence and InfV vaccination of the individuals aged between 3‒17 years during the influenza epidemic season from 2022 to 2023. Vaccine effectiveness (VE) was calculated, and a log-binomial regression model was used to calculate the corrected VE. ResultsThe incidence rate of influenza in InfV vaccinated and un-vaccinated groups was 7.32% (1 937/ 26 446) and 9.65% (4 421/45 837), respectively. After adjusting for age and gender factors, the unadjusted VE (95%CI) was 54.57% (52.24%‒56.78%). The unadjusted VE (95%CI) was 53.66% (50.36%‒56.74%) for males and 55.60% (52.24%‒58.72%) for females, respectively. The unadjusted VE (95%CI) for the age group of 3‒ years, 6‒ years, 9‒ years, 12‒ years, and 15‒17 years were 64.08% (60.89%‒67.01%), 57.40% (53.71%‒60.80%), 57.77% (52.49%‒62.47%), 24.36% (9.49%‒36.79%), and 24.09% (-17.59%‒51.00%), respectively. The unadjusted VE (95%CI) for quadrivalent split-virion inactivated influenza vaccine, trivalent split-virion inactivated influenza vaccine, trivalent subunit influenza vaccine, and trivalent live attenuated influenza vaccine were 53.84% (51.32%‒56.24%), 62.17% (56.28%‒67.26%), 79.83% (69.94%‒86.46%), and 31.59% (19.07%‒42.18%), respectively. ConclusionThe InfV used during the 2022‒2023 influenza season had a good protective effect against influenza A among the individuals aged between 3‒17 years old, especially in those aged between 3‒11 years old.