Background: Platform work offers flexibility and autonomy to workers, but there are concerns about the impacts of excessive labor control exercised by digital platforms using algorithmic management. This study assessed the level of labor control exerted by digital platforms and explored its associations with psychosocial work conditions and workers' mental well-being. Methods: A total of 487 location-based platform workers in Taiwan, encompassing ride-hailing, delivery, and house chore services, participated in this questionnaire survey. A 5-item scale was created to assess platform-mediated labor control. Psychosocial work conditions, including demands, job control, work rewards, and mental well-being, including burnout and self-rated mental health status, were assessed using validated instruments. Cronbach's α and principal component analysis were employed to assess the validity of the labor control scale. Logistic regression analysis and path analysis were conducted to investigate the relationship between labor control and workers' mental health. Results: Exploratory factor analyses confirmed the structural validity and the internal consistency of the scale. Logistic regression models revealed that higher levels of labor control were associated with an increased risk of burnout and poorer mental health. Path analyses further indicated that higher labor control through digital platforms raised mental health risks by increasing job demands. Conclusion Algorithmic management is associated with increased stress among platform workers as they try to meet the platform's performance metrics.

Background: Platform work offers flexibility and autonomy to workers, but there are concerns about the impacts of excessive labor control exercised by digital platforms using algorithmic management. This study assessed the level of labor control exerted by digital platforms and explored its associations with psychosocial work conditions and workers' mental well-being. Methods: A total of 487 location-based platform workers in Taiwan, encompassing ride-hailing, delivery, and house chore services, participated in this questionnaire survey. A 5-item scale was created to assess platform-mediated labor control. Psychosocial work conditions, including demands, job control, work rewards, and mental well-being, including burnout and self-rated mental health status, were assessed using validated instruments. Cronbach's α and principal component analysis were employed to assess the validity of the labor control scale. Logistic regression analysis and path analysis were conducted to investigate the relationship between labor control and workers' mental health. Results: Exploratory factor analyses confirmed the structural validity and the internal consistency of the scale. Logistic regression models revealed that higher levels of labor control were associated with an increased risk of burnout and poorer mental health. Path analyses further indicated that higher labor control through digital platforms raised mental health risks by increasing job demands. Conclusion Algorithmic management is associated with increased stress among platform workers as they try to meet the platform's performance metrics.

Background: Platform work offers flexibility and autonomy to workers, but there are concerns about the impacts of excessive labor control exercised by digital platforms using algorithmic management. This study assessed the level of labor control exerted by digital platforms and explored its associations with psychosocial work conditions and workers' mental well-being. Methods: A total of 487 location-based platform workers in Taiwan, encompassing ride-hailing, delivery, and house chore services, participated in this questionnaire survey. A 5-item scale was created to assess platform-mediated labor control. Psychosocial work conditions, including demands, job control, work rewards, and mental well-being, including burnout and self-rated mental health status, were assessed using validated instruments. Cronbach's α and principal component analysis were employed to assess the validity of the labor control scale. Logistic regression analysis and path analysis were conducted to investigate the relationship between labor control and workers' mental health. Results: Exploratory factor analyses confirmed the structural validity and the internal consistency of the scale. Logistic regression models revealed that higher levels of labor control were associated with an increased risk of burnout and poorer mental health. Path analyses further indicated that higher labor control through digital platforms raised mental health risks by increasing job demands. Conclusion Algorithmic management is associated with increased stress among platform workers as they try to meet the platform's performance metrics.

Background: Platform work offers flexibility and autonomy to workers, but there are concerns about the impacts of excessive labor control exercised by digital platforms using algorithmic management. This study assessed the level of labor control exerted by digital platforms and explored its associations with psychosocial work conditions and workers' mental well-being. Methods: A total of 487 location-based platform workers in Taiwan, encompassing ride-hailing, delivery, and house chore services, participated in this questionnaire survey. A 5-item scale was created to assess platform-mediated labor control. Psychosocial work conditions, including demands, job control, work rewards, and mental well-being, including burnout and self-rated mental health status, were assessed using validated instruments. Cronbach's α and principal component analysis were employed to assess the validity of the labor control scale. Logistic regression analysis and path analysis were conducted to investigate the relationship between labor control and workers' mental health. Results: Exploratory factor analyses confirmed the structural validity and the internal consistency of the scale. Logistic regression models revealed that higher levels of labor control were associated with an increased risk of burnout and poorer mental health. Path analyses further indicated that higher labor control through digital platforms raised mental health risks by increasing job demands. Conclusion Algorithmic management is associated with increased stress among platform workers as they try to meet the platform's performance metrics.

Background: Platform work offers flexibility and autonomy to workers, but there are concerns about the impacts of excessive labor control exercised by digital platforms using algorithmic management. This study assessed the level of labor control exerted by digital platforms and explored its associations with psychosocial work conditions and workers' mental well-being. Methods: A total of 487 location-based platform workers in Taiwan, encompassing ride-hailing, delivery, and house chore services, participated in this questionnaire survey. A 5-item scale was created to assess platform-mediated labor control. Psychosocial work conditions, including demands, job control, work rewards, and mental well-being, including burnout and self-rated mental health status, were assessed using validated instruments. Cronbach's α and principal component analysis were employed to assess the validity of the labor control scale. Logistic regression analysis and path analysis were conducted to investigate the relationship between labor control and workers' mental health. Results: Exploratory factor analyses confirmed the structural validity and the internal consistency of the scale. Logistic regression models revealed that higher levels of labor control were associated with an increased risk of burnout and poorer mental health. Path analyses further indicated that higher labor control through digital platforms raised mental health risks by increasing job demands. Conclusion Algorithmic management is associated with increased stress among platform workers as they try to meet the platform's performance metrics.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Background/Aims: Oral EDP-514 is a potent core protein inhibitor of hepatitis B virus (HBV) replication, which produced a >4-log viral load reduction in HBV-infected chimeric mice with human liver cells. This study evaluated the safety, pharmacokinetics, and antiviral activity of three doses of EDP-514 in treatment-naive viremic patients with HBeAgpositive or -negative chronic HBV infection. Methods: Patients with HBsAg detectable at screening and at least 6 months previously were eligible. HBeAg-positive and -negative patients had a serum/plasma HBV DNA level ≥20,000 and ≥2,000 IU/mL, respectively. Twenty-five patients were randomized to EDP-514 200 (n=6), 400 (n=6) or 800 mg (n=7) or placebo (n=6) once daily for 28 days. Results: A dose-related increase in EDP-514 exposure (AUClast and Cmax) was observed across doses. At Day 28, mean reductions in HBV DNA were –2.9, –3.3, –3.5 and –0.2 log10 IU/mL with EDP-514 200 mg, 400 mg, 800 mg, and placebo groups, respectively. The corresponding mean change from baseline for HBV RNA levels was –2.9, –2.4, –2.0, and –0.02 log10 U/mL. No virologic failures were observed. No clinically meaningful changes from baseline were observed for HBsAg, HBeAg or HBcrAg. Nine patients reported treatment emergent adverse events of mild or moderate severity with no discontinuations, serious AEs or deaths. Conclusions In treatment-naïve viremic patients, oral EDP-514 was generally safe and well-tolerated, displayed PK profile supportive of once-daily dosing, and markedly reduced HBV DNA and HBV RNA.

Objective:To study influencing factors of prognosis of patients with atrial fibrillation(AF)and heart fail-ure(HF)after rhythm control therapy.Methods:According to rhythm control methods,a total of 379 AF+HF patients treated in our hospital were divided into medication group(n=133)and surgery group(n=246,received catheter ablation of AF);according to AF type,patients were divided into paroxysmal AF group(n=208)and per-sistent AF group(n=171),and general clinical data and rhythm control effect were compared among all groups.According to NYHA cardiac function classification,patients were divided into NYHA classⅡ group(n=160),classⅢ group(n=188)and class Ⅳ group(n=31),and rhythm control effect was compared among above groups.Re-sults:Compared with medication,there were significant reductions in percentage of AF recurrence and LAD after treatment,and significant rise in percentage of heart function improvement in paroxysmal AF group,persistent AF group,NYHA class Ⅱ and Ⅲ group in those underwent surgery(P＜0.05 or＜0.01).Multivariate Logistic re-gression analysis indicated that medication,persistent AF were independent risk factors for AF recurrence and no cardiac function improvement after rhythm control therapy(OR=2.426～7.908,P=0.001 all),and NYHA classⅡ was an independent protective factor for AF recurrence after rhythm control therapy(OR=0.393,P=0.049).Conclusion:Atrial fibrillation catheter ablation can significantly improve prognosis of AF+HF patients.Persistent AF and antiarrhythmic medication are independent risk factors for poor prognosis after rhythm control therapy.

Objective:To analyze the research hotspots of the application of mobile health in cardiac function monitoring of patients with heart disease and to conduct a visual analysis.Method:Subject field retrieval was carried out based on Web of Science Core Collection. The retrieval time range was set from 2000 to 2022, and the type of paper was limited to treatise. The retrieved literatures were imported into Endnote 7 for screening, and the repetitive and unrelated literatures were excluded. A total of 291 literatures were included in this study. Open refine 2.8 was used for data cleaning of synonyms and then VOSviewer 1.6.18 was used for visualization analysis of country/region, author and keywords.Results:Research reports on the application of mobile medical in the field of cardiac function monitoring in patients with heart disease have been growing steadily since 2005, and the number of articles published in the field increased rapidly from 2020 to 2021. The top five countries with the highest number of publications were the United States (71) , Australia (20) , China (18) , the United Kingdom (14) and Sweden (13) . The cooperation and exchange among global researchers were relatively close, but they were still dominated by regional cooperation and had not formed a large core group of authors. A total of 71 high-frequency keywords were included, and the top five were mhealth (30 times) , heart failure (27 times) , smartphone (26 times) , telemedicine (15 times) and mobile phone (17 times) . Each cluster keyword was related to each other, which represented the current research hotspot content respectively.Conclusions:At present, number of publications in China ranks first in the world, but there is still a big gap compared with the United States. The development and optimization of monitoring equipment and mobile medical APP have become the focus and hot topics.

ObjectiveTo explore medication regularity of traditional Chinese medicine （TCM） in the treatment of non-small cell lung cancer （NSCLC） and thereby to lay a theoretical basis for clinical medication and drug development. MethodArticles on clinical treatment of intermediate and advanced NSCLC with TCM in the past 40 years were retrieved from CNKI， which were taken the data source. Then the articles were screened to establish a formula database， followed by frequency statistics， association rule analysis， cluster analysis， factor analysis， and complex network construction. ResultA total of 307 eligible articles were screened out， involving 483 formulas. The common syndrome of intermediate and advanced NSCLC was the deficiency of both Qi and Yin， with the common syndrome elements of Qi deficiency， Yin deficiency， phlegm， blood stasis， pathogenic heat （fire）， toxin， and pathogenic dampness. The frequently used medicinals mainly had the functions of tonifying deficiency， clearing heat， resolving phlegm and relieving cough and dyspnea， promoting urination and draining dampness， and activating blood and resolving stasis. The high-frequency medicinals were Astragali Radix， Glycyrrhizae Radix et Rhizome， Ophiopogonis Radix， Fritillariae Thunbergii Bulbus， and Poria， which were mainly cold， bitter， sweet， and pungent， with tropism at lung， spleen， and stomach. The association rule analysis yielded 17 rules with strong association. Ten common factors were extracted from the factor analysis， and cluster analysis classified the medicinals into 5 groups. Complex network analysis suggested that the core formula was modified Liujunzi Tang and Yiqi Yangyin Jiedu prescription. ConclusionThe treatment principle for intermediate and advanced NSCLC is replenishing Qi and nourishing Yin， invigorating spleen and resolving phlegm， clearing heat and detoxifying， promoting blood circulation and removing blood stasis. The core combinations new prescription discovered by data mining are of important guiding significance， but they should be further verified in clinical practice and by experiments based on the theory of TCM.