The purpose of this study was to establish a luciferase immunosorbent assay(LISA)using the Crimean-Congo hemorrhagic fever virus(CCHFV)glycoprotein C(Gc),a specific antigen,for the detection of CCHFV IgG antibodies.Three antigenic fragments based on CCHFV glycoprotein C were designed,and three recombinant plasmids were constructed by liga-tion with the NanoLuc luciferase(NLuc)expression vector pNLF1-N through molecular cloning.The accuracy of the sequences in the recombinant plasmids was confirmed through sequencing.The recombinant plasmids were transfected into eukaryotic cells to obtain fusion proteins containing specific antigens and luciferase,and the expression of the fusion proteins was verified by western blotting,thereby facilitating the establishment of the CCHFV-LISA detection technique.The assay's sensitivity,specificity,and stability were evaluated and compared with those of a commercial CCHFV IgG antibody test kit.Three recom-binant antigen fragments of CCHFV Gc—NLuc-Gc-Full,NLuc-Gc-C1,and NLuc-Gc-C2—were expressed,with molecular weights of 80.1 kDa,62.8 kDa,and 53.9 kDa,respectively.The optimal fragment for CCHFV detection was NLuc-Gc-C2.The sensitivity of the CCHFV-LISA was 90.9％,and the specificity was 100％;the findings were highly concordant with those for the commercial CCHFV enzyme-linked immunosorbent assay kit.Repeatability tests indicated no statistically significant differ-ences in inter-and intra-assay variability within the same batch.The LISA was highly specific,sensitive,and user-friendly in detecting IgG antibodies against the CCHFV.Therefore,this method may facilitate serological diagnosis and epidemiological studies in endemic regions,and provide essential technical support for disease surveillance and early warning.

BACKGROUND: Depression among older adults is an important public health issue, and air and noise pollution have been found to contribute to exacerbation of depressive symptoms. This study examined the association of exposure to air and noise pollutants with clinically-newly-diagnosed depressive disorder. The mediating role of individual pro-inflammatory markers was explored. METHODS: We linked National Health Insurance claim data with 2998 healthy community-dwellers aged 55 and above who participated in the Healthy Aging Longitudinal Study between 2009 and 2013. Newly diagnosed depressive disorder was identified using diagnostic codes from the medical claim data. Pollutants were estimated using nationwide land use regression, including PM_2.5 and PM₁₀, carbon monoxide, ozone, nitrogen dioxide, sulfur dioxide, and road traffic noise. Cox proportional hazard models were employed to examine the association between pollutants and newly developed depressive disorders. The mediating effect of serum pro-inflammatory biomarkers on the relationship was examined. RESULTS: Among the 2998 participants, 209 had newly diagnosed depressive disorders. In adjusted Cox proportional hazard models, one interquartile range increase in PM_2.5 (8.53 µg/m³) was associated with a 17.5% increased hazard of developing depressive disorders. Other air pollutants and road traffic noise were not linearly associated with depressive disorder incidence. Levels of serum tumor necrosis factor receptor 1 mediated the relationship between PM_2.5 and survival time to newly onset depressive disorder. CONCLUSION PM_2.5 is related to an increased risk of newly developed depressive disorder among middle-aged and older adults, and the association is partially mediated by the pro-inflammatory marker TNF-R1.
Humans ; Particulate Matter/analysis* ; Male ; Female ; Middle Aged ; Longitudinal Studies ; Aged ; Incidence ; Air Pollutants/analysis* ; Environmental Exposure/adverse effects* ; Taiwan/epidemiology* ; Receptors, Tumor Necrosis Factor, Type I/blood* ; Proportional Hazards Models ; Biomarkers/blood* ; Depression/epidemiology* ; Aged, 80 and over ; Depressive Disorder/chemically induced* ; Risk Factors ; Air Pollution/adverse effects*

The purpose of this study was to establish a luciferase immunosorbent assay(LISA)using the Crimean-Congo hemorrhagic fever virus(CCHFV)glycoprotein C(Gc),a specific antigen,for the detection of CCHFV IgG antibodies.Three antigenic fragments based on CCHFV glycoprotein C were designed,and three recombinant plasmids were constructed by liga-tion with the NanoLuc luciferase(NLuc)expression vector pNLF1-N through molecular cloning.The accuracy of the sequences in the recombinant plasmids was confirmed through sequencing.The recombinant plasmids were transfected into eukaryotic cells to obtain fusion proteins containing specific antigens and luciferase,and the expression of the fusion proteins was verified by western blotting,thereby facilitating the establishment of the CCHFV-LISA detection technique.The assay's sensitivity,specificity,and stability were evaluated and compared with those of a commercial CCHFV IgG antibody test kit.Three recom-binant antigen fragments of CCHFV Gc—NLuc-Gc-Full,NLuc-Gc-C1,and NLuc-Gc-C2—were expressed,with molecular weights of 80.1 kDa,62.8 kDa,and 53.9 kDa,respectively.The optimal fragment for CCHFV detection was NLuc-Gc-C2.The sensitivity of the CCHFV-LISA was 90.9％,and the specificity was 100％;the findings were highly concordant with those for the commercial CCHFV enzyme-linked immunosorbent assay kit.Repeatability tests indicated no statistically significant differ-ences in inter-and intra-assay variability within the same batch.The LISA was highly specific,sensitive,and user-friendly in detecting IgG antibodies against the CCHFV.Therefore,this method may facilitate serological diagnosis and epidemiological studies in endemic regions,and provide essential technical support for disease surveillance and early warning.

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.