OBJECTIVE: Presenteeism, the phenomenon of employees working despite illness, is a significant issue globally, impacting individual well-being and organizational efficiency. This study examines presenteeism among Swiss employees, exploring its occurrence, primary factors, reasons, and impact on employees\' health.
METHODS: This study used cross-sectional data from 1,521 employees in different sectors in Switzerland. Descriptive statistics and multiple linear models for influencing factors and detrimental effects, such as burnout symptoms, job satisfaction, general health, and quality of life, were calculated for data analysis. Presenteeism was measured using the Hägerbäumer multi-item scale, ranging from 1 = \"Never in case of illness\" - 5 = \"Very often in case of illness.\"
RESULTS: The employees reported that in case of illness, they rarely worked in the last 12 months M = 2.04 (SD = 1.00). A positive approach to presenteeism in the team was associated with less presenteeism (β = -0.07) and problematic leadership culture in dealing with presenteeism with increased presenteeism (β = 0.10). In addition to well-known factors, presenteeism was significant for burnout symptoms (β = 1.49), general health status (β = -1.5), and quality of life (β = -0.01).
CONCLUSIONS: The study offers insights into the phenomenon of presenteeism among Swiss employees in various sectors by applying a multi-item scale for presenteeism. The findings indicate that a positive team dynamic and organizational culture may significantly reduce presenteeism. Presenteeism behavior is a significant factor of adverse outcomes. This highlights the importance of acknowledging presenteeism in the context of occupational health.

OBJECTIVE: We aimed to explore the associations of baseline and cumulative cardiovascular health with nonalcoholic fatty liver disease (NAFLD) development and regression using the new Life\'s Essential 8 score.
METHODS: From a health screening database, participants who underwent at least 4 health examinations between 2012 and 2022 were recruited and categorized into two cohorts: (a) the NAFLD development cohort with no history of NAFLD prior to Exam 4 and (b) the NAFLD regression cohort with diagnosed NAFLD prior to Exam 4. The LE8 score was calculated from each component. The outcomes were defined as newly incident NAFLD or regression of existing NAFLD from Exam 4 to the end of follow-up.
RESULTS: In the NAFLD development cohort, of 21,844 participants, 3,510 experienced incident NAFLD over a median follow-up of 2.3 years. Compared with the lowest quartile of cumulative LE8, individuals in the highest quartile conferred statistically significant 76% lower odds (hazard ratio [HR] 0.24, 95% confidence interval [CI], 0.21-0.28) of NAFLD incidence, and corresponding values for baseline LE8 were 42% (HR 0.58, 95% CI 0.53-0.65). In the NAFLD regression cohort, of 6,566 participants, 469 experienced NAFLD regression over a median follow-up of 2.4 years. Subjects with the highest quartile of cumulative LE8 had 2.03-fold (95% CI, 1.51-2.74) higher odds of NAFLD regression, and corresponding values for baseline LE8 were 1.61-fold (95% CI, 1.24-2.10).
CONCLUSIONS: Cumulative ideal cardiovascular health exposure is associated with reduced NAFLD development and increased NAFLD regression. Improving and preserving health behaviors and factors should be emphasized as an important part of NAFLD prevention and intervention strategies.

Developing countries like India are rapidly transitioning from traditional energy sources to sustainable energy sources, due to the increase in demand and the depletion of fossil fuels. Grid-connected photovoltaic (PV) systems attract many investors, organizations, and institutions for deployment. This article studies and compares the performance evaluations of three 52-kW PV plants installed at an educational institution, SRMIST (SRM Institute of Science and Technology), in Tamil Nadu, India. This site receives an annual average temperature of 28.5°C and an average global horizontal irradiation of 160 kWh/m2/m. The prediction model for the 52-kW power plant is obtained using solar radiation, temperature, and wind speed. Linear regression model-based prediction equations are derived using the Minitab 16.2.1 software, and the results are compared with the real-time AC energy yield acquired from the three 52-kW plants for the year 2020. Furthermore, this 52-kW plant is designed using PVsyst V7.1.8 version software. The simulation results are compared with the energy yield from the plants in 2020 to identify the shortfall in the plant performance. The loss analysis for the plant is performed by obtaining the loss diagram from the PVsyst software. This study also proposes a methodology to study the commissioned PV plant\'s performance and determine the interaction between variables such as direct and diffused solar radiations, air temperature, and wind speed for forecasting hourly produced power. This article will motivate researchers to analyze installed power plants using modern technical tools.

Anxiety and depression in children and adolescents warrant special attention as a public health concern given their devastating and long-term effects on development and mental health. Multiple factors, ranging from genetic vulnerabilities to environmental stressors, influence the risk for the disorders. This study aimed to understand how environmental factors and genomics affect children and adolescents anxiety and depression across three cohorts: Adolescent Brain and Cognitive Development Study (US, age of 9-10; N=11,875), Consortium on Vulnerability to Externalizing Disorders and Addictions (INDIA, age of 6-17; N=4,326) and IMAGEN (EUROPE, age of 14; N=1888). We performed data harmonization and identified the environmental impact on anxiety/depression using a linear mixed-effect model, recursive feature elimination regression, and the LASSO regression model. Subsequently, genome-wide association analyses with consideration of significant environmental factors were performed for all three cohorts by mega-analysis and meta-analysis, followed by functional annotations. The results showed that multiple environmental factors contributed to the risk of anxiety and depression during development, where early life stress and school support index had the most significant and consistent impact across all three cohorts. In both meta, and mega-analysis, SNP rs79878474 in chr11p15 emerged as a particularly promising candidate associated with anxiety and depression, despite not reaching genomic significance. Gene set analysis on the common genes mapped from top promising SNPs of both meta and mega analyses found significant enrichment in regions of chr11p15 and chr3q26, in the function of potassium channels and insulin secretion, in particular Kv3, Kir-6.2, SUR potassium channels encoded by the KCNC1, KCNJ11, and ABCCC8 genes respectively, in chr11p15. Tissue enrichment analysis showed significant enrichment in the small intestine, and a trend of enrichment in the cerebellum. Our findings provide evidences of consistent environmental impact from early life stress and school support index on anxiety and depression during development and also highlight the genetic association between mutations in potassium channels, which support the stress-depression connection via hypothalamic-pituitary-adrenal axis, along with the potential modulating role of potassium channels.

BACKGROUND: To develop and validate a deep learning model for automated assessment of endodontic case difficulty from periapical radiographs.
METHODS: A dataset of 1,386 periapical radiographs was compiled from two clinical sites. Two dentists and two endodontists annotated the radiographs for difficulty using the \"simple assessment\" criteria from the American Association of Endodontists\' case difficulty assessment form in the Endocase application. A classification task labeled cases as \"easy\" or \"hard\", while regression predicted overall difficulty scores. Convolutional neural networks (i.e. VGG16, ResNet18, ResNet50, ResNext50, and Inception v2) were used, with a baseline model trained via transfer learning from ImageNet weights. Other models was pre-trained using self-supervised contrastive learning (i.e. BYOL, SimCLR, MoCo, and DINO) on 20,295 unlabeled dental radiographs to learn representation without manual labels. Both models were evaluated using 10-fold cross-validation, with performance compared to seven human examiners (three general dentists and four endodontists) on a hold-out test set.
RESULTS: The baseline VGG16 model attained 87.62% accuracy in classifying difficulty. Self-supervised pretraining did not improve performance. Regression predicted scores with ± 3.21 score error. All models outperformed human raters, with poor inter-examiner reliability.
CONCLUSIONS: This pilot study demonstrated the feasibility of automated endodontic difficulty assessment via deep learning models.

BACKGROUND: Open-system electronic cigarette (EC) product features, such as battery capacity, maximum output wattage, and so forth, are major components that drive product costs and may influence use patterns. Moreover, continued innovation and monitoring of product features and prices will provide critical information for designing appropriate taxation policies and product regulations.
OBJECTIVE: This study will examine how product features are associated with the prices of devices sold in web-based vape shops.
METHODS: We draw samples from 5 popular, US-based, web-based vape shops from April to August 2022 to examine starter kits, device-only products, and e-liquid container-only products. We implemented a linear regression model with a store-fixed effect to examine the association between device attributes and prices.
RESULTS: EC starter kits or devices vary significantly by type, with mod prices being much higher than pod and vape pen prices. The prices of mod starter kits were even lower than those of mod devices, suggesting that mod starter kits are discounted in web-based vape shops. The price of mod kits, mod device-only products, and pod kits increased as the battery capacity and output wattage increased. For vape pens, the price was positively associated with the volume size of the e-liquid container. On the other hand, the price of pod kits was positively associated with the number of containers.
CONCLUSIONS: A unit-based specific tax, therefore, will impose a higher tax burden on lower-priced devices such as vape pens or pod systems and a lower tax burden on mod devices. A volume- or capacity-based specific tax on devices will impose a higher tax burden on vape pens with a larger container size. Meanwhile, ad valorem taxes pegged to wholesale or retail prices would apply evenly across device types, meaning those with advanced features such as higher battery capacities and output wattage would face higher rates. Therefore, policy makers could manipulate tax rates by device type to discourage the use of certain device products.

Data analysis methods for the study of treatments or exposures in relation to a clinical outcome in the presence of competing risks have a long history, often with inference targets that are hypothetical, thereby requiring strong assumptions for identifiability with available data. Here data analysis methods are considered that are based on single and higher dimensional marginal hazard rates, quantities that are identifiable under standard independent censoring assumptions. These lead naturally to joint survival function estimators for outcomes of interest, including competing risk outcomes, and provide the basis for addressing a variety of data analysis questions. These methods will be illustrated using simulations and Women\'s Health Initiative cohort and clinical trial data sets, and additional research needs will be described.

[This corrects the article DOI: 10.3389/fonc.2023.1275222.].

BACKGROUND: Type 2 diabetes disproportionately affects South Asian subgroups. Lifestyle prevention programs help prevent and manage diabetes; however, there is a need to tailor these programs for mobile health (mHealth).
OBJECTIVE: This study examined technology access, current use, and preferences for health communication among South Asian immigrants diagnosed with or at risk for diabetes, overall and by sex. We examined factors associated with interest in receiving diabetes information by (1) text message, (2) online (videos, voice notes, online forums), and (3) none or skipped, adjusting for sociodemographic characteristics and technology access.
METHODS: We used baseline data collected in 2019-2021 from two clinical trials among South Asian immigrants in New York City (NYC), with one trial focused on diabetes prevention and the other focused on diabetes management. Descriptive statistics were used to examine overall and sex-stratified impacts of sociodemographics on technology use. Overall logistic regression was used to examine the preference for diabetes information by text message, online (videos, voice notes, or forums), and no interest/skipped response.
RESULTS: The overall sample (N=816) had a mean age of 51.8 years (SD 11.0), and was mostly female (462/816, 56.6%), married (756/816, 92.6%), with below high school education (476/816, 58.3%) and limited English proficiency (731/816, 89.6%). Most participants had a smartphone (611/816, 74.9%) and reported interest in receiving diabetes information via text message (609/816, 74.6%). Compared to male participants, female participants were significantly less likely to own smartphones (317/462, 68.6% vs 294/354, 83.1%) or use social media apps (Viber: 102/462, 22.1% vs 111/354, 31.4%; WhatsApp: 279/462, 60.4% vs 255/354, 72.0%; Facebook: Messenger 72/462, 15.6% vs 150/354, 42.4%). A preference for receiving diabetes information via text messaging was associated with male sex (adjusted odds ratio [AOR] 1.63, 95% CI 1.01-2.55; P=.04), current unemployment (AOR 1.62, 95% CI 1.03-2.53; P=.04), above high school education (AOR 2.17, 95% CI 1.41-3.32; P<.001), and owning a smart device (AOR 3.35, 95% CI 2.17-5.18; P<.001). A preference for videos, voice notes, or online forums was associated with male sex (AOR 2.38, 95% CI 1.59-3.57; P<.001) and ownership of a smart device (AOR 5.19, 95% CI 2.83-9.51; P<.001). No interest/skipping the question was associated with female sex (AOR 2.66, 95% CI 1.55-4.56; P<.001), high school education or below (AOR 2.02, 95% CI 1.22-3.36; P=.01), not being married (AOR 2.26, 95% CI 1.13-4.52; P=.02), current employment (AOR 1.96, 95% CI 1.18-3.29; P=.01), and not owning a smart device (AOR 2.06, 95% CI 2.06-5.44; P<.001).
CONCLUSIONS: Technology access and social media usage were moderately high in primarily low-income South Asian immigrants in NYC with prediabetes or diabetes. Sex, education, marital status, and employment were associated with interest in mHealth interventions. Additional support to South Asian women may be required when designing and developing mHealth interventions.
BACKGROUND: ClinicalTrials.gov NCT03333044; https://classic.clinicaltrials.gov/ct2/show/NCT03333044, ClinicalTrials.gov NCT03188094; https://classic.clinicaltrials.gov/ct2/show/NCT03188094.
UNASSIGNED: RR2-10.1186/s13063-019-3711-y.

The strength of rock under uniaxial compression, commonly known as Uniaxial Compressive Strength (UCS), plays a crucial role in various geomechanical applications such as designing foundations, mining projects, slopes in rocks, tunnel construction, and rock characterization. However, sampling and preparation can become challenging in some rocks, making it difficult to determine the UCS of the rocks directly. Therefore, indirect approaches are widely used for estimating UCS. This study presents two Machine Learning Models, Simple Linear Regression and Step-wise Regression, implemented in Python to calculate the UCS of Charnockite rocks. The models consider Ultrasonic Pulse Velocity (UPV), Schmidt Hammer Rebound Number (N), Brazilian Tensile Strength (BTS), and Point Load Index (PLI) as factors for forecasting the UCS of Charnockite samples. Three regression metrics, including Coefficient of Regression (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), were used to evaluate and compare the performance of the models. The results indicate a high predictive capability of both models. Notably, the Step-wise model achieved a testing R2 of 0.99 and a training R2 of 0.988 for predicting Charnockite strength, making it the most accurate model. The analysis of the influential factors indicates that UPV plays a significant role in predicting the UCS of Charnockite.