The aim is to investigate the effects of a hybrid-type multicomponent exercise program on muscle strength, resting metabolic rate (RMR), cardiopulmonary capacity (VO2peak), and body composition after bariatric surgery. Twenty adults (5 males, 15 females; BMI ≥ 30 kg/m2) who underwent sleeve gastrectomy were evenly assigned to a hybrid-type multicomponent exercise group (HEG) or a Control Group (CG). Three months after the surgery, the HEG commenced a hybrid exercise program, which was undertaken three days per week for a period of four months. A range of health and fitness metrics were measured at the beginning of the study and after the intervention, including musclular strength, body composition, skeletal muscle index (SMI), VO2max, and RMR. Exercise effects were analysed using two-way repeated-measures ANOVA, with sex included as a covariate to account for any differences between the groups. Significant time effects were observed for all anthropometric and body composition variables in both groups (p < 0.001), reflecting the dominant effect of surgery. Reductions in body weight and BMI and regional adiposity were greater in the HEG (p < 0.01), whereas lean body mass, SMI and RMR decreased similarly in both groups (p > 0.05). In contrast, significant time × group interactions with large effect sizes were found for all upper- and lower-extremity strength measures (p < 0.001), favoring the HEG, with the greatest gains in the lower extremities (p < 0.001). Handgrip strength and relative VO₂peak increased significantly only in the HEG (p < 0.001). Physical activity levels increased markedly in the HEG (p = 0.003), with all participants classified as highly active post-intervention, while the CG largely remained in low or moderate categories (p < 0.001). Although the hybrid-type multicomponent exercise program does not prevent early postoperative reductions in RMR and lean body mass after bariatric surgery, it provides associated with functional and metabolic benefits by improving muscle strength, cardiorespiratory fitness, physical activity levels, and adiposity-related outcomes, thereby supporting functional capacity. The hybrid-type multicomponent exercise program implemented after bariatric surgery significantly enhanced cardiopulmonary capacity and muscle strength, thereby supporting functional recovery. Resting metabolic rate and fat-free mass did not increase in either group after surgery, and the hybrid exercise intervention was unable to prevent these physiological declines. Improvements in body weight, body fat percentage, and other anthropometric parameters were observed in both groups, with more pronounced gains in the hybrid exercise group.
To evaluate the diagnostic accuracy of the Diagnocat™ artificial intelligence (AI) system for caries detection on bitewing radiographs compared with expert human examiners, with emphasis on differences between enamel and dentin lesions. A sample of 100 digital bitewing radiographs (1540 surfaces) was retrospectively selected from the European University Cyprus dental clinic database using a systematic backward screening method. Radiographs were obtained with a standardized phosphor plate system and anonymized before analysis. Two independent experts (operative dentistry and oral radiology) established the reference standard. AI and human assessments were binarized (caries/no caries; enamel/dentin) and compared. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated, and statistical significance was tested across detection categories. Diagnocat™ showed high specificity (94.3
This paper refers to three different ways that are used to measure school effectiveness. Specifically, it refers to school effect by considering a) student achievement-gains (i.e., the quality dimension of effectiveness), b) achievement-gap in terms of students' socioeconomic status (i.e., the equity dimension), and c) the impact of one extra year of schooling on final achievement. Criterion consistency is examined by analysing data on mathematics achievement of all Grade 4-6 students (n = 5520) in 40 Cypriot schools at the beginning and at the end of the school year. Student background factors were also measured. The school effectiveness status which was measured by considering the schooling effect was found to be related to the school effectiveness status measured by considering the quality and/or the equity dimension of effectiveness. The importance of using multiple criteria to measure school effectiveness for formative reasons is discussed. Implications for research, policy and practice are drawn.
Extracellular vesicles (EVs) have emerged as essential mediators of intercellular communication, transporting a complex repertoire of lipids, proteins, and nucleic acids that mirror the physiological and pathological status of their parent cells. This review provides a comprehensive overview of EVs from their biogenesis and molecular composition to their translational potential in human disease. This review outlines the major classes of EVs, including exosomes, microvesicles, apoptotic bodies, and oncosomes, together with recent developments in their isolation, molecular characterization, and omics-based profiling. Special focus is given to the role of EVs in viral infection, inflammation, and immune regulation, as well as their contribution to disease development and cancer biology. Moreover, we highlight the emerging clinical applications of mesenchymal stem cell-derived EVs (MSC-EVs) in regenerative medicine and oncology, alongside the therapeutic modulation of EV signaling by photobiomodulation (PBM). Finally, we address key translational challenges related to standardization, scalability, and regulatory validation. As exosome-based therapeutics fall under strict FDA and EMA oversight, their translation further depends on harmonized quality controls and robust safety evaluation. By integrating molecular mechanisms with clinical applications, this review emphasizes the transformative potential of EVs as next-generation diagnostic and therapeutic tools in precision medicine.
Machine learning (ML) drives progress in occupational accident prevention across diverse sectors. However, significant challenges persist in aligning these tools with practical safety needs, including accurate risk assessment, incident prediction, and targeted prevention strategies. While prior reviews focused narrowly on specific industries or data types, this study presents a comprehensive analysis of ML models in accident analysis, categorizing them by accident type, industry application, and modeling methodology. This study addresses critical challenges in ML model development—such as data quality, hyperparameter tuning, and managing class imbalances—and examines less-discussed topics, including explanatory variable selection and strategies for mitigating overfitting. This review thoroughly assesses the current state of ML-based accident prediction, highlighting critical gaps, methodological limitations, and potential research directions. By analyzing 504 studies across three perspectives—Accident Type, Industry Application, and Modeling Methodology—this review identifies pressing challenges, including (1) limitations in data quality and availability, especially for real-time sources; (2) inadequate model interpretability across applications; (3) difficulties in handling imbalanced accident datasets; and (4) the lack of an integrated framework for incorporating proactive data and industry-specific risk factors. The findings outline a roadmap for advancing ML in occupational safety by enhancing model robustness, improving interpretability, and expanding data sources. This review aims to better align ML applications with safety objectives, promoting data-driven approaches for effective accident analysis and prevention across industries.
