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Background: Women with chronic medical conditions are at increased risk of adverse pregnancy outcomes, making contraceptive counselling an essential component of comprehensive chronic disease management. However, evidence regarding the knowledge and clinical practices of non-obstetric physicians remains limited. This study evaluated physicians’ knowledge, attitudes, and practices regarding contraceptive counselling for women with chronic medical conditions in a tertiary referral hospital in Türkiye. Methods: A cross-sectional Knowledge–Attitude–Practice survey was conducted among 264 non-obstetric physicians at İzmir City Hospital between July and October 2025. The questionnaire assessed contraceptive counselling practices, awareness of the World Health Organization Medical Eligibility Criteria for Contraceptive Use (WHO MEC), postgraduate contraception training, perceived barriers, and educational needs. Categorical associations were examined using chi-square or exact procedures as appropriate, and multivariable binary logistic regression was performed to evaluate factors associated with self-reported routine contraceptive counselling while accounting for potential confounding. Results: Among the 260 physicians who responded to the relevant item, 89.2% agreed that contraceptive method selection may influence the course of chronic disease, whereas 37.9% of the total sample reported routinely providing contraceptive counselling. Awareness of the WHO MEC was limited, with 75.8% reporting no prior awareness, and 18.2% had received postgraduate contraception training. In the adjusted analysis, WHO MEC awareness (aOR = 2.76, 95% CI: 1.39–5.46; p = 0.004), postgraduate contraception training (aOR = 2.38, 95% CI: 1.12–5.07; p = 0.024), seeing ≥30 women of reproductive age per week (aOR = 2.07, 95% CI: 1.15–3.73; p = 0.016), female sex (aOR = 3.81, 95% CI: 1.90–7.65; p < 0.001), and >10 years of clinical practice (aOR = 2.38, 95% CI: 1.23–4.62; p = 0.010) were associated with higher odds of self-reported routine counselling. Specialty was not significantly associated after adjustment. Conclusions: Self-reported routine contraceptive counselling remained limited despite broad recognition of the relevance of contraception in chronic disease care. Guideline awareness, postgraduate training, clinical exposure, sex, and clinical experience were associated with routine counselling after adjustment, although causal relationships cannot be inferred from this cross-sectional study. These findings support further evaluation of educational and organizational strategies for integrating reproductive healthcare into chronic disease management. Full article

Cannabis use is expanding rapidly across the United States, yet product quality and consumer safety knowledge remain significant public health concerns. This cross-sectional study surveyed 325 adults in Florida dispensary customers (n = 185) and general public participants (n = 140) to compare cannabis use patterns, purchasing behaviors, and safety awareness. Cannabinoid gummy products were characterized using texture analysis; oil and topical formulations were evaluated for label accuracy by high-performance liquid chromatography (HPLC) against USP specifications (90–110% of label claim). Inter-laboratory variability accelerated stability testing, and a machine learning microsimulation projecting knowledge trend (2026–2031) were also conducted. Texture analysis revealed substantial variability in mechanical properties across gummy brands. Oil and topical formulations demonstrated generally acceptable labeling accuracy, though significant inter-laboratory variability in CBD quantification was observed. Dispensary respondents reported more frequent cannabis use, stronger brand loyalty, greater perceived quality-of-life benefits, and higher product knowledge than the general public; however, critical safety knowledge gaps regarding THC–cardiovascular drug interactions and pregnancy risks persisted across both groups. These findings highlight the need for stronger product quality standards, standardized analytical testing, and targeted public health education to promote safe and informed cannabis use. Full article

Background: Recently, artificial intelligence-based tools such as large language models (LLMs) have further transformed health information-seeking practices. This study aimed to assess digital health information-seeking behaviors among healthcare professionals working in İzmir, Türkiye, and to examine individual and environmental factors associated with these behaviors. Methods: A cross-sectional study was conducted among 380 healthcare professionals in İzmir. Data were collected using an online questionnaire comprising sociodemographic and health information-seeking items and the eight-item e-health literacy scale (eHEALS). Descriptive statistics and non-parametric tests were used for unadjusted comparisons. Multivariable binary logistic regression was performed to identify factors independently associated with LLM use, while multivariable linear regression with HC3-robust standard errors was used to identify factors independently associated with e-health literacy. To reduce sparse-data instability, conceptually compatible small categories were collapsed before refitting the multivariable models. Statistical significance was set at p < 0.05. Results: Overall, 73.2% of participants reported using LLMs (e.g., ChatGPT) for health-related information seeking. After multivariable adjustment, daily internet use of 3–6 h was associated with higher odds of LLM use compared with ≤3 h/day (adjusted OR = 2.0437, 95% CI: 1.1467–3.6423, p = 0.0153). E-health literacy was not independently associated with LLM use (adjusted OR = 0.9875, 95% CI: 0.9599–1.0158, p = 0.3825). In the e-health literacy model, the combined divorced/widowed group had lower adjusted scores than married participants (B = −4.4087, 95% CI: −7.4003 to −1.4171, p = 0.0039). Uncertainty about institutional scientific database access was also associated with lower e-health literacy (B = −6.1805, 95% CI: −8.9255 to −3.4356, p < 0.0001), whereas often/always reading online health information was associated with higher scores compared with never/rarely reading it (B = 2.6264, 95% CI: 0.5112–4.7416, p = 0.0149). Conclusion: LLM use was common among healthcare professionals, but it was not independently associated with e-health literacy. Patterns of internet use, marital status, institutional database awareness, and frequency of reading online health information showed independent associations with the study outcomes. These findings support targeted digital health and AI-literacy initiatives. Full article

Vehicle-to-everything (V2X) connectivity provides electrified vehicles with previews of traffic signals, road geometry, surrounding traffic, and route conditions, yet control benefit arises only when these data are converted into variables that can shape motion and powertrain decisions. This review presents a structured, framework-driven narrative synthesis organized along an information–motion–energy chain: external preview, control-oriented prediction, energy-aware speed planning, trip-level energy and state-of-charge scheduling, power-source allocation, cross-layer coordination, and staged validation. The reviewed studies are compared in terms of coupling depth, from traffic-layer optimization and sequential speed–energy management strategy (EMS) schemes to hierarchical/weakly coupled and joint/tightly coupled formulations. Across hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), battery electric vehicles (BEVs), and fuel cell electric vehicle/hybrid electric vehicle (FCEV/FCHEV) platforms, the information interface is broadly shared, whereas energy-replenishment, thermal, and component-health constraints require powertrain-specific formulations. The evidence base also shows a persistent maturity gap between algorithmic simulation and hardware or vehicle validation. Key needs are uncertainty-aware closed-loop design, physically interpretable model–data fusion, fallback control under information degradation, standardized cross-layer benchmarks, and staged validation that reports both control performance and evidence level. Full article

Accurate and robust state-of-health (SOH) estimation is essential for efficient battery management systems (BMS), especially in practical scenarios suffering from severe cell-to-cell inconsistencies and data distribution shifts. Although prevailing data-driven estimation methods can achieve satisfactory accuracy within specific domains, their generalization capability degrades drastically when applied to unseen battery cells. To fill this research gap, this paper develops an integrated physics-guided and domain-aware SOH estimation framework. The proposed framework combines redundancy-aware feature screening, cycle-aware soft covariance alignment specifically designed for physics-based health indicators, as well as monotonicity-constrained LightGBM regression embedded with split conformal uncertainty quantification. Experimental validations are conducted on the NASA B0005, B0006 and B0007 battery datasets under the leave-one-cell-out (LOCO) cross-cell evaluation strategy. Comparative results reveal that the presented method achieves prominent performance improvement on the most difficult B6 domain, where the root mean square error (RMSE) is reduced from 0.1068 to 0.0845 with a decline rate of 20.9%, and the coefficient of determination ($R^2$) rises from 0.2464 to 0.5286, while maintaining stable estimation accuracy on less challenging target domains. In terms of overall performance, the average RMSE across all tested cells drops from 0.0499 to 0.0425, corresponding to a 14.8% reduction, indicating improved cross-cell performance across the investigated cells. Furthermore, the adaptive alignment mechanism can be dynamically activated only when necessary, effectively avoiding redundant distortion of the original feature distribution. The research findings indicate that the integrated framework can alleviate cross-cell battery degradation discrepancies under the investigated conditions. The proposed strategy demonstrates promising potential for improving cross-cell SOH estimation under the investigated laboratory conditions; however, the uncertainty intervals are not fully calibrated under severe domain shift, and broader validation on larger and more heterogeneous battery datasets is required before practical deployment claims. Full article

Respiratory-sound event classification is challenged by non-stationarity, class imbalance, heterogeneous acquisition, and participant-correlated recordings. This study evaluates whether direct fusion of short-time Fourier transform (STFT), mel-frequency cepstral coefficient (MFCC), and wavelet-packet features improves a temporal one-dimensional convolutional neural network (1D-CNN), and whether temporal convolution offers an advantage over conventional classifiers. A radial basis function support vector machine (RBF-SVM) and random forest were included deliberately to separate the value of the engineered representation from classifier complexity. Experiments used 920 recordings and 6898 annotated cycles from the International Conference on Biomedical and Health Informatics (ICBHI) 2017 Respiratory Sound Database. The predefined 60:40 recording-level benchmark partition was retained; model selection used five-fold participant-grouped cross-validation, and 95% confidence intervals were estimated from 1000 participant-level bootstrap resamples. The complete hybrid 1D-CNN achieved a macro-averaged F1-score of 0.313. MFCC alone yielded 0.320, but the paired difference was not statistically resolved. The RBF-SVM and random forest achieved 0.401 and 0.378, respectively. These findings apply to direct early concatenation with the shared 1D-CNN backbone and do not imply that feature fusion is generally ineffective. The study provides leakage-aware baselines, controlled ablations, clustered uncertainty estimates, and frozen artifacts for reproducible comparison. Full article

Background and Objectives: Yoga has been proposed as a supportive intervention to enhance mindfulness and reduce stress among nursing students, but evidence from mixed-method studies remains limited. This study aimed to examine the effects of a 12-week course-based yoga intervention on mindfulness and perceived stress and to explore students’ experiences of practicing yoga. Materials and Methods: A total of 42 second-year nursing students at a university in Istanbul, Türkiye, participated in a mixed-methods study, with 22 students enrolled in the elective Health and Yoga course constituting the intervention group and 20 students enrolled in another elective course constituting the control group. Group allocation was non-randomized and determined by students’ elective-course enrollment. The intervention group attended weekly 90 min yoga sessions for 12 weeks, delivered by a certified instructor (E-RYT 200). Quantitative outcomes were measured using the Mindful Attention Awareness Scale (MAAS) and Perceived Stress Scale (PSS), with baseline-adjusted ANCOVA and change score analyses performed to control for pre-test differences and gender. Effect sizes (partial η²) were reported, and 95% CIs were calculated for the mean differences in change scores. Qualitative data were collected via semi-structured interviews and analyzed thematically, integrating findings with quantitative results. Results: Baseline-adjusted ANCOVA revealed greater improvements in mindfulness (F(1, 39) = 29.686, p < 0.001, partial η² = 0.432) and perceived stress (F(1, 39) = 158.293, p < 0.001, partial η² = 0.802) in the intervention group, controlling for baseline scores. Change score analyses confirmed these findings: mindfulness increased (MD = 10.68, 95% CI [6.62, 14.75], p < 0.001) and perceived stress decreased (MD = −11.40, 95% CI [−13.32, −9.48], p < 0.001) significantly more in the intervention group than controls. Qualitative findings complemented the quantitative results, indicating perceived physical (flexibility, posture, pain reduction), mental (positive emotions, awareness, emotion regulation), and social (motivation, interaction) benefits of yoga. Some participants reported hesitation in applying yoga in nursing practice due to cultural barriers. Conclusions: The findings suggest that participating in a course-based yoga program may help improve mindfulness and support stress reduction among nursing students. Qualitative findings complemented the quantitative results by providing insight into students’ perceived physical, mental, and social experiences of yoga. Given the small, single-center, and self-selected sample, these findings should be interpreted cautiously. Larger, multicenter randomized studies with longer follow-up are needed to confirm these findings and assess their generalizability. Full article

Occupational exposure to blood and body fluids (OEBBF) continues to represent an important public health problem due to the associated medical, social and economic consequences. Young healthcare workers are at high risk of OEBBF because of their lack of experience, hesitation before performing a task and the nature of the task performed. This study aims to comparatively evaluate the knowledge, attitudes and perceptions on OEBBF among students from the Faculty of Medicine and the Faculty of Dental Medicine at Carol Davila University of Medicine and Pharmacy, Bucharest, Romania, during the 2021–2022 academic year in order to facilitate the adaptation of educational materials used in training programs. Out of the 911 students who completed the questionnaire, 55% (502) correctly identified hepatitis B virus (HBV) as having the highest risk of transmission after an OEBBF, with statistically significantly higher percentages among Dental Medicine students (152; 60% vs. 350; 53%; p = 0.003). However, lower percentages of Dental Medicine students recognized the protective concentration of antibodies against HBV (173; 68% vs. 536; 82%; p < 0.001) or reported knowing to be vaccinated against hepatitis B (110; 43% vs. 459; 70%; p < 0.001). Additionally, higher percentages of Dental Medicine students reported not having an antibodies concentration measurement since they started their clinical practice (206; 81% vs. 439; 67%; p < 0.001). If an OEBBF were to occur, 43% (391) reported that they would induce bleeding, either instinctively (210; 23%) or because they considered it necessary (181; 20%). Ninety-three students (10%) reported at least one past OEBBF; 59 (63%) reported it to their supervisors. Statistically significantly higher percentages of Dental Medicine students reported a high perceived risk of exposure compared to Medicine students (117; 46% vs. 202; 31%; p < 0.001), with the finding supported by univariable and multivariable analyses. In conclusion, the reporting frequency of past OEBBF in our study population was low. However, enhanced efforts are required to improve knowledge and awareness regarding OEBBF risk and preventive measures. Additionally, efforts should focus on assessing the level of protection against hepatitis B among Dental Medicine students and on promoting timely reporting to ensure appropriate post-exposure management. Full article

UAVs are being employed for inspection, mapping, emergency response, and low-altitude activities. However, propeller fractures, edge wear, tip loss, imbalance, motor-bearing deterioration, eccentric shafts, and operating circumstances might damage their propulsion systems. Acoustic health monitoring is promising for this task because propulsion failures may change pressure fluctuations, blade-passing tones, broadband aerodynamic noise, and vibration-radiated sound without structural changes. Wind, reverberation, interference from surrounding rotors, speed variations, sensor-direction effects, limited datasets, and the gap between laboratory precision and field robustness in operational situations prevent acoustic UAV diagnostics from being widely used. Acoustic diagnostics of small UAV propulsion systems are the focus of this article. Acoustic emission monitoring, microphone-array and beamforming, wavelet and time–frequency feature extraction, normalized feature design, machine learning, lightweight deep learning, and noise-resilient deployment evaluation criteria are included. The reviewed studies reported promising results, including on-board microphone-array isolation of damaged rotors, acoustic-camera propeller-tip identification, and deep learning on propeller-anomaly datasets. Standardization of sensor locations, publicly accessible multi-condition acoustic datasets, cross-UAV validation, systematic selection of wavelet bases and decomposition levels, and clear computational cost reporting are still lacking. A deployment-oriented roadmap emphasizes multi-condition benchmarking, sensor placement optimization, wavelet-based noise robustness, hybrid acoustic-vibration validation, domain adaptation, uncertainty-aware decision-making, and edge AI for real-time UAV propulsion health monitoring. Full article

Antimicrobial resistance poses a critical challenge to global public health. Urinary tract infections are one of the leading causes of morbidity in Mexico. This study aimed to identify the most prevalent bacterial pathogens in urinary bacterial isolates and to determine their antimicrobial and selected multidrug-resistance pattern profiles in outpatient and inpatient isolates at a tertiary care hospital in Mexico City. In this retrospective laboratory-based surveillance study, a census of 3434 urine samples collected between January and December 2023 was analyzed. Bacterial identification and antimicrobial susceptibility testing were performed using a VITEK 2 XL automated system. Escherichia coli was the predominant uropathogen in both groups, followed by Enterococcus spp. and Klebsiella spp. A significant disparity was observed in the resistance profiles: E. coli resistance to third-generation cephalosporins and fluoroquinolones was higher in inpatient isolates than in outpatient isolates. The selected multidrug-resistance patterns were generally higher in the inpatient isolates than in the outpatient isolates. Although common enteric pathogens with lower resistance levels predominated in the outpatient isolates, inpatient isolates required coverage targeting higher resistance. These findings reflect the need to tailor the treatment to the patients’ clinical context, launch awareness campaigns for the strategic use of antibiotics, and strengthen epidemiological surveillance. Full article

TikTok is the fifth-most-popular social media platform worldwide and is particularly popular among young adults. In recent years, research on the association between TikTok use and mental health among young adults has increased substantially. Following the PRISMA guidelines, this systematic review examined empirical studies investigating this association. We conducted a comprehensive literature search across four electronic databases: PubMed, Web of Science, Scopus, and CNKI. We included and synthesized 11 eligible studies using narrative synthesis. The findings indicated that TikTok use was associated with both potential risks and benefits for the mental health of young adults. These psychological outcomes varied according to patterns of TikTok use and the content encountered on the platform. Individual psychological characteristics, sex, age, and algorithm awareness were also identified as potential factors that may shape this association. The existing literature was limited by the predominance of cross-sectional study designs, which precluded causal inference. Future research should prioritize longitudinal studies and randomized controlled trials to inform the development and evaluation of interventions to support healthier, more responsible for TikTok use among young adults. Full article

Accurate state of health (SOH) estimation of lithium-ion batteries is essential for ensuring the safe and efficient operation of electric transportation and grid-scale energy storage systems (ESS). However, extracting reliable degradation information from Battery Management System (BMS) data remains challenging due to measurement noise, operating variations, and nonlinear characteristics of charging signals. To address this issue, a multi-scale spatiotemporal deep learning framework based on a multi-scale convolutional neural network and bidirectional long short-term memory (MS-CNN-BiLSTM) is proposed. A degradation-aware sliding-window strategy is first designed to extract aging-related features from charging signals, while parallel multi-scale convolution branches with different receptive fields are employed to capture temporal characteristics at multiple scales. Subsequently, an attention-enhanced BiLSTM module is introduced to aggregate long-term degradation dependencies and adaptively capture informative temporal representations. The proposed framework is evaluated using lithium-ion batteries with different chemistries, including the NASA LCO and MOLICEL NCM datasets. Experimental results demonstrate that the proposed method achieves accurate SOH estimation with MAE values as low as 0.0026 and R² values above 0.979. Furthermore, the model maintains consistent estimation performance across different battery chemistries with relatively low computational complexity, suggesting its potential suitability for embedded BMS applications. Full article

African leafy vegetables (ALVs) are increasingly recognised for their nutritional, phytochemical, and socioeconomic importance, particularly in resource-limited communities. This narrative review synthesises current evidence on the effects of preservation methods on nutritional composition, phytochemical stability, microbial quality, sensory attributes, consumer acceptability, and their contribution to sustainable food systems in South Africa, supported by evidence from across Africa and the broader scientific literature. Evidence indicates that ALVs are rich sources of essential nutrients and bioactive compounds with important health-promoting properties. Preservation methods such as sun drying, blanching, freezing and solar drying are widely used to extend shelf life and improve year-round availability. However, their effects on nutrient retention, phytochemical stability, microbial safety, and sensory quality vary considerably. The review further highlights persistent challenges within ALV value chains, including inadequate processing and storage infrastructure, limited value addition, weak market integration, and low consumer awareness. Collectively, these constraints restrict the wider utilisation and commercialisation of ALVs despite their potential to strengthen food security and improve dietary quality. Standardised preservation approaches, investment in appropriate processing infrastructure, stronger value chains, improved consumer awareness, and supportive institutional and policy frameworks are required to enhance the adoption and sustainable utilisation of ALVs. Full article

Background/Objectives: Musicians face occupational health risks from repetitive movement, sustained posture, and intensive practice. This study examined self-reported knowledge of musician-related disorders, physical complaints, and selected health practices among music educators and pre-service music teachers, using occupational health literacy (OHL) as an interpretive framework. Methods: A cross-sectional survey included 432 participants from public higher education institutions in Türkiye (83 educators; 349 pre-service teachers). The primary outcome was a common 15-item self-reported disorder knowledge score (range 15–75). Psychometric and nonparametric analyses included effect sizes, confidence intervals, and multiplicity correction. Results: Instrument-related physical problems were reported by 49.4% of educators and 53.0% of pre-service teachers. After false discovery rate correction, hand/wrist complaints were more frequent among pre-service teachers. The score showed a one-factor structure and excellent internal consistency. Educators (n = 79; M = 38.73, SD = 14.71) scored higher than pre-service teachers (n = 341; M = 26.81, SD = 12.35; U = 20,062.0, p < 0.001; rank biserial r = 0.489, 95% CI 0.364–0.602). Conclusions: Knowledge was uneven, especially for specialized conditions. Targeted musician-health education, communication, and referral pathways are warranted. The score is not a comprehensive OHL measure or a test of clinical competence, and causal effects on preventive behavior or occupational well-being cannot be inferred. Full article

Background/Objectives: Hepatitis A virus (HAV) infection is a relevant biological risk in some occupational settings and has long been discussed as an occupational health concern among municipal waste collection workers. Data on HAV seroprevalence and vaccination history in this occupational group remain limited in Italy. This study investigated HAV seroprevalence, self-reported vaccination history, and factors associated with HAV seropositivity among waste collection workers in Sardinia. Methods: This retrospective cross-sectional study analyzed HAV seroprevalence and self-reported vaccination history among 326 workers in Sardinia, Italy, using occupational health surveillance data (2017–2024). Recorded variables included age, sex, smoking habits, years of service, work setting, vaccination status, and serological results. Chi-square and Mann–Whitney U tests assessed group differences; logistic regression identified factors independently associated with HAV seropositivity. Results: HAV seroprevalence was 44.2%; only 0.9% of participants reported previous HAV vaccination. Multivariable analysis identified older age, but not years of service, as independently associated with HAV seropositivity once age was modeled as a continuous variable—a pattern more consistent with birth-cohort differences in lifetime HAV exposure than with cumulative occupational exposure. Worksite 7 showed a higher prevalence ratio of HAV seropositivity relative to the reference site, but this finding was based on a small subsample and should be considered exploratory; observed worksite-level differences may reflect organizational rather than geographic factors. Conclusions: Older age, rather than occupational tenure, was independently associated with HAV seropositivity among Sardinian waste workers, a pattern consistent with birth-cohort differences in lifetime exposure. The high seroprevalence and very low vaccination uptake highlight the need to strengthen prevention through improved vaccination access, awareness, and better integration of occupational and public health strategies. Full article