Search Results (5,977)

Research surrounding impacts of staffing on pharmacy residents is limited. This prospective survey study aims to elucidate relationships between burnout and weekends staffed among California pharmacy residents. Postgraduate year 1 and 2 (PGY1 and PGY2) pharmacy residents completed electronic surveys in August 2023 and February 2024. The primary outcome was the difference in burnout score changes based on weekends required to staff (measured using the Maslach Burnout Inventory-Human Services Survey for Medical Personnel). Secondary subgroup analyses measured differences in burnout scores by the overall cohort, no weekend staffing vs. weekend staffing required, PGY1 vs. PGY2, and by changes in planned professional pursuits. Of 66 respondents, no significant differences in burnout scores were observed based on the number of weekends required to staff. Final mean emotional exhaustion (EE), but not depersonalization (DP) or personal accomplishment (PA), scores were significantly higher for all residents combined, increasing from 24.8 (SD 10.2) to 28.4 (SD 11.5). Final mean EE scores were also significantly higher among PGY2s compared to PGY1s, at 35.1 (SD 0.70) vs. 25.8 (SD 12.0), respectively. Final mean burnout scores were significantly worse in those becoming less likely to pursue board specialty certification across all domains, with EE = 32.6 (SD 6.50), DP = 4.29 (4.79), and PA = 36.3 (SD 3.21). Based on these results, staffing intensity alone may not be associated with burnout among California pharmacy residents, but PGY2 pharmacy residents may be at higher risk of burnout. Higher burnout scores may predict the likelihood of pursuing board specialty certification. Future studies assessing additional confounding factors with a broader scope are needed to fully define risk factors for burnout in pharmacy residents. Full article

Background/Objectives: Subjective well-being (SWB) in children is a key indicator of healthy development, influenced by physical activity and sports, with physical literacy (PL) as a potential mediator. Traditional linear models overlook non-linear and heterogeneous effects in diverse populations. This study uses causal machine learning (ML) to examine PL’s mediating role between sports participation and SWB in a multinational cohort. Methods: Data from the International Survey of Children’s Well-Being (ISCWeB) (n = 128,184 children aged 6–14, 35 countries) were analyzed. SWB was a composite (six items, α = 0.85); PL was a proxy (three items excluding sports frequency, α = 0.70); sports participation was continuous (0–5). Confounders were age, gender, parental listening, and school satisfaction. CausalForestDML estimated the effects; GroupKFold and bootstrap were used for robustness; SHAP/PDP was used for interpretability. Results: Total ATE = 0.083 (95% CI [0.073, 0.094]); indirect via PL = 0.055 (CI [0.049, 0.061]); direct = 0.028 (CI [0.020, 0.038]); mediation proportion = 0.660. Sensitivity with lean PL (2 items) was as follows: indirect = 0.045 (CI [0.040, 0.050]). For SHAP, school satisfaction was (+0.28), and parents were (+0.20) top. For PDP, there was a non-linear rise at PL 4–6 (+1.2 units) and a plateau ~9.2. The cross-cultural mean ATE = 0.083 ± 0.01 (from within-country meta-analysis); this was stronger in older children (CATE 0.30 for 12–14). For Rho sensitivity at 0.1, it was indirect −0.129; at Rho sensitivity of 0.2, it was −0.314 (robust to low confounding). Conclusions: The findings, grounded in SDT/PYD, support interventions targeting PL through sports to enhance SWB, addressing inactivity. Limitations are its cross-sectional nature and proxy measures; we recommend longitudinal studies. Full article

To overcome the inadequacy of traditional financial metrics in appraising green infrastructure, this study develops and validates an integrated framework combining financial and sustainability indicators to more accurately predict project performance. Employing a mixed-methods design, this study synthesized metrics from expert interviews (N = 24) and literature, then collected data from 42 completed projects in Gulf Cooperation Council countries. The framework’s predictive validity was tested using a novel application of a Gradient Boosting Machine (XGBoost) model, with SHAP (SHapley Additive exPlanations) analysis ensuring model interpretability. The integrated framework yielded higher out-of-sample discriminatory performance (AUC-ROC = 0.88) than a baseline using only traditional metrics (AUC-ROC = 0.71). In SHAP analyses, RBCR and LCC contributed most to the model’s predictions, whereas NPV and IRR contributed least. These results indicate stronger predictive associations for sustainability-oriented metrics in this study’s model. Because the design is cross-sectional and predictive, all findings are associational rather than causal; residual confounding is possible. The validated, interpretable model is therefore positioned as a decision support tool that complements, rather than replaces, expert appraisal. Full article

Measures of narcissism often overlap with global self-esteem, risking that observed associations with outcomes may reflect associations of self-regard rather than actual narcissistic dispositions. The present study examined whether common narcissism instruments differ in their overlap with self-esteem and how this alters their associations with key personality domains. A sample of 337 participants completed multiple measures of narcissism, a global self-esteem measure as the control variable, and assessments of the Big Five, empathy, and aggression as personality correlates. Our results showed that overlap the measures of narcissism share with self-esteem varied considerably. Vulnerable scales showed the largest overlap and the greatest changes in correlations with the personality correlates after controlling for self-esteem. Grandiose and antagonistic measures were generally less affected, though noteworthy differences emerged between these instruments as well. We conclude that self-esteem overlap is a substantive but uneven measurement issue. Researchers cannot assume measures to be interchangeable. Our findings suggest that in order to isolate narcissistic dispositions from self-regard, researchers may need to select less affected instruments and/or report (additional) analyses controlling for self-esteem. Full article

The intensification of agricultural land use (A-LUI) is a central driver of global environmental change, affecting soil health, water quality, biodiversity, and greenhouse gas balances. Monitoring A-LUI remains challenging because it is shaped by multiple management practices, ecological processes, and spatio-temporal dynamics. This review provides a comprehensive synthesis of existing definitions and standards of A-LUI at national and international levels (FAO, OECD, World Bank, EUROSTAT) and evaluates in situ methods alongside the rapidly expanding potential of remote sensing (RS). We introduce a novel RS-based taxonomy of A-LUI indicators, structured into five complementary categories: trait, genesis, structural, taxonomic, and functional indicators. Numerous examples illustrate how traits and management practices can be translated into RS proxies and linked to intensity signals, while highlighting key challenges such as sensor limitations, cultivar variability, and confounding environmental factors. We further propose an integrative framework that connects management practices, plant and soil traits, RS observables, validation needs, and policy relevance. Emerging technologies—such as hyperspectral imaging, solar-induced fluorescence, radar, artificial intelligence, and semantic data integration—are discussed as promising pathways to advance the monitoring of A-LUI across scales. By compiling and structuring RS-derived indicators, this review establishes a conceptual and methodological foundation for transparent, standardised, and globally comparable assessments of agricultural land use intensity, thereby supporting both scientific progress and evidence-based agricultural policy. Full article

Background/Objectives: Menopause is a significant, universal hormonal transition, with symptoms impacting ~80% of women. Research shows that menopause can be professionally disruptive, contributing to decreased productivity, absenteeism, and early exit from the workplace. The objective of this study was to describe the landscape of menopause among Canadian women physicians and explore its potential impact on work performance, job satisfaction, and absenteeism. Methods: In this exploratory cross-sectional study, Canadian physicians self-identifying as women and peri-menopausal or menopausal were invited to participate in an online survey between May–September 2023. Demographic and practice characteristics data were collected. A modified Menopause Rating Scale (MRS) was used to quantify symptom burden. Qualitative data describing the menopausal experience were collected as well. Primary outcome was self-reported work performance. Secondary outcomes included perceived impact of menopause on promotional opportunities, absenteeism, and job satisfaction. Multivariable regression was used to examine associations between MRS scores and outcomes of interest. Results: Among 217 respondents, 47.7% reported a severe menopausal symptom burden; 40% felt menopause negatively impacted work performance, and 16.1% expressed job dissatisfaction. However, fewer than 10 respondents (4.6%) ever took time off for menopausal symptoms. Increasing MRS scores were significantly associated with negative perceived work performance (p < 0.001), fewer promotional opportunities (p < 0.001), and lower job satisfaction (p = 0.006) when controlling for confounders. Qualitative responses were provided by 43 participants, 6 of whom reported positive aspects of the menopausal transition, whereas 20 elaborated on the challenges. Conclusions: Canadian women physicians can experience severe menopausal symptoms, often without support. This needs assessment highlights an important occupational health issue and suggests that opportunities remain for medical institutions and employers to formally recognize and study this life stage of women physicians to improve well-being for this valuable workforce. Full article

Background: Demographic factors such as education, sex, and age can significantly influence cognitive test performance, yet their impact on the Montreal Cognitive Assessment (MoCA) and Rey Complex Figure (CF) test has not been fully characterized in large, cognitively normal samples. Understanding these effects is critical for refining normative standards and improving the clinical interpretation of neuropsychological assessments. Methods: Data from 926 cognitively healthy adults (MoCA ≥ 24) were analyzed using supervised machine learning classifiers and complementary statistical models to identify the most predictive MoCA and CF features associated with education, sex, and age, while including race as a covariate. Feature importance analyses were conducted to quantify the relative contributions of accuracy-based and time-based measures after adjusting for demographic confounding. Results: Distinct patterns emerged across demographic groups. Higher educational attainment was associated with longer encoding times and improved recall performance, suggesting more deliberate encoding strategies. Sex differences were most apparent in the recall of visuospatial details and language-related subtests, with women showing relative advantages in fine detail reproduction and verbal fluency. Age-related differences were primarily reflected in slower task completion and reduced spatial memory accuracy. Conclusions: Leveraging one of the largest reported samples of cognitively healthy adults, this study demonstrates that education, sex, and age systematically influence MoCA and CF performance. These findings highlight the importance of incorporating demographic factors into normative frameworks to enhance diagnostic precision and the interpretability of cognitive assessments. Full article

Accurate estimation of lithium-ion battery (LIB) state of health (SOH) underpins safe operation, predictive maintenance, and lifetime-aware energy management. Despite recent advances in machine learning (ML), systematic benchmarking across heterogeneous real-world cells remains limited, often confounded by data leakage and inconsistent validation. Here, we establish a leakage-averse, cross-battery evaluation framework encompassing 32 commercial LIBs (B5–B56) spanning diverse cycling histories and temperatures (≈4 °C, 24 °C, 43 °C). Models ranging from classical regressors to ensemble trees and deep sequence architectures were assessed under blocked 5-fold GroupKFold splits using RMSE, MAE, R² with confidence intervals, and inference latency. The results reveal distinct stratification among model families. Sequence-based architectures—CNN–LSTM, GRU, and LSTM—consistently achieved the highest accuracy (mean RMSE ≈ 0.006; per-cell R² up to 0.996), demonstrating strong generalization across regimes. Gradient-boosted ensembles such as LightGBM and CatBoost delivered competitive mid-tier accuracy (RMSE ≈ 0.012–0.015) yet unrivaled computational efficiency (≈0.001–0.003 ms), confirming their suitability for embedded applications. Transformer-based hybrids underperformed, while approximately one-third of cells exhibited elevated errors linked to noise or regime shifts, underscoring the necessity of rigorous evaluation design. Collectively, these findings establish clear deployment guidelines: CNN–LSTM and GRU are recommended where robustness and accuracy are paramount (cloud and edge analytics), while LightGBM and CatBoost offer optimal latency–efficiency trade-offs for embedded controllers. Beyond model choice, the study highlights data curation and leakage-averse validation as critical enablers for transferable and reliable SOH estimation. This benchmarking framework provides a robust foundation for future integration of ML models into real-world battery management systems. Full article

Accurate wind turbine noise (WTN) measurements are essential for environmental compliance and noise impact assessments. However, these measurements are often polluted by background biological noise, especially from insects. Insect noise is typically assumed to be irrelevant due to frequency separation. This study challenges this assumption by demonstrating that insect sounds, specifically those of the cricket Oecanthus pellucens, can overlap with turbine noise in the 2.5 kHz band and introduce significant measurement bias at low wind speeds. The featured application is a machine learning-based methodology to filter confounding biological sounds (e.g., insect calls) from wind turbine noise measurements. By correcting for these acoustic contaminants, which typically lead to an overestimation of turbine noise at low wind speeds, the method enables more accurate environmental noise impact assessments. This directly supports the development of evidence-based regulatory policies and guidelines. Using long-term acoustic monitoring and an unsupervised Gaussian Mixture Model (GMM) clustering approach, we classified and excluded insect noise from recorded data. We found that the presence of cricket calls can increase measured wind turbine sound power levels (WTSPL) by more than 3 dBA at wind speeds below 6 m/s, with peak deviations reaching up to 10 dBA. These findings have significant implications for rural or low-wind regions where turbine operation at partial load is frequent. Our results underscore the importance of insect noise filtering when performing WTN assessments to ensure regulatory accuracy, particularly when long-term average noise modeling is used for compliance. The presented methodology provides a robust framework for distinguishing insect noise and can improve the consistency and credibility of WTN measurements under real-world environmental conditions. Full article

Background/Objectives: Furosemide is frequently used in preterm neonates for respiratory and fluid management but has been linked to nephrocalcinosis (NC), a renal complication with unclear long-term impact. Clarifying this association is crucial for safe diuretic use. Methods: A focused literature review included observational studies published between 1982 and 2025 reporting NC incidence by renal ultrasound in preterm infants receiving furosemide. Data on sample size, gestational age, birth weight, NC prevalence, and furosemide dosing/duration were extracted. Results were synthesized descriptively. Results: Twenty-two studies with 1489 infants were included. NC prevalence ranged 6–83%, higher in infants <32 weeks’ gestation or <1500 g. Across studies, incidence clustered at 17–41% between 4 weeks and term-equivalent age. Cumulative furosemide doses were generally three- to fourfold higher in NC groups (10–19 mg/kg cumulative vs. ≤5 mg/kg cumulative, p < 0.001). A dose-dependent risk was noted, with odds ratios increasing above a cumulative dose of 10 mg/kg. Some studies found no significant dose–response, indicating variability and confounding factors. NC was detected during NICU stay or around term-equivalent age; ~60% resolved after discontinuation, while persistent cases were associated with prolonged exposure and renal dysfunction. A recent multicenter, dose-escalation randomized trial showed that carefully dosed furosemide (≤2 mg/kg/day for 28 days) did not increase NC risk, though electrolyte disturbances were more frequent. Conclusions: Evidence supports a dose-related association between furosemide and NC in preterm infants. When administered cautiously within defined limits, risk may be mitigated. Careful dosing, monitoring, and further studies are essential for safe use. Full article

Background: Tuberculosis (TB) is a leading cause of death among people living with HIV (PLHIV). While body mass index (BMI) affects TB risk, its association with latent tuberculosis infection (LTBI) in PLHIV is unclear. High-transmission settings, such as prisons, may further increase LTBI risk, yet this relationship has not been studied across both prison and community populations of PLHIV. Methods: We conducted a dual cross-sectional study of PLHIV in Jiangsu Province, China, recruiting participants from a prison hospital in 2021 and community healthcare facilities from July to November 2023. BMI was calculated from measured height and weight. LTBI was identified by a positive ESAT6-CFP10 (EC) skin test or the QuantiFERON-TB Gold In-Tube (QFT-GIT) assay. Logistic regression and generalized additive models (GAMs) assessed the association between BMI and LTBI, adjusting for demographic, clinical, and behavioral confounders. Results: A total of 1799 PLHIV were included in the analysis, of whom 343 (19.07%) were recruited from prison settings and 1456 (80.93%) from community-based screening. The overall prevalence of LTBI was 13.79% (n = 248). Obesity (BMI ≥ 28 kg/m²) was linked to a significantly lower risk of LTBI (adjusted OR = 0.47, 95% CI: 0.23–0.95, p = 0.036), particularly when identified by EC testing (adjusted OR = 0.13, 95% CI: 0.03–0.54, p = 0.005). The BMI–LTBI association followed a nonlinear “U-shaped” pattern, with the lowest prevalence in individuals who were obese. Among those with CD4+ T cell counts < 500 cells/μL, the inverse association between obesity and LTBI was even more marked (adjusted OR = 0.20, 95% CI: 0.05–0.83, p = 0.027). Conclusion: In summary, obesity is significantly associated with a lower risk of LTBI among PLHIV, with an approximate 54% risk reduction. This inverse relationship was most pronounced when using the EC skin test. Full article

Soil organic carbon (SOC) is a critical component of soil health, influencing soil structure, soil water retention capacity, and nutrient cycling while playing a key role in the global carbon cycle. Accurate SOC estimation over croplands is essential for sustainable land management and climate change mitigation. This study explores a novel approach to SOC estimation using multi-frequency synthetic aperture radar (SAR) data, specifically Sentinel-1 and ALOS-2/PALSAR-2 imagery, combined with advanced machine learning techniques for cropland SOC estimation. Diverse agricultural practices, with major crop types such as rice (Oryza sativa), finger millet (Eleusine coracana), Niger (Guizotia abyssinica), maize (Zea mays), and vegetable cultivation, characterize the study region. By integrating C-band (Sentinel-1) and L-band (ALOS-2/PALSAR-2) SAR data with key polarimetric features such as the C2 matrix, entropy, and degree of polarization, this study enhances SOC estimation. These parameters help distinguish variations in soil moisture, texture, and mineral composition, reducing their confounding effects on SOC estimation. An ensemble model incorporating Random Forest (RF) and neural networks (NNs) was developed to capture the complex relationships between SAR data and SOC. The NN component effectively models complex non-linear relationships, while the RF model helps prevent overfitting. The proposed model achieved a correlation coefficient (r) of 0.64 and a root mean square error (RMSE) of 0.18, demonstrating its predictive capability. In summary, our results offer an efficient approach for enhanced SOC mapping in diverse agricultural landscapes, with ongoing work targeting challenges in data availability to facilitate large-scale SOC mapping. Full article

Background/Objectives: Sleep disorders, particularly insomnia, are increasingly recognized as key determinants of mental health. Disturbances in sleep architecture may exacerbate hormonal dysregulation, contributing to menstrual cycle irregularities and premenstrual syndrome (PMS). The study investigate the relationship between insomnia symptoms, menstrual problems, and PMS among nursing students. Methods: The cross-sectional study was conducted using a web-based survey (CAWI) among 72 female graduate nursing students. The questionnaire included questions about menstrual history, the presence of menstrual disorders, PMS symptoms, and lifestyle and body mass index (BMI). Insomnia was assessed using the Polish version of the Athens Insomnia Scale (AIS), taking ≥8 as the cutoff point. Logistic regression analysis with confounding variables was performed. Results: 70% of participants had PMS symptoms, 19.5% had irregular menstrual cycles, and 86.5% reported problems with menstrual bleeding. The mean AIS score was 10.1 (SD = 4.05). Women with insomnia were almost 4 times more likely to experience PMS symptoms (OR = 3.93; 95% CI 1.14–13.59), more than 7 times more likely to experience bleeding problems (OR = 7.56; 95% CI: 1.51–37.97), and each additional AIS score increased the risk of cycle irregularity by 24% (OR = 1.24, 95% CI 1.01–1.50). Conclusions: The findings indicate a significant association between insomnia symptoms, menstrual disturbances, and PMS, underscoring the complex links between sleep, reproductive, and mental health. Preventive interventions, particularly sleep hygiene education, may serve as an effective strategy to support women’s overall health and well-being. Full article

The gut microbiota of social bees is vital for host health, yet pesticide exposure can disrupt these communities. Because most active ingredients are poorly soluble, toxicological tests often use cosolvents, but their effects remain unclear. We assessed four common cosolvents (DMSO, DMF, acetone, and Tween 80) at laboratory-relevant concentrations on honeybee survival, pollen consumption, body weight, and gut microbiota. In parallel, in vitro assays tested their impact on five dominant gut symbionts. The results showed no significant changes in survival, feeding, body weight, bacterial load, community composition, or core taxa abundance. Similarly, cosolvents did not inhibit bacterial growth in vitro. These findings demonstrate that commonly used cosolvents exert no detectable influence on honeybee physiology or gut microbiota. Although negative, this evidence is critical: it rules out cosolvents as hidden confounders, improving confidence in pesticide toxicology studies and providing essential reference data for pollinator risk assessment. Full article

Background: Resting-state functional connectivity (RSFC) is widely used to identify abnormal brain function associated with depression. Resting-state functional magnetic resonance imaging (fMRI) scans have many potential confounds, and task-based FC might provide complementary information leading to better insight on brain function. Methods: We used MATLAB’s (version 2024b) CONN toolbox (version 22a) to evaluate FC in 40 adults with and without major depressive disorder (MDD) (n_MDD = 23, n_HC = 17). fMRI acquisition was performed while participants were at rest and while performing the Selves Task, an individualized goal priming task. Seed-based analyses were performed using two seeds: medial prefrontal cortex (mPFC) and left hippocampus. Results: Both groups showed strong positive RSFC between the mPFC and other DMN regions, including the anterior cingulate cortex and precuneus, which had more focal positive FC to the mPFC during the task in both groups. Additionally, the MDD group had significantly lower RSFC between the mPFC and several regions, including the right inferior temporal gyrus. The left hippocampus seed-based analysis revealed a pattern of hypoconnectivity to multiple brain regions in MDD, including the cerebellum, which was present at rest and during the task. Conclusions: Our results indicated multiple FC differences between adults with and without MDD, as well as distinct FC patterns and contrast results in resting state and task-based analyses, including differential FC between mPFC–cerebellum and hippocampus–cerebellum. These results emphasize that resting-state and task-based fMRI capture distinct patterns of brain connectivity. Further investigation into combining resting-state and task-based FC could inform future neuroimaging research. Full article