Search Results (6,061)

Background/Objectives: Antimicrobial stewardship programmes (ASPs) are essential for improving antimicrobial use and limiting the emergence of antimicrobial resistance. Although national ASP key performance indicators (KPIs) have been implemented across Saudi Ministry of Health (MOH) hospitals, large-scale evaluations of programme performance remain limited. This study evaluated national ASP performance across MOH hospitals from 2021 to 2024 using formal KPIs and complementary stewardship measures. Methods: This retrospective multicentre surveillance analysis included 124,452 antimicrobial prescription reviews from 96 Saudi MOH hospitals actively reporting stewardship activities. Hospitals were classified according to ASP capacity as category A, B, or C. The evaluated KPIs included protocol adherence, restricted-antibiotic dispensing compliance, active intervention rate, annual trends in optimization interventions, and physician acceptance of ASP recommendations. Secondary assessments included appropriateness of therapy management, therapeutic drug monitoring (TDM) for vancomycin and aminoglycosides, and hospital-level antimicrobial use expressed as defined daily doses per 100 available bed-days. Multivariable logistic regression was used to identify factors associated with guideline non-adherence and physician non-acceptance. Results: Overall protocol adherence was 87.1%, exceeding the national target of 80%, while the active intervention rate was 64.6%, exceeding the 60% benchmark. Restricted-antibiotic dispensing compliance reached 87.6% but remained below the 100% target. Physician acceptance of ASP recommendations was high at 93.0%, although it did not achieve the national target of 98%. The optimization-intervention KPI was not met because de-escalation, antibiotic discontinuation, and intravenous-to-oral conversion did not demonstrate a sustained annual increase. Appropriate ongoing therapy management was documented in 68.3% of cases with available data, whereas TDM was requested in 44.5% of eligible vancomycin or aminoglycoside cases. Antimicrobial use varied across years and hospital categories, with higher monitored broad-spectrum and reserve-agent use in higher-capacity hospitals. Lower ASP capacity, non-critical care settings, and combination therapy were consistently associated with less favourable stewardship outcomes. Conclusions: Saudi MOH hospitals actively reporting stewardship activities demonstrated favourable performance in protocol adherence, intervention activity, and physician acceptance. However, persistent gaps in restricted-antibiotic dispensing, optimization interventions, TDM, and inter-hospital variation indicate the need for continued strengthening of ASP infrastructure, monitoring, and feedback systems. Future research should link stewardship indicators with clinical outcomes, antimicrobial resistance trends, and patient safety. Full article

Background/Objective: Melanoma is an aggressive skin malignancy characterized by significant molecular heterogeneity. Among the molecular alterations identified in melanoma, BRAF mutations represent one of the most common genetic abnormalities and play an important role in activating the MAPK signaling pathway. BRAF mutation status has become clinically important because of its prognostic significance and implications for targeted therapy. This study aimed to evaluate the frequency of BRAF mutations and their associations with demographic, histopathological, and clinicopathological characteristics in melanoma patients at the only referral center for BRAF testing in Kosova, the Institute of Pathology, University Clinical Center of Kosova (UCCK). Methods: This retrospective study included 127 melanoma patients. Descriptive statistics, frequency analysis, Spearman’s correlation and multivariable binary logistic regression analyses were performed to evaluate associations between BRAF mutation status and clinicopathological variables, including age, gender, Breslow thickness, histological type, ulceration, and anatomical localization. Results: BRAF mutation was identified in 76 of 127 melanoma patients (59.8%). The BRAF V600E/V600E2/V600D variants represented the predominant molecular subtype (75%). BRAF-positive melanoma was more frequently observed in younger patients and was significantly associated with increased Breslow thickness, nodular melanoma, ulceration, and trunk localization. Conclusions: BRAF mutations were highly prevalent in melanoma patients from Kosova and were associated with clinicopathological features of a more aggressive disease. The findings establish an important baseline for molecular epidemiology in the country and support the integration of routine BRAF testing into personalized melanoma management and future regional research. Full article

Accurate identification and effective prediction of the maize tasseling stage are of great significance for guiding precision field management and ensuring stable crop yields. Conventional manual observation methods suffer from high labor intensity, poor timeliness, and strong subjectivity. In this study, based on an unmanned aerial vehicle (UAV) remote sensing platform, LiDAR point cloud data and RGB imagery were simultaneously acquired to construct digital surface models (DSMs) and digital terrain models (DTMs). Multi-dimensional statistical features were extracted to establish a high-precision plant height estimation method applicable to the entire growth cycle of maize. On this basis, the Logistic growth curve function was introduced to fit the dynamic changes in plant height, enabling the identification and early prediction of the maize tasseling stage based on the plant height growth curve. The research results indicate the following: (1) For maize plant height estimation, the LiDAR sensor outperforms RGB. The optimal accuracy is achieved by combining the 99th percentile of DSM with the minimum DTM, yielding a root mean square error (RMSE) of 0.17 m. (2) Based on the high-accuracy plant height time series, the point of inflection (POI) achieves the highest accuracy in tasseling stage identification, with an RMSE of 2.586 d under the reconstructed time series. (3) Prediction accuracy of the tasseling stage improves with increasing plant height threshold, and optimal performance is observed when the threshold is ≥1.6 m with a growth rate between 0.11 and 0.13. This study establishes a technical framework of “time-series perception–dynamic simulation–feature identification–early prediction”, providing a scientific basis for automated monitoring and precision management of the maize tasseling stage. It holds significant theoretical and practical value for the advancement of smart agriculture and crop phenotyping research. Full article

Background/Objectives: Sleep disorders are common among older adults and have been increasingly linked to metabolic abnormalities and non-communicable diseases (NCDs). However, evidence on the relationship between sleep quality and health-related clinical outcomes and biomarkers in rural Northern Thailand remains limited. This study aimed to assess the association between sleep quality and health-related clinical outcomes and biomarkers among older adults in rural Northern Thailand. Methods: A cross-sectional study was conducted among 424 older adults in Phayao Province, rural Northern Thailand. Quota sampling was employed to ensure equal representation of participants with and without NCDs (n = 212 per group). Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Physical health status was evaluated through measurements of blood pressure (BP), including systolic blood pressure (SBP) and diastolic blood pressure (DBP), and fasting blood sugar (FBS). Results: The participants’ mean PSQI score was 8.03 ± 4.86, and 29.7% had poor sleep quality (PSQI > 5). Hypertension (35.4%) and diabetes mellitus (17.5%) were the most common underlying diseases. The mean fasting blood sugar level was 116.33 ± 30.84 mg/dL, with 21.0% of participants presenting abnormal levels (≥126 mg/dL). Multivariable binary logistic regression demonstrated that each one-point increase in the overall PSQI score was significantly associated with higher odds of having an underlying disease (adjusted odds ratio [AOR] = 2.37; 95% confidence interval [CI]: 2.00–2.81), hypertension (AOR = 1.51; 95% CI: 1.37–1.66), diabetes mellitus (AOR = 1.42; 95% CI: 1.28–1.58), other non-communicable diseases (AOR = 1.94; 95% CI: 1.64–2.29), abnormal fasting blood sugar (AOR = 1.47; 95% CI: 1.33–1.63), elevated systolic blood pressure (AOR = 1.39; 95% CI: 1.28–1.51), and elevated diastolic blood pressure (AOR = 1.30; 95% CI: 1.19–1.43). Subjective sleep quality, sleep duration, and habitual sleep efficiency were also significantly associated with multiple health-related clinical outcomes and biomarkers. Although sparse data and quasi-complete separation may have influenced the precision of some regression estimates, the overall pattern of associations remained consistent and should be interpreted with appropriate caution. Conclusions: Higher overall PSQI scores were significantly associated with adverse health-related clinical outcomes and biomarkers among older adults in rural Northern Thailand. These findings highlight the clinical importance of routinely assessing sleep quality in older adults, particularly those with chronic conditions or increased cardiometabolic risk. Because of the cross-sectional design and the statistical uncertainty associated with sparse data in some regression models, the findings should be interpreted as evidence of associations rather than causal relationships. The observed associations are consistent with the likely bidirectional relationship between sleep disturbances and chronic diseases. Integrating sleep quality screening into routine health assessments, alongside comprehensive chronic disease management, may facilitate the early identification of older adults at increased cardiometabolic risk and support evidence-based health promotion strategies. Full article

Landslide susceptibility mapping (LSM) is fundamental to disaster prevention and spatial risk management in mountainous regions. However, conventional LSM approaches that rely mainly on static landslide influencing factors and empirical classification thresholds may have limited temporal relevance and interpretability. In response to these limitations, this study proposed an LSM framework constrained by interferometric synthetic aperture radar (InSAR)-derived deformation information. Wangmo County, Guizhou Province, China, was selected as the study area. Multi-source data, including small baseline subset InSAR (SBAS-InSAR) deformation results, optical remote sensing imagery, geo-environmental factors, and field investigation data, were used to construct and validate four machine learning models: logistic regression (LR), random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN). The validation results showed that the RF and BPNN models performed better than the LR and SVM models in terms of AUC, accuracy, precision, recall, and F1-score. Accordingly, an RF–BPNN combined model was constructed using an equal-weight averaging strategy. Shapley value analysis indicated that terrain- and rainfall-related factors made dominant contributions to landslide susceptibility prediction, a finding consistent with the landslide development characteristics in the study area. InSAR-derived deformation information was extracted from 31 Sentinel-1A images using SBAS-InSAR. A classification adjustment strategy based on kernel density estimation (KDE) and the Pearson correlation coefficient (PCC) was then used to identify the susceptibility classification scheme with relatively high spatial consistency with deformation activity during the observation period. The optimized classification scheme achieved a PCC value of 0.65, compared with 0.61 for the natural breaks classification, indicating a modest improvement in the spatial consistency between susceptibility zoning and deformation activity. The Xiangle and Namu landslides were used as representative cases to illustrate the adjustment effects of the deformation-constrained classification scheme. The proposed framework provides a practical approach for incorporating observation-period InSAR-derived deformation information into regional LSM and can support landslide monitoring and decision-making in complex terrains. Full article

This paper presents a retrospective proof-of-concept development and preliminary evaluation of an interpretable decision-support system for daily fatigue-risk management in one elite women’s football club. The system integrates morning wellness, GPS-derived external load, and a daily wellness-duration internal-load proxy at the player-day level. It combines a player-day integration layer, an interpretable predictive layer, and a recommendation layer that returns one of three staff-facing actions: Reduce, Maintain, or Progress. The predictive model outputs a calibrated probability of elevated next-morning self-reported soreness. The target is a subjective questionnaire outcome, not an injury, medical diagnosis, or objective marker of recovery. The decision-support layer maps this probability to a three-state recommendation, informed by a review threshold, operational guardrails, and staff oversight. Using retrospective monitoring data from two competitive seasons (2024/25 and 2025/26) in a single professional team, we evaluated the proposed approach using rolling-origin temporal validation, leave-one-player-out cross-validation, and between-season validation. To separate genuine predictive signal from the day-to-day persistence of soreness, we report a baseline ladder ranging from a trivial persistence rule to the full model, with bootstrap confidence intervals for performance differences. Under rolling-origin validation across 16 monthly folds, the final logistic regression model achieved a mean ROC-AUC of $0.759$ ($SD=0.089$). Critically, a model excluding current soreness still outperformed the persistence baseline (ROC-AUC $0.738$ vs. $0.721$), and the isolated contribution of current soreness was modest but reliable ($\Delta$ROC-AUC $=+0.044$, 95% CI $[+0.027,+0.059]$). Between-season validation (train: 2024/25; test: 2025/26) yielded an ROC-AUC of 0.801. The three-state recommendation layer separated outcomes monotonically, with observed next-morning soreness rates of $0.054$ for Progress, $0.217$ for Maintain, and $0.326$ for Reduce ($p<0.001$ for the Progress-versus-Maintain contrast). These preliminary findings support the feasibility of the proposed approach within the club studied. However, because the model requires complete wellness, GPS, and proxy data, it operates only on the fully monitored on-pitch stratum (3386 of 17,703 player-days); the reported performance therefore applies to this stratum rather than to a typical player-day, and prospective evaluation and external validation by independent teams are required before broader implementation can be considered. Full article

Background/Objectives: Cutaneous adverse drug reactions (CADRs) account for approximately 45% of all adverse drug reactions. Although most are self-limiting, some may progress to severe cutaneous adverse reactions (SCARs). In children, delayed rashes associated with antimicrobial therapy, particularly beta-lactams, are often misclassified as drug allergies, leading to unnecessary antibiotic avoidance and potentially suboptimal antimicrobial prescribing. This study aimed to characterize delayed antimicrobial-associated CADRs in children and to investigate reporting patterns and factors associated with their clinical management across age groups. Methods: Real-world data from pediatric patients hospitalized in 2023 at the “Ospedale dei Bambini Vittore Buzzi” (Milan, Italy) were integrated with Individual Case Safety Reports from the FDA Adverse Event Reporting System (FAERS). Only cases with documented treatment durations were included. Clinical characteristics, antimicrobial exposure patterns, and factors associated with the reporting of delayed rashes were evaluated. Results: Five pediatric patients developed delayed CADRs after 19–22 days of antimicrobial therapy. Infectious and immunological investigations were negative, and symptoms resolved following drug discontinuation. FAERS analysis identified 97 delayed rash reports, associated with prolonged treatment duration, frequent polytherapy, and higher reporting rates for vancomycin, teicoplanin, and beta-lactam combination regimens. Logistic regression showed that age and polypharmacy were significantly associated with reporting patterns of delayed rash and therapy continuation among reported cases. Conclusions: Among reported cases, delayed antimicrobial-associated CADRs were associated with age and polypharmacy. Improved recognition of these reactions may facilitate appropriate clinical management, support more informed prescribing decisions, and reduce inappropriate antibiotic allergy labeling. Further studies are needed to validate these findings and refine risk-based management strategies in pediatric patients. Full article

The agricultural and livestock sectors of Latin America produce a large number of residues that could be converted into energy through bioenergy production processes. However, the bioenergy sector still faces several limitations across the region, including fragmented logistics systems, weak coordination among institutions, and limited integration of environmental, digital, and compliance-related performance indicators. This review systematically analyses residue-based bioenergy value chains in Latin America between 2015 and 2025 using the PRISMA methodology to evaluate selected peer-reviewed studies and regional reports indexed in Scopus, ScienceDirect, SpringerLink, and IEEE Xplore, and institutional repositories. The final synthesis included 37 studies and institutional contributions, which were further disaggregated into 208 country–residue observations for the regional and feedstock distribution analysis. The review identified three main research gap categories: the limited integration of collection and logistics systems, the insufficient treatment of uncertainty, circularity, and traceability within optimization models, and the weak incorporation of governance and institutional coordination into bioenergy value-chain design. The analysis includes biogas, biomethane and related residue-based systems, with attention to supply-chain optimization, policy alignment, methane mitigation metrics, and traceability requirements. Results indicate that although technologies such as biomass pretreatment, process intensification, and upgrading processes continue to improve conversion performance, most studies still focus mainly on technical feasibility and biomass potential. Less attention is given to governance constraints, uncertainty analysis, and monitoring systems capable of supporting regulatory compliance. This research introduced the Sustainable Bioenergy Chain Management Framework (SBCMF) to respond to these limitations and bring together different aspects of bioenergy management within one analytical structure. The framework combines supply-chain optimization under spatial and temporal constraints, circular economy valorisation, methane-related climate performance, and digital traceability, while also linking techno-economic system design with governance and monitoring requirements. In this way, it can help support the development of more transparent and low-carbon bioenergy systems across Latin America. Full article

Background: Lean management is widely recognized as an effective approach to process optimization. However, its implementation in Small and Medium Enterprises (SMEs), particularly in logistics and supply chain contexts, remains challenging. Resource constraints and inconsistent organizational commitment often hinder effective implementation. This study examines Lean adoption, leadership commitment, and implementation outcomes in SME logistics and supply chains. Methods: A mixed-methods design was used, combining a systematic literature review guided by PRISMA and PEO frameworks, followed by a structured survey. A total of 780 valid responses from SME professionals were analyzed using descriptive statistics, correlation, regression, and reliability assessment. Results: Top and middle management commitment was identified as a significant predictor of perceived Lean implementation success. A measurable gap was observed between respondents’ knowledge of Lean methods and their practical application, emphasizing the importance of strategic alignment, organizational culture, and employee engagement. Conclusions: The findings provide practical implications for strengthening Lean implementation in SMEs through enhanced managerial commitment and employee involvement. The study is limited by its focus on SMEs from a single country and the literature retrieved from one bibliographic database. Future research should include broader geographical coverage, multiple databases, and objective organizational performance indicators. Full article

Background: Non-massive hemoptysis is generally considered low-risk and manageable with conservative treatment. However, some patients progress to massive hemoptysis during follow-up. Identifying high-risk patients early in the emergency department matters for calibrating monitoring intensity, guiding timely intervention, and allocating acute care resources. This study aimed to identify clinical, laboratory, and radiological predictors of progression to massive hemoptysis within the first 72 h in emergency department patients presenting with non-massive hemoptysis who were managed conservatively. Methods: This prospective cohort study enrolled patients at a tertiary university hospital emergency department between November 2023 and June 2025. Adult patients presenting with non-massive hemoptysis were enrolled consecutively. The primary outcome was development of massive hemoptysis within 72 h of admission. Patients were divided into two groups: those who developed massive hemoptysis within 72 h and those who did not. Demographic, bleeding, laboratory, imaging, and bronchoscopy data were recorded for all patients. Multivariate logistic regression was used to identify independent predictors. Results: Of 199 patients, 10.6% developed massive hemoptysis within the first 72 h. On multivariate analysis, bright red hemoptysis (3.17-fold increase in risk), a cavity or mass on thoracic CT (7.13-fold increase in risk), and bleeding volume ≥20 mL in a single episode (3.3-fold increase in risk) were independent predictors of massive hemoptysis. Conclusions: A meaningful proportion of patients presenting with non-massive hemoptysis go on to develop massive hemoptysis in the early period. Simple clinical and radiological parameters available at admission can support early risk stratification and inform decisions regarding monitoring intensity and early inpatient management during the critical first 72 h after emergency department admission. These findings may assist early risk stratification but should complement, rather than replace, clinical judgement. Full article

The Levelized Cost of Energy (LCOE) is the standard metric for evaluating renewable energy project economics; however, conventional formulations inadequately represent the dynamic effects of logistics performance, supply chain disruptions, geopolitical risk, and institutional constraints on project costs. This study addresses this gap by proposing the Integrated Levelized Cost of Energy (I-LCOE), a conceptual framework designed for macro-level renewable energy planning and policy analysis. An interpretivist qualitative research design was adopted, combining a systematic literature review with twelve semi-structured interviews involving renewable energy, logistics, regulatory, and academic experts from the MENA and GCC regions. Thematic analysis identified five recurring challenges: limited knowledge management, weak integration of logistics within conventional LCOE models, fragmented sustainability metrics, stakeholder coordination inefficiencies, and reliance on tacit knowledge. The findings indicate that transportation delays, customs bottlenecks, infrastructure limitations, and geopolitical disruptions generate dynamic risk premiums that are insufficiently reflected in existing macro-level cost assessment approaches. In response, the study develops the four-layer I-LCOE framework, supported by an operational proxy variable mapping framework, a comparative assessment against established uncertainty methods, and an integrated digital knowledge management platform. The proposed framework provides a structured approach for incorporating logistics-induced uncertainty into renewable energy cost assessment, supporting more informed strategic planning, investment prioritization, and policy development aligned with Sustainable Development Goal 7 and the Paris Agreement. Full article

Our first objective was to quantify the associations between health-related events (HRE) before insemination, the relative increase in estrus intensity (REI) at insemination, and the probability of cow-level pregnancy per artificial insemination (P/AI) in organic Holstein dairy cows. Quantifying these associations may aid on-farm decision-making, such as setting the voluntary waiting period, choice of type of semen, do-not-breed and culling decisions. A second objective was to develop predictive models to estimate P/AI based on readily available data, and present common goodness-of-fit results also used in the machine learning community. All data were collected from a certified organic dairy farm in the western USA from 2019 to 2021. Health-related and reproduction data were obtained through Dairy Records Management Systems (DRMS; Raleigh, NC, USA). Activity data were collected using pedometers (IceRobotics, Stirling, UK) mounted on the rear legs. The REI, defined as walking steps per hour before insemination divided by the cow’s baseline steps per hour, was available for 17,238 inseminations from 4759 cows. The REI was categorized as ≤200%, >200–400%, >400–600%, or >600%. The HRE were available for 65,684 inseminations from 13,365 cows. The HRE were categorized as mastitis, metabolic disease (i.e., hypocalcemia, ketosis, displaced abomasum, digestive problems), reproductive disease (i.e, metritis, endometritis, pyometra, retained fetal membranes), lameness, 2 different diseases, ≥3 different diseases, or as healthy (none of these diseases prior to insemination). Combinations (COMBO) between REI categories and 0, 1, or ≥2 HRE were also created. Data were split into training and test sets. The training data were used to fit three logistic regression models that included either HRE, or REI, or COMBO. Each of the three models also included the covariates of 3-mo herd-average P/AI prior to insemination, days in milk, and the fixed effects of parity, insemination season, days after the previous insemination or days to 1st insemination. A random effect accounted for repeated inseminations within cow. Parameter estimates, odds ratios, and the estimated marginal means of the estimated P/AI of the fixed effects were obtained from the logistic regression models. The models’ estimates were applied to the test datasets, and discrimination and calibration statistics were calculated to judge goodness-of-fit. Unadjusted mean P/AI were 0.31, 0.28 and 0.28 for the HRE, REI and COMBO training datasets. For the HRE model, estimated P/AI ranged from 0.20 (≥3 different HRE) to 0.30 (healthy). The estimated P/AI associated with four REI categories were not different from 0.27 in the REI model. The estimated P/AI associated with the combinations of HRE and REI in the COMBO model varied from 0.18 after ≥2 HRE and >200–400% REI, to 0.30 when inseminations were in healthy cows with REI >600%. Inseminations in older cows, in the spring, and outside 18–24 d after the previous insemination were also associated with lower estimated P/AI. The area underneath the Receiver Operating Characteristic curve ranged from 0.57 (COMBO) to 0.60 (HRE) for the test data, indicating fair discrimination ability of the models. Calibration plots showed that the prediction models produced unbiased predicted P/AI. In conclusion, the results showed no conclusive evidence of greater estimated P/AI related to greater REI as a measure of estrus activity. More HRE were associated with lower estimated P/AI. Combinations of low REI and more HRE were associated with notably decreased estimated P/AI. The logistic regression models produced unbiased predicted P/AI. We found no evidence that the strength of the relationship between REI and P/AI depended on the HRE category. The applications of the results are as follows. First, these predictive models may help inform insemination decisions in organic dairy cows, although further external validation is recommended, and the discriminatory performance is weak. Second, a variety of goodness-of-fit statistics were calculated to allow comparisons of the current logistic regression analyses with future analyses made by other machine learning techniques. Full article

Tourism is a key driver of regional development, particularly in emerging destinations around the globe. Despite its critical role, aligning tourism services with tourists’ needs requires effective planning and management that reflect key stakeholder perceptions and resource allocation to enhance the overall competitiveness of a destination. This study aims to integrate the perspectives of tourists and tourism service providers using a logistics performance framework to support data-driven tourism management. Using the Best–Worst Method (BWM), stakeholder-based evaluations are conducted to prioritize key decision criteria, including economic efficiency, reliability, responsiveness, and safety, which are essential for supporting operational efficiency and tourism planning. Next, a Multi-Objective Traveling Salesman Problem (MOTSP) model is applied to evaluate travel routing and ensure efficient resource allocation. Data collection and analysis are conducted for popular tourist attractions, using a case study of Ubon Ratchathani, Thailand. The findings provide practical insights for regional planners and tourism managers seeking to enhance infrastructure planning through stakeholder-informed and logistics-driven decision-making for regional tourism development. Full article

Microbial contamination in hospitality settings remains understudied despite the high density of human contact and diverse operational activities that characterize hotel environments. This study aimed to characterize microbial contamination patterns and environmental hygiene risks across multiple functional areas of family-oriented hotels. A cross-sectional environmental microbiological investigation was conducted in family hotels in Bavaria, Germany. A total of 225 environmental surface samples were collected from guest rooms, child areas, food-related areas, service environments, and water-exposed locations. Bacterial isolates were identified using culture-based microbiology and MALDI-TOF mass spectrometry. Microbial prevalence, contamination severity, microbial richness, and pathogen prevalence were assessed using mixed-effects ordinal logistic regression, generalized additive model (GAM), and integrated hygiene profile, all performed in R (version 4.5.1). Microbial occurrence patterns differed markedly across functional areas. Contamination category distributions differed significantly among areas, with food-related environments showing the strongest enrichment in the highest contamination category (72.7%). Food-related areas showed significantly greater odds of severe contamination than guest rooms (OR = 8.37, 95% CI: 1.89–37.00), child areas (OR = 6.16, 95% CI: 1.20–31.46), and water-exposed environments (OR = 12.34, 95% CI: 2.45–62.10). Microbial richness differed across operational zones (p = 0.048) and was positively associated with contamination severity (β = 0.141, p < 0.001). GAM revealed a significant non-linear richness–contamination relationship (p < 0.001). Integrated hygiene profile consistently identified food-related and service areas as the highest risk environments. Environmental hygiene risks in hospitality settings display functional-area heterogeneity, highlighting the need for targeted, area-specific hygiene management strategies. Full article

This study explores the behavioral intersection of nutritional health and environmental literacy among pre-service teachers within the national higher education context. Utilizing the Sustainable Healthy Diet Index (SHED Index) for the first time in Croatia, this research systematically examines the dietary habits, lifestyle choices, and socio-cultural patterns of future educators (N = 164) at the University of Split. The survey instrument evaluated the core SHED domains, Healthy Eating (HE) and Sustainable Eating (SE), alongside supplementary indicators monitoring food logistics, hydration, and waste management. Descriptive analysis revealed moderately high standardized overall SHED scores (M = 62.86), aligning with the original normative distribution. Notably, students achieved significantly higher descriptive sub-scores in the HE domain (M = 24.68) than in the SE domain (M = 18.06). Although domestic food consumption and circular recycling practices were well integrated, critical biospheric behaviors—such as reducing animal protein, consuming legumes, purchasing organic food, and composting—remain limited by cultural resistance and municipal infrastructure deficits. Furthermore, an independent t-test indicated no significant differentiation across study levels, highlighting a potential institutional stagnation throughout the five-year teacher education program. Regression analysis demonstrated that sustainable dietary choices appear to be strongly anchored in personal health concerns rather than biospheric altruism, with healthy eating emerging as the single strongest explanatory factor for of sustainable behavior. These findings indicate that to cultivate authentic ecological literacy in the future teaching workforce, higher education curricula require a systemic redesign that explicitly links sustainability to personal well-being through localized, experiential learning. Full article