The relationship between the global climate crisis, which is associated with environmental risks, and occupational hygiene has not been extensively studied. This study develops a framework for identifying how climate change and the climate crisis could impact the workplace environment, workers, and occupational morbidity, mortality, and injury. A framework is used in this paper that is based on a review of the scientific literature published from 2014 to 2024, addressing climate risks, their interaction with occupational hazards, and their effects on the workforce. Eight categories of climate-related hazards are identified: increasingly high temperatures, dust and air pollution, sun and cosmic ultraviolet exposure, pandemics and infectious diseases, diseases transmitted by insects and changes in ecosystems, industrial occupational diseases, changes and crises in the built environment, and extreme weather events. Policies need to consider the gaps in the possibility of interactions between known hazards and new conditions and the productivity of workers, especially those who are most at risk of heat-related illnesses.

Actinic keratoses (AKs) are the most common neoplastic lesions and are recognized as a precursor to squamous cell skin cancer. Photodynamic therapy (PDT) is a therapeutic option for multiple AKs in line with field cancerization. The aim of this study was to assess the effectiveness of PDT on patients with AKs using a meta-analysis, in order to evaluate the possible superiority of one treatment over the others. For this purpose, the PubMed, MEDLINE, Scopus, OVID, Science Direct, British Journal of Dermatology, Research Gate, and Embase databases were searched in March 2022. The search terms used were 'photodynamic therapy' and 'actinic keratosis'. We utilized the random-effects meta-analysis model to compare methyl aminolevulinate PDT (MAL-PDT) and the combination of a nanoscale-lipid vesicle formulation with the prodrug 5-aminolevulinic acid (BF-200 ALA) on a complete response (CR) of the lesions. Our meta-analysis indicated that the comparison of BF-200 ALA versus MAL-PDT showed marginally higher CRs than MAL-PDT.

BACKGROUND Patients with spinal cord injury (SCI) tend to develop pressure ulcers (PrUs) because of prolonged immobility. This study assessed the efficacy of vacuum assisted closure for healing of PrUs in individuals with SCI. METHODS In a systematic review, CINAHL, PubMed, Cochrane Clinical Trials, DARE, MEDLINE, Scopus, Embase, Science Direct, PsycInfo and Spinal Cord Journal were searched in March 2019. The search combined related terms for pressure ulcer, spinal cord injury, and vacuum assisted closure. Each database was searched from its inception with no restrictions on year of publication. RESULTS The search yielded 7 studies for inclusion in a qualitative analysis. The studies included a variety of methodologies, specifically 2 randomized controlled trials, 2 assessor-blinded crossover and retrospective cohort study, 1 prospective non-randomized trial, 1 randomized case study and 1 case report. The meta-analysis was unsuccessful. Only descriptive results mean +/- SD were reported as well as time to heal and time to discharge after admission. CONCLUSION The studies that we included in our qualitative synthesis showed that vacuum assisted closure promoted the healing of PrUs in individuals with SCI.

Type 2 diabetes mellitus (T2DM) is a growing global health concern requiring early detection strategies. This study applies a Random Forest machine learning model to predict diabetes progression using a structured clinical dataset of 100 patients. The dataset includes demographic, physiological, and biochemical variables such as age, BMI, blood pressure, glucose levels, and lipid profiles. After preprocessing and training, the model achieved strong performance metrics: accuracy of 0.80, precision of 0.84, recall of 0.94, and an AUC of 0.88. Feature importance analysis revealed that systolic blood pressure, fasting glucose, and BMI are the most critical predictors. These findings are consistent with previous literature and demonstrate the model’s clinical relevance in identifying high-risk patients. The model’s ability to generalize across cross-validation folds highlights its robustness and potential integration into decision-support systems for proactive diabetes management. The study concludes that machine learning models, when properly validated, can offer significant improvements in chronic disease prediction and personalized care delivery.

Microplastics (MPs), defined as plastic particles smaller than 5 mm, are an emerging environmental contaminant of concern due to their widespread presence in air, water, food, and human tissue. Although the evidence has identified MPs in the bloodstream, placentas, and excrement, suggesting chronic internal exposure, MPs' health outcomes and biological mechanisms remain inadequate. This systematic review and meta-analysis follow PRISMA 2020 guidelines, and the Coherence risk of bias tools to evaluate MPs ' exposure pathways and health risks. A comprehensive search across PubMed, Scopus, Web of Science, Embase, Cochrane, and other databases identified 2,441 initial records. 78 studies met the inclusion criteria. Ingestion, inhalation, and dermal contact pathways were analysed, and observational and toxicological studies were assessed. Ingestion accounted for ~74% of MP exposure pathways, followed by inhalation (22%) and dermal absorption (4%). MPs were detected in 33–65% of human faecal and blood samples across national surveys.Meta-analysis-Random effects models and test of overall effect size z=1.18, p-value =0,24 were used to account for inter-study variance. Homogeneity: Q=1.16 and Heterogeneity: H-squared=1,00. Animal studies suggest MPs induce oxidative stress, inflammation, immune disruption, and endocrine effects. However, causality in human populations remains unverified due to inconsistent study designs and exposure quantification methods. Subgroup analysis revealed stronger associations with reproductive, gastrointestinal, and neurotoxic markers in rodent models. MPs exposure is recognised as a ubiquitous and potentially hazardous public health concern. This study underscores the pressing need for long-term studies and effective public risk strategies to mitigate human exposure and safeguard the ecosystem.