Francisco Javier Bueno-Guillén

Environmental exposures are recognized as determinants of health, particularly in vulnerable populations such as hemodialysis patients, whose impaired kidney function may heighten susceptibility. This study assesses the association between meteorological and air-pollution factors and the risk of hospital admission and mortality in end-stage kidney disease patients, considering both acute peaks and sustained exposure through weekly and monthly averages. A retrospective cohort of 336 hemodialysis patients treated between 2016 and 2024 at Hospital Universitario Príncipe de Asturias (Madrid, Spain) was analyzed, including 563 admissions and 90 deaths. Time-to-event analyses were conducted separately for mortality and admissions. Cox proportional hazards and parametric survival models (Weibull, log-normal, log-logistic) were applied for mortality, while recurrent-event models (Andersen–Gill, Prentice–Williams–Peterson, frailty) used for admissions. Sulfur dioxide (SO ₂ ) was consistently associated with increased risk in both acute and cumulative exposure models for mortality and admissions. Nitrogen dioxide (NO ₂ ) predicted mortality only in long-term analyses, suggesting a cumulative effect. Solar radiation was linked to accelerated mortality in non-linear models, whereas atmospheric pressure appeared protective for admissions. These findings highlight the influence of environmental exposures, particularly SO ₂ , on adverse outcomes in hemodialysis patients and support incorporating environmental indicators into clinical risk prediction and public health strategies.

Background/Objectives: Trichomonas vaginalis is one of the most prevalent non-viral sexually transmitted infections worldwide. Although Gram staining is routinely performed in clinical microbiology laboratories for the evaluation of vaginal samples, it is not considered a diagnostic method for T. vaginalis, which represents a missed diagnostic opportunity in routine practice. This study aimed to evaluate an artificial intelligence (AI)-assisted diagnostic approach for the identification of T. vaginalis directly from routine Gram-stained vaginal smears. Methods: A retrospective dataset of Gram-stained vaginal smear images was analysed using a cascaded AI-based framework combining image processing and classification. The image selection and quality control were performed under the supervision of a specialised clinical microbiologist. All cases were independently confirmed by polymerase chain reaction (PCR), which served as the reference diagnostic standard. Model performance was assessed using standard diagnostic metrics, including accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), Cohen’s kappa, and Matthews correlation coefficient (MCC). Held-out independent testing was used to assess generalisability beyond the internal validation subset. Results: The proposed AI-assisted approach demonstrated high diagnostic performance for the identification of T. vaginalis, achieving an AUC of 0.973, Cohen’s kappa of 0.87, and an MCC of 0.87. The system showed high diagnostic concordance with PCR results across both internal and external validation datasets, supporting the feasibility and reproducibility of the approach under routine laboratory conditions. Conclusions: This study shows that artificial intelligence may enhance the diagnostic utility of routinely performed Gram-stained vaginal smears by enabling reliable identification of T. vaginalis. The proposed approach could be integrated into standard microbiology workflows as an objective decision-support or triage adjunct, facilitating early identification and supporting clinical decision-making without altering existing laboratory procedures.

Background: Aging in Western societies poses a growing challenge, placing increasing pressure on healthcare costs. Early identification of infections in elderly nursing home residents is crucial to reduce complications, mortality, and the burden on emergency departments. Methods: We performed a comparative analysis of machine learning models using XGBoost classifiers for infection detection, addressing incomplete daily physiological measurements (Heart Rate, Oxygen Saturation, Body Temperature, and Electrodermal Activity) through strict imputation protocols. We evaluated three model variants—Basic (clinical only), Air Pollution-added, and Social Media-integrated—while incorporating a novel Basal Module to personalize physiological baselines for each resident. Results: Results from the binary model indicate that physiological data provides a necessary baseline for immediate screening. Notably, social media integration emerged as a powerful forecasting tool, extending the predictive horizon to a 6-day lead time with an F1-score of 0.97. Complementarily, air pollution data ensured robust immediate detection (“nowcasting”). In the multiclass scenario, external data resolved the “semantic gap” of vital signs, improving sensitivity for specific infections (e.g., acute respiratory and urinary tract infections) to over 90%. Conclusions: These findings highlight that the strategic integration of environmental and digital signals transforms the system from a reactive monitor into a proactive early warning tool for long-term care facilities.