Background/Objectives: Urinary tract infections (UTIs) are frequently diagnosed empirically, often leading to overtreatment and rising antimicrobial resistance. This study aimed to develop and evaluate machine learning (ML) models that predict urine culture outcomes using routine urinalysis and demographic data, supporting more targeted empirical antibiotic use. Methods: A real-world dataset comprising 8065 urinalysis records from a hospital laboratory was used to train five ensemble ML models, including random forest, XGBoost (eXtreme gradient boosting), extra trees, voting classifier, and stacking classifier. Models were developed using 10-fold stratified cross-validation and assessed via clinically relevant metrics including specificity, sensitivity, likelihood ratios, and diagnostic odds ratios (DORs). To enhance screening utility, threshold optimization was applied to the best-performing model (XGBoost) using the Youden index. Results: XGBoost and random forest demonstrated the most balanced diagnostic profiles (AUROC: 0.819 and 0.791, respectively), with DORs exceeding 21. The voting and stacking classifiers achieved the highest specificity (>95%) and positive likelihood ratios (>10) but exhibited lower sensitivity. Feature importance analysis identified positive nitrites, white blood cell count, and specific gravity as key predictors. Threshold tuning of XGBoost improved sensitivity from 70.2% to 87.9% and reduced false negatives by 82%, with an associated NPV of 96.4%. The adjusted model reduced overtreatment by 56% compared to empirical prescribing. Conclusions: ML models based on structured urinalysis and demographic data can support clinical decision-making for UTIs. While high-specificity models may reduce unnecessary antibiotic use, sensitivity trade-offs must be considered. Threshold-optimized XGBoost offers a clinically adaptable tool for empirical treatment decisions by improving sensitivity and reducing overtreatment, thus supporting the more personalized and judicious use of antibiotics.

The use of antibiotics in open-water aquaculture is often unavoidable when faced with pathogens with high mortality rates. In addition, seasonal pathogen surges have become more common and more intense over the years. Apart from the apparent cost of antibiotic treatment, it has been observed that, in aquaculture practice, the surviving fish often display measurable growth impairment. To understand the role of gut microbiota on the observed growth impairment, in this study, we follow the incidence of Photobacterium damselae subsp. piscicida in a seabass commercial open-water aquaculture setting in Galaxidi (Greece). Fish around 10 months of age were fed with feed containing oxytetracycline (120 mg/kg/day) for twelve days, followed by a twelve-day withdrawal period, and another eighteen days of treatment. The fish were sampled 19 days before the start of the first treatment and one month after the end of the second treatment cycle. Sequencing of the 16S rRNA gene was used to measure changes in the gut microbiome. Overall, the gut microbiota community, even a month after treatment, was highly dysbiotic and characterized by very low alpha diversity. High abundances of alkalophilic bacteria in the post-antibiotic-treated fish indicated a rise in pH that was coupled with a significant increase in gut parasites. This study’s results indicate that oxytetracycline (OTC) treatment causes persistent dysbiosis even one month after withdrawal and provides a more suitable environment for an increase in parasites. These findings highlight the need for interventions to restore a healthy and protective gut microbiome.

The increasing prevalence of metabolic syndrome (MetS), a serious condition associated with elevated risks of cardiovascular diseases, stroke, and type 2 diabetes, underscores the urgent need for effective diagnostic tools. This research carefully examines the effectiveness of 16 diverse machine learning (ML) models in predicting MetS, a multifaceted health condition linked to increased risks of heart disease and other serious health complications. Utilizing a comprehensive, unpublished dataset of imbalanced blood test results, spanning from 2017 to 2022, from the Laboratory Information System of the General Hospital of Amfissa, Greece, our study embarks on a novel approach to enhance MetS diagnosis. By harnessing the power of advanced ML techniques, we aim to predict MetS with greater accuracy using non-invasive blood test data, thereby reducing the reliance on more invasive diagnostic methods. Central to our methodology is the application of the Borda count method, an innovative technique employed to refine the dataset. This process prioritizes the most relevant variables, as determined by the performance of the leading ML models, ensuring a more focused and effective analysis. Our selection of models, encompassing a wide array of ML techniques, allows for a comprehensive comparison of their individual predictive capabilities in identifying MetS. This study not only illuminates the unique strengths of each ML model in predicting MetS but also reveals the expansive potential of these methods in the broader landscape of health diagnostics. The insights gleaned from our analysis are pivotal in shaping more efficient strategies for the management and prevention of metabolic syndrome, thereby addressing a significant concern in public health.

The complete blood count (CBC), a cost-effective blood test, offers insights into the cell composition of blood, including white and red blood cells and platelets. Novel inflammatory biomarkers derived from combinations of CBC parameters include the neutrophil-to-lymphocyte ratio, reflect systemic and local inflammation. In this retrospective study, we successfully leveraged bioinformatics analysis to examine potential correlations between CBC biomarkers and climate and environmental factors, including temperature, humidity, and rainfall, in Fokis, Greece for a 4-year period (2019–2022). Our findings provide valuable insights into how these environmental factors might influence blood cell parameters in the general population.

Background/Objectives: Urinary tract infections (UTIs) are frequently diagnosed empirically, often leading to over-treatment and rising antimicrobial resistance. This study aimed to develop and evaluate machine learning (ML) models that predict urine culture outcomes using routine urinaly-sis and demographic data, supporting more targeted empirical antibiotic use. Methods: A real-world dataset comprising 8,065 urinalysis records from a hospital laboratory was used to train five ensemble ML models: Random Forest, XGBoost (eXtreme Gradient Boosting), Extra Trees, Voting Classifier, and Stacking Classifier. Models were developed using 10-fold stratified cross-validation and assessed via clinically relevant metrics in-cluding specificity, sensitivity, likelihood ratios, and diagnostic odds ratio (DOR). To en-hance screening utility, threshold optimization was applied to the best-performing model (XGBoost) using the Youden index. Results: XGBoost and Random Forest demonstrated the most balanced diagnostic profiles, with DORs exceeding 21. The Voting and Stacking Classifiers achieved highest specificity (>95%) and positive likelihood ratios (>10), but exhibited lower sensitivity. Feature im-portance analysis identified positive nitrites, white blood cell count, and specific gravity as key predictors. Threshold tuning of XGBoost improved sensitivity from 70.2% to 87.9% and reduced false negatives by 82%, with an associated NPV of 96.4%. The adjusted mod-el reduced overtreatment by 56% compared to empirical prescribing. Conclusions: ML models based on structured urinalysis and demographic data can support clinical decision-making for UTIs. While high-specificity models may reduce unnecessary antibi-otic use, sensitivity trade-offs must be considered. Threshold-optimized XGBoost offers a clinically adaptable tool for empirical treatment decisions, particularly in settings lacking rapid diagnostics.

The increasing prevalence of Metabolic Syndrome (MetS), a serious condition associated with elevated risks of cardiovascular diseases, stroke, and type 2 diabetes, underscores the urgent need for effective diagnostic tools. This research carefully examines the effectiveness of 16 diverse machine learning (ML) models in predicting MetS, a multifaceted health condition linked to increased risks of heart disease and other serious health complications. Utilizing a comprehensive, unpublished dataset of imbalanced blood test results, spanning from 2017 to 2022, from the Laboratory Information System of the General Hospital of Amfissa, Greece, our study embarks on a novel approach to enhance MetS diagnosis. By harnessing the power of advanced ML techniques, we aim to predict MetS with greater accuracy using non-invasive blood test data, thereby reducing the reliance on more invasive diagnostic methods. Central to our methodology is the application of the Borda count method, an innovative technique employed to refine the dataset. This process prioritizes the most relevant variables, as determined by the performance of the leading ML models, ensuring a more focused and effective analysis. Our selection of models, encompassing a wide array of ML techniques, allows for a comprehensive comparison of their individual predictive capabilities in identifying MetS. This study not only illuminates the unique strengths of each ML model in predicting MetS but also reveals the expansive potential of these methods in the broader landscape of health diagnostics. The insights gleaned from our analysis are pivotal in shaping more efficient strategies for the management and prevention of Metabolic Syndrome, thereby addressing a significant concern in public health.