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 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.

Background Accurate characterization of human sperm motility is crucial for understanding male fertility potential. Traditional motility assessment methods primarily focus on static velocity parameters, often overlooking temporal declines in motility during the sperm trajectory. Objective We aimed to develop and validate a novel fatigue-based profiling approach to assess intra-trajectory motility decline in human spermatozoa. Methods Using computer-assisted sperm analysis (CASA)-derived motion tracking data, we introduced the Fatigue Index, a metric quantifying the decline in forward progression over time. We performed descriptive and inferential analyses to examine motility patterns, complemented by multivariate dimensionality reduction and clustering techniques. Results Our analysis revealed significant intra-trajectory fatigue effects that traditional static parameters failed to capture. Fatigue-based profiling distinguished hidden subpopulations of sperm trajectories with distinct motility dynamics. The proposed Fatigue Index demonstrated robust consistency across samples and correlated with key kinematic features. Conclusions Fatigue-based temporal profiling offers a new dimension for understanding sperm motility, highlighting the dynamic nature of forward progression and identifying subtle motility impairments that may be overlooked by conventional assessment methods.

Background: Understanding heterogeneity in sperm motility requires tools capable of capturing both dynamic patterns and interpretable structure. Symbolic encoding methods offer a novel way to represent motion trajectories through discrete motifs. Objective: This study explores whether symbolic time-series representations can uncover latent structure in sperm motility data, focusing on patterns of symbolic monotony and entropy. Methods: We applied Symbolic Aggregate approXimation (SAX) to 1176 sperm trajectories and computed motif entropy and dominance across multiple parameterizations (a = 3–7, k = 2–4). Trajectories were embedded using UMAP and evaluated for symbolic convergence. Results: A compact trajectory cluster (82/1176, 7%) consistently emerged across SAX configurations, characterized by very low entropy (median: 0.00) and high motif dominance (median: 0.97), with >85% of motifs consisting of the “DDD” triplet. The cluster exhibited a markedly negative VSL slope (median: –0.00021), in contrast to non-clustered trajectories (median: +0.00002). No external labels were available to determine functional significance. Conclusions: Symbolic encoding revealed a highly consistent pattern of motion monotony. While these findings may reflect constrained or declining motility, their biological interpretation remains uncertain. Symbolic representations may serve as useful hypothesis-generating tools in the discovery of emergent sperm motility phenotypes.

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: Traditional sperm motility metrics often fail to reflect the dynamic complexity of motion patterns. Here, we present an unsupervised framework combining dynamic time warping (DTW) clustering with phase-space and fatigue-sensitive descriptors to uncover latent motility phenotypes. Methods: We analyzed 1,176 sperm tracks from the VISEM dataset using DTW distance matrices applied to velocity time series, followed by agglomerative hierarchical clustering (n = 2). After cluster assignment, we extracted phase-space features—recurrence rate, spectral entropy, fractal index, and Lyapunov approximation—and computed fatigue metrics such as VSL slope. Results: DTW clustering revealed two well-separated motility phenotypes with a mean silhouette score of 0.861. Chaotic-like tracks exhibited higher spectral entropy (4.45 vs. 2.58), elevated fractal index (0.079 vs. 0.434), and increased local instability as reflected by the Lyapunov approximation (0.131 vs. 0.009; all p < 0.001). Recurrence rate showed no significant difference. VSL slope was markedly more negative in Chaotic-like tracks, indicating a stronger fatigue component. Conclusions: Our pipeline stratifies sperm motility into biologically interpretable dynamic classes using raw temporal profiles—without relying on predefined scalar indices. This approach may enhance phenotypic analysis in reproductive diagnostics by capturing structural and fatigue-driven variability in sperm motion.