Clinical assessment commonly relies on physiological variables interpreted as isolated values or against population-derived thresholds. Although indispensable, these approaches provide limited information about regulatory flexibility, inter-system coordination, and recovery after stress. Physiological signals contain structured fluctuations across multiple temporal scales that reflect dynamic regulation and interactions among organ systems. This article proposes adaptive physiological reconfiguration as an integrative framework for interpreting disease-related changes in physiological dynamics. We hypothesize that disease-related vulnerability is associated with a reduced capacity of physiological systems to reconfigure signal dynamics and inter-system relationships appropriately across changing functional states and to restore a context-appropriate organization after perturbation. Heart rate variability provides a conceptual starting point, while network physiology extends this perspective toward multisystem regulation. Continuous multimodal monitoring and artificial intelligence may support the characterization of these dynamics but cannot establish causality. The framework generates testable predictions: dynamic and multisystem features should provide reproducible information beyond corresponding mean values and isolated signals, while recovery trajectories should help characterize physiological resilience. These hypotheses require standardized measurement, external validation, and prospective evaluation.

Counting bacterial colonies by hand is one of the most common tasks in microbiology, but it is slow and often yields different results depending on who does the counting. This paper presents an automated system for detecting and classifying bacterial colonies from Petri plate images. We tested four models: Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and a hybrid CNN+Vision Transformer (CNN+ViT). The system uses OpenCV for image preprocessing and extracts features such as area, perimeter, and circularity. Colonies are then classified by size and health condition. Confusion matrix results show that SVM and ANN achieved the highest overall accuracy (95.6%), while CNN+ViT eliminated false positives entirely. A Streamlit-based web interface allows real-time colony analysis directly in laboratory environments.

Background: Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality among patients undergoing peritoneal dialysis (PD). Despite advances in clinical management, accurate prediction of cardiovascular events in patients receiving PD continues to be a major challenge due to the complexity and heterogeneity of patient data. This study proposes a novel Pro-PD diagnostic model to enhance the prediction of cardiovascular events, including myocardial dysfunction, stroke, and heart attack. Methods: The model integrates deep learning (DL) with the Grey Wolf Marking (GWM) optimization technique and incorporates a dual-mode authentication and decision-support mechanism governed by GWM to optimize feature selection and classification performance. It leverages multidimensional datasets comprising demographic characteristics, clinical indicators, and dialysis-specific parameters to extract complex patterns associated with CVD risk. Results: Experimental evaluation demonstrates that the proposed approach achieves an accuracy of 95.62% in identifying selective pattern attributes and classifying PD patients into CVD risk categories. Conclusions: The proposed Pro-PD framework demonstrated promising predictive performance, achieving 95.62% training accuracy and 92.64% validation accuracy for the local model under an 80% training–20% validation split. The global feature-mapping model achieved an AUC (area under ROC curve) of 92.41%.