Background: The SARS-CoV-2 pandemic began in early 2020, paralyzing human life all over the world and threatening our security. Thus, the need for an effective, novel approach to diagnosing, preventing, and treating COVID-19 infections became paramount. Methods: This article proposes a machine learning-based method for the classification of chest X-ray images. We also examined some of the pre-processing methods such as thresholding, blurring, and histogram equalization. Results: We found the F1-score results rose to 97%, 96%, and 99% for the three analyzed classes: healthy, COVID-19, and pneumonia, respectively. Conclusion: Our research provides proof that machine learning can be used to support medics in chest X-ray classification and improving pre-processing leads to improvements in accuracy, precision, recall, and F1-scores.

Background/Objectives: Periapical lesions (PLs) are frequently detected in dental radiology. Accurate diagnosis of these lesions is essential for proper treatment planning. Imaging techniques such as orthopantomogram (OPG) and cone-beam CT (CBCT) imaging are used to identify PLs. The aim of this study was to assess the diagnostic accuracy of artificial intelligence (AI) software Diagnocat for PL detection in OPG and CBCT images. Methods: The study included 49 patients, totaling 1223 teeth. Both OPG and CBCT images were analyzed by AI software and by three experienced clinicians. All the images were obtained in one patient cohort, and findings were compared to the consensus of human readers using CBCT. The AI’s diagnostic accuracy was compared to a reference method, calculating sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Results: The AI’s sensitivity for OPG images was 33.33% with an F1 score of 32.73%. For CBCT images, the AI’s sensitivity was 77.78% with an F1 score of 84.00%. The AI’s specificity was over 98% for both OPG and CBCT images. Conclusions: The AI demonstrated high sensitivity and high specificity in detecting PLs in CBCT images but lower sensitivity in OPG images.

Preterm birth is a significant public health concern, given its correlation with neonatal mortality and morbidity. The aetiology of preterm birth is complex and multifactorial. The objective of this study was to develop and compare machine learning models for predicting the risk of preterm birth. Data were collected from 50 patients in a maternity ward, with an analysis performed based on the timing of delivery (preterm vs. term). The applicability of XGBoost, CatBoost, logistic regression, support vector machines (SVM), and decision trees for predicting preterm delivery was evaluated through training. The linear SVM with boosted parameters demonstrated the highest performance, achieving an accuracy of 82%, precision of 83%, recall of 86%, and an F1-score of 84%. The logistic regression model, also boosted, demonstrated comparable performance to the linear SVM, with similar accuracy (80%), precision (82%), recall (82%), and F1-score (82%). The performance of other models, including decision trees and more complex algorithms, was inferior, which is likely attributable to the limited dataset and the number of parameters involved. In particular, machine learning models, most notably the linear SVM, can be effectively employed to assess the risk of preterm birth. The findings indicate that the linear SVM model exhibits the greatest efficacy among the tested models.

Data obtained with the use of massive parallel sequencing (MPS) can be valuable in population genetics studies. In particular, such data harbor the potential for distinguishing samples from different populations, especially from those coming from adjacent populations of common origin. Machine learning (ML) techniques seem to be especially well suited for analyzing large datasets obtained using MPS. The Slavic populations constitute about a third of the population of Europe and inhabit a large area of the continent, while being relatively closely related in population genetics terms. In this proof-of-concept study, various ML techniques were used to classify DNA samples from Slavic and non-Slavic individuals. The primary objective of this study was to empirically evaluate the feasibility of discerning the genetic provenance of individuals of Slavic descent who exhibit genetic similarity, with the overarching goal of categorizing DNA specimens derived from diverse Slavic population representatives. Raw sequencing data were pre-processed, to obtain a 1200 character-long binary vector. A total of three classifiers were used—Random Forest, Support Vector Machine (SVM), and XGBoost. The most-promising results were obtained using SVM with a linear kernel, with 99.9% accuracy and F1-scores of 0.9846–1.000 for all classes.

Chronic diseases increasingly reflect a shared biological origin: persistent cellular stress. This review summarizes the biochemical mechanisms that normally preserve cellular homeostasis, namely redox regulation, endoplasmic reticulum proteostasis, mitochondrial quality control, autophagy, and DNA damage response, and explains how they fail under sustained lifestyle-related overload. Repeated exposure to psychological stress, sleep disruption, hypercaloric intake, and physical inactivity shifts adaptive signaling toward maladaptation, promoting oxidative damage, protein misfolding, mitochondrial dysfunction, low-grade inflammation, and genomic instability. These interconnected processes contribute to the development and progression of major chronic non-communicable diseases, including obesity, type 2 diabetes, cardiovascular disease, neurodegeneration, and cancer. Particular emphasis is placed on circadian and neuroendocrine regulation, especially overactivation of the hypothalamic–pituitary–adrenal axis and impaired nocturnal regenerative pathways such as glymphatic clearance and DNA repair. Together, the evidence supports a unifying model in which chronic pathology emerges from cumulative failure of cellular resilience systems rather than isolated organ-specific defects. This perspective highlights sleep optimization, stress reduction, and metabolic regulation as mechanistically grounded strategies for prevention and supportive interventions for chronic disease.

Digital image correlation may be useful in many different fields of science, one of which is medicine. In this paper, the authors present the results of research aimed at detecting skin micro-shifts caused by pulsation of the veins. A novel technique using digital image correlation (DIC) and filtering the resulting shifts map to detect pulsating veins was proposed. After applying the proposed method, the veins in the forearm were visualized. The proposed technique may be used in the diagnosis of venous stenosis and may also contribute to reducing the number of adverse events during blood collection. The great advantage of the proposed method is the lack of the need to have specialized equipment, only a typical mobile phone camera is needed to perform the test.