Precision medicine keeps promising individualized care and keeps delivering population averages. The gap is structural: single-layer omics analysis cannot capture what a biological system does when its parts interact. Digital Twins (DTs) close that gap, not with more data but with dynamic, patient-specific models that fuse multi-omics, clinical records, and longitudinal monitoring into something that runs forward in time. AI drives the simulation; network biology gives it structure. The result predicts disease trajectories and therapeutic response, rather than merely fitting them.
Rimantadine is an adamantane derivative, known for its antiviral activity against infections caused by influenza A viruses. The purpose of the present study was to synthesize 6 new rimantadine derivatives, containing short-chain (Gly, Ala, β-Ala) and bulky (Leu, Ile, Val) amino acids and to investigate their antimicrobial activity against the strains Bacillus subtilis NBIMCC 3562 and Escherichia coli NBIMCC 8785. All derivatives were successfully obtained in good yields by using the TBTU/TEA condensation system. Their antimicrobial properties were established by determining the minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC) against both test strains. The MIC was obtained via a microdilution method, whereas the MBC – via a spread plate method. Most derivatives showed antimicrobial activity, with stronger effects against the Gram-positive strain B. subtilis NBIMCC 3562. Among them, L-Ile-Rim was the most effective derivative against both bacterial strains.
Rapid and accurate detection of intracranial hemorrhage on non-contrast head CT (NCCT) is critical for acute stroke triage and safe thrombolysis. AI tools may support high-volume emergency workflows, but independent real-world validation remains limited. This study externally validated BrainScan CT (v1.4) for hemorrhage detection, with secondary evaluation of ischemia detection.
Epigenetic clocks are DNA methylation-based models that estimate biological age and provide a quantitative framework for investigating interindividual differences in aging. Unlike chronological age, which reflects the passage of time, epigenetic age may capture the cumulative effects of genetic background, environmental exposure, lifestyle, inflammation, and metabolic stress on cellular function. Over the past decade, these models have evolved from tools designed primarily to predict chronological age into increasingly sophisticated biomarkers of morbidity, mortality, and the pace of physiological decline.This review summarizes the conceptual and methodological development of epigenetic clocks and discusses their relevance to aging research and clinical medicine. First-generation clocks, including the Horvath and Hannum models, were developed to estimate chronological age with high accuracy. Second-generation clocks, such as PhenoAge and GrimAge, incorporated clinical bio-markers, mortality-related variables, and plasma protein surrogates, thereby improving their ability to reflect health status and disease risk. More recent models, particularly DunedinPACE, focus on the rate of aging rather than cumulative biological age. Advances in principal component-based clocks, deep learning approaches, and single-cell methylation analyses have further expanded the analytical capacity of the field.Particular attention is given to epigenetic age acceleration and its intrinsic and extrinsic components, as well as the genetic architecture underlying these measures. Variants involving TERT, SELP, HLA, APOE, POU5F1, and cytochrome P450-related pathways support the close relationship between epigenetic aging, telomere biology, inflammation, immune function, metabolism, and neurodegeneration. Evidence from cancer, clonal hematopoiesis, neurodegenerative disorders, and cardiometabolic disease suggests that epigenetic clocks may serve as useful biomarkers for risk stratification and longitudinal monitoring. Nevertheless, broader clinical implementation will require improved standardization, validation across diverse populations, and careful interpretation in tissue- and context-specific settings.
Artificial intelligence (AI) is transforming plant science by enabling rapid data analysis, predictive modeling, and precision breeding. Image recognition accelerates phenotyping, while machine learning optimizes bioprocesses, improving both reproducibility and scalability. Deep Learning technologies, and cloud services expand accessibility, with Copilot exemplifying AI’s role by documenting leaf dehydration in Phaseolus vulgaris L. under controlled conditions. Statistical analysis confirmed a significant weight reduction from 0.25 g to 0.13 g (52%). Copilot generated structured annotations of morphological changes, including loss of turgor, increased venation prominence, and surface wrinkling. Mathematical overlays revealed fractal branching and Voronoi tessellation patterns, with dehydration exaggerating vein relief and sharpening boundaries, linking morphological traits to physiological stress responses. Additionally, Copilot supported bilingual figure legends, terminology harmonization, and bibliometric keyword suggestions, streamlining reproducibility and clarity. Overall, this AI-human synergy demonstrates Copilot’s value as a methodological partner, enhancing accessibility, precision, and international impact in plant physiology research.
