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.
Ischemic stroke (IS) is characterized by complex inflammatory mechanisms that contribute to both primary injury and reperfusion-related damage. Among inflammatory mediators, mast cell–derived histamine and tryptase have been implicated in blood–brain barrier disruption and neuroinflammation, yet their dynamics following thrombolysis remain unclear.
Background: Trauma remains one of the leading causes of morbidity and mortality in the pediatric population. Early assessment of injury severity is essential for timely triage, risk stratification, and clinical decision-making. The Revised Trauma Score (RTS), Pediatric Trauma Score (PTS), and Shock Index (SI) are widely used tools for evaluating physiological status and trauma severity in injured children.
Synthetic biology has evolved from a set of engineering aspirations to an operationally sophisticated discipline, and artificial intelligence (AI) is its fastest-growing accelerant. This review traces that convergence across six interlocking domains: systems-level biological modeling, de novo protein engineering, metabolic and microbial programming, multi-omics data integration, regulatory element design, and clinical translation. For each domain, we survey established results, integrate findings from 2010–2026 literature, and articulate the trajectories that will define the next decade. Emerging themes include physics-informed neural networks for mechanistically constrained biological modeling, drug design, and federated learning architectures that allow global omics collaboration without centralizing sensitive data, self-driving laboratories that close the Design-Build-Test-Learn loop with minimal human intervention, and large language models that accelerate hypothesis generation from scientific literature. Alongside these opportunities, the review gives equal weight to the governance challenges they create: dual-use risks amplified by generative sequence design, the reproducibility crisis in AI-driven biodesign, and the equitable distribution of autonomous experimentation capacity. The overarching argument is that the promise of synthetic biology, making biological design as deliberate and reliable as any mature engineering discipline, is closer than ever, but will only be realized if technical ambition is matched by scientific rigor, transparent governance, and inclusive access.
Arterial Tortuosity Syndrome (ATS) is a rare autosomal recessive connective tissue disorder caused by mutations in the SLC2A10 gene encoding the glucose transporter GLUT10. GLUT10 deficiency leads to the accumulation of reactive oxygen species (ROS), resulting in oxidative stress, extracellular matrix (ECM) disorganization, and dysregulated signaling that compromise vascular integrity. This study aimed to characterize transcriptomic alterations in fibroblasts derived from ATS patients to elucidate molecular mechanisms underlying the disease and identify potential compensatory responses to ECM disruption. Fibroblasts from two ATS patients carrying homozygous SLC2A10 missense variants were analyzed by RNA sequencing (RNA-seq). Differential gene expression (DEG) analysis was followed by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway enrichment analyses to identify altered biological processes. Functional categorization focused on gene sets related to vascular integrity, ECM organization, and cardiovascular function. RNA-seq revealed significant transcriptional dysregulation in ATS fibroblasts compared to controls. Genes involved in Wnt signaling, angiogenesis, and vascular remodeling were significantly upregulated, suggesting potential compensatory mechanisms against ECM disorganization. In contrast, genes related to cardiovascular integrity, ECM-receptor interaction, and basement membrane stability were markedly downregulated. KEGG pathway analysis showed suppression of critical cardiovascular pathways, including hyper-trophic cardiomyopathy (HCM), arrhythmogenic right ventricular cardiomyopathy (ARVC), ECM-receptor interaction, and complement and coagulation cascades. Conversely, the upregulation of cell adhesion molecules (CAMs) indicated potential adaptive responses to vascular abnormalities. Our findings demonstrate that SLC2A10 mutations impair ECM integrity and vascular remodeling, contributing to the molecular pathology of ATS. The observed transcriptional signatures highlight both disrupted pathways and compensatory responses, providing novel insights into ATS pathophysiology and suggesting potential therapeutic targets for future intervention.
Acute myeloid leukemia (AML) is a complex and aggressive malignancy, particularly in children, characterized by chromosomal abnormalities and diverse gene mutations, which contribute to its variable clinical outcomes. Despite advancements in treatment, the prognosis for AML patients remains poor, highlighting the urgent need for reliable biomarkers. Adrenomedullin (ADM), a peptide expressed in various cancer cells, has been implicated in tumor progression. In this study, we investigated the expression patterns and quantified the levels of ADM and its associated receptors receptor activity-modifying proteins (RAMP)2, RAMP3, and calcitonin receptor-like receptor (CLR) in Saudi AML patients. Using real-time PCR and protein analysis, we compared the gene and protein expression levels of these molecules in serum samples from AML patients and healthy controls. Our findings revealed a significant increase (p = 0.0001) in the gene expression of ADM, RAMP2, RAMP3, and CLR in AML patients compared to healthy controls. Furthermore, protein levels of ADM and RAMP3 were significantly elevated (p = 0.0001) in the patient group. These findings suggest that ADM and its receptor components may serve as promising diagnostic biomarkers and potential therapeutic targets for acute myeloid leukemia, warranting further clinical validation.
The frequent rise of antibiotic-resistant bacteria is due to the increasing use of antibiotics in the healthcare system. Probiotics could offer a potential alternative, although their efficacy tends to be diminished in the presence of antibiotics, rendering co-fortification an impractical solution. Stand-alone probiotics cannot completely counteract the effects of antibiotics and often die off in the stomach due to the lower acidic pH. Similarly, antibiotics significantly reduce the action of probiotics; as a result, their therapeutic potential is diminished. Based on the biofilm protection characteristic, chitosan and alginate nanogel are used to encapsulate probiotics with temporary protection against antibiotics, enabling the simultaneous delivery of probiotics and antibiotics. This study involved encapsulation of the probiotic within chitosan-coated alginate nanoparticles (Cs-Alg+ProB NPs), which were made using the ionic gelation technique. The physicochemical characteristics, probiotic release profile across varying pH levels, swelling properties, coincubation of probiotics with antibiotics, and in vitro toxicity evaluation of the produced nanocomplex were examined. The hydrodynamic size of nanoparticles increased from 295.3 +/- 7.13 nm to 328.7 +/- 13.07 nm after probiotic encapsulation, confirming successful loading, as supported by zeta potential changes. Enhanced probiotic release and swelling were observed under acidic pH. The Korsmeyer-Peppas model indicated Fickian diffusion as the release mechanism. Coincubation with amoxicillin demonstrated that encapsulation protects probiotics, a finding that can be extended to provide therapeutic benefits against MDR bacteria to protect public health.
Precision medicine is transforming drug discovery from empirical, population-based approaches toward data-driven, mechanistically informed strategies tailored to individual molecular profiles. Central to this transformation is multi-omics integration-the systematic analysis of genomic, transcriptomic, proteomic, metabolomic, and epigenomic data-which enables comprehensive characterization of disease mechanisms, therapeutic vulnerabilities, and inter- and intra-patient (single-cell) heterogeneity. By moving beyond reductionist, single-layer analyses, multi-omics captures emergent properties of biological systems, revealing causal relationships between molecular variation and clinical phenotypes that are essential for robust target discovery, validation, and lead optimization.This mini-review examines how precision medicine and multi-omics are reshaping the drug discovery pipeline, emphasizing the critical roles of artificial intelligence (AI), FAIR data principles (Findable, Accessible, Interoperable, Reusable), and governance frameworks. We highlight advances in network-based integration, multi-view machine learning, and AI-driven target prioritization, demonstrating how these approaches accelerate hypothesis generation while maintaining reproducibility and traceability. Real-world applications-from HER2-targeted therapies in breast cancer to PARP inhibitors for BRCA-mutated tumors-illustrate the clinical impact of multi-omics-guided drug development.Emerging technologies, including single-cell and spatially resolved multi-omics, promise unprecedented resolution for dissecting tissue heterogeneity, microenvironmental context, and therapeutic resistance mechanisms. Integration of these modalities with foundation models and knowledge graphs comprised of FAIR data will enable cross-modal reasoning, predictive modeling, and patient stratification at scale. However, persistent challenges-data heterogeneity, computational complexity, ethical considerations, and regulatory frameworks-require coordinated solutions. By synthesizing conceptual advances, practical applications, and emerging challenges, we articulate a vision for FAIR-enabled, AI-driven precision medicine as the foundation for next-generation therapeutic discovery.
Diabetic retinopathy (DR), a leading cause of vision loss, is characterized by retinal inflammation, vascular leakage, and pathological neovascularization, with vascular endothelial growth factor A (VEGFA) playing a central role in its progression. While anti-VEGF therapies are effective, their invasive nature and associated risks emphasize the need for safer and more accessible alternatives. This study aimed to investigate the potential of all-trans retinoic acid (RA), a bioactive metabolite of vitamin A, to suppress high glucose-induced VEGFA expression in retinal pigment epithelial (ARPE-19) cells and explore the underlying molecular mechanisms. ARPE-19 cells were treated with high glucose (30 mM) in the presence or absence of RA (5 or 20 mu M). Cell viability was assessed by CCK-8 assay, while VEGFA mRNA and protein levels were measured using quantitative real-time PCR and ELISA, respectively. The activation of p38 MAPK and nuclear translocation of NF-kappa B p65 was evaluated through Western blot analysis. RA treatment significantly reduced high glucose-induced VEGFA expression at both the mRNA and protein levels, without affecting cell viability. Mechanistically, RA inhibited the phosphorylation of p38 MAPK and the nuclear translocation of NF-kappa B p65, suggesting that these pathways contribute to VEGFA regulation under hyperglycemic conditions. These findings highlight the anti-inflammatory and anti-angiogenic effects of RA in ARPE-19 cells and propose RA as a potential, safe, and non-invasive therapeutic candidate for the early intervention of diabetic retinopathy. Further in vivo studies are needed to validate its clinical applicability.
The type I interferon receptor gene (Ifnar1) encodes a subunit of the heterodimeric receptor complex responsible for mediating type I interferon (IFN-α/β) signaling, a critical pathway in antiviral defense and immune regulation. Ifnar1 knockout (KO) mice are widely used in immunology and virology research to study host-pathogen interactions, immune signaling, and inflammatory processes. Although a conventional Ifnar1 KO model was generated decades ago, advances in genome engineering technologies now allow for more efficient and precise generation of genetically modified animals. We employed CRISPR/Cas9 genome editing to generate a novel Ifnar1 knockout mouse line. Single-guide RNAs targeting the third exon of mouse Ifnar1 gene were electroporated into fertilized C57BL/6J zygotes along with Cas9 protein. The newborn founder mice were screened by PCR and Sanger sequencing to identify mutations at the target site. We successfully established a mouse line harboring a 14-nucleotide deletion in the third exon of Ifnar1. This deletion causes a frameshift mutation, introducing a premature stop codon that is predicted to produce a truncated, non-functional protein. The mutation was confirmed by direct sequencing of the targeted locus. Homozygous mutant mice are viable and fertile. This newly generated Ifnar1 knockout mouse model provides a CRISPR-engineered alternative to the original targeted deletion model described by Müller et al. (1994). The frameshift mutation is expected to ablate IFNAR1 protein function. The model will serve as a valuable resource for immunology and virology research, particularly in studies focused on interferon signaling, antiviral responses, and host-pathogen interactions.
Scar-free wound healing remains a major challenge in regenerative medicine. In this study, a carboxymethyl cellulose (CMC)-based hydrogel nanocomposite containing silver nanoparticles (CMC@Ag) was developed, along with a phytocompound-enriched variant (CMC@Ag+P) incorporating aloe vera, curcumin, and plantain peel extracts. The phytocompound-infused hydrogel exhibited enhanced antibacterial activity, biocompatibility, and scar-free healing potential, supporting tissue regeneration. An in vitro scratch assay using the A375 cell line showed 89% cell proliferation and migration at high doses and 69% at low doses of CMC@Ag+P. Zebrafish toxicity assays confirmed its safety, with hatchability rates of 82% (low dose) and 71% (high dose). The chorioallantoic membrane (CAM) assay demonstrated strong angiogenic activity, particularly in CMC@ Ag+P, indicating improved vascularization essential for tissue repair. Statistical analysis using the Student’s t-test revealed significant differences between hydrogel-treated groups and controls (p < 0.05), confirming the enhanced healing and scar-minimization effects. Previous animal studies further validated the scar-free wound healing potential of these hydrogel highlighting the synergistic role of phytocompounds in promoting effective tissue regeneration.
Proteins are one of the fundamental molecules that regulate cellular processes in living organisms. Given the pivotal role played by protein-protein, DNA-protein, and RNA-protein interactions in a significant proportion of biological processes, variants occurring in the regions where these interactions occur have the potential to give rise to serious consequences for the phenotype. Various supervised learning techniques are employed to ascertain the correlation between protein variants and the development of a specific disease. In this study, a convolutional neural network-based prediction model is proposed to predict the pathogenicity effect of variants on the phenotype. This is achieved by converting amino acid sequences into two-dimensional images. A protein embedding method utilizing transfer learning (TAPE) was employed to generate the feature vector. The feature vector was transformed into a square-shaped, single-channel image and trained with a deep learning algorithm comprising a convolutional neural network. This study performed a binary classification (benign versus pathogenic) using missense variants in the BRCA1 protein obtained from the open-access ClinVar database as the dataset. The findings demonstrate that the developed prediction model is highly effective in predicting the pathogenicity effects of variants within the functional regions of the BRCA1 protein on phenotype. The evaluation of the model’s prediction results demonstrated that variants in the benign class can be classified with 91% accuracy (93% sensitivity). Furthermore, the model demonstrated robust performance in classifying both benign and pathogenic variants, with an AUC value of 92%. These findings suggest that the developed prediction model may offer potential in classifying BRCA1 variants and assessing their potential pathogenicity. The variant effect prediction model obtained in this study shows promise and may benefit from further refinement in future research.
Plant genetic improvement integrates conventional breeding with advanced biotechnological approaches to enhance traits such as yield, disease resistance, and stress tolerance. Among these, in vitro-induced somaclonal variation—genetic and epi-genetic alterations arising during tissue culture—has emerged as a valuable tool for crop improvement. This variation can lead to novel phenotypes suitable for selection and propagation. Recent studies have demonstrated its utility in crops such as sugarcane, rice, banana, potato, wheat, tomato, barley, chrysanthemum, soybean, and maize. This review distinguishes itself by providing the first integrated evaluation of somaclonal variation applications across major crops alongside a detailed case study of pineapple, a species seldom emphasised in prior reviews. As one of the most widely cultivated tropical fruits with significant commercial value in both fresh and processed markets, pineapple plays a vital role in the agricultural economies of many developing countries. We highlight results from somaclonal variants derived from the Red Spanish cultivar, including P3R5 and Dwarf, which exhibited significant morphological and physiological differences. Amplified Fragment Length Polymorphism confirmed genetic divergence, with Dwarf showing enhanced water-use efficiency and antioxidant activity. These findings underscore somaclonal variation’s potential as a complementary strategy to conventional breeding, contributing to crop diversification and agricultural resilience.
The integration of advanced artificial intelligence (AI) and humanoid robotics into healthcare represents a critical evolution in biotechnology with profound societal implications. This review explores the bioethical implications of these technologies, and their potential to displace human agency in life-critical decisions. It adopts an interdisciplinary approach encompassing ethics, law, and technology. The review examines how innovations in AI and robotics might shift autonomy from humans to machines, and addresses the accountability challenges inherent in such transitions. We synthesize discussions on the ethical management of AI and robotics, underscoring the importance of maintaining human oversight and integrating ethical standards in technology development to prevent worsening of social inequalities. While AI and robotics present challenges to traditional concepts of autonomy, ethical responsibility, and justice, careful and inclusive policymaking and ethical oversight can harness these technologies to enhance human well-being. This analysis highlights the necessity for continued cross-disciplinary research to navigate the complex ethical landscapes these technologies create, emphasizing that the proactive engagement of diverse stakeholders is essential to guide AI and robotics towards improving human health.
Objective: Breast cancer remains one of the most prevalent malignancies among women worldwide, underscoring the need for physiologically relevant in vitro models that closely mimic the tumor microenvironment. While two-dimensional (2D) cultures are commonly used, they fall short in replicating in vivo conditions. This study aimed to develop a three-dimensional (3D) breast cancer model using various biomaterial-based scaffolds to evaluate their effects on cell viability, morphology, and adhesion. Material and Method: MCF-7 breast cancer cells were cultured on five different 3D printed scaffolds composed of PLA, PCL, PET, HIPS, and TPU. Scaffold designs were created using SolidWorks and Slic3r, followed by 3D printing. Cell viability was assessed using the crystal violet assay, and morphological analysis was conducted through scanning electron microscopy (SEM) and confocal microscopy. Statistical analysis was performed using one-way ANOVA. Results: Among all tested scaffolds, TPU scaffolds exhibited superior performance, significantly enhancing MCF-7 cell viability compared to HIPS, PCL, and PET (***p <= 0.001), and slightly outperforming PLA (ns, p > 0.05). SEM analysis revealed enhanced cell spreading and surface attachment on TPU, while HIPS showed minimal interaction. Confocal imaging further confirmed superior nuclear localization and mitochondrial activity on TPU scaffolds, indicating improved metabolic activity and 3D cellular organization. Conclusion: The findings confirm that TPU scaffolds provide the most supportive microenvironment for MCF-7 cells in 3D culture, offering superior viability, morphology, and cellular interaction. PLA also showed promising results but was slightly less effective than TPU. In contrast, HIPS was the least effective and appears unsuitable as a standalone scaffold material. These results support the use of TPU for physiologically relevant 3D in vitro models of breast cancer for future research and therapeutic applications.
