The EuroBiotech Journal Feed

European Biotechnology Congress 2026

Thu, 23 Jul 2026 00:00:00 GMT

AI-Powered Synthetic Biology: Current Situation, Challenges, and Future Perspectives

Thu, 23 Jul 2026 00:00:00 GMT

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.

Antibacterial Activity of New Rimantadine Derivatives, Conjugated with Compact and Bulky Amino Acids

Thu, 23 Jul 2026 00:00:00 GMT

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.

Pediatric Trauma Severity Assessment and Prognosis Using Automated Scoring Systems

Thu, 23 Jul 2026 00:00:00 GMT

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.Materials and Methods: A prospective observational study was conducted between December 2025 and March 2026 and included 100 pediatric trauma patients registered in the Pediatric Trauma Support System (PTSS). Patients were stratified into four age groups: ≤2 years (n=15), 2-6 years (n=39), 6-12 years (n=34), and 12-18 years (n=12). RTS, PTS, and SI were calculated automatically within the PTSS platform. Severe trauma was defined as hemodynamic instability and/or a Pediatric Trauma Score below 8. Six patients met the criteria for severe trauma. Descriptive statistical analysis was performed.Results: Patients with severe trauma demonstrated lower PTS values (5-6/12), RTS values ranging from 6.00 to 7.11, and a mean SI of 1.38. In children aged ≤2 years, PTS values decreased to 9/12 even in cases of minor head injury, while RTS remained 7.84 and mean SI was 1.27, reflecting age-related physiological differences. In the 2-6 and 6-12 year groups, PTS values ranged from 10-11/12, RTS remained stable at 7.84, and SI ranged from 0.80 to 1.11. Similar patterns were observed among adolescents aged 12-18 years. The combined assessment of RTS, PTS, and SI provided complementary information regarding injury severity across age groups.Conclusions:RTS, PTS, and SI remain useful tools for the early assessment of pediatric trauma patients. Their integration within the PTSS clinical decision-support platform enables standardized calculation and facilitates rapid evaluation in emergency settings. Age-related physiological variations should be considered when interpreting trauma scores in children.

Digital Twins for Precision Medicine and Drug Discovery

Thu, 23 Jul 2026 00:00:00 GMT

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.This review follows the full DT lifecycle, from data ingestion and FAIRification through machine learning personalization to clinical deployment. We propose a five-point operational definition that separates genuine DTs from relabeled statistical models, resting on one requirement: a bidirectional virtual-physical loop, not a static prediction. We also examine the gut microbiome, still underserved despite its complexity, and show how ecological frameworks map host-microbe dynamics for precision nutrition, IBD management, and cancer immunotherapy.Case studies in oncology and neurodegeneration ground the argument, but the bottlenecks matter more: the drug discovery “Attrition Paradox,” misaligned multi-modal data, and uncertainty quantification most pipelines skip. Ethical exposure runs alongside these gaps. Privacy law (GDPR/HIPAA) and algorithmic bias are not peripheral; left unaddressed, they widen disparities rather than close them. We close with stakeholder-specific recommendations, for researchers, industry, funders, regulators, aimed at Digital Twin ecosystems that are safe and equitable by design, not by afterthought.

Dynamic Changes in Mast Cell-Derived Histamine and Tryptase Following Reperfusion Therapy in Acute Ischemic Stroke

Thu, 23 Jul 2026 00:00:00 GMT

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.This study investigated plasma histamine and tryptase levels in patients with acute IS before and after intravenous thrombolytic therapy and examined their association with clinical and laboratory parameters. At admission, both mediators were significantly elevated. Following thrombolytic therapy, histamine and tryptase decreased significantly, corresponding to an approximately 1.5-fold decrease.Correlation analysis revealed positive associations between both mediators and triglycerides, hemoglobin, and bleeding time, while post-thrombolysis tryptase levels are additionally associated with clotting time. Although mean neurological improvement was not significant, 45% of patients showed clinical improvement following treatment.In conclusion, our findings suggest that thrombolysis may modulate mast cell-related inflammatory activity, metabolic pathways, and coagulation processes in acute IS.

The Next Fifty Years: Drug Discovery in the Age of the Computational Self

Thu, 23 Jul 2026 00:00:00 GMT

For most of biomedical history, drug discovery has been organized around populations: diseases defined through shared symptoms, therapeutics evaluated in statistical cohorts, approval granted for averaged responses. The paradigm produced extraordinary successes. It also embedded a foundational assumption now under pressure from several independent directions at once. AI-native drug discovery, multi-scale digital twins, pharmacogenomics, patient-derived organoids, adaptive trial design, closed-loop biosensing, decentralized science — these are not isolated innovations. They are convergent expressions of a single directional tendency: the progressive migration of therapeutic authority from population-scale structures toward the individual. This is the biomedical expression of a broader civilizational vector, in which the organizing unit of decision-making resolves continuously toward smaller, more personalized scales. Twelve convergent indicators of this transition are introduced, along with a fifty-year roadmap projecting five successive phases of drug discovery, from AI-assisted precision medicine through full therapeutic sovereignty. The Asymmetric Epistemic Burden (AEB) model formalizes the growing divergence between AI-accelerated hypothesis generation and the bottlenecked experimental and regulatory validation that now constitutes the primary constraint on progress. The defining transformation of twenty-first century medicine may not be the discovery of a better drug class. It may be the conceptual and institutional replacement of the average patient as the fundamental unit of therapeutic reasoning — and the emergence of a pharmacotherapy organized not around populations, but around continuously modeled, computationally instantiated, biologically sovereign individuals.

External Validation of BrainScan AI for Detection of Hemorrhagic Stroke on Non-Contrast Head CT

Thu, 23 Jul 2026 00:00:00 GMT

Background and PurposeRapid 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.MethodsIn this retrospective single-centre study at UMHAT “St. George” (Plovdiv, Bulgaria), 1,419 consecutive NCCT scans from 1,260 adults (January–December 2025) were analysed. The reference standard was routine radiology reports. Hemorrhage was present in 120 scans (8.5%) and ischemia in 201 (14.2%). Performance metrics included sensitivity, specificity, predictive values, likelihood ratios, AUC, and McNemar testing. Subgroup analyses examined hemorrhage subtype (ECASS) and anatomical location. Sensitivity analyses assessed threshold variation and repeated scans.ResultsBrainScan CT showed high accuracy for hemorrhage detection: sensitivity 96.7% (95% CI 91.7–99.1), specificity 99.8% (99.4–100.0), PPV 98.3% (94.0–100.0%), NPV 99.7% (99.3–100.0%) and AUC 0.983 (0.967–0.999). Likelihood ratios were 627.9 (positive) and 0.03 (negative), with no systematic disagreement versus the reference standard (p=0.68). Sensitivity reached 100% for parenchymal hemorrhage, while false negatives occurred only in hemorrhagic infarction (HI-1 86.7%; HI-2 87.5%). Performance was consistent across regions, lowest for occipital hemorrhage (84.6%). Results were invariant across thresholds and unchanged when restricted to first scans (AUC 1.000). For ischemia, sensitivity was lower (59.2%) despite high AUC (0.930), significantly inferior to hemorrhage detection (p<0.001).ConclusionsBrainScan CT demonstrated excellent real-world performance for hemorrhage detection on NCCT, supporting its role in triage, while expert oversight remains essential, particularly for subtle hemorrhagic infarction.

Copilot as a methodological partner in plant physiology: Proof of concept using common bean leaf dehydration

Thu, 23 Jul 2026 00:00:00 GMT

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.

Epigenetic Clocks: Biological Aging, Disease, and Clinical Applications

Thu, 23 Jul 2026 00:00:00 GMT

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.

Protein Function Prediction with Pretrained Transformers: Performance, Pitfalls, and Practical Guidance

Thu, 30 Apr 2026 00:00:00 GMT

Transformer-based protein language models (PLMs) learn meaningful representations from millions of unlabeled sequences, capturing evolutionary patterns and functional relationships. Recent advances include ESM-2’s systematic scaling to 15 billion parameters, structure-aware vocabularies (SaProt), and multimodal foundation models (ESM-3, 98B parameters). PLMs achieve state-of-the-art performance: Gene Ontology prediction (F-max 0.64–0.68), enzyme classification (81% accuracy), and variant effect prediction (Spearman ρ 0.52–0.55). Deep-layer attention correlates 44–63% with 3D contacts despite no structural training. This review synthesizes recent PLM developments, benchmarks, and practical applications, providing guidance for experimental biologists on model selection and validation strategies.

Expression Analysis of Adrenomedullin and Its Receptors RAMP2/CLR and RAMP3/CLR in Acute Myeloid Leukemia: A Saudi Cohort Study

Thu, 30 Apr 2026 00:00:00 GMT

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.

Probiotic-infused Mixed Polymeric Nanoparticles Co-incubated with Antibiotics: An Alternative Approach to Combat Drug-resistant Bacteria

Thu, 30 Apr 2026 00:00:00 GMT

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.

Transcriptomic Analysis of GLUT10 Deficiency in Arterial Tortuosity Syndrome

Thu, 30 Apr 2026 00:00:00 GMT

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.

Integrated Biotechnological Strategies for Planetary Health and Ecosystem Restoration

Thu, 30 Apr 2026 00:00:00 GMT

The escalation of ecological degradation, marked by climatic instability, precipitous biodiversity loss, and systemic chemical infiltration, necessitates a shift toward integrated biotechnological intervention. This analysis considers how the trajectory of diverse advanced methodologies, including environmental DNA (eDNA) surveillance, CRISPR-based genetic restitution, and synthetic biology-driven bioremediation, might be synthesized into a unified Planetary Health Framework. By exploring the convergence of high-precision genomic engineering, AI-augmented bioinformatic modeling, and the tenets of a circular bio-economy, we examine the capacity of technological intervention to ameliorate ecological stressors and restore critical ecosystems.Moving beyond the constraints of conventional conservation, this discourse explores frontier solutions in climate-resilient agrotechnology, photo-driven biomanufacturing, and technologically integrated urban ecology, highlighting the criticality of microbiome modulation and comprehensive genomic repositories in stabilizing the biosphere. By reconciling molecular-level precision with macro-scale systems ecology, this framework points to a trajectory for sustainable development where technological scalability is constrained by, yet optimized for, planetary boundaries. Ultimately, this review addresses the imperatives of ethical oversight and highlights the multi-sectoral governance architectures required for the responsible global deployment of these “living technologies.”

Precision Medicine and Multi-Omics Integration: Transforming Drug Discovery Through FAIR-Enabled Systems

Tue, 20 Jan 2026 00:00:00 GMT

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.

A novel Ifnar1 knockout mouse model generated by CRISPR/Cas9 genome editing

Tue, 20 Jan 2026 00:00:00 GMT

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.

Bioactive Hydrogel for Regenerative Wound Healing: Evaluating Angiogenesis and Toxicity

Tue, 20 Jan 2026 00:00:00 GMT

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.

All-trans Retinoic Acid Attenuates High Glucose-Induced VEGFA Expression via Inhibition of p38 MAPK and NF-κB in ARPE-19 Cells

Tue, 20 Jan 2026 00:00:00 GMT

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 μM). 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-κB 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-κB 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 European Biotechnology Congress 2025

Sat, 06 Dec 2025 00:00:00 GMT