Esculetin is a coumarin compound, which belongs to the class of benzopyrone enriched in various plants such as Sonchus grandifolius, Aesculus turbinata, etc. Free radicals lead to the development of oxidative stress causing inflammation, arthritis, cancer, diabetes, fatty liver disease, etc. These further reduce the efficacy of anticancer drugs, activate inflammatory signaling pathways, degrade joints and cartilage, and disrupt the glycemic index and normal function of liver enzymes. For instance, the current treatment modalities used in arthritis such as non-steroidal anti-inflammatory drugs, disease-modifying anti-rheumatoid drugs, and lipoxygenase inhibitors present limited efficacy and adverse effects. Thus, there is a constant need to find newer and safer alternatives. Esculetin has an immense antioxidative potential thereby alleviating arthritis, diabetes, malignancies, and hepatic disorders. Structurally, esculetin contains two hydroxyl groups, which enhance its ability to function as an antioxidant by inhibiting oxidative stress in pathological conditions. Leukotriene B4 synthesis, NF-κB and MPAK pathway activation, and inflammatory cytokine production are the main causes of bone and joint deterioration in arthritis, whereas esculetin treatment reverses these factors and relieves the disease condition. In contrast, lipid peroxidation caused by upregulation of TGF-β-mediated expression and dysfunction of antioxidant enzymes is inhibited by esculetin therapy, thus reducing liver fibrosis by acting on the PI3K/FoxO1 pathway. Therefore, targeting NF-κB, pro-inflammatory cytokines, TGF-β and oxidative stress may be a therapeutic strategy to alleviate arthritis and liver fibrosis.

The inhibition of tumor necrosis factor (TNF)-α trimer formation renders it inactive for binding to its receptors, thus mitigating the vicious cycle of inflammation. We designed a peptide (PIYLGGVFQ) that simulates a sequence strand of human TNFα monomer using a series of in silico methods, such as active site finding (Acsite), protein–protein interaction (PPI), docking studies (GOLD and Flex-X) followed by molecular dynamics (MD) simulation studies. The MD studies confirmed the intermolecular interaction of the peptide with the TNFα. Fluorescence-activated cell sorting and fluorescence microscopy revealed that the peptide effectively inhibited the binding of TNF to the cell surface receptors. The cell culture assays showed that the peptide significantly inhibited the TNFα-mediated cell death. In addition, the nuclear translocation of the nuclear factor kappa B (NFκB) was significantly suppressed in the peptide-treated A549 cells, as observed in immunofluorescence and gel mobility-shift assays. Furthermore, the peptide protected against joint damage in the collagen-induced arthritis (CIA) mouse model, as revealed in the micro focal-CT scans. In conclusion, this TNFα antagonist would be helpful for the prevention and repair of inflammatory bone destruction and subsequent loss in the mouse model of CIA as well as human rheumatoid arthritis (RA) patients. This calls upon further clinical investigation to utilize its potential effect as an antiarthritic drug.

Mass spectrometry-based metabolomics is a valuable tool for advancing pediatric health research. Along with nuclear magnetic resonance, it enables detailed biochemical analysis from minimal sample volumes, a critical feature for pediatric diagnosis. Metabolomics supports early detection of inherited metabolic disorders, monitors metabolic changes during growth, and identifies disease markers for a range of conditions, including metabolic, neurodevelopmental, oncological, and infectious diseases. Integrating metabolomic data with genomic, proteomic (i.e., multi-omics approaches), and clinical information enables more precise and preventive care by enhancing risk assessment and informing targeted treatments. However, routine clinical use faces several challenges, including establishing age- and sex-specific reference ranges, standardizing sample collection and processing, ensuring consistency across platforms and laboratories, expanding reference databases, and improving data comparability. Ethical and regulatory issues, including informed consent, data privacy, and equitable access, also require careful consideration. Advances in high-resolution and single-cell metabolomics, artificial intelligence for data analysis, and cost-effective testing are expected to address these barriers and support broader clinical adoption. As standards and data-sharing initiatives grow, metabolomics will play an increasingly important role in pediatric diagnostics and personalized care, enabling earlier disease detection, improved treatment monitoring, and better long-term outcomes for children.

Vascular diseases (VDs) and cardiovascular diseases (CVDs) are the leading causes of morbidity and mortality worldwide. Current diagnostic and therapeutic approaches are limited by insufficient resolution and a lack of mechanistic understanding at the cellular level. Traditional imaging and clinical assays do not fully capture the dynamic molecular and structural complexities underlying vascular pathology. Recent technological innovations, including single-cell and spatial transcriptomics, super-resolution and photoacoustic imaging, microfluidic organ-on-chip platforms, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated protein 9 (Cas9)-based gene editing, and artificial intelligence (AI), have created new opportunities for investigating the cellular and molecular basis of VDs. These techniques enable high-resolution mapping of cellular heterogeneity and functional alterations, facilitating the integration of large-scale data for biomarker discovery, disease modeling, and therapeutic development. This review focuses on evaluating the translational readiness, limitations, and potential clinical applications of these emerging technologies. Understanding the cellular and molecular mechanisms of VDs is essential for developing targeted therapies and precise diagnostics. Integrating single-cell and multiomics approaches highlights disease-driving cell types and gene programs. Optogenetics and organ-on-chip platforms allow for controlled manipulation and physiologically relevant modeling, while AI enhances data integration, risk prediction, and clinical interpretability. Future efforts should prioritize multi-center, large-scale validation studies, harmonization of assay protocols, and integration with clinical datasets and human samples. Multi-omics approaches and computational modeling hold promise for unraveling disease complexity, while advances in regulatory science and digital simulation (such as digital twins) may further accelerate personalized medicine in vascular disease research and treatment.

Troxerutin (TXR) is a phytochemical reported to possess anti-inflammatory and hepatoprotective effects. In this study, we aimed to exploit the antiarthritic properties of TXR using an adjuvant-induced arthritic (AIA) rat model. AIA-induced rats showed the highest arthritis score at the disease onset and by oral administration of TXR (50, 100, and 200 mg/kg body weight), reduced to basal level in a dose-dependent manner. Isobaric tags for relative and absolute quantitative (iTRAQ) proteomics tool were employed to identify deregulated joint homogenate proteins in AIA and TXR-treated rats to decipher the probable mechanism of TXR action in arthritis. iTRAQ analysis identified a set of 434 proteins with 65 deregulated proteins (log₂ case/control≥1.5) in AIA. Expressions of a set of important proteins (AAT, T-kininogen, vimentin, desmin, and nucleophosmin) that could classify AIA from the healthy ones were validated using Western blot analysis. The Western blot data corroborated proteomics findings. In silico protein–protein interaction study of tissue-proteome revealed that complement component 9 (C9), the major building blocks of the membrane attack complex (MAC) responsible for sterile inflammation, get perturbed in AIA. Our dosimetry study suggests that a TXR dose of 200 mg/kg body weight for 15 days is sufficient to bring the arthritis score to basal levels in AIA rats. We have shown the importance of TXR as an antiarthritic agent in the AIA model and after additional investigation, its arthritic ameliorating properties could be exploited for clinical usability.