Dyumn Dwivedi

Ayurveda is increasingly investigated using genomics, pharmacology and systems biology, yet its global scientific landscape remains quantitatively underexplored. We conducted a bibliometric and scientometric analysis of 2,027 Scopus-indexed articles (1991–2024), identifying 7,541 authors, 762 sources and 51,735 citations. Publication growth followed a Gompertz trajectory (R² = 0.998), indicating consolidation rather than exponential expansion, with declining relative growth and increasing doubling time. Author productivity deviated from Lotka’s law, while source distribution followed Bradford’s law. Citation inequality was high (Gini = 0.728). Network analysis revealed eight thematic clusters, with antioxidant pharmacology, COVID-19-related network pharmacology and complementary medicine as major motor themes; molecular docking and network pharmacology emerged as the newest topics. India dominated output but exhibited limited international collaboration. No significant open-access citation advantage was observed. Overall, Ayurveda research is shifting toward mechanistic and translational science but remains structurally concentrated and insufficiently internationalized.

Prakriti, the constitutionally determined phenotypic framework described in Ayurveda, is thought to capture biologically meaningful inter-individual variation in physiology, disease susceptibility and therapeutic response. Although prior Ayurgenomics studies have identified constitution-linked differences in gene expression, genetic variation and epigenetic state, the regulatory architecture underlying these phenotypes remains poorly understood. We applied a multi-layer systems biology framework to Prakriti-stratified bulk transcriptomic data. Weighted gene co-expression network analysis was used to identify Prakriti-associated modules and hub genes. Pathway activity was inferred using PROGENy and GSVA; transcription factor activity was estimated using VIPER; immune composition was assessed using ssGSEA, CIBERSORTx, and xCell; and immune-evasion markers were analysed using differential expression and clustering approaches. Dark kinase activity was profiled as a dedicated analytical layer, and cross-layer integration was performed using z-score-standardised feature matrices, correlation analysis, principal component analysis and linear interaction modelling. Eighteen co-expression modules were identified, of which three showed strong Prakriti associations: a Vata-linked module enriched for innate immune sensing and leukocyte trafficking, a Pitta-linked module enriched for chromatin regulation and ubiquitin-mediated proteolysis, and a Kapha-linked module enriched for RNA processing, DNA repair, adhesion and calcium-channel biology. PROGENy revealed a largely shared inflammatory and hypoxic macro-state across constitutions, indicating that broad canonical signalling did not itself explain group separation. Finer-resolution analyses uncovered marked divergence across regulatory layers. Vata combined innate immune activation with strong immunosuppressive features, including selective elevation of IDO2, ARG1, TGFB3, SIGLEC15 and CD274. Pitta showed mitochondrial and proteostatic signatures together with dominant PDCD1LG2 expression. Kapha displayed the most consistent GSVA elevation across immune-metabolic pathways and was enriched for M2 macrophages, NKT cells and B cells. Dark kinome profiling identified reciprocal constitutional patterns: ALPK3 and STK31 were enriched in Kapha, and BRSK1, WNK2, and STKLD1 were enriched in Vata. Cross-layer integration revealed a coherent TNFa/NFkB-to-AP1 regulatory bridge. It showed that dark kinase activity was significantly coupled to immune-evasion outputs, with checkpoint regulation displaying a Prakriti-dependent interaction that reversed in Pitta relative to Kapha. Prakriti-associated molecular divergence is not primarily encoded in isolated genes or broad pathway shifts, but in constitution-specific regulatory wiring across co-expression, transcription factor, immune, metabolic and dark kinase-linked layers. These findings move Ayurgenomics beyond descriptive stratification towards a systems-level mechanistic framework for constitutional biology and provide a basis for future validation in precision immunology and personalised medicine.

Predictive multiscale cellular modeling is emerging as a consequential direction in precision medicine, converging hypothesis grammars, digital twins, and integrative genomics to interrogate tumor-immune dynamics, therapeutic resistance, and cellular plasticity. This perspective synthesizes recent progress across these domains and critically maps their translational potential alongside their current limitations. Hypothesis grammars translate mechanistic theories into executable agent-based models (ABMs) and hybrid ODE-PDE systems, enabling rapid in silico hypothesis testing while lowering the authoring barrier for domain scientists. Patient-specific digital twins, driven by multi-omics data, employ stochastic ensemble methods to simulate clonal evolution and microenvironmental interactions, though prospective clinical validation of these capabilities remains at an early stage. Integrative genomics, leveraging algorithms such as SCODE and SimiC, infers causal gene regulatory networks (GRNs) using Bayesian variational autoencoders, embedding dynamic intracellular logic into tissue-scale simulations. Emerging applications include in silico oncology trials for optimizing checkpoint blockade and combination therapies. Large language models are being explored to enhance rule induction, while FAIR-compliant digital cell repositories aim to ensure reproducibility and reuse. Verification, validation, and uncertainty quantification (VVUQ) via Sobol sensitivity analysis and Kennedy-O’Hagan calibration are identified as essential components for addressing non-identifiability and supporting regulatory credibility. Federated learning is discussed as a means of mitigating privacy and bias concerns in multi-institutional settings. Together, these converging approaches outline a plausible pathway toward virtual clinical trials and adaptive theranostics, contingent on the prospective validation, data infrastructure, and governance frameworks that clinical deployment will require. Graphical Abstract The alternative text for this image may have been generated using AI. Patient multi-omics data are processed through hypothesis grammars, digital twins, and integrative multiscale modeling into an AI-driven workflow that simulates tumor–immune dynamics, forecasts therapeutic outcomes, and iteratively guides precision oncology decisions under FAIR principles.

Colorectal carcinoma (CRC) exerts a growing global disease burden, with microsatellite-stable/proficient mismatch repair (MSS/pMMR) tumors exhibiting intrinsic refractoriness to immune-checkpoint blockade (ICB) owing to low tumor mutational burden, limited neoantigenicity, and an immunosuppressive tumor microenvironment (TME) dominated by regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs). This review evaluates induced pluripotent stem cell (iPSC)–derived polyvalent vaccines as ontogenetically recapitulative immunogens capable of reinstating broad antitumor immunity. Reprogramming induces re-expression of oncofetal tumor-associated antigens, including cancer-testis antigens (NY-ESO-1, MAGE-A3), aberrant glycoforms of CEA and MUC1, and clinically actionable neoepitopes such as KRAS^G12D/V, thereby promoting epitope spreading and immunogenic cell death. Irradiated autologous or syngeneic iPSCs, delivered with Toll-like receptor 9 agonists, facilitate robust MHC I/II cross-presentation, driving CD8⁺ cytotoxic T-cell activation, Th1 polarization, perforin/granzyme-mediated cytolysis, and favorable effector-to-suppressor ratios. Preclinical models of melanoma, pancreatic ductal adenocarcinoma, and MSS CRC demonstrate prophylactic and therapeutic efficacy, with neoantigen-enhanced iPSCs synergizing with radiotherapy-induced DAMPs to achieve durable regressions and memory T-cell formation. Translational priorities include CRISPR-engineered hypoimmunogenic iPSC platforms, GMP-compatible non-integrating reprogramming, and combinatorial integration with STING agonists, ICB, CAR-NK cells, and LNP-mRNA constructs to enable biomarker-guided clinical deployment in minimal-residual-disease CRC.

Advances in single-cell and spatial multi-omics technologies have transformed the understanding of neutrophils from short-lived effector cells to highly heterogeneous and transcriptionally plastic immune populations. Within the inflamed intestinal microenvironment, gradients of cytokines, oxygen tension, and microbial metabolites such as short-chain fatty acids dynamically modulate neutrophil differentiation and function, shaping either tissue-protective or tissue-destructive phenotypes. Recent studies highlight the de novo expression of the NADPH oxidase enzyme DUOX2 in intestinal neutrophils as a pivotal mediator of redox signaling. DUOX2-derived reactive oxygen species activate epithelial and immune signaling cascades through NF-κB and p38 MAPK pathways, thereby amplifying inflammation, promoting barrier disruption, and sustaining microbial dysbiosis. Although this oxidative response enhances antimicrobial defense, it concurrently contributes to neutrophil extracellular trap (NET)-driven thrombo-inflammation and chronic tissue injury. Experimental evidence indicates that selective ablation of myeloid DUOX2 attenuates colitis, underscoring its potential as a therapeutic target. Emerging interventions that modulate this axis, including JAK/STAT inhibitors, CXCR2 antagonists, p38/MK2 inhibitors, and butyrate-based metabolic regulators, offer promising avenues to restore neutrophil homeostasis while maintaining host defense. Integrating single-cell transcriptomics, redox proteomics, and advanced imaging approaches will be essential for translating neutrophil plasticity into biomarker-guided and precision-based therapeutic strategies for durable mucosal healing in IBD.

Predictive multiscale cellular modeling redefines precision medicine by integrating hypothesis grammars, digital twins, and integrative genomics to forecast tumor–immune dynamics, therapeutic resistance, and cellular plasticity. Hypothesis grammars translate mechanistic theories into executable agent-based models (ABMs) and hybrid ODE–PDE systems, enabling rapid in silico hypothesis testing. Patient-specific digital twins, driven by multi-omics data, employ stochastic ensemble methods to simulate clonal evolution and microenvironmental interactions. Integrative genomics, leveraging algorithms like SCODE and SimiC, infers causal gene regulatory networks (GRNs) using Bayesian variational autoencoders, embedding dynamic intracellular logic into tissue-scale simulations. Applications include in silico oncology trials optimizing checkpoint blockade and combination therapies. Large language models enhance rule induction, while FAIR-compliant digital cell repositories ensure reproducibility. Verification, validation, and uncertainty quantification (VVUQ) via Sobol sensitivity and Kennedy–O’Hagan calibration address non-identifiability. Federated learning mitigates privacy and bias concerns. This framework enables virtual clinical trials and adaptive theranostics, transforming biological understanding into actionable, equitable precision medicine.