AI-Optimized Nanoparticle-Mediated Gene Therapy for Gangrene Limb Salvage: A Falsifiable Computational Framework

Gangrene, tissue necrosis arising from ischemia, infection, or trauma, remains a major driver of amputation and mortality worldwide, with diabetic-foot gangrene reported to precede up to 50% of major lower-limb amputations and necrotizing soft-tissue infections carrying case-fatality rates that range from approximately 7.5% in population-based cohorts to 20 to 40% in referral-center series. Current interventions, including surgical debridement, hyperbaric oxygen, endovascular revascularization, and early-generation angiogenic gene therapy, reduce but do not eliminate progression to amputation, in part because they lack lesion-level spatial precision. This paper presents AI-Optimized Nanoparticle-Mediated Gene Therapy (AI-NMGT), a conceptual, simulation-based framework that couples AI-guided multimodal imaging for necrotic-zone delineation, PLGA-nanoparticle delivery of VEGF-A165 plasmid DNA and an antimicrobial payload, and closed-loop biosensor feedback for adaptive dosing.Tissue necrosis is first represented by a well-mixed Susceptible, Exposed, Infected, Recovered (SEIR) system in which treatment efficacy tau attenuates necrosis transmission, then extended to a spatial reaction diffusion model that resolves local oxygen, nanoparticle concentration, and, for infectious or septic gangrene, bacterial load fields, distinguishing an ischemic from an infectious or septic aetiology. Under the well-mixed model's nominal parameterization, increasing tau from 0 to 0.5 reduces cumulative necrotic-tissue loss by 80.9% and peak burden by 76.4%. Global (Sobol, PRCC), local (elasticity), and practical identifiability analyses jointly confirm tau as the dominant driver of outcomes, while revealing that gamma plus alpha, not gamma or alpha individually, is the identifiable quantity under aggregate clinical calibration. That calibration is performed by Approximate Bayesian Computation against a real published statistic, the 7.5% case-fatality rate reported by Sorensen et al. (2009) for a 1,641-case Fournier's-gangrene cohort, yielding a posterior predictive case-fatality of 7.2% (95% interval 5.8% to 9.2%). The spatial model predicts that a fixed nominal dose is markedly less effective against infectious or septic gangrene than ischemic gangrene over a matched time window (10.5% versus 31.6% simulated tissue-loss reduction), an aetiology-dependent-efficacy claim posed as directly testable. A synthetic-phantom imaging proof-of-concept, explicitly not a claim of real clinical segmentation performance since no GPU deep-learning framework or clinical imaging dataset was available, quantifies how segmentation error propagates through a rule-based dosing controller into excess simulated tissue loss, up to plus 4.8% at the 95th percentile under degraded imaging. A cost-effectiveness model built from cited planning parameters identifies a treatment-efficacy threshold, tau approximately 0.4 to 0.5, separating a non-cost-effective regime ($92,994 per QALY at tau equals 0.3) from a highly cost-effective one ($4,722 per QALY at tau equals 0.5).Every numerical result, table, and figure in this paper, across all of the above analyses, is generated by fully self-contained, seeded (seed equals 20260129) Python code reproduced in Appendices A and B, using NumPy, SciPy, and scikit-learn only, with no proprietary dependencies, so that any reader can regenerate them exactly. AI-NMGT is presented explicitly as a theoretical design and simulation study: no wet-laboratory, animal, or human data were generated, and all preclinical figures are stated as design specifications and testable hypotheses rather than measured outcomes. The paper closes with a structured risk assessment and a falsifiability-anchored roadmap, including three specific experiments capable of falsifying the framework's central claims within 6 to 12 months, positioning empirical validation as the necessary next step toward translational relevance.