Multiscale Credible Systems Framework for the Tetra-Shield Protocol: Integrating ASME V&V 40 Standards for Arterial Rehabilitation and Prevention of Cardiovascular Events

Background: Cardiovascular diseases remain the leading global cause of mortality, with atherosclerosis as the primary pathological substrate. Current therapeutic modalities address individual risk factors but do not fully account for the integrated neuro-mechanical-biochemical milieu of arterial degeneration.Objective: This conceptual paper delineates the Tetra-Shield protocol, a four-pillar integrative framework---neuromuscular modulation, targeted nanotherapy, hemodynamic optimization, and AI-driven biosensing---systematically addressing the multifactorial pathophysiology of atherosclerosis through a systems biology perspective.Methods: Mathematical models are derived from biophysical first principles (damped harmonic oscillator for vasospasm, reaction-kinetics for plaque evolution, Poiseuille's law for hemodynamics, Bayesian updating for risk inference). Python-implemented simulations (scipy.odeint, XGBoost) are calibrated to literature-derived physiological ranges. Rigor is established through: (i) local sensitivity via analytic partial derivatives; (ii) global sensitivity via Sobol indices (Saltelli estimator, \( n=1024 \)), Morris screening (\( r=10 \) trajectories, bootstrap 95% CI), and Monte Carlo variance decomposition (\( N=1000 \)); (iii) uncertainty quantification via error propagation and bootstrap percentile CIs (10,000 replicates); (iv) Bayesian inference with conjugate priors; and (v) explicit Popperian falsifiability criteria. A physics-informed neural network (PINN) template with a complete training loop is provided for hemodynamic simulation. Credibility is assessed per ASME V&V 40 standards.Results: Simulations demonstrate coherent inter-pillar behavior: vasospasm attenuates to 0.8,mm equilibrium; plaque fibrous caps thicken to an asymptotic 1.13,mm; wall shear stress follows the predicted inverse-cubic relationship with vessel radius; and the XGBoost classifier achieves AUC,=,0.862 on simulated pillar-correlated features. Sobol analyses identify the dominant parameters in each pillar (\( T \): S\( _1 \),=,0.761 in Pillar~1; \( h_{\max} \): S\( _1 \),=,0.559 in Pillar~2; \( r \): S\( _1 \),=,0.806 in Pillar~3). Under idealized in silico conditions (all pillars at nominal capacity, no inter-pillar interference), a hypothetical mortality reduction of 45--65% (95% CrI: 40--70%, mean 54%) is generated.Limitations: All outcomes are computational. Parameter distributions are assumed (uniform) for convenience. The ML model is trained on synthetic data. Inter-pillar coupling is modeled but not empirically validated.Conclusions: The Tetra-Shield framework demonstrates internal consistency, reproducibility, and methodological rigor as a computational proof-of-concept. The reported quantitative projections describe model behavior under defined assumptions and do not constitute clinical efficacy evidence. Prospective empirical validation through preclinical studies and randomized controlled trials is required before any translational inference can be drawn.