Artificial intelligence-assisted reinterpretation of preclinical progeria research suggests a hierarchical nuclear-vascular resilience framework with translational implications

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  Figure 1. Structured workflow for investigator-led AI-assisted reinterpretation of experimental datasets. The workflow illustrates the analytical strategy adopted in this study. Investigators defined the biological questions, selected the published experimental data, critically evaluated successive AI-assisted outputs, and retained only interpretations consistent with the validated observations and established biological knowledge. The AI platform functioned exclusively as an analytical partner by reorganizing experimentally validated findings, exploring complementary conceptual relationships, and generating hypotheses for future investigation. The final conceptual synthesis resulted from iterative dialogue and continuous investigator supervision rather than autonomous AI inference.

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  Figure 2. Figure 2. Comparison between the original intervention-anchored interpretation and the complementary hierarchical framework emerging from structured investigator-led analytical dialogue. The blue pathway summarizes the original mechanistic interpretation emphasizing attenuation of nuclear stress and cellular senescence, whereas the green pathway illustrates the complementary hierarchical organization emerging from the structured analytical dialogue, in which vascular remodeling and extracellular matrix adaptation are repositioned as candidate upstream determinants of functional improvement. Solid boxes and arrows indicate experimentally validated observations or established biological relationships. The figure illustrates how identical experimental observations can be reorganized into alternative, non-mutually exclusive conceptual frameworks without modifying the original experimental findings.

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  Figure 3. Variability-informed nuclear-vascular resilience framework emerging from structured analytical reinterpretation. Dispersion analysis showed relatively consistent cardiac BPIFB4 target engagement following gene delivery despite substantially greater variability in structural and functional responses. This dissociation suggested that LAV-BPIFB4 may function primarily as a permissive trigger enabling coordinated biological adaptation rather than as a quantitative determinant of therapeutic response. Integration of variability patterns identified four candidate resilience nodes—nuclear stress-senescence adaptation, vascular remodeling, extracellular matrix stiffness, and diastolic performance threshold—that may influence the magnitude of functional improvement. Together, these observations support a variability-informed conceptual framework in which preservation of cardiac function emerges from coordinated interactions among multiple adaptive mechanisms rather than from a simple linear relationship between transgene expression and biological outcome. The proposed resilience nodes represent hypothesis-generating conceptual interpretations that require prospective experimental…