Disease as Information Disorder: A Thermodynamic Framework for Signal Restoration Across Five Pathologies.

This paper proposes a falsifiable systems-level hypothesis: that multiple chronic diseases — including cancer, Alzheimer's disease, amyotrophic lateral sclerosis, autoimmune disease, and cardiovascular disease — share a common structural feature: loss of informational coherence under energetic constraint. We propose that transcriptional entropy, measured via Shannon entropy across gene expression distributions, may serve as a unifying quantitative biomarker of regulatory disorder across pathologies, and that effective therapeutic intervention corresponds to measurable entropy reduction. The framework is supported by converging evidence from bioelectric patterning research, differentiation therapy, metabolic theories of neurodegeneration, immune tolerance studies, and haemodynamic shear stress research. We present falsifiable predictions for each disease domain and propose an immediately executable first experiment: retrospective analysis of transcriptional entropy dynamics in APL patients treated with ATRA, testable on existing public datasets within weeks. This is a hypothesis-generating framework. It does not propose novel therapies or claim to replace existing treatment. It invites empirical testing.