The Elephant Modern Geroscience Refuses to See: Why Preclinical Animal Models Fail and Why Healthspan Keeps Collapsing in a Bio-Incompatible, Blocking Habitat
Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Planarian Biology and Electrostimulation
Abstract
Modern geroscience celebrates marginal increments in chronological life expectancy while overlooking a catastrophic structural failure: the global healthspan-lifespan gap has widened to 9.6–12.4 years of chronic morbidity, disability, and biological decline. Despite hundreds of billions invested into downstream senolytics, epigenetic reprogramming, and metabolic pharmaceuticals, clinical translation consistently collapses. Here, we identify the fundamental systemic flaw responsible for this translational impasse: the profound biophysical disconnect between sanitized preclinical rodent models and real-world human habitats. Laboratory rodents housed in inert cages do not carry lifelong heterogeneous dental alloys, highly isolating restorative composite polymers, or titanium implants situated millimeters away from cranial regulatory centers, nor are they encased 24/7 in bio-incompatible dielectric textiles and consumer electronic field interference. We delineate the multi-tiered mechanism of Intraoral Electrodynamic Blockade: while mixed dental alloys generate unceasing micro-galvanic currents that batter the trigeminal pathway and brainstem autonomic nuclei, modern dental composites, bonding agents, and cements act as absolute dielectric insulators. These non-conductive barriers sever cranial phase coherence, extinguish native piezoelectric bone micro-currents, and scatter bioelectric signals, frequently inducing unexplained distal muscle inhibition and phantom pain syndromes. This relentless physical disruption triggers reflex microcapillary vasospasm, chronic hypoperfusion, and thermal asymmetry. Concurrently, tissue hypoxia destabilizes chaperone-dependent protein folding, while skeletal matrices "petrify"—entombing mesenchymal and hematopoietic stem cells within rigidified endosteal niches. Concurrently, downstream interventions such as Yamanaka epigenetic reprogramming (OSKM) fail because a biochemical reset inside the nucleus cannot overcome the dominant mechanotransductive signaling of a calcified, stiffened extracellular matrix. Eliminating boundary impedance and intraoral blockades is the non-negotiable biophysical prerequisite to genuinely compress morbidity and deliver a 20–30 year expansion of healthy human life.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· SSE@SIGSOFT FSE· 56 citations· ⚡4
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.