I. Chaordic Homeodynamics (Periodic Chaos Homeostasis)
Using our proprietary SL‑nanometry (3 nm spatial, 2 ms temporal resolution), we were the first to demonstrate heat‑induced self‑oscillations (HSOs) whose period remains constant while the amplitude varies chaotically. By recurrence plots, Lyapunov exponents, and mathematical modeling, we are quantifying how chaotic fluctuations in individual sarcomeres collectively generate a smooth, stable heartbeat—Chaordic Homeodynamics.
Key papers
Shintani SA. Biochem. Biophys. Res. Commun. 760, 151712 (2025) ▶ Details
Shintani SA. Biophys. Physicobiol. 21, e210006 (2024) ▶ Details
Shintani SA. Biochem. Biophys. Res. Commun. 611, 8‑13 (2022) ▶ Details
Shintani SA et al. Sci. Rep. 10, 20468 (2020) ▶ Details
Shintani SA et al. Biochem. Biophys. Res. Commun. 457(2), 165‑170 (2015) ▶ Details
Shintani SA et al. J. Gen. Physiol. 143(4), 513‑524 (2014) ▶ Details
Significance
Addresses the fundamental question “Why can cardiac muscle beat without fatigue?”
Explains how biological rhythms reconcile order and fluctuation (Chaordic Homeodynamics).
Provides a new theoretical framework for arrhythmogenesis and dynamical diagnosis.
Toward Societal Implementation
We are exploring ultra‑early diagnostics for heart failure/arrhythmia based on rhythm “chaoticity,” and new design principles for next‑generation pacemakers.
Techniques born from sarcomere studies—temperature‑field control, nanometer imaging, AI analytics—are powering diverse collaborations:
Titanium bone‑repair materials (antibacterial & bioactive coatings) ▶ Details
Non‑invasive vital sensing (AI + optical methods) ▶ Details
Cryo‑flash‑freezing & cryo‑EM for dynamic molecular imaging ▶ Details
We actively partner with universities, companies, and medical institutions to create new technologies in medicine, healthcare, and biomaterials.
To accelerate basic science, we develop custom microscopy systems, temperature‑control (heating) systems, and real‑time live‑imaging techniques for electron microscopy.
In parallel, we propose AI to Learn (AI2L)—an operational guideline that confines AI to a learning‑support role and removes any black‑box components from the final deliverables (preprint prior to peer review).
AI2L: Four Pillars
Human sovereignty over final decisions
Accountability ensured through human verification
Privacy/Security with information protection placed first
Green AI for energy efficiency and long‑term sustainability
Examples
Use Grad‑CAM and related XAI tools to externalize tacit knowledge and distill it into human‑understandable rules
Enumerate candidate laws via symbolic regression, followed by human theory‑building (contributing to the deepening of the Chaordic concept)
Audit and slim AI‑generated code so it becomes a human‑owned, lightweight asset
Reduce information‑leak risk during cloud use through reversible anonymization, etc.
Related material (preprint)
Shintani SA. AI to Learn (AI2L): Guidelines and Practice for Human‑Centered AI Utilization as a Learning Support Tool — Four Pillars of Black‑Box Elimination, Accountability, Information Protection, and Energy Efficiency. Jxiv (preprint), DOI: 10.51094/jxiv.1435 ▶ AI2L Details
Note: This preprint has not undergone peer review; its contents may be updated or revised.
Cryo‑EM exploration (flash‑freezing × dynamic molecular analysis) ▶ Details
Fusion of AI, mathematics, and physiology (AI2L, Grad‑CAM, symbolic regression) ▶ Details
Promotion of international collaborations, conference outreach, and academia–industry projects ▶ Details
Fostering young talents and next‑generation researchers ▶ Details