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When the Dominant Paradigm Hits a Deadlock: Examining a Practice-Rooted Theoretical Path with the Aid of AI

发布时间：

2026-08-19

When the Dominant Paradigm Hits a Deadlock: Examining a Practice-Rooted Theoretical Path with the Aid of AI

Sun Zuodong

Two theoretical papers — Potassium Channel Origami Windmill Model and DNA Origami Windmill Tetramer Model — have been released to the public online. Together, the two models open an entirely new research track for bioelectrical genetics. Far from being mere speculative thought, this whole body of theory has been forged through more than thirty years of continuous practical work, completing a full exploratory loop: practice taking the lead, theory distilled from practice, and theory in turn informing practice.

Looking back at the origin of this research, conceptual work first began in 1994. In 1995, the Aobo transcranial electrical brain function rehabilitation therapeutic apparatus (one of the world’s earliest commercialized extracranial brain pacemakers) obtained marketing approval. Long-term clinical observations on brain disorders were carried out alongside its real-world application. Theoretical generalizations and refinements gradually emerged from equipment development and ongoing clinical monitoring.

In 2003, the popular-science book Awakening the Dormant Brain was first published by Heilongjiang People’s Publishing House; a revised edition came out from Heilongjiang Science and Technology Press in 2010. Written for general audiences, it explains phenomena at the cellular level. The theory of brain cell activation, an academic paper published in 2015, delivers systematic arguments at both cellular and molecular levels. The two works share the same intellectual lineage yet differ in purpose and analytical depth. In 2011, the world’s first therapeutic devices for Parkinson’s disease and depression were completed and granted regulatory approvals. The world’s first therapeutic apparatus for Alzheimer’s disease was developed in 2014. To translate cutting-edge brain-science findings out of laboratories for public outreach, I founded the Sun Zuodong Brain Science Experience Hall, a physical platform for theoretical demonstration, hands-on experience and academic exchange. Years of engineering development and clinical observation uncovered objective phenomena that existing theories could not adequately explain, driving me to search for fundamental mechanisms underlying cellular life activities.

The theory of brain cell activation was formally published in 2015. In 2019, I first put forward The Theory of Dove-like Particles, which explains the pathogenesis of neurodegenerative diseases based on the competitive cation-occupancy mechanism. That same year, I proposed the Potassium Channel Origami Windmill Model. In 2026, further iterative expansion yielded the DNA Origami Windmill Tetramer Model. This thirty-one-year quest follows a clear timeline: exploration grew out of front-line industrial practice, yielding successive, progressive theoretical hypotheses; once formulated, these theories guided further technological innovation. Take the paper on the Potassium Channel Origami Windmill Model as an example: more than 100 000 characters of raw source material were condensed down to an 800-character core text. The process spanned roughly one year, involving at least 150 rounds of revision, with every archived version preserved. The core ideas and theoretical framework took shape long before the large-scale popularization of artificial intelligence. Today AI aids logical deduction, critical review and further refinement of the theoretical system. Humans should not cling to intellectual arrogance. Artificial intelligence has repeatedly demonstrated its value in scientific research, much as AlphaGo once overturned popular assumptions about human intellectual superiority. Approaching AI rationally and harnessing it skillfully is a necessary mindset for present-day researchers.

Turning to the dominant paradigm in contemporary membrane biology: foundational work on cellular bioelectricity rests on the classical ionic theory established by Alan Hodgkin and Andrew Huxley, recipients of the 1963 Nobel Prize in Physiology or Medicine, through experiments on the giant squid axon. Later landmark molecular-level achievements include Jens C. Skou’s discovery of the sodium-potassium pump (Nobel Prize in Chemistry 1997), Peter Agre’s work on aquaporins and Roderick MacKinnon’s research on ion channels (both awarded the Nobel Prize in Chemistry in 2003). Bioelectrical genetics extends its scope to James Watson and Francis Crick’s discovery of the DNA double-helix structure (Nobel Prize in Physiology or Medicine 1962). For decades, the dominant paradigm has relied on adding auxiliary hypotheses to reconcile a growing body of anomalous experimental observations. The theoretical architecture has grown increasingly unwieldy, with many internal contradictions remaining unresolved, bringing the old paradigm close to its limits. According to Thomas Kuhn’s theory of scientific paradigms, when an old paradigm loses self-consistency, a window opens for paradigm shift and cognitive renewal. The stirrings of a scientific revolution within life science are already discernible on the horizon. A paradigm shift is inevitable. Alongside the public release of the two origami-windmill models, complete experimental protocols for verification and falsification have been provided, open to examination by researchers worldwide.

Artificial intelligence is now deeply integrated across research workflows. Many scientists and journal referees use AI for literature searches, manuscript drafting, peer-review support and plagiarism screening. Yet a notable double standard persists within the research community: some practitioners take advantage of AI’s conveniences while harbouring prejudice against others who reasonably deploy AI for literature synthesis and theoretical logical validation. Tools themselves are neutral. Artificial intelligence is a public research resource for all investigators and should neither be deliberately shunned nor concealed. Humans are already outperformed by AI in synthesizing massive bodies of literature and carrying out long-chain logical reasoning.

Against this backdrop, we must define AI’s role objectively: artificial intelligence cannot serve as the final arbiter of whether a scientific hypothesis is true, nor can it ever replace laboratory empirical research. Trained on centuries of publicly available life-science literature and free from the constraints of academic allegiances, personal biases and ingrained conceptual inertia, AI can function as a neutral third-party for logical analysis. Logical inferences generated by artificial intelligence suggest that the shared-pore origami-windmill model offers greater internal consistency under present-day conditions and represents the theoretically optimal solution. Fellow researchers are invited to submit this article to various AI systems for assessment. Cross-examination across multiple independent AI platforms tends to yield converging, informative conclusions. Whether the hypothesis holds true must ultimately be settled by decisive laboratory experiments — and whether such experiments are conducted is a choice for the academic community.

Systematic review and deductive simulation of the two origami-windmill models by artificial intelligence yield clear, objective outcomes: rooted in decades of hands-on practice, the two theories form an internally consistent system compatible with many published experimental records. At this stage they constitute competitive, theoretically preferable explanations. AI also explicitly indicates that whether these models correspond to real-world biological mechanisms awaits experimental testing by research teams around the globe; they are by no means definitive answers.

Intellectual rigidity is among the greatest pitfalls in scientific inquiry. Insistently patching loopholes within a paradigm with growing structural flaws risks leading research into a dead end. As the old paradigm runs into obstacles, glimmers of a new one have appeared. The academic community should keep an open mind toward alternative lines of inquiry. Young researchers in particular ought not to be confined by existing conceptual frameworks. Bioelectrical genetics is an emerging field born amid this potential paradigm shift. This largely untamed academic frontier holds promise for major breakthroughs in basic medicine and brain-disorder interventions.

Scientific-paradigm change cannot occur silently. Avoiding discussion will not erase genuine theoretical contradictions, nor can silence halt intellectual progress. Artificial intelligence has delivered a third-party logical reassessment. I have fully disclosed my lines of reasoning together with experimental protocols. I do not expect broad acceptance in the short term; my goal is simply to prompt the academic community to confront existing disagreements. I hope truth-seeking researchers will set aside preconceptions, judge theories through logical argument and experimental observation, and turn attention toward the new track of bioelectrical genetics. Only by openly engaging with divergent viewpoints can we collectively advance brain science and cell biology into a new era.