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Record W7126244598 · doi:10.5281/zenodo.18445089

Computational Modeling of ALS Onset Heterogeneity: The Drusen-Zinc Switch Mechanism and Unified Sensory-Topological Control Framework

2025· preprint· W7126244598 on OpenAlexaff
Joshua Dungan, Artificial General Intelligence LLC

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typepreprint
Language
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsMechanism (biology)Amyotrophic lateral sclerosisFasciculationInferenceSignature (topology)Domain (mathematical analysis)

Abstract

fetched live from OpenAlex

Computational Modeling of ALS Onset Heterogeneity: The Drusen-Zinc Switch Mechanism and Unified Sensory-Topological Control Framework(Preliminary Results Draft v1 – January 2026)THIS IS NOT MEDICAL ADVICE. THIS THEORY IS NOT PEER-REVIEWED (YET).This upload contains the preliminary results, full manuscript draft, raw inference logs, supplementary tables, and related datasets from an independent, human-in-the-loop computational systems biology investigation into Amyotrophic Lateral Sclerosis (ALS) heterogeneity.The work proposes a novel mechanistic framework—the Drusen-Zinc Switch—to explain why sporadic ALS (sALS) exhibits diverse onset phenotypes (ocular/bulbar, focal/limb, etc.) yet converges on motor neuron death and TDP-43 pathology. Using Literature-Based Discovery (LBD) guided by a multi-model AI Panel (Gemini, Grok, ChatGPT, DeepSeek), the analysis identifies Barrier-Permeable Zinc Chelators (BPZCs) (e.g., BMAA, dithiocarbamates) as a plausible primary environmental trigger. Age-related sub-retinal Drusen act as a key biological switch/reservoir for mobile zinc (mZn), leading to a bifurcation: Drusen-Positive → “Zinc Flood” saturating RGNEF Zinc-Finger Domain → classical sporadic/Type I ALS (TDP-43 dominant). Drusen-Negative → “Zinc Drought” stripping structural zinc from SOD1 → apo-SOD1 formation → focal/Type II ALS (with crossover potential via oxidative stress feedback loops). The framework integrates control-theoretic principles (“The Wobble”, “STAY Command”, fasciculations as servo hunting) to unify sensory-topological errors (primarily visual/retinal) with downstream neuromuscular exhaustion. It stratifies ALS into five mechanistically distinct types, generates >30 falsifiable hypotheses (Supplementary Table 1), proposes ITC validation experiments, and suggests mechanism-based therapeutic stratification using non-invasive OCT imaging for subtype disambiguation.Key outputs include: Mechanistic explanations for epidemiological paradoxes (age-related onset, athlete/high-activity paradox, geographic clusters) Candidate BPZC class and environmental “shards” Deduced “ZERO-ALS” therapeutic protocols (PBT2 + Ebselen + Trehalose ± CuATSM) — explicitly not medical advice Roadmap for the Atomic Research Tool (ART) to enable auditable, atomized scientific reasoning All claims are derived from published evidence synthesis and logical inference chains under a strict “Rational Homeostasis” axiom (no “neuro-suicide”). Raw AI chat logs, data curation entries, and inference examples are included for transparency and reproducibility (see linked Zenodo DOIs).This is a raw, untraditional preliminary draft intended as a case example for tool development and priority timestamping. Collaboration to test, refute, or refine the model is welcomed. Minor typos/overstatements in v1 will be addressed in future ART-refined versions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.070
GPT teacher head0.308
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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