A.3 Beta-amyloid is a cytokine and Alzheimer’s is an autoimmune disease
Bibliographic record
Abstract
Background: Despite the limited successes of recent amyloid-targetting biologics, the need for a new pathogenesis mechanistic model of Alzheimer’s disease (AD) is a continuing priority, to facilitate improved rational drug design. Methods: To devise a new AD model, we performed an extensive, comprehensive series of in silico, in vitro, and in vivo studies explicitly evaluating the atomistic–molecular mechanisms of cytokine-mediated and amyloid-beta (Aβ)-mediated neurotoxicities in AD. Results: A new model of AD has been devised: In response to pathogen-/damage-associated molecular pattern-stimulating events (e.g., infection, trauma, ischaemia), Aβ is released as an early responder cytokine triggering an innate immunity cascade in which Aβ exhibits immunomodulatory/antimicrobial duality. However, Aβ’s antimicrobial properties result in a misdirected cytotoxic attack upon “self” neurons, arising from the electrophysiological similarities between neurons and bacteria in terms of transmembrane potential and anionic charges on outer membrane macromolecules. The subsequent breakdown products (amyloid-ganglioside complexes) released from the damaged neurons diffuse to adjacent neurons eliciting further release of Aβ, leading to a chronic, self-perpetuating disease cycle. In short, AD occurs because Aβ cannot differentiate neurons from bacteria. Conclusions: An innovative new model of AD has been devised, recognizing Aβ as a physiologically oligomerizing cytokine and conceptualizing AD as brain-centric autoimmune disorder of innate immunity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".