β-Amyloid is an Immunopeptide and Alzheimer’s is an Autoimmune Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: As new biomolecular targets for Alzheimer's disease (AD) emerge, there is a tendency to regard these as mutually exclusive and in competition, culminating in declarations that since the "amyloid hypothesis is dead" it needs to be replaced by completely different theories. However, given the well-described role of misfolding peptides, particularly β-amyloid (Aβ), in the pathogenesis of AD, the need for a broad-based conceptualization of AD, coalescing different theories into a single harmonized explanation emerges as a viable alternative. Incorporating protein aggregation mechanisms of AD into a more widely-encompassing immunopathic model of AD could accomplish such a goal-a goal which could be achieved by repositioning the role of Aβ as an immunopeptide. CONCLUSION: This review presents the concept that Aβ is an immunopeptide and that AD is an autoimmune disease in which Aβ is a key molecular player. Being a peptide with the capacity to alter immune function, Aβ is an immunopeptide; having both antimicrobial and immunomodulatory activities, Aβ is a host defense peptide; having most of the defining properties of cytokines, Aβ satisfies the broad definition of cytokine-the prototypic immunopeptide subtype. In addition to these immunoactivities, Aβ is also directly and independently cytotoxic to neurons by both necrotic and apoptotic mechanisms. Therefore, following brain exposure to immune-instigating stimuli, the innate immune system is activated, leading to the release of Aβ as an immunopeptide (functioning as a host defense peptide or cytokine), which subsequently inflicts a misdirected attack upon the host neurons-an autoimmune event.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 it