Alzheimer’s disease – because β-amyloid cannot distinguish neurons from bacteria: an <i>in silico</i> simulation study
Bibliographic record
Abstract
Abstract Based on semi-empirical quantum mechanics calculations and extensive molecular mechanics calculations and molecular dynamics simulations, a novel molecular level conceptualization of key biochemical events in the pathogenesis of Alzheimer’s disease (AD) is presented. In response to immune stimulating events ( e.g ., infection, trauma), β-amyloid (Aβ) protein is released in brain as a protective immunopeptide triggering an immunity cascade in which Aβ exhibits antimicrobial activity, which mistakenly results in a misdirected attack upon “self” neurons, arising from the macromolecular and electrochemical similarities between neurons and bacteria in terms of transmembrane potential gradients and anionic charge distribution geometries on outer membrane macromolecules (gangliosides in neurons; cardiolipins or lipopolysaccharides in bacteria). Molecular mechanics/dynamics calculations are used to demonstrate how the inability of Aβ to distinguish between bacteria and neurons is a central pathological process in the pathogenesis of AD.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".