MétaCan
Menu
Back to cohort
Record W4414516440 · doi:10.1515/pac-2025-0471

Alzheimer’s disease – because β-amyloid cannot distinguish neurons from bacteria: an <i>in silico</i> simulation study

2025· article· en· W4414516440 on OpenAlexafffund
M. Neal, Donald F. Weaver

Bibliographic record

VenuePure and Applied Chemistry · 2025
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersKrembil Foundation
KeywordsMolecular mechanicsMacromoleculePathogenesisConceptualizationDiseaseMolecular dynamicsMacromolecular SubstancesImmune system

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.312
Teacher spread0.290 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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 routes2
Has abstractyes

Explore more

Same venuePure and Applied ChemistrySame topicAlzheimer's disease research and treatmentsFrench-language works237,207