MétaCan
Menu
← Back to cohort
Record W4406050559 · doi:10.1002/alz.088345

Alzheimer’s Disease: Because Amyloid‐β Cannot Differentiate Between Bacteria and Neurons

2024· review· en· W4406050559 on OpenAlexaff
Matthew Neal, Autumn Meek, Donald F. Weaver

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsKrembil Foundation
Fundersnot available
KeywordsNeuroinflammationNeuroscienceMembraneIn silicoDiseaseIn vivoBiologyAmyloid (mycology)PathogenesisComputational biologyImmunologyInflammationMedicineGeneBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: An explicit molecular level understanding of Alzheimer's Disease (AD) remains elusive. What initiates the disease and why does it progress? Answering these questions will be crucial to the development of much needed new diagnostics and therapeutics. Though the amyloid hypothesis is often debated, recent biologic trial results support a role for Aβ in AD pathogenesis. However, there are other tenable hypotheses, most notably neuroinflammation, but also gliopathy, synaptopathy, mitochondriopathy, and oxidative stress. Is it possible to formulate a model of AD that incorporates multiple proposed hypotheses into a single unifying conceptual approach? We seek to answer this question. METHOD: We performed a comprehensive series of in silico, in vitro and in vivo studies explicitly evaluating the atomistic-molecular mechanisms of Aβ-mediated neurotoxicities as well as Aβ's antimicrobial and immunomodulatory effects. The molecular effects of Aβ and various cytokines on mitochondria, neuronal membranes and synapses were also explicitly studied. RESULT: Membranes are a mosaic of lipophilic and hydrophilic (negatively-charged) regions organized in specific geometric patterns. Our studies show that bacterial membranes and neuronal membranes have essentially identical patterns, making neurons inadvertently susceptible to molecules targeting bacterial membranes. From this, a new model of AD emerges: In response to various immunostimulatory events (infection, trauma, ischemia, diabetes, air pollution), Aβ is released as an immunopeptide (kinocidin-type cytokine) which exhibits both immunomodulatory and antimicrobial properties (whether bacteria are present, or not); this inflicts a misdirected attack upon 'self' neurons, arising from the essentially identical membrane surface electrotopologies between neurons (especially at the synapse) and bacteria - causing neuronal death by mistaken identity. Following this self-attack, the resulting necrotic neuronal breakdown products diffuse to adjacent neurons eliciting further release of Aβ, leading to a self-perpetuating cycle. CONCLUSION: We propose that AD occurs because Aβ is an immunopeptide that cannot differentiate neurons from bacteria - a case of mistaken identity that leads to an innate autoimmune response in which Aβ extracellularly attacks neuronal membranes, particularly at the synapse, while also intracellularly attacking mitochondria.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.095
GPT teacher head0.371
Teacher spread0.276 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAlzheimer s & Dementia→Same topicAlzheimer's disease research and treatments→French-language works237,207→