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Record W2726024278 · doi:10.1093/geroni/igx004.1008

AMYLOID BETA PEPTIDES AS ANTIMICROBIAL PEPTIDES: RELEVANCE FOR ALZHEIMER’S DISEASE?

2017· article· en· W2726024278 on OpenAlexaff
B. Karine, Gilles Dupuis, Éric Frost, Tamàs Fülöp

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAntimicrobial peptidesAmyloid (mycology)Herpes simplex virusDiseaseAntimicrobialPeptideAmyloid precursor proteinAmyloid betaBiologyAlzheimer's diseaseImmunologyComputational biologyNeuroscienceMedicineMicrobiologyVirusBiochemistryInternal medicinePathology

Abstract

fetched live from OpenAlex

Amyloid β (Aβ) peptides generated by the amyloidogenic pathway of amyloid precursor protein (APP) processing contribute significantly to neurological degeneration characteristic of Alzheimer’s disease (AD). Their precise role, whether it be direct or the indirect target of an inflammatory response, has been a subject of considerable debate. Data published in the last 6 years by three different groups have added a new twist by revealing that Aβ peptides could act as antimicrobial peptides (AMP). These observations are of significance with respect to the notion that pathogens may be important contributors to the development of AD, particularly in the case of Herpes simplex virus (HSV) infection which often resides in the same cerebral sites where AD arises. Our recent data support the interpretation that Aβ peptides behave as AMP, with an emphasis on studies concerning HSV-1 and a putative molecular mechanism that suggests that interactions between Aβ peptides and the HSV-1 lead to impairment of HSV-1 infectivity by preventing the virus from fusing with the plasma membrane.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.182
GPT teacher head0.456
Teacher spread0.274 · 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 designBench or experimental
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
Published2017
Admission routes1
Has abstractyes

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