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Record W2473084660 · doi:10.3233/jad-150967

Mitochondrial Regulatory Pathways in the Pathogenesis of Alzheimer’s Disease

2016· review· en· W2473084660 on OpenAlexafffund
Reginald C. Adiele, Chiedukam A. Adiele

Bibliographic record

VenueJournal of Alzheimer s Disease · 2016
Typereview
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of Saskatchewan
FundersNational Institute on AgingCanadian Institutes of Health Research
KeywordsPathogenesisNeuroscienceDiseaseDementiaAlzheimer's diseaseMitochondrionOxidative stressProgrammed cell deathBiologyAmyloid betaMedicineBioinformaticsPathologyCell biologyImmunologyApoptosisGeneticsEndocrinology

Abstract

fetched live from OpenAlex

Alzheimer's disease (AD) is an age-associated neurodegenerative brain disorder with progressive cognitive decline that leads to terminal dementia and death. For decades, amyloid-beta (Aβ) and neurofibrillary tangle (NFT) aggregation hypotheses have dominated studies on the pathogenesis and identification of potential therapeutic targets in AD. Little attention has been paid to the mitochondrial molecular/biochemical pathways leading to AD. Mitochondria play a critical role in cell viability and death including neurons and neuroglia, not only because they regulate energy and oxygen metabolism but also because they regulate cell death pathways. Mitochondrial impairment and oxidative stress are implicated in the pathogenesis of AD. Interestingly, current therapeutics provide symptomatic benefits to AD patients resulting in the use of preventive trials on presymptomatic subjects. This review article elucidates the pathophysiology of AD and emphasizes the need to explore the mitochondrial pathways to provide solutions to unanswered questions in the prevention and treatment 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 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: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.082
GPT teacher head0.357
Teacher spread0.275 · 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

Citations23
Published2016
Admission routes2
Has abstractyes

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