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Record W2093818907 · doi:10.3390/ijerph120202289

Impact of Air Pollutants on Oxidative Stress in Common Autophagy-Mediated Aging Diseases

2015· review· en· W2093818907 on OpenAlexafffund
Mohamed Numan, Jacques P. Brown, Laëtitia Michou

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2015
Typereview
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchFondation CHU de QuébecUniversité Laval
KeywordsAutophagyOxidative stressAmyotrophic lateral sclerosisCell biologyOxidative phosphorylationFrontotemporal dementiaDiseaseBiologyDementiaMedicineBiochemistryPathologyApoptosis

Abstract

fetched live from OpenAlex

Atmospheric pollution-induced cellular oxidative stress is probably one of the pathogenic mechanisms involved in most of the common autophagy-mediated aging diseases, including neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), Alzheimer's, disease, as well as Paget's disease of bone with or without frontotemporal dementia and inclusion body myopathy. Oxidative stress has serious damaging effects on the cellular contents: DNA, RNA, cellular proteins, and cellular organelles. Autophagy has a pivotal role in recycling these damaged non-functional organelles and misfolded or unfolded proteins. In this paper, we highlight, through a narrative review of the literature, that when autophagy processes are impaired during aging, in presence of cumulative air pollution-induced cellular oxidative stress and due to a direct effect on air pollutant, autophagy-mediated aging diseases may occur.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.122
GPT teacher head0.493
Teacher spread0.371 · 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

Citations33
Published2015
Admission routes2
Has abstractyes

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