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Record W4392571181 · doi:10.3389/fnins.2024.1351721

Air pollution and neurological diseases, current state highlights

2024· review· en· W4392571181 on OpenAlexaff
Raymond S. Roy, Amedeo D’Angiulli

Bibliographic record

VenueFrontiers in Neuroscience · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsChildren's Hospital of Eastern OntarioCarleton University
Fundersnot available
KeywordsAir pollutionDiseasePsychological interventionEnvironmental healthCardiovascular healthOxidative stressNeuroscienceMedicinePsychologyPsychiatryPathologyBiologyEcology

Abstract

fetched live from OpenAlex

This paper delves into the increasingly recognized yet complex relationship between air pollution and Neurological Diseases. Although the detrimental effects of air pollution on respiratory and cardiovascular health are well-documented, its impact on neurological and cognitive disorders is an emerging area of concern. In this mini review, we explore the intricate mechanisms by which various air pollutants, such as particulate matter, nitrogen oxides, and polycyclic aromatic hydrocarbons, contribute to neurological pathologies. The focus lies on the role of oxidative stress and inflammation in exacerbating conditions like Alzheimer's disease and Parkinson's disease. By unraveling these connections, the paper sheds light on the broader implications of environmental factors on neurological health and underscores the urgent need for policy interventions to mitigate air pollution's impact on the nervous system.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.054
GPT teacher head0.345
Teacher spread0.291 · 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.

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

Citations60
Published2024
Admission routes1
Has abstractyes

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