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Record W2578569841 · doi:10.1016/j.envint.2017.01.010

Influence of exposure to coarse, fine and ultrafine urban particulate matter and their biological constituents on neural biomarkers in a randomized controlled crossover study

2017· article· en· W2578569841 on OpenAlexafffund
Ling Liu, Bruce Urch, Mieczysław Szyszkowicz, Mary Speck, Karen Leingartner, Robin Shutt, Guillaume Pelletier, Diane R. Gold, James A. Scott, Jeffrey R. Brook, Peter S. Thorne, Frances Silverman

Bibliographic record

VenueEnvironment International · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsSt. Michael's HospitalEnvironment and Climate Change CanadaPublic Health OntarioUniversity of TorontoHealth Canada
FundersHealth CanadaPublic Health Agency of CanadaNational Institutes of HealthClean Air Regulatory AgendaNational Institute of Environmental Health SciencesEnvironment and Climate Change Canada
KeywordsParticulatesCrossover studyEnvironmental scienceUltrafine particleEnvironmental chemistryEnvironmental healthMedicineChemistryBiologyMaterials scienceNanotechnologyEcology

Abstract

fetched live from OpenAlex

Background Epidemiological studies have reported associations between air pollution and neuro-psychological conditions. Biological mechanisms behind these findings are still not clear. Objectives We examined changes in blood and urinary neural biomarkers following exposure to concentrated ambient coarse, fine and ultrafine particles. Methods Fifty healthy non-smoking volunteers, mean age 28 years, were exposed to coarse (2.5–10 μm, mean 213 μg/m 3 ) and fine (0.15–2.5 μm, mean 238 μg/m 3 ) concentrated ambient particles (CAPs), and filtered ambient and/or medical air. Twenty-five participants were exposed to ultrafine CAP (mean size 59.6 nm, range 47.0–69.8 nm), mean (136 μg/m 3 ) and filtered medical air. Exposures lasted 130 min, separated by ≥ 2 weeks, and the biological constituents endotoxin and β-1,3- d -glucan of each particle size fraction were measured. Blood and urine samples were collected pre-exposure, and 1-hour and 21-hour post-exposure to determine neural biomarker levels. Mixed-model regressions assessed associations between exposures and changes in biomarker levels. Results Results were expressed as percent change from daily pre-exposure biomarker levels. Exposure to coarse CAP was significantly associated with increased urinary levels of the stress-related biomarkers vanillylmandelic acid (VMA) and cortisol when compared with exposure to filtered medical air [20% (95% confidence interval: 1.0%, 38%) and 64% (0.2%, 127%), respectively] 21 hours post-exposure. However exposure to coarse CAP was significantly associated with decreases in blood cortisol [− 26.0% (− 42.4%, − 9.6%) and − 22.4% (− 43.7%, − 1.1%) at 1 h and 21 h post-exposure, respectively]. Biological molecules present in coarse CAP were significantly associated with blood biomarkers indicative of blood brain barrier integrity. Endotoxin content was significantly associated with increased blood ubiquitin C-terminal hydrolase L1 [UCHL1, 11% (5.3%, 16%) per ln(ng/m 3 + 1)] 1-hour post-exposure, while β-1,3- d -glucan was significantly associated with increased blood S100B [6.3% (3.2%, 9.4%) per ln(ng/m 3 + 1)], as well as UCHL1 [3.1% (0.4%, 5.9%) per ln(ng/m 3 + 1)], one-hour post-exposure. Fine CAP was marginally associated with increased blood UCHL1 when compared with exposure to filtered medical air [17.7% (− 1.7%, 37.2%), p = 0.07] 21 hours post-exposure. Ultrafine CAP was not significantly associated with changes in any blood and urinary neural biomarkers examined. Conclusion Ambient coarse particulate matter and its biological constituents may influence neural biomarker levels that reflect perturbations of blood-brain barrier integrity and systemic stress response.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.022
GPT teacher head0.289
Teacher spread0.266 · 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 designRandomized trial
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".

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Citations57
Published2017
Admission routes2
Has abstractyes

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