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Record W2345408346 · doi:10.1289/ehp175

Particulate Oxidative Burden as a Predictor of Exhaled Nitric Oxide in Children with Asthma

2016· article· en· W2345408346 on OpenAlexafffundabout
Caitlin L. Maikawa, Scott Weichenthal, Amanda J. Wheeler, Nina A. Dobbin, Audrey Smargiassi, Greg J. Evans, Ling Liu, Mark S. Goldberg, Krystal J. Godri Pollitt

Bibliographic record

VenueEnvironmental Health Perspectives · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsMcGill University Health CentreUniversité de MontréalMcGill UniversityInstitut National de Santé Publique du QuébecHealth CanadaUniversity of Toronto
FundersClean Air Regulatory Agenda
KeywordsExhaled nitric oxideInterquartile rangeOxidative stressAsthmaMedicineGlutathioneNitric oxideReactive oxygen speciesPhysiologyImmunologyInternal medicineChemistrySpirometryBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Epidemiological studies have provided strong evidence that fine particulate matter (PM2.5; aerodynamic diameter ≤ 2.5 μm) can exacerbate asthmatic symptoms in children. Pro-oxidant components of PM2.5 are capable of directly generating reactive oxygen species. Oxidative burden is used to describe the capacity of PM2.5 to generate reactive oxygen species in the lung. OBJECTIVE: In this study we investigated the association between airway inflammation in asthmatic children and oxidative burden of PM2.5 personal exposure. METHODS: Daily PM2.5 personal exposure samples (n = 249) of 62 asthmatic school-aged children in Montreal were collected over 10 consecutive days. The oxidative burden of PM2.5 samples was determined in vitro as the depletion of low-molecular-weight antioxidants (ascorbate and glutathione) from a synthetic model of the fluid lining the respiratory tract. Airway inflammation was measured daily as fractional exhaled nitric oxide (FeNO). RESULTS: A positive association was identified between FeNO and glutathione-related oxidative burden exposure in the previous 24 hr (6.0% increase per interquartile range change in glutathione). Glutathione-related oxidative burden was further found to be positively associated with FeNO over 1-day lag and 2-day lag periods. Results further demonstrate that corticosteroid use may reduce the FeNO response to elevated glutathione-related oxidative burden exposure (no use, 15.8%; irregular use, 3.8%), whereas mold (22.1%), dust (10.6%), or fur (13.1%) allergies may increase FeNO in children with versus children without these allergies (11.5%). No association was found between PM2.5 mass or ascorbate-related oxidative burden and FeNO levels. CONCLUSIONS: Exposure to PM2.5 with elevated glutathione-related oxidative burden was associated with increased FeNO. CITATION: Maikawa CL, Weichenthal S, Wheeler AJ, Dobbin NA, Smargiassi A, Evans G, Liu L, Goldberg MS, Godri Pollitt KJ. 2016. Particulate oxidative burden as a predictor of exhaled nitric oxide in children with asthma. Environ Health Perspect 124:1616-1622; http://dx.doi.org/10.1289/EHP175.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.273
Teacher spread0.261 · 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 designObservational
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

Citations71
Published2016
Admission routes3
Has abstractyes

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