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Record W1972617907 · doi:10.1155/2014/797050

Inhalation of Nebulized Diesel Particulate Matter: A Safety Trial in Healthy Humans

2014· article· en· W1972617907 on OpenAlexaff
Sandra C. Dorman, Kaylyn M. Sutcliffe, Jacques Abourbih, Stacey A. Ritz

Bibliographic record

VenueJournal of Respiratory Medicine · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsNOSM UniversityLaurentian University
Fundersnot available
KeywordsInhalationNebulizerInhalation exposureMedicineSalineAnesthesiaSputumPharmacologyPathology

Abstract

fetched live from OpenAlex

Diesel particulate matter (DPM) has adverse health effects. Examining the underlying pathophysiological mechanisms would be facilitated by the introduction of an exposure method that is safe, portable, and cost-effective. The purpose of this study was to establish a novel method to study DPM exposure via nebulization and an inhalation dose that was safe, yet capable of eliciting an inflammatory response. Ten participants enrolled in this nonblinded, nonrandomized study. Subjects inhaled nebulized 0.9% saline and increasing doses of DPM suspended in 0.9% saline (75, 150, and 300 μ g) in a sequential manner. FEV 1 was measured repeatedly during the first 2 h after exposure and blood, oximetry, sputum, and heart rate were taken before, 2 h, and 24 h after inhalation challenge. DPM inhalation was well-tolerated at all doses. A decrease in FEV 1 was observed after each inhalation challenge (including saline). Inhalation of 300 μ g DPM produced a significantly different FEV 1 response curve. An increase in particle inclusion-positive sputum macrophages for all DPM doses confirmed that the nebulized particles were reaching the lower airways. Serum GM-CSF was elevated after exposures to 150 and 300 μ g DPM. No other inflammatory changes were detected. DPM inhalation via nebulizer is a safe method of delivering low doses of DPMs in healthy people.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.357
Teacher spread0.297 · 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 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

Citations2
Published2014
Admission routes1
Has abstractyes

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