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Record W2161978817 · doi:10.1164/ajrccm.161.4.9904084

The Human Bone Marrow Response to Acute Air Pollution Caused by Forest Fires

2000· article· en· W2161978817 on OpenAlexaff
WAN C. TAN, Diwen Qiu, BENG L. LIAM, TZE P. NG, Szu Hee Lee, STEPHAN F. van EEDEN, Yulia Dyachkova, James C. Hogg

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBone marrowMedicinePathogenesisHazeImmunologyWhite blood cellImmune systemChemistry

Abstract

fetched live from OpenAlex

Atmospheric pollution increases cardiopulmonary morbidity and mortality by unexplained mechanisms. Phagocytosis of fine particles (PM(10)) by rabbit alveolar macrophages elevates white blood cells (WBC) by releasing precursors from the bone marrow and this could contribute to the pathogenesis of cardiopulmonary disease. The present study examined the association between acute air pollution caused by biomass burning and peripheral WBC counts in humans. Serial measurements of the WBC count made during the 1997 Southeast Asian Smoke-haze (Sep 29, Oct 27) were compared with a period after the haze cleared (Nov 21, Dec 5) using peripheral blood PMN band cells to monitor marrow release. The results showed that indices of atmospheric pollution were significantly associated with elevated band neutrophil counts expressed as a percentage of total polymorphonuclear leukocytes (PMN), with maximal association on zero and 1 lag day for PM(10) and 3, and 4 lag days for SO(2) (p value < 0.000). We conclude that atmospheric pollution caused by biomass burning is associated with elevated circulating band cell counts in humans because of the increased release of PMN precursors from the marrow. We speculate that this response contributes to the pathogenesis of the cardiorespiratory morbidity associated with acute air pollution.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.696

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.336
Teacher spread0.316 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations285
Published2000
Admission routes1
Has abstractyes

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