Differential pattern of deposition of nanoparticles in the airways of exposed workers
Bibliographic record
Abstract
Ultrafine particles (UFP) have been postulated to significantly contribute to the adverse health effects associated with exposure to particulate matter (PM). Due to their extremely small size (aerodynamic diameter <100 nm), UFP are able to deposit deep within the lung after inhalation and evade many mechanisms responsible for the clearance of larger particles. There is a lack of biologically relevant personal exposure metrics for exposure to occupational- and environmental-related micro- and nano-sized PM. The aim of the present study is to assess UFP in induced sputum (IS) and exhaled breath condensate (EBC) as possible biomarkers for assessing lung function impairment. Sputum induction and EBC testing were performed by conventional methods. UFP particles were assessed with the NanoSight LM20 (NanoSight Ltd, London, UK). The subjects included 35 exposed and 25 non-exposed workers. There were no group differences in pulmonary function test results and differential cell counts, but 63.6% of the exposed subjects had a higher percentage of neutrophils (OR3.28 p = 0.03) compared to the non-exposed subjects. The exposed subjects had higher percentages of UFP between 10 and 50 nm (69.45 ± 18.70 vs 60.11 ± 17.52 for the non-exposed group, p = 0.004). No differences were found in the IS samples. Years of exposure correlated positively to UFP content ( r = 0.342 p = 0.01) and macrophage content ( r = −0.327 p = 0.03). The percentage of small fraction of UFP in EBC, but not IS, is higher in exposed workers, and EBC may be a sensitive biomarker to assess exposure to nanoparticles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".