Inhaled Pollutants: The Molecular Scene behind Respiratory and Systemic Diseases Associated with Ultrafine Particulate Matter
Bibliographic record
Abstract
Air pollution of anthropogenic origin is largely from the combustion of biomass (e.g., wood), fossil fuels (e.g., cars and trucks), incinerators, landfills, agricultural activities and tobacco smoke. Air pollution is a complex mixture that varies in space and time, and contains hundreds of compounds including volatile organic compounds (e.g., benzene), metals, sulphur and nitrogen oxides, ozone and particulate matter (PM). PM0.1 (ultrafine particles (UFP)), those particles with a diameter less than 100 nm (includes nanoparticles (NP)) are considered especially dangerous to human health and may contribute significantly to the development of numerous respiratory and cardiovascular diseases such as chronic obstructive pulmonary disease (COPD) and atherosclerosis. Some of the pathogenic mechanisms through which PM0.1 may contribute to chronic disease is their ability to induce inflammation, oxidative stress and cell death by molecular mechanisms that include transcription factors such as nuclear factor κB (NF-κB) and nuclear factor (erythroid-derived 2)-like 2 (Nrf2). Epigenetic mechanisms including non-coding RNA (ncRNA) may also contribute towards the development of chronic disease associated with exposure to PM0.1. This paper highlights emerging molecular concepts associated with inhalational exposure to PM0.1 and their ability to contribute to chronic respiratory and systemic disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".