Pulmonary and systemic response to atmospheric pollution
Bibliographic record
Abstract
The inhalation of toxic particles and gases reduces the innate defences of the lung by increasing epithelial permeability, decreasing mucociliary clearance and depressing macrophage function. There is also substantial experimental evidence that lung epithelial cells and alveolar macrophages generate a rich milieu of inflammatory mediators when exposed to atmospheric particles that can be measured in induced sputum, BAL fluid and blood. Here we review evidence that these mediators produce an integrated local lung and systemic inflammatory immune response. That results in an increase in the release of leucocytes and platelets from the bone marrow, an increased production of acute phase proteins from the liver and activation of the vascular endothelium to favour the formation of new and destabilization and rupture of existing atherosclerotic plaques. We postulate that when this response is generated in elderly persons whose lungs are compromised by COPD, it may account for the acute exacerbations of COPD that destroy the quality of life and increase the need for medical attention and hospital admissions. Moreover, the accelerated spread of the atherosclerotic process, and destabilization of existing atherosclerotic plaques in experimental animals that develop atherosclerosis naturally when exposed to atmospheric particles, may account for the acute coronary syndromes, myocardial infarction, transient cerebral ischaemia and stroke that have been documented in humans exposed to episodes of air pollution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".