Diesel exhaust inhalation induces heat shock protein 70 expression<i>in vivo</i>
Bibliographic record
Abstract
Exposure to urban air pollution is an independent risk factor for increased cardiovascular diseases. Heat shock protein 70 (HSP70) has been implicated in the pathogenesis of vascular dysfunction and cardiovascular diseases. This study has been designed to determine whether inhalation of urban air induces HSP70 expression in the lung and blood as well as the association of HSP70 and air pollution-induced vascular dysfunction. Apolipoprotein E (Apo-E) deficient mice were exposed to diesel exhaust (DE) either acutely (3 days, 200 or 400 µg/m(3) for 6 h/day) or chronically (7 weeks, 200 or 400 µg/m(3) for 6 h/day). HSP70 was measured in the lung using immunohistochemistry, and in the plasma by ELISA. Abdominal aorta rings were used to determine vascular functional responses. Chronic DE-exposure increased the fraction of HSP70 positive alveolar macrophages (AM) that was related to the fraction of particle-laden AM in the lung (r(2) = 0.48, p <0.01). Chronic DE-exposure increased plasma HSP70 levels and reduced blood vessel responses to phenylephrine (PE). The fraction of particle-laden HSP70 positive AM was associated with abnormal vasoconstriction responses to PE induced by DE-exposure (r(2) = 0.12, p = 0.02). Our results show that chronic inhalation of DE increases HSP70 expression in the lung and systemic circulation, and we postulate that HSP70 possibly contributes to air pollution induced vascular dysfunction and cardiovascular diseases.
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 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 | 0.000 | 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 | 0.001 |
| 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".