In Vitro Assessment of the Inflammatory Response of Respiratory Endothelial Cells Exposed to Particulate Matter
Bibliographic record
Abstract
Particulate matter (PM) is a ubiquitous environmental pollutant that has been associated with increased risk of cardiopulmonary mortality and morbidity in urban communities. An increasing body of evidence suggests that inflammation induced by PM may play an important role in the development of cardiovascular diseases. However, airway epithelial cell lines, instead of vascular endothelial cells, are commonly used to investigate the effects of PM with respect to cardiovascular effects. Thus, the present study was conducted using primary human vascular endothelial cells (HMVEC-LB1), human white blood cells (WBC), and their cocultures to evaluate their inflammatory responses to various PM exposures. Enzyme-linked immunosorbent assay (ELISA) kits were used to measure the concentrations of interleukin (IL)-6, soluble intercellular adhesion marker (sICAM-1), and soluble vascular cell adhesion marker (sVCAM-1) in HMVEC-LB1, WBC, and their cocultures after exposure to size-fractionated PM. Pretreatment of cells with inhibitors was performed in order to examine pathways involved in PM-induced cellular responses. IL-6 levels increased significantly in HMVEC-LB1 cells exposed to PM in both a time- and concentration-dependent manner. However, particle exposure for up to 24 h failed to induce any detectable production of sICAM-1 or sVCAM-1 in HMVEC-LB1 cells. IL-6 production in response to size-fractioned PM exposures failed to show evidence of relative importance of particle sizes in their abilities to induce proinflammatory responses. Lastly, cocultures with WBC significantly enhanced particle induced IL-6 release in HMVEC-LB1 cells in a synergistic manner. The present study suggests that HMVEC-LB1 cells can be successfully used as an in vitro model to examine effects of PM exposure.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".