Pleural Mesothelial Cells Express Both BLT2 and PPARα and Mount an Integrated Response to Pleural Leukotriene B4
Bibliographic record
Abstract
Leukotriene B(4) (LTB(4)) plays a crucial role in the recruitment of neutrophils into the pleural space. We identified for the first time the mechanisms by which LTB(4) interacts with mesothelial cells and recruits neutrophils in the pleural compartment. Primary pleural mesothelial cells express both the proinflammatory receptor for LTB(4) BLT2, and the anti-inflammatory receptor for LTB(4), PPARalpha. Parapneumonic pleural effusions highly increase BLT2 expression and, via BLT2 activation, increase the adhesion between mesothelial cells and neutrophils and the expression of ICAM-1 on mesothelial cells. The block of PPARalpha further increases both cell adhesion and ICAM-1 expression. BLT2 activation promotes the activation, on mesothelial cells, of STAT-1 but not the activation of NF-kappaB transcription factor. The increase of ICAM-1 expression is achieved via increased tyrosine phosphorylation activity since herbimycin, a tyrosine kinase inhibitor, reduces and since Na orthovanadate, a tyrosine phosphatase inhibitor, further increases ICAM-1 expression. This study demonstrates that pleural mesothelial cells, expressing both proinflammatory and anti-inflammatory LTB(4) receptors, are able to mount an integrated response to LTB(4) with a prevalence of BLT2 activities in the presence of an inflammatory milieu within the pleura.
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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 | 0.000 | 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) | 0.002 | 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".