Endothelial signal transduction system enhances neutrophil‐induced pulmonary vascular permeability
Bibliographic record
Abstract
The mechanism by which stimulated polymorphonuclear leukocytes and neutrophils (PMNs) damage pulmonary vascular endothelium was investigated. The authors assessed the ability of unstimulated and mechanically stimulated PMNs to adhere to pulmonary endothelial cells and, thereby, alter pulmonary vascular permeability, measured as the pulmonary filtration coefficient (K) and haemodynamics. PMNs were stimulated by gentle agitation in a glass vial for 10 s. Perfusing lungs with the stimulated PMNs (n=6) resulted in significant accumulation of PMNs within the lungs, assessed by myeloperoxidase levels, and elicited a 4-fold increase in K and a 2-fold increase in pulmonary vascular resistance as compared to lungs perfused with unstimulated cells (n=6). The increases in K were completely blocked by GF109203X, a protein kinase C inhibitor (n=6); however, GF109203X only partially attenuated the increase in vascular resistance and had little effect on the accumulation of stimulated PMNs. An agonist of protein kinase C, phorbol myristate acetate, elicited dose dependent increases in both K and pulmonary vascular resistance even in the absence of PMNs (n=6). These findings indicate that the increases in pulmonary filtration coefficient and pulmonary vascular resistance induced by polymorphonuclear neutrophils result from endothelial cell injury mediated by activation of protein kinase C within the endothelial cells themselves.
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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) | 0.001 | 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".