Platelets Enhance Endothelial Adhesiveness in High Tidal Volume Ventilation
Bibliographic record
Abstract
Although platelets induce lung inflammation, leading to acute lung injury (ALI), the extent of platelet-endothelial cell (EC) interactions remains poorly understood. Here, in a ventilation-stress model of lung inflammation, we show that platelet-EC interactions are important. We obtained freshly isolated lung endothelial cells (FLECs) from isolated, blood-perfused rat lungs exposed to ventilation at low tidal volume (LV) or stress-inducing high tidal volume (HV). Immunofluorescence and immunoprecipitation studies revealed HV-induced increases in cell-surface von Willebrand factor (vWf) expression on FLEC. This increased expression was inhibited by platelet removal from the lung perfusion and by including a P-selectin-blocking antibody in the lung perfusion. The expression was also blocked in lungs from P-selectin knockout (P sel(-/-)) mice perfused with autologous blood, but not with heterologous wild-type blood containing P-selectin-expressing platelets. These findings indicate that in ventilation stress, platelets transfer vWf to the EC surface and that platelet P-selectin plays a critical role in this transfer. Further evidence for such intercellular transfers was the HV-induced FLEC expressions of platelet glycoprotein 1b and of platelet P-selectin. We conclude that in ventilation stress, platelets deposit leukocyte- and platelet-binding proteins on the EC surface, thereby establishing the proinflammatory phenotype of the vascular lining.
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 | 0.000 |
| 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".