Pretreatment of Human Cerebrovascular Endothelial Cells with <scp>CO</scp>‐releasing Molecule‐3 Interferes with <scp>JNK</scp>/<scp>AP</scp>‐1 Signaling and Suppresses <scp>LPS</scp>‐induced Proadhesive Phenotype
Bibliographic record
Abstract
OBJECTIVE: Exogenously administered CO interferes with PMN recruitment to the inflamed organs. The mechanisms of CO-dependent modulation of vascular proadhesive phenotype, a key step in PMN recruitment, are unclear. METHODS: We assessed the effects/mechanisms of CO liberated from a water-soluble CORM-3 on modulation of the proadhesive phenotype in hCMEC/D3 in an in vitro model of endotoxemia. To this end, hCMEC/D3 were stimulated with LPS (1 μg/mL) for six hours. In some experiments hCMEC/D3 were pretreated with CORM-3 (200 μmol/L) before LPS-stimulation. PMN rolling/adhesion to hCMEC/D3 were assessed under conditions of laminar shear stress (0.7 dyn/cm(2) ). In parallel, expression of adhesion molecules E-selectin, ICAM-1, and VCAM-1 (qPCR), activation of transcription factors, NF-κB and AP-1 (ELISA), and MAPK-signaling (expression/phosphorylation of p38, ERK1/2, and JNK1/2; western blot) were assessed. RESULTS: The obtained results indicate that CORM-3 pretreatment reduces PMN rolling/adhesion to LPS-stimulated hCMEC/D3 (p < 0.05). Decreased PMN rolling/adhesion to hCMEC/D3 was associated with CORM-3-dependent inhibition of MAPK JNK1/2 activation (Tyr-phosphorylation), inhibition of transcription factor, AP-1 (c-Jun phosphorylation), and subsequent suppression of VCAM-1 expression (p < 0.05). CONCLUSIONS: These findings indicate that CORM-3 pretreatment interferes with JNK/AP-1 signaling and suppresses LPS-induced upregulation of the proadhesive phenotype in hCMEC/D3.
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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 | 0.000 |
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".