Carbon monoxide (CO) liberated by CO‐releasing molecule (CORM‐3) interferes with up‐regulation of vascular pro‐adhesive phenotype in experimental model of sepsis
Bibliographic record
Abstract
Recently, it has been shown that CORM‐released CO can modulate inflammatory response. In this study we assessed the effects and potential mechanisms of water‐soluble CO‐releasing molecule, CORM‐3, in experimental model of sepsis. Sepsis in mice was induced by cecal ligation and perforation (CLP). CORM‐3 (8mg/kg; i.v.) was administered immediately after CLP‐induction and PMN accumulation (MPO assay) in systemic organs was assessed 24 hrs later. In in vitro experiments human umbilical vein endothelial cells (HUVEC) were stimulated with LPS (10μg/ml; 4hrs) in the presence or absence of CORM‐3 (100 μM). Subsequently, induction of oxidative stress (DHR123 oxidation), activation of NFκB (EMSA), expression of ICAM‐1 (cell ELISA), and 51 Cr‐PMN adhesion to HUVEC were assessed. In some experiments, HUVEC were pretreated with CORM‐3 for 2hrs before stimulation with LPS. The obtained results indicate that administration of CORM‐3 significantly reduces PMN accumulation in the lung, heart and liver of septic mice. In parallel, LPS‐induced increase in HUVEC oxidative stress, NFκB activation, ICAM‐1 surface expression and PMN adhesion to HUVEC were prevented by CORM‐3. Interestingly, pretreatment of HUVEC with CORM‐3 was also effective with respect to the above experimental end‐points. Taken together these findings indicate that CORM‐3‐released CO confers anti‐inflammatory effects by interfering with up‐regulation of vascular endothelial cell pro‐adhesive phenotype during sepsis (HSFO‐NA5580 and MOP‐68848)
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 | 0.001 |
| 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".