Effect of the selective protein kinase C inhibitor, Ro-31-8220, on chemokine-induced Leukocyte recruitment in vivo
Bibliographic record
Abstract
The most critical and most important event in acute inflammation is the migration of neutrophils and other inflammatory cells from blood to the site of injury, immune response or infection. Leukocyte recruitment occurs in response to pro-inflammatory mediators such as cytokines and interleukins which are produced at the site of inflammation. Protein kinase C (PKC) is a family of kinases that are involved in the pathophysiology of a variety of inflammatory diseases or disorders such as arthritis, asthma and myocarditis. The effect of Ro-31-8220, the selective PKC inhibitor, on leukocyte transmigration in various inflammatory models is still incompletely understood. The present study explored the effect of the selective and pan inhibitor of PKC, Ro-31-8220, on CXCL1/KC induced leukocyte recruitment especially neutrophils in acute peritonitis model in mice. Ro-31-8220 treatment significantly attenuated the emigration of leukocytes predominately neutrophils in response to CXCL1/KC chemokine. Thereby, Ro-31-8220 treatment ameliorated CXCL1/KC induced acute peritonitis by interfering with emigration of leukocytes. Collectively, our study demonstrates that pharmacological inhibition of PKC in general, may provide the basic key of therapeutic strategy for many inflammatory diseases or immune linked disorders in which PKC was implicated
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".