Retinoid derivatives offer anti‐inflammatory benefits by promoting neutrophil apoptosis and inhibiting proinflammatory mediator production in a model of bovine respiratory disease
Bibliographic record
Abstract
Clearance of apoptotic neutrophils (PMN) following infection is critical for the resolution of inflammation. Despite demonstrating immunomodulatory properties, the effects of retinoids in PMN in the context of an inflammatory response remain unknown. Objective to evaluate the immunomodulatory properties of two retinoids, oxidatively‐transformed carotene‐β (OxC‐β) and retinoic acid (RA) in a model of Mannheimia haemolytica ‐induced bovine respiratory disease, which is characterized by severe inflammation. Results in vitro , RA and OxC‐β induced caspase‐and time‐dependent apoptosis, but not necrosis, in bovine PMN, but not epithelial cells or fibroblasts. Neither RA nor OxC‐β affected PMN function. In M. haemolytica ‐infected calves (2×10 7 CFU), animals that received a 28‐day dietary OxC‐β treatment (10 mg/kg) had elevated apoptotic leukocytes and reduced LTB 4 levels in their lower airways 3h post‐infection vs infected‐untreated calves. Conclusion RA and OxC‐β promote cell‐specific apoptosis and inhibit the production of pro‐inflammatory LTB 4 , both mechanisms that promote the resolution of inflammation, thereby suggesting an anti‐inflammatory role for retinoid derivatives. Supported by NSERC and AIHS.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".