Tulathromycin promotes phagocytosis and mediates IL‐8, NO, and PGE2 secretion in bovine monocyte‐derived macrophages
Bibliographic record
Abstract
Tulathromycin (TUL) is an antibiotic used in the treatment of bovine respiratory disease, whose efficacy may be due to anti‐inflammatory capabilities. In the lung, macrophage secretion of inflammatory mediators interleukin (IL)‐8, nitric oxide (NO), and prostaglandin E 2 (PGE 2 ), as well as phagocytosis apoptotic neutrophils, are imperative mechanisms contributing to the resolution of inflammation. Effects of TUL on modulation of macrophage function have yet to be investigated. Aim 1) to determine the effects of TUL on neutrophil phagocytosis by bovine monocyte‐derived macrophages (BMDM) and 2) to examine the direct effects of TUL on the expression of cyclo‐oxygenase‐2 (COX‐2) and secreted levels of IL‐8, PGE 2 , and NO. Results Light microscopy suggested that TUL‐induced neutrophil apoptosis is associated with increased neutrophil phagocytosis by BMDM. Western blotting showed TUL increases expression of COX‐2, while ELISA and Greiss reaction showed that TUL decreased secreted levels of IL‐8, and NO and increased levels of PGE 2 in lipopolysaccharide (LPS)‐stimulated BMDM. Conclusion TUL promotes macrophage phagocytosis of apoptotic neutrophils and modulates COX‐2 protein expression and secreted levels of IL‐8, NO, and PGE 2 in LPS‐stimulated BMDM. Together, the findings illustrate novel mechanisms through which an antibiotic may deliver anti‐inflammatory benefits. This work is supported by NSERC.
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".