Anti‐inflammatory benefits of an antibiotic via modulation of neutrophil and macrophage function: The example of tulathromycin
Bibliographic record
Abstract
The pathogenesis of bacterial‐induced inflammatory diseases is due, in part, to the host immune response. The ability of an antibiotic to modulate immune cell function during infection may confer anti‐inflammatory benefits. Tulathromycin (TUL), an antibacterial agent for bovine respiratory disease, is a novel model for investigating immuno‐modulating effects of antibiotics. Studies have shown that TUL exerts pro‐apoptotic effects in bovine neutrophils (PMN). Nuclear factor‐¿B (NF¿B), a transcription factor implicated in the onset of inflammation, is known to inhibit apoptosis. The effects of TUL on macrophage function are unclear. Aims To investigate the mechanisms of TUL‐induced PMN apoptosis and to determine the effects of TUL on bovine macrophage (MØ) function. Results ELISA revealed TUL‐induced PMN apoptosis is caspase‐3 and ‐8 dependent. Western blotting showed TUL reduces phosphorylation of NF¿B inhibitor, I¿B, in zymosan‐stimulated PMN. Light microscopy suggested TUL stimulates phagocytosis of apoptotic PMN in MØ. ELISA and Greiss reaction showed TUL reduced secreted levels of pro‐inflammatory interleukin‐8 and nitric oxide in E. coli lipopolysaccharide‐challenged MØ. Conclusion Modulation of PMN and MØ function by TUL is associated with inhibition of NF¿B signaling . These findings illustrate novel mechanisms through which an antibiotic may deliver anti‐inflammatory benefits.
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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 | 0.000 |
| 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".