Tulathromycin inhibits NF‐κB signaling and induces apoptosis in bovine neutrophils in Caspase‐3 dependent fashion
Bibliographic record
Abstract
Induction of neutrophil apoptosis by macrolides may generate anti‐inflammatory benefits via mechanisms that remain obscure. Tulathromycin (TUL), a new antibacterial agent for bovine respiratory disease, offers superior clinical efficacy for unknown reasons. Nuclear factor‐κB (NF‐κB) is a transcription factor implicated in the onset of inflammation. Studies have linked inhibition of NF‐κB signaling to increased neutrophil apoptosis. Aims To assess whether TUL induces apoptosis in bovine neutrophils and to determine whether the effects are associated with a modulation of the NF‐κB cascade. Results Cell Death ELISA and Annexin‐V staining showed that TUL, but not other antibiotics, induced apoptosis but not necrosis in bovine neutrophils in vitro in a dose‐ and time‐dependent manner (50μg/mL ‐ 2 mg/mL; 30–120 minutes). TUL failed to induce apoptosis in bovine epithelial, fibroblasts, or endothelial cells. TUL increased caspase‐3 activity in bovine neutrophils (ρ<0.05); caspase‐3 inhibition (50uM DEVD) blocked this pro‐apoptotic effect. In neutrophils stimulated with bacterial lipopolysaccharide or with IL‐1β, TUL strikingly reduced IκB phosphorylation. Conclusion Caspase‐3 dependent induction of apoptosis in bovine neutrophils by tulathromycin is associated with inhibition of the inflammatory NF‐κB signaling pathway. The effect is at least in part drug‐ and cell‐specific.
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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) | 0.002 | 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".