New anti‐inflammatory benefits of antibiotics: Tulathromycin promotes apoptosis and efferocytosis, and inhibits pro‐inflammatory CXCL8 production in a model of porcine pleuropneumonia
Bibliographic record
Abstract
Neutrophil (PMN) apoptosis and subsequent clearance by surrounding macrophages (MΦ; efferocytosis) is critical to the resolution of inflammation following infection. Abnormal resolution of inflammation is responsible for the severe tissue damage characteristic of pneumonia, as in Actinobacillus pleuropneumoniae porcine pleuropneumonia. The superior clinical efficacy of some antibiotics has been attributed to inherent anti‐inflammatory properties via mechanisms that remain obscure. Objective to characterize anti‐inflammatory properties of tulathromycin (TUL) in a model of porcine pleuropneumonia. Results In vivo and in vitro , TUL induced porcine PMN apoptosis in a time‐and dose‐dependent manner, as determined by cell death ELISA, TUNEL fluorescent staining, and cleavage (activation) of caspase‐3, detected by western blot. TUL‐treated apoptotic PMN were readily efferocytosed by MΦ. TUL also induced delayed apoptosis and inhibited pro‐inflammatory CXCL8 production in porcine MΦ. Conclusion TUL's superior clinical efficacy may be due in part to anti‐inflammatory benefits mediated by PMN apoptosis and inhibition of CXCL8 in MΦ, which together promote the resolution of inflammation. Supported by Pfizer.
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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 | 0.001 |
| 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".