Apoptosis, oxidative metabolism and interleukin-8 production in human neutrophils exposed to azithromycin: effects of Streptococcus pneumoniae
Bibliographic record
Abstract
Pathogen virulence factors and the host inflammatory response cause tissue injury associated with respiratory tract infections. The azalide azithromycin has demonstrated efficacy in the treatment of these infections. It has been demonstrated previously that induction of polymorphonuclear leucocyte (PMN) apoptosis is associated with minimization of tissue damage and inflammation in the lung. We hypothesized that, in addition to its antibacterial effects, azithromycin may promote apoptosis. The aim of the study was to determine the effects of azithromycin on PMN apoptosis, oxidative function and interleukin-8 (IL-8) production in the presence or absence of Streptococcus pneumoniae, in comparison with penicillin, erythromycin, dexamethasone or phosphate-buffered saline. Human circulating PMNs were assessed for apoptosis (by annexin V labelling and ELISA), oxidative function (by nitroblue tetrazolium reduction) and IL-8 production (by ELISA). Azithromycin significantly induced PMN apoptosis in the absence of S. pneumoniae after 1 h (10.27% +/- 1.48%, compared with 2.19% +/- 0.42% in controls) to levels similar to those after 3 h induction with tumour necrosis factor-alpha (8. 73% +/- 1.86%). This effect was abolished in the presence of S. pneumoniae. Apoptosis in PMNs exposed to the other drugs was not significantly different from that in controls. Azithromycin did not affect PMN oxidative metabolism or IL-8 production. In summary, azithromycin-induced PMN apoptosis may be detected in the absence of any effect on PMN function, and the pro-apoptotic properties of azithromycin are inhibited in the presence of S. pneumoniae.
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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".