Azithromycin Mechanisms of Action in CRS Include Epithelial Barrier Restoration and Type 1 Inflammation Reduction
Bibliographic record
Abstract
OBJECTIVE: Previous in vitro transcriptomic profiling suggests azithromycin exerts its effects in patients with chronic rhinosinusitis (CRS) via modulation of type 1 inflammation and restoration of epithelial barrier function. We wished to verify these postulated effects using in vitro models of epithelial repair and in vivo transcriptional profiling. STUDY DESIGN: Functional effects of azithromycin in CRS were verified using in vitro models of wounding. The mechanism of the effect of azithromycin was assessed in vivo using transcriptomic profiling. SETTING: Academic medical center. METHODS: Effects of azithromycin on the speed of epithelial repair were verified in a wounding model using primary nasal epithelial cells (pNEC) from CRS patients. Nasal brushings collected pre-and posttreatment during a placebo-controlled trial of azithromycin for CRS patients unresponsive to surgery underwent transcriptomic profiling to identify implicated pathways. RESULTS: Administration of azithromycin improved the wound healing rates in CRS pNECs and prevented the negative effect of Staphylococcus aureus on epithelial repair. In vivo, response to azithromycin was associated with downregulation in pathways of type 1 inflammation, and upregulation of pathways implicated in the restoration of the cell cycle. CONCLUSION: Restoration of healthy epithelial function may represent a major mode of action of azithromycin in CRS. In vitro models show enhanced epithelial repair, while in vivo transcriptomics shows downregulation of pathways type 1 inflammation accompanied by upregulation of DNA repair and cell-cycle pathways. The maximal effect in patients with high levels of type 1-enhanced inflammation suggests that azithromycin may represent a novel therapeutic option for surgery-unresponsive CRS patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".