Low‐dose and long‐term azithromycin significantly decreases <i>Staphylococcus aureus</i> in the microbiome of refractory CRS patients
Bibliographic record
Abstract
BACKGROUND: The sinonasal microbiome is believed to play an important role in the pathophysiology of refractory chronic rhinosinusitis (CRS). We evaluated changes in the microbiome following a 4-month course of low-dose azithromycin. Assessing microbiome alterations following such a treatment may help identify underlying mechanisms of this drug. METHODS: A total of 48 adults with refractory CRS were enrolled in a double-blind, randomized, placebo-controlled trial. Patients were randomized to 250 mg of azithromycin or placebo 3 times weekly for 4 months. During this time, daily budesonide saline irrigations were continued. Sinonasal swabs were collected by endoscopically-assisted method prior to treatment initiation and at the end of it, and sent for 16S ribosomal RNA gene sequencing. High-resolution ANCHOR pipeline was used to infer and annotate putative species. The 2 patient groups were compared using DESeq2 differential abundance analysis. RESULTS: From initiation to the end of azithromycin treatment, patients showed a significant difference in beta diversity analysis (p = 0.0004) along with a significant decrease in 71 different operational taxonomic units (OTUs) of Staphylococcus aureus (false discovery rate [FDR] < 0.05) obtained from the differential abundance analysis. This was not observed in placebo-treated patients. By the end of treatments, azithromycin-treated patients had a significant decrease in 29 different OTUs of S. aureus (FDR < 0.05) when compared to placebo. CONCLUSION: A 4-month course of 250 mg of azithromycin 3 times weekly in patients with refractory CRS significantly decreases S. aureus abundance in the sinonasal microbiome. Considering the pathogenic role of S. aureus in the refractory CRS population, azithromycin may constitute an additional therapeutic option to help control this disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".