The association of Leptospermum honey with cytokine expression in the sinonasal epithelium of chronic rhinosinusitis patients
Bibliographic record
Abstract
Objective To identify the differences in cytokine expression between sinonasal tissue from patients treated with Leptospermum (Manuka) honey (LH) irrigation versus normal saline irrigation twice‐daily for twelve weeks following sinus surgery (FESS). Methods Forty‐six CRS patients were recruited. Sinus tissue biopsies were collected during FESS and then at 5 and 12 weeks postoperatively during the course of treatment. A multi‐plex cytokine assay quantified the abundance of 17 cytokines in biopsied tissue. Cytokine expression fold‐change was analyzed between each time point using a robust linear regression model and compared between the two treatment groups. Results Compared to the saline irrigation group, five cytokines were differently expressed (CI = 95%) in sinonasal tissue obtained from subjects in the LH irrigation group during the 12‐week treatment period. Cytokines IL‐6 (P = 0.0400), IL‐8 (P = 0.0398), MCP‐1 (P = 0.0284), and MIP‐1β (P = 0.016) were significantly increased in the LH irrigation group compared to the saline irrigation group. IL‐13 was significantly increased in the saline irrigation group compared to the LH group (P = 0.0086). Conclusion LH may potentially act to modulate the expression of IL‐6, IL‐8, IL‐13, MCP‐1 and MIP‐1β in sinonasal tissue.
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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) | 0.000 | 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".