Differences in RANTES and IL-6 levels among chronic rhinosinusitis patients with predominant gram-negative and gram-positive infection
Bibliographic record
Abstract
BACKGROUND: Bacteria are suspected players in the pathogenesis of chronic rhinosinusitis (CRS), yet their exact role is not understood. We investigated the effect of planktonic and biofilm of staphylococcus aureus (SA) and Pseudomonas aeruginosa (PA) on the mucosa of CRS patients with gram-positive and gram-negative infections by measuring the levels of IL-6 and RANTES, a chemokine with activity on eosinophils and T lymphocytes. METHODS: Ethmoid mucosa of six CRS patients with gram-positive bacteria on culture and five with gram-negative bacteria were compared to ethmoid mucosa of 8 control patients. The tissue explants were stimulated with SA and PA extracts in planktonic and biofilm form for 6 hours, then RANTES levels were measured by ELISA. RESULTS: Compared to the control group, CRS patients with gram-negative predominance demonstrated a significantly higher level of RANTES expression in response to all forms of bacterial stimuli (P-value <0.05). Patients with gram-positive predominance showed a higher level of RANTES compere to control group, however, this difference was not significant (P-value >0.05). CONCLUSIONS: The mucosa of CRS patients with gram-negative infections has a heightened innate immune response compared to controls and patients with gram-positive infections. It is possible that this response leads to the pathological eosinophilic inflammation seen in CRS.
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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".