Prevalence of Biofilm-forming Bacteria in Chronic Rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Recently, biofilms have been implicated in the pathogenesis of recalcitrant chronic rhinosinusitis (CRS). We sought to determine the prevalence of biofilm-forming cultures obtained from patients with CRS and clinical factors that may contribute to biofilm formation. METHODS: Endoscopically guided sinonasal cultures were obtained in duplicate from CRS patients with evidence of mucopurulence. Bacterial swabs were sent for microbiological characterization and were simultaneously evaluated for biofilm-forming capacity by a modified Calgary Biofilm Detection Assay. Biofilm formation was based on concomitant values of biofilm-forming Pseudomonas aeruginosa O1 (PAO1) (positive control) and non-biofilm-forming mutants sad-31 (type IV pili) and sad-36 (flagella K; negative control). Samples, with growth greater than the sad-31 mutant, were designated as biofilm formers. RESULTS: Sinonasal cultures were obtained from 157 consecutive patients (83 female patients) over a 4-month period. Forty-five samples (28.6%) showed biofilm formation. Among patients with a prior history of functional endoscopic sinus surgery (FESS), 30.7% (n = 42) showed biofilm growth. For patients naive to surgical intervention (n = 20), only 15% showed biofilm formation. A positive, statistically significant correlation existed between biofilm formation and number of prior FESS procedures. Polymicrobial cultures, Pseudomonas aeruginosa, and/or Staphylococcus aureus comprised 71% of samples. Chi-squared analysis showed an association with prior infections, but not with any pharmacologic therapy or comorbidies. CONCLUSION: We show a high percentage of CRS patients (28.6%) whose sinonasal mucopurulence has biofilm-forming capacity. Postsurgical patients had a high prevalence of biofilm-forming bacteria, a possible reflection of the severe nature of their disease. Additional studies are warranted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".