Bacterial biofilms and the pathophysiology of chronic rhinosinusitis
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the evidence for the presence of bacterial biofilms in chronic rhinosinusitis (CRS) and mechanisms by which they may contribute to the chronic inflammation characteristic of this disease. Lastly, to provide an overview of the current and potential future treatments for bacterial biofilms in CRS. RECENT FINDINGS: Advances in the techniques for identifying biofilms have confirmed the presence of bacterial biofilms on the sinonasal mucosa of patients with CRS. The impact on mucosal inflammation of the polymicrobial or multiorganism milieu is not yet well understood. Numerous novel topical therapies for the treatment of bacterial biofilms in CRS have been suggested with some demonstrating clinical efficacy. Blocking of quorum sensing represents a potential future therapy for biofilm treatment in CRS and biofilm infection at large. SUMMARY: Biofilms represent an important influence on the pathophysiology of CRS. Further understanding of biofilm interactions and microbial organism behavior will provide us with future treatment modalities for this disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".