Pulmonary Bacterial Communities in Surgically Resected Noncystic Fibrosis Bronchiectasis Lungs Are Similar to Those in Cystic Fibrosis
Bibliographic record
Abstract
Background. Recurrent bacterial infections play a key role in the pathogenesis of bronchiectasis, but conventional microbiologic methods may fail to identify pathogens in many cases. We characterized and compared the pulmonary bacterial communities of cystic fibrosis (CF) and non-CF bronchiectasis patients using a culture-independent molecular approach. Methods. Bacterial 16S rRNA gene libraries were constructed from lung tissue of 10 non-CF bronchiectasis and 21 CF patients, followed by DNA sequencing of isolates from each library. Community characteristics were analyzed and compared between the two groups. Results. A wide range of bacterial diversity was detected in both groups, with between 1 and 21 bacterial taxa found in each patient. Pseudomonas was the most common genus in both groups, comprising 49% of sequences detected and dominating numerically in 13 patients. Although Pseudomonas appeared to be dominant more often in CF patients than in non-CF patients, analysis of entire bacterial communities did not identify significant differences between these two groups. Conclusions. Our data indicate significant diversity in the pulmonary bacterial community of both CF and non-CF bronchiectasis patients and suggest that this community is similar in surgically resected lungs of CF and non-CF bronchiectasis patients.
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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 | 0.001 | 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".