Swimming Motility in a Longitudinal Collection of Clinical Isolates of Burkholderia cepacia Complex Bacteria from People with Cystic Fibrosis
Bibliographic record
Abstract
Chronic bacterial lung infections in cystic fibrosis (CF) are the leading cause of morbidity and mortality. While a range of bacteria are known to be capable of establishing residence in the CF lung, only a small number have a clearly established link to deteriorating clinical status. The two bacteria with the clearest roles in CF lung disease are Pseudomonas aeruginosa and bacteria belonging to the Burkholderia cepacia complex (BCC). A number of common adaptations by P. aeruginosa strains to chronic lung infection in CF have been well described. Typically, initial isolates of P. aeruginosa are nonmucoid and display a range of putative virulence determinants. Upon establishment of chronic infection, subsequent isolates ultimately show a reduction in putative virulence determinants, including swimming motility, along with an acquisition of the mucoid phenotype and increased levels of antimicrobial resistance. Infections by BCC are marked by an unpredictable, but typically worse, clinical outcome. However, in contrast to P. aeruginosa infections in CF, studies describing adaptive changes in BCC bacterial phenotype during chronic lung infections are far more limited. To further enhance our understanding of chronic lung infections by BCC bacteria in CF, we assessed the swimming motility phenotype in 551 isolates of BCC bacteria from cystic fibrosis (CF) lung infections between 1981 and 2007. These data suggest that swimming motility is not typically lost by BCC during chronic infection, unlike as seen in P. aeruginosa infections. Furthermore, while we observed a statistically significant link between mucoidy and motility, we did not detect any link between motility phenotype and clinical outcome. These studies highlight the need for further work to understand the adaptive changes of BCC bacteria during chronic infection in the CF lung.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".