Serum Susceptibility in Clinical Isolates of Burkholderia Cepacia Complex Bacteria: Development of a Growth-Based Assay for High Throughput Determination
Bibliographic record
Abstract
Burkholderia cepacia complex (BCC) bacteria can cause devastating chronic infections in people with cystic fibrosis. Of particular concern is "cepacia syndrome," a rapidly progressive and usually fatal decline in health, characterized by a necrotizing bacteremic pneumonia. An important component of defense against bloodstream infections is the bactericidal action of serum. Traditional methods to determine the capacity of bacterial isolates to resist the bactericidal effects of serum are relatively low-throughput viability assays. In this study, we developed a novel growth-based assay for serum susceptibility, which allows for high throughput analysis. We applied this assay to a range of clinical isolates of BCC as well as isolates comprising the BCC experimental strain panel. Our data demonstrate that isolates from all species of BCC examined can possess serum resistant or serum sensitive/intermediate phenotypes. Of particular clinical significance, we also found no direct link between the last saved pulmonary isolate from patients who subsequently developed "cepacia syndrome" and their capacity to resist the inhibitory effects of human serum, suggesting serum resistance cannot be used as a marker of an isolate's capacity to escape from the lung and cause bacteremia.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".