Impact of cranberry juice on initial adhesion of the EPS producing bacterium<i>Burkholderia cepacia</i>
Bibliographic record
Abstract
The impact of cranberry juice was investigated with respect to the initial adhesion of three isogenic strains of the bacterium Burkholderia cepacia with different extracellular polymeric substance (EPS) producing capacities, viz. a wild-type cepacian EPS producer PC184 and its mutant strains PC184rml with reduced EPS production and PC184bceK with a deficiency in EPS production. Adhesion experiments conducted in a parallel-plate flow chamber demonstrated that, in the absence of cranberry juice, strain PC184 had a significantly higher adhesive capacity compared to the mutant strains. In the presence of cranberry juice, the adhesive capacity of the EPS-producing strain PC184 was largely reduced, while cranberry juice had little impact on the adhesion behavior of either mutant strain. Thermodynamic modeling supported the results from adhesion experiments. Surface force apparatus (SFA) and scanning electron microscope (SEM) studies demonstrated a strong association between cranberry juice components and bacterial EPS. It was concluded that cranberry juice components could impact bacterial initial adhesion by adhering to the EPS and impairing the adhesive capacity of the cells, which provides an insight into the development of novel treatment strategies to block the biofilm formation associated with bacterial infection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".