Characterization of the growth dynamics and biofilm formation of Staphylococcus epidermidis strains isolated from contaminated platelet units
Bibliographic record
Abstract
Bacterial contamination of platelet concentrates (PCs) poses the highest transfusion-associated infectious risk, with Staphylococcus epidermidis being a predominant contaminant. Herein, the growth dynamics of 20 S. epidermidis strains in PCs and regular media were characterized. Strains were categorized as fast (short lag phase) or slow (long lag phase) growers in PCs. All strains were evaluated for the presence of the biofilm-associated icaAD genes by PCR, their capability to produce extracellular polysaccharide (slime) on Congo red agar plates and their ability to form surface-attached aggregates (biofilms) in glucose-supplemented trypticase soy broth (TSBg) using a crystal violet staining assay. A subset of four strains (two slow growers and two fast growers) was further examined for the ability for biofilm formation in PCs. Two of these strains carried the icAD genes, formed slime and produced biofilms in TSBg and PCs, while the other two strains, which did not carry icaAD, did not produce slime or form biofilms in TSBg. Although the two ica-negative slime-negative strains did not form biofilms in media, they displayed a biofilm-positive phenotype in PCs. Although all four strains formed biofilms in PCs, the two slow growers formed significantly more biofilms than the fast growers. Furthermore, growth experiments of the two ica-positive strains in plasma-conditioned platelet bags containing TSBg revealed that a slow grower isolate was more likely to escape culture-based screening than a fast grower strain. Therefore, this study provides novel evidence that links S. epidermidis biofilm formation with slow growth in PCs and suggests that slow-growing biofilm-positive S. epidermidis would be more likely to be missed with automate culture.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".