<i>Staphylococcus epidermidis</i> forms biofilms under simulated platelet storage conditions
Bibliographic record
Abstract
BACKGROUND: Staphylococcus epidermidis grows slowly in platelet (PLT) preparations compared to other bacteria, presenting the possibility of missed detection by routine screening. S. epidermidis is a leading cause of nosocomial sepsis, with virulence residing in its ability to establish chronic infections through production of slime layers, or biofilms, on biomedical devices. This study aims to establish biofilm formation (BF) as a mode of growth by S. epidermidis in PLT preparations. STUDY DESIGN AND METHODS: Biofilm-positive (BFpos) and -negative (BFneg) S. epidermidis strains were grown in whole blood-derived PLTs (WBDPs) and in glucose-rich medium (TSBg). An assay for BF was adapted for cultures grown in WBDPs or filtered WBDPs in polystyrene culture plates. Bacterial attachment to polyvinylchloride PLT bags and PLTs was examined by scanning electron microscopy. RESULTS: Both strains display similar growth profiles in WBDPs and TSBg. Unexpectedly, evidence of BF was observed on PLT bags and on PLTs directly, not only by the BFpos strain but also by the BFneg strain. The BFpos strain displayed greater plastic adherence than the BFneg strain in WBDPs (p < 0.05). BF by the BFneg strain was approximately 10-fold greater in WBDPs compared to TSBg (p < 0.05), likely by use of PLTs as a scaffold. Furthermore, BF by S. epidermidis was significantly decreased when PLT concentration was reduced 1000-fold. CONCLUSIONS: S. epidermidis forms biofilms on PLT aggregates and on PLT bags under PLT storage conditions. Our results demonstrate that the PLT storage environment can promote a BF growth mechanism for contaminant bacteria.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".