<i>Serratia marcescens</i> strains implicated in adverse transfusion reactions form biofilms in platelet concentrates and demonstrate reduced detection by automated culture
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Serratia marcescens is a gram-negative bacterium that has been implicated in adverse transfusion reactions associated with contaminated platelet concentrates. The aim of this study was to investigate whether the ability of S. marcescens to form surface-attached aggregates (biofilms) could account for contaminated platelet units being missed during screening by the BacT/ALERT automated culture system. MATERIALS AND METHODS: Seven S. marcescens strains, including biofilm-positive and biofilm-negative control strains and five isolates recovered from contaminated platelet concentrates, were grown in enriched Luria-Bertani medium and in platelets. Biofilm formation was examined by staining assay, dislodging experiments and scanning electron microscopy. Clinical strains were also analysed for their ability to evade detection by the BacT/ALERT system. RESULTS: All strains exhibited similar growth in medium and platelets. While only the biofilm-positive control strain formed biofilms in medium, this strain and three clinical isolates associated with transfusion reactions formed biofilms in platelet concentrates. The other two clinical strains, which had been captured during platelet screening by BacT/ALERT, failed to form biofilms in platelets. Biofilm-forming clinical isolates were approximately three times (P<0·05) more likely to be missed by BacT/ALERT screening than biofilm-negative strains. CONCLUSION: S. marcescens strains associated with transfusion reactions form biofilms under platelet storage conditions, and initial biofilm formation correlates with missed detection of contaminated platelet concentrates by the BacT/ALERT system.
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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) | 0.000 | 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 | 0.001 |
| 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".