Biofilm‐associated accumulation‐associated protein (Aap): A contributing factor to the predominant growth of <i>Staphylococcus epidermidis</i> in platelet concentrates
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Staphylococcus epidermidis is a predominant contaminant of platelet concentrates (PCs), outcompeting other skin flora bacteria such as Staphylococcus capitis. The accumulation-associated protein (Aap), encoded by the aap gene, is involved in formation of bacterial aggregates (biofilms) in S. epidermidis and is absent in S. capitis. In this study, the role of S. epidermidis aap in enhancing biofilm formation and conferring an advantageous growth in PCs was investigated. MATERIALS AND METHODS: Biofilm formation assays of S. epidermidis 1457, S. epidermidis 1457∆aap, S. capitis 517 and S. capitis 517 carrying S. epidermidis aap (S. capitis 517/pRBaap) were performed in glucose-supplemented trypticase soy broth (TSBg) and PCs. Additionally, competition assays with paired cultures (1:1 ratio) of S. epidermidis and S. capitis strains were seeded in PCs, followed by determination of viable counts of each organism at the end of PC storage. RESULTS: Staphylococcus epidermidis aap had no effect on biofilm formation in TSBg. By contrast in PCs, S. epidermidis 1457 showed higher biofilm formation than S. epidermidis 1457∆aap (P < 0·05). Biofilm formation was also enhanced in S. capitis 517/pRBaap compared to S. capitis 517 (P = 0·054). Competition assays showed that S. epidermidis 1457 outcompeted S. capitis 517, and importantly, S. capitis 517/pRBaap outcompeted S. capitis 517 and S. epidermidis 1457∆aap. CONCLUSION: This study demonstrated that S. epidermidis aap plays a role in biofilm formation in PCs conferring an advantageous proliferation to skin flora bacteria in this milieu. The molecular mechanisms of action of Aap merit further investigation.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".