Quantitative PCR for detection and discrimination of the bloodborne pathogen <i>Staphylococcus epidermidis</i> in platelet preparations using <i>divIVA</i> and <i>icaA</i> as target genes
Bibliographic record
Abstract
Bacterial contamination of blood components is the major microbiological cause of transfusion-associated morbidity, with Staphylococcus epidermidis being the most frequently isolated organism from contaminated platelet preparations (PPs). We have recently shown that S. epidermidis forms biofilms during platelet storage, which might account for reported missed detection during routine screening. In this study, we developed a highly sensitive and specific multiplex quantitative PCR (QPCR) assay to detect S. epidermidis in PPs at levels of 10(2)-10(3) cfu/mL. A specific primer pair and hydrolysis probe were designed to amplify an internal region of the cell division divIVA gene that is unique to S. epidermidis. In addition, an internal sequence of the virulence gene icaA, which is involved in the synthesis of the S. epidermidis biofilm matrix, was selected to allow for differentiation of potentially biofilm-forming S. epidermidis isolates. A conserved region of the 8 alleles of the HLA-DQalpha1 locus present in residual white blood cells in PPs was selected as an internal control for the assay. The specificity of this assay was confirmed, as other staphylococcal species that were tested with the optimized parameters were not detected. This QPCR assay could be adaptable for the detection of other bloodborne bacterial pathogens.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".