Noninvasive pH monitoring for bacterial detection in platelet concentrates
Bibliographic record
Abstract
BACKGROUND: Bacterial contamination of platelet concentrates (PCs) remains the prevalent posttransfusion infectious risk. The pH SAFE system, a noninvasive method used to measure pH of PC for quality control, was evaluated herein as a rapid method to detect bacterial contamination in PCs. STUDY DESIGN AND METHODS: Pairs of ABO-D-matched apheresis and buffy coat PCs were pooled and split into two pH SAFE platelet bags. One of the bags served as the control unit, while the other was inoculated with one of nine clinically relevant bacteria (target concentration approx. 1 colony-forming units [CFUs]/mL). The pH of both PCs was measured over 7 days of storage at approximately 4-hour intervals during daytime. One-milliliter samples were taken at the testing points to determine bacterial concentration. RESULTS: PCs with pH values of less than 6.6 or with a pH change over time (ΔpH/Δtime) greater or equal than 0.046 pH units/hr are suspected of being contaminated. pH decreased significantly during storage in all bacterially inoculated PC at concentrations of more than 10(7) CFUs/mL (p < 0.0001). A significant decrease in pH (p < 0.0001) was noticed as early as 28 hours in units with Bacillus cereus and as late as 125 hours in units containing Staphylococcus epidermidis. Interestingly, PCs containing Gram-negative species showed a decline in pH followed by a rebound. CONCLUSIONS: The pH SAFE system allows for repeated, noninvasive pH screening during PC storage. A significant decrease in pH could serve as an indicator of clinically significant levels of bacterial contamination. Since differences in pH decline were observed among bacterial species, continuous pH monitoring in PCs is recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".