Microbial Contamination of Clinical Islet Transplant Preparations Is Associated with Very Low Risk of Infection
Bibliographic record
Abstract
BACKGROUND: Several published studies have analyzed microbial contamination rates of islet products, ranging from 0% to 16%. However, few studies make reference to potential clinical consequences for transplant recipients and possible impact on islet survival. MATERIALS AND METHODS: The current study defines rates of microbiological contamination of islet products under current good manufacturing practice conditions in 164 patients receiving 343 transplants at a single institution. RESULTS: Nineteen (5.5%) islet preparations showed positive microbial growth with a majority (79.4%) due to Gram-positive organisms. The most frequently identified microorganism was coagulase-negative Staphylococcus (nine of 19 [47.3%]), followed by polymicrobial organisms (eight of 19 [42.1%]). No patient developed signs of clinical infection, and there were no hepatic abscesses evident on imaging by ultrasound or magnetic resonance imaging (none of 19 [0%]), despite the use of potent T-depletional induction. Finally, we could not demonstrate any negative impact of microbiological contamination on long-term islet graft survival. CONCLUSIONS: Microbiological contamination of the final islet preparation appears to have little or no effect on patients or on islet survival when appropriate antibiotics are given. However, preparation sterility should be guaranteed at all cost in order maximize patient safety and avoid potential complications in immunosuppressed patients.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".