Detection of Microbial Contamination during Human Islet Isolation
Bibliographic record
Abstract
Current good manufacturing practice (cGMP) islet processing facilities provide an ultraclean environment for the safe production of clinical grade islets for transplantation into immunosuppressed diabetic recipients. The objective of this study was to monitor the rate of microbial contamination in islet products after implementation of good manufacturing practice conditions. Fluid samples for microbial contamination were collected at the following steps: from the pancreas transport solution upon arrival of the organ (n=157), after surface decontamination of the pancreas with antiseptic agents (n=89), from islet supernatant at the end of the isolation (n=104), and from islet supernatant as a final transplantable product after culture (n=53). Bacterial, fungal, and mycoplasma cultures were conducted for 2, 2, and 3 weeks, respectively. Microbial contamination was detected in 31% of transport solution. The contamination was not associated with the presence of the duodenum during the preservation, cold ischemia time, or procurement team (local vs. distant). Surface decontamination of the pancreas resulted in clearance of 92% of the microbial contamination. Six preparations at the end of the isolation revealed microbial growth. All were de novo contamination during the processing. Fifty-three preparations that met our release criteria in terms of product sterility were transplanted into type 1 diabetic patients. In two instances, positive culture of the islet preparation was reported after transplantation had occurred. No patient showed any clinical findings suggestive of infection or any radiological abnormalities suggestive of abscess; a single dose of antibiotic coverage was given routinely to recipients prior to islet infusion. Although transport solution carries a high risk of microbial contamination, most contaminants become undetectable during islet processing. Microbial contamination in final products is rare, but de novo contamination still occurs during processing even under cGMP conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".