Impaired Proinsulin Processing is a Characteristic of Transplanted Islets
Bibliographic record
Abstract
We sought to determine whether recipients of islet transplants have defective proinsulin processing. Individuals who had islet allo- or autotransplantation were compared to healthy nondiabetic subjects. Insulin (I), total proinsulin (TP), intact proinsulin and C-peptide (CP) were measured in samples of fasting serum by immunoassay, and the ratios of TP/TP+I and TP/CP were calculated. Islet allotransplant recipients had elevated TP levels relative to nondiabetic controls (16.8 [5.5-28.8] vs. 8.4 [4.0-21.8] pmol/L; p < 0.05) and autologous transplant recipients (7.3 [0.3-82.3] pmol/L; p < 0.05). Islet autotransplant recipients had significantly higher TP/TP+I ratios relative to nondiabetic controls (35.9 +/- 6.4 vs. 13.9 +/- 1.4%; p < 0.001). Islet allotransplant recipients, some of whom were on insulin, tended to have higher TP/TP+I ratios. The TP/CP ratio was significantly higher in both islet autotransplant (8.9 [0.6-105.2]; p < 0.05) and allotransplant recipients (2.4 [0.8-8.8]; p < 0.001) relative to nondiabetic controls (1.4 [0.5-2.6]%). Consistent with these findings, TP/TP+I and TP/CP values in islet autotransplant recipients increased significantly by 1-year posttransplant compared to preoperative levels (TP/CP: 3.8 +/- 0.6 vs. 23.3 +/- 7.9%; p < 0.05). Both allo- and autotransplant subjects who received <10,000 IE/kg had higher TP/CP ratios than those who received >10,000 IE/kg. Islet transplant recipients exhibit defects in the processing of proinsulin similar to that observed in subjects with type 2 diabetes manifest as higher levels of total proinsulin and increased TP/TP+I and TP/CP ratios.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 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".