Detection of Insulin mRNA in the Peripheral Blood after Human Islet Transplantion Predicts Deterioration of Metabolic Control
Bibliographic record
Abstract
Recent updates of the Edmonton trial have shown that insulin independence is progressively lost in approximately 90% of islet transplant recipients over the first 5 years. Early prediction of islet graft injury could prompt the implementation of strategies attempting to salvage the transplanted islets. We hypothesize that islet damage is associated with the release and detection of insulin mRNA in the circulating blood. Whole blood samples were prospectively taken from 19 patients with type 1 diabetes receiving 31 islet transplants, immediately prior to transplantation and at regular time-points thereafter. After RNA extraction, levels of insulin mRNA were determined by quantitative reverse tran-scriptase-polymerase chain reaction. All patients exhibited a primary peak of insulin mRNA immediately after transplantation, without correlation of duration and amplitude with graft size or outcome. Twenty-five subsequent peaks were observed during the follow-up of 17 transplantations. Fourteen secondary peaks (56%) were closely followed by events related to islet graft function. Duration and amplitude of peaks were higher when they heralded occurrence of an adverse event. Peaks of insulin mRNA can be detected and are often associated with alterations of islet graft function. These data suggest that insulin mRNA detection in the peripheral blood is a promising method for the prediction of islet graft damage.
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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 | 0.002 |
| 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 | 0.001 | 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".