Beta Cell Death by Cell-free DNA and Outcome After Clinical Islet Transplantation
Bibliographic record
Abstract
In Brief Background Optimizing engraftment and early survival after clinical islet transplantation is critical to long-term function, but there are no reliable, quantifiable measures to assess beta cell death. Circulating cell-free DNA (cfDNA) derived from beta cells has been identified as a novel biomarker to detect cell loss and was recently validated in new-onset type 1 diabetes and in islet transplant patients. Methods Herein we report beta cell cfDNA measurements after allotransplantation in 37 subjects and the correlation with clinical outcomes. Results A distinctive peak of cfDNA was observed 1 hour after transplantation in 31 (83.8%) of 37 subjects. The presence and magnitude of this signal did not correlate with transplant outcome. The 1-hour signal represents dead beta cells carried over into the recipient after islet isolation and culture, combined with acute cell death post infusion. Beta cell cfDNA was also detected 24 hours posttransplant (8/37 subjects, 21.6%). This signal was associated with higher 1-month insulin requirements (P = 0.04), lower 1-month stimulated C-peptide levels (P = 0.01), and overall worse 3-month engraftment, by insulin independence (receiver operating characteristic-area under the curve = 0.70, P = 0.03) and beta 2 score (receiver operating characteristic-area under the curve = 0.77, P = 0.006). Conclusions cfDNA-based estimation of beta cell death 24 hours after islet allotransplantation correlates with clinical outcome and could predict early engraftment. Death and injury can lead to release of cell-free DNA in transplantation. The level of cell-free DNA released at 1 hour after islet transplantation reflects beta cell death from the processing, culture, and implantation of islets and does not correlate with outcomes, but the level at 24 hours after transplantation does.
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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 | 0.002 |
| 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 | 0.000 |
| 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".