Monitoring of clinical islet transplantation
Bibliographic record
Abstract
Islet transplantation has recently emerged as one of the most promising therapeutic approaches to improving glycometabolic control in diabetic patients.The Edmonton trials demonstrated a marked improvement in the short-term rate of success of islet transplantation,with an 80% rate of insulin-independence being at 1 year after transplantation,as reported by several institutions worldwide.Unfortunately,this rate consistently decreases to 10% by 5 years post-transplantation.1 Other data reported that less than half of the patients achieved insulin independence at 1 year and <15% remained insulin independent at 2 years.2It is not clear whether this decline in insulin independence is the result of islet loss.Also unclear are the mechanisms that underlie the deterioration of graft function,though possibilities include auto or alloimmune destruction,immunosuppressant toxicity,or a progressive disruption of insulin secretion.Several questions with respect to islet transplantation remain regarding why and when insulin independence is attained,the effective volume of marginal islet mass,and the mechanism of loss of islet mass following transplantation.As such,there is an urgent need for efficient monitoring tools to label and track islets,detect graft damage,and monitor the long-term function and survival of transplanted islets.Furthermore,because complications of islet transplantation may be either immediate or delayed as a consequence of the migration of engrafted islets to the liver or as a side effect of immunosuppressive therapy,routine post-transplantation imaging is important to detect complications that are not apparent with clinical or laboratory examinations.3 Successful monitoring of the stability and function of the graft and graft-related complications will assist in the evaluation of various immunosuppressive regimens and islet delivery strategies and ultimately assist in optimizing the islet transplantation procedure.In this article,we summarize the recent advances in the monitoring of islet post-transplantation,as shown in Table 1.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".