Carbon Monoxide in Pancreatic Islet Transplantation: A New Therapeutic Alternative to Patients With Severe Type 1 Diabetes Mellitus
Bibliographic record
Abstract
Pancreatic islet transplantation is a minimally invasive procedure to replace β-cells in a subset of patients with autoimmune type 1 diabetic mellitus, who are extremely sensitive to insulin and lack counter-regulatory measures, and thereby increasing their risk of neuroglycopenia and hypoglycemia unawareness. Thus, pancreatic islet transplantation restores normoglycemia and insulin independence, and prevents long-term surgical complications associated with whole-organ pancreas transplantation. Nonetheless, relative inefficiency of islet isolation and storage process as well as progressive loss of islet function after transplantation due to unvoidable islet inflammation and apoptosis, hinder a successful islet transplantation. Carbon monoxide (CO), a gas which was once feared for its toxicity and death at high concentrations, has recently emerged as a medical gas that seems to overcome the challenges in islet transplantation. This minireview discusses recent findings about CO in preclinical pancreatic islet transplantation and the underlying molecular mechanisms that ensure islet protection during isolation, islet culture, transplantation and post-transplant periods in type 1 diabetic transplant recipients. In addition, the review also discusses clinical translation of these promising experimental findings that serve to lay the foundation for CO in islet transplantation to replace the role of insulin therapy, and thus acting as a cure for type 1 diabetes mellitus and preventing long-term diabetic complications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".