Long-term storage, cryopreservation, and culture of isolated human islets: a systematic review
Bibliographic record
Abstract
Introduction: Islet transplantation offers a potential curative treatment for patients with type 1 diabetes (T1D). To make this therapy widely available, a stable supply chain of human islets is essential. Developing techniques like cryopreservation and culture for long-term islet storage, or islet banking, with minimal functional loss would strengthen this supply chain. This study provides a systematic review of the current methods for long-term human islet storage. Methods: A search strategy and query were developed according to the PICO framework. We included studies published on PubMed, Embase, and Web of Science from inception until August 2024. Results: 6,945 studies were screened with 47 meeting criteria for full text extraction. The primary outcomes recorded were measures of islet viability and glucose stimulated insulin secretion. Optimization of culture parameters such as temperature, medium selection, and scaffolds can extend islet viability and function. Discussion: Recent studies on human islet cryopreservation report promising results for long-term storage; however, the field remains underexplored. Several cytoprotective supplements with potential utility across both culture and cryopreservation conditions have also been reviewed. Although long-term islet storage has been a critical focus since the advent of the Edmonton protocol, the literature lacks the rigor needed to drive clinical translation. Notably, we observe substantial variability in experimental design and reported outcomes, which complicates meaningful comparison between interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".