Methods of Human Islet Culture for Transplantation
Bibliographic record
Abstract
The ability to maintain isolated human islet preparations in tissue culture has recently been adopted by most islet transplant centers, and improves the safety as well as the practicality of islet transplantation. Maintaining islet viability and recovery, however, remains challenging in a clinical setting, due to stringent conditions required for culture. Islet culture is further complicated by the fact that islets do not form a monolayer. This review aims to clarify media, supplementation, and conditions that have been shown to be relevant to human islets, as well as to offer avenues of future research. Factors examined that may influence islet survival include base medium, glucose concentration, vitamin, inorganic ion, lipid, hormone, growth factor, amino acid, and binding protein composition and concentration, as well as culture temperature and seeding density. In addition, this article reviews novel techniques, such as coculture and matrices, that have been employed in an attempt to improve islet survival and functional viability.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.019 |
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".