Enhancing the efficiency of human pancreatic islet dissociation
Bibliographic record
Abstract
Over half a million children worldwide are affected by type 1 diabetes, an autoimmune disease characterized by the destruction of insulin-producing pancreatic beta cells. Islet transplantation is a treatment that is currently limited by the lack of vascular network to support large islets post-transplantation. A promising proposal is to disperse native islets into single-cell suspensions and re-aggregate them into smaller, uniform “pseudo-islets”. Substantial cell loss during islet dispersion, however, remains an important obstacle that limits the yield of pseudo-islet aggregates, especially considering the scarcity of donor islets. To optimize the dissociation protocol, we experimented with different cell dissociation reagents, concentrations, and times in order to establish standards for future pseudo-islet formation procedure. Isolated human islets were dissociated using Trypsin, TrypLE, Accutase, Accumax, and Dispase. These dissociation reagents were identified through literature and the concentrations as well as dissociation times used were in ranges previously outlined. Cell counts of viable cells were recorded using Trypan Blue and PicoGreen DNA assay to quantify cell loss during islet dispersion, filtration and post-culture. Assessment of the viability of the re-aggregated pseudo-islets post-culture was performed using the Alamar Blue assay. Preliminary results showed the potential for 5.8-fold increase in cell recovery which provides evidence of the significant need to optimize the dissociation protocol. TrypLE showed the highest recovery of cells both post-dissociation and filtration. Results from the project are promising and further investigations will allow the results to become applicable to clinical trials. Improving the recovery and quality of dissociated islet cells will directly help increase the number of treatable patients from the limited supply of donor islets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".