Histopaque provides optimal mouse islet purification kinetics: Comparison study with Ficoll, iodixanol and dextran
Bibliographic record
Abstract
Islet transplantation has become a very promising treatment for type 1 diabetes. To facilitate further clinical improvements in this exciting field, rodent islets are used to evaluate new strategies and modifications. One method to purify islets is on a density gradient, although the optimal gradient component can be debated. N=6 separate mouse islet isolations were used and the resulting islets were separated and purified on either a Ficoll, Histopaque, Dextran or Iodixanol gradient. Islets were assessed for recovery, viability, purity and in vitro functionality. Aliquots were transplanted into diabetic mice to assess in vivo functionality and survival. There was no difference in the number of islets recovered across groups nor in the size of recovered islets. Use of a Ficoll or Histopaque gradient led to the most pure and viable islets in comparison to Dextran and Iodixanol. Functionally, islets isolated on a Ficoll gradient had the highest glucose-stimulated insulin release in vitro while performing equally to Histopaque and Dextran gradients in vivo. Using a Ficoll gradient, however, comes at a higher monetary cost. We recommend using a Histopaque gradient, which led to the isolation of viable and functional islets with a reduced cost as compared to a Ficoll gradient.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".