Microfluidics for Live-Cell Imaging Pancreatic Islets of Langerhans for Human Transplant
Bibliographic record
Abstract
Since the introduction of the Edmonton Protocol in 2000, islet transplantation has been emerging as promising therapy for Type I diabetes mellitus (T1DM) and currently is the only therapy that can achieve glycemic control without the need for exogenous insulin. Transplanting islet cells has several advantages over transplanting a whole pancreas in that it involves only a minor surgical procedure with low morbidity and mortality, and at a significantly lower cost. However, an obstacle to realizing this goal is a lack of an islet potency index as required by the U.S. Food and Drug Administration (FDA) biologics licensing, as well as a more complete understanding of the physiological mechanisms governing islet and β-cell physiology. Recently, the University of Illinois at Chicago (UIC) has developed a microfluidic platform that can mimic in vivo islet microenvironments through precise and dynamic control of perifusing culture media and oxygen culture levels; all while measuring functionally relevant factors including intracellular calcium levels, mitochondrial potentials, and insulin secretion. By developing an understanding of the physiology and pathophysiology of islets we can more effectively develop strategies that reduce metabolic stress and promote optimization in order to achieve improved success of islet transplantation and open new clinical avenues. The presentation begins by introducing key issues in the field of pancreatic islet transplantation as a clinical therapy for T1DM. This is followed by brief review various technologies that have been developed to study islet cells. The presentation then describes the design, application, and evolution of UIC’s microfluidic-based multimodal islet perifusion and live-cell imaging system for the study of pancreatic islet and β-cell physiology. The article then concludes presenting initial findings from studies seeking to develop an islet potency test.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".