Microfluidic platform for assessing pancreatic islet functionality through dielectric spectroscopy
Bibliographic record
Abstract
Human pancreatic islets are seldom assessed for dynamic responses to external stimuli. Thus, the elucidation of human islet functionality would provide insights into the progression of diabetes mellitus, evaluation of preparations for clinical transplantation, as well as for the development of novel therapeutics. The objective of this study was to develop a microfluidic platform for in vitro islet culture, allowing the multi-parametric investigation of islet response to chemical and biochemical stimuli. This was accomplished through the fabrication and implementation of a microfluidic platform that allowed the perifusion of islet culture while integrating real-time monitoring using impedance spectroscopy, through microfabricated, interdigitated electrodes located along the microchamber arrays. Real-time impedance measurements provide important dielectric parameters, such as cell membrane capacitance and cytoplasmic conductivity, representing proliferation, differentiation, viability, and functionality. The perifusion of varying glucose concentrations and monitoring of the resulting impedance of pancreatic islets were performed as proof-of-concept validation of the lab-on-chip platform. This novel technique to elucidate the underlying mechanisms that dictate islet functionality is presented, providing new information regarding islet function that could improve the evaluation of islet preparations for transplantation. In addition, it will lead to a better understanding of fundamental diabetes-related islet dysfunction and the development of therapeutics through evaluation of potential drug effects.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".