Microfluidic perfusion systems for secretion fingerprint analysis of pancreatic islets: applications, challenges and opportunities
Bibliographic record
Abstract
A secretome signature is a heterogeneous profile of secretions present in a single cell type. From the secretome signature a smaller panel of proteins, namely a secretion fingerprint, can be chosen to feasibly monitor specific cellular activity. Based on a thorough appraisal of the literature, this review explores the possibility of defining and using a secretion fingerprint to gauge the functionality of pancreatic islets of Langerhans. It covers the state of the art regarding microfluidic perfusion systems used in pancreatic islet research. Candidate analytical tools to be integrated within microfluidic perfusion systems for dynamic secretory fingerprint monitoring were identified. These analytical tools include patch clamp, amperometry/voltametry, impedance spectroscopy, field effect transistors and surface plasmon resonance. Coupled with these tools, microfluidic devices can ultimately find applications in determining islet quality for transplantation, islet regeneration and drug screening of therapeutic agents for the treatment of diabetes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".