Development of dielectric spectroscopy platforms for in vitro monitoring and assessment of human pancreatic islet functionality and cellular aggregate formation
Bibliographic record
Abstract
Diabetes mellitus is a growing disease that is characterized by the body's inability to control blood glucose levels.This disease is associated with pathologies of the pancreatic islets of Langerhans, which secrete insulin.Several treatments are available, including islet transplantations and islet targeting drugs.Islet transplantations are plagued by donor islet shortages and the lack of reliable methods to store islets in vitro.Therefore, novel methods to regenerate islet are being explored to provide an unlimited source of tissue for transplantation.In vitro monitoring of isolated intact human islets provides many opportunities to further develop diabetes treatments.A critical parameter is islet functionality, which describes if the islet can maintain homeostasis by appropriately secreting various hormones.Given that insulin secretion is an electrically excitable process, alternative tools such as dielectric spectroscopy could monitor islet functionality by assessing their dielectric response.Moreover, other features that affect islet functionality, such as intercellular gap junction coupling, are gaining recognition.Gap junctions connect the cytoplasm of adjacent islet cells, allowing the exchange of ions.Dielectric spectroscopy is sensitive to ionic flow through gap junctions, and therefore can assess gap junction coupling, giving a multifactorial assessment of islet functionality.Another feature that can be monitored in vitro with dielectric spectroscopy is cellular aggregate formation.This process is an important step in regenerating islets, since cell aggregates reflect islet morphology and cell-cell interactions.An in vitro platform to monitor cell aggregation could screen the ability of various protocols to induce the formation of isletlike tissue.This work presents novel in vitro platforms, along with computer simulations, for dielectric spectroscopy assessment of islet functionality and cellular aggregate formation.First, a microfluidic platform was fabricated which continuously obtains dielectric spectra from immobilized human islets undergoing glucose stimulated insulin release.The enhanced dielectric response enables detection of gap junction coupling, which is reflected by a double dispersion in the dielectric spectra.Moreover, the islet dielectric response is sensitive to glucose stimulation, and may reflect cell activities associated with insulin secretion.laboratory 4 at the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland.In particular, I would like to thank Dr. Ludovica Colella, at the time a doctoral student, who provided guidance while I was at EPFL.I benefited from the laboratory's long experience with dielectric spectroscopy measurement of cells in microfluidic devices.The knowledge I gained at EPFL was very helpful for designing the dielectric spectroscopy platforms presented in this work.I would also like to acknowledge the members of the BiomatX laboratory, who maintained a friendly and supportive environment in which to conduct this work.I would like to thank Dr. Jamal Daoud, who taught and trained me considerably regarding dielectric spectroscopy, islet tissue culture and computer modelling of cell dielectric response.In addition, I would like to thank Rafael Castiello, whose investigations deepened my knowledge regarding dielectric spectroscopy and with whom I published a review paper detailing microfluidic biosensors for islets.I am also grateful for editing provided by Feriel Melaine, Laila Benameur and Paresa Modarres.I would like to thank Craig Hasilo, Marco Gasparrini and Dr
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".