Development of dielectric spectroscopy platforms for in vitro monitoring and assessment of human pancreatic islet functionality and cellular aggregate formation
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».