DIFFERENTIATION OF HUMAN UMBILICAL CORD MESENCHYMAL STEM CELLS INTO INSULIN PRODUCING BETA CELLS FOR DIABETIC THERAPY.
Bibliographic record
Abstract
Aim:The aim of the present study was to evaluate the potential of using human umbilical cord tissue as a source of mesenchymal stem cells to differentiate into functional pancreatic β-cells.Method: Umbilical Cord Mesenchymal Stem Cells (MSCs), which have a low risk of immune rejection, were isolated, cultured in-vitro and characterized by Flowcytometry and immunocytochemistry.Further they were differentiated successfully in-vitro into functional pancreatic β-cells using H-DMEM (25 mM glucose/L) supplemented with β-Mercaptoethanol (0.1 mM/L), b-Fibroblast growth factor (10 µg/L), Taurine (10 mM/L) and Nicotinamide (10 mM/L).Results: Umbilical cord sample of 8 cm length yielded 1.5*10 5 ± 0.5*10 5 cells after enzymatic digestion.These were passaged to confluence and were found by Flowcytometry to be CD-73 + , CD-90 + , CD-105 + , Vimentin + , CD-34 -, CD-45 -and HLA-DR -indicating that 95% of cells were MSCs.These were differentiated for 25 days and 20% stained positive for Dithizone, which is a specific marker for pancreatic beta cells.Insulin secreted in the medium was evaluated with and without glucose stimulation.Conclusion: It is evident from this work that Umbilical cord tissue which is easily and ethically available is suitable to isolate MSCs, which have a potential to differentiate into insulin secreting β-cell invitro.These differentiated cells can be placed in-vivo either by Edmonton protocol into the pancreatic duct or embedded in omentum for managing both Type-1 and Type-2 Diabetes Mellitus.This is a promising treatment option, especially since recent literature proposes that pancreatic β-cell apoptosis is the underlying cause of both these pathologies.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".