Differentiation of Wharton’s jelly-derived mesenchymal stromal cells into hepatocyte-like cells using a refined method
Bibliographic record
Abstract
BACKGROUND: The production of functional hepatocyte cells in enough quantities is of paramount importance for the replacement of lost hepatocytes. In this investigation, a series of 7-mimic microRNAs was harnessed to induce the differentiation of Wharton's jelly-derived mesenchymal stromal cells (WJ-MSCs) into hepatocyte-like cells (HLC) through the application of two distinct techniques: transfection agents and electroporation. The results were then compared with those of HLCs differentiated through the consumption of chemical compounds. RESULTS: Different time points (48 h, 72 h, and 96 h), unlike concentrations of mimic miRNAs (100 pM, and 200 pM), and dissimilar combinations of mimic-miRNAs (4-mimic and 7-mimic miRNAs) were selected to assess the stage of differentiated cells through electroporation and lipofection methods. For chemical differentiation, a two-step chemical hepatic differentiation protocol was used (for 21 days). The expression level of eleven key genes that were selected to estimate the stage of produced HLCs by each method were tested at different time points, concentrations and combination of mimic-miRNA. Results demonstrated that the 7-miR-mimics/72 h culture method by electroporation, then the 7-miR-mimics/72 h culture method by lipofection, and finally the chemical differentiation (72 h culture) showed the best result for differentiation. Furthermore, the period in which HLCs are maintained under culture conditions is important, as prolonged culture (more than 72 h) leads to cell loss. CONCLUSION: In conclusion, the results demonstrated that the 7-miR cocktail delivered by electroporation after 72 h effectively promoted the acquisition of hepatocyte-like characteristics which was evidenced by a significant decrease in the Oct4 stemness factor and an increase in the expression of ALB, TAT, AAT, CYP, G6P and HNF4A.
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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) | 0.001 | 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".