The Combination of bFGF and Hydrocortisone is a Better Alternative Compared to 5-Azacytidine for Cardiomyogenic Differentiation of Bone Marrow and Adipose Stem Cells
Bibliographic record
Abstract
Stem cells can be differentiated into cardiomyocytes by induction with 5-azacytidine (5-aza) but its carcinogenicity is of concern for future translational application. Alternatively, growth factors and hormones such as basic fibroblast growth factor (bFGF) and hydrocortisone have been reported to act as a therapeutic inducer for cardiomyocytes differentiation. In this study, we aim to investigate the ability of bFGF and hydrocortisone in combination to stimulate the differentiation of mesenchymal stem cells (MSC) into cardiomyocytes lineage. Sheep adipose tissue stem cell (ATSC) and bone marrow stem cell (BMSC) were isolated, cultured and induced with the three groups of induction factors; 5-aza alone, the combination of hydrocortisone and bFGF and all three factors in combination for cardiomyogenic differentiation. Morphological, protein and functional ability of both ATSC and BMSC were observed and analysed to confirm cardiomyocyte differentiation. Viability of BMSC and ATSC in each treated group was significantly higher (P < 0.05) on both cells after treated with 10 nM of bFGF and 50 μM of hydrocortisone. Cardiomyocyte proteins; α-Sarcomeric actin (αSA) and Phospolamban (Plb) was detected in both ATSC and BMSC exposed to induction factors but not in the control negative group. Both ATSC and BMSC without induction factors showed only minute cell number possesses αSA and Plb. Calcium ion (Ca2+) spark was observed in primary heart cells. Similarly, Ca2+ spark was also detected in induced ATSC and BMSC, proving some functionality of induced cells. In conclusion, bFGF and hydrocortisone are safer induction factor compared to the currently used 5-aza as both showed higher viability after induction, therefore more cells are available for future use in cardiac tissue engineering.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".