Unraveling molecular mechanism underlying biomaterial and stem cells interaction during cell fate commitment using high throughput data analysis
Bibliographic record
Abstract
Stem cell (SC) differentiation towards somatic cells has proven to be an effective technique in the understanding and progression of regenerative medicine. Despite improvements, concerns regarding the efficiency of differentiation and the differences between SC products and their in vivo counterparts must be addressed. Biomaterials that mimic endogenous growth conditions represent one recent method used to improve the quality and efficiency of SC differentiation. Here, we aim to use bioinformatics approaches to accomplish two aims: 1) determine the effect of different biomaterials on SC growth and differentiation, and 2) understand the effect of cell of origin on the differentiation potential of multipotent SCs. First, we demonstrate that the dimensionality (2D versus 3D) and the degradability of biomaterials affects the way that the cells are able to grow and differentiate at the transcriptional level. Additionally, the particular cell of origin is an important factor in determining the response of SCs to same biomaterial transcriptionally. Our data demonstrates the ability of bioinformatics to understand novel molecular mechanisms and context by which SCs are most efficiently able to differentiate. These results and strategies may suggest proper combinations of biomaterials and SCs to achieve high differentiation efficiency and functionality of desired cell types.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".