Human Pluripotent Stem Cell Mechanobiology: Manipulating the Biophysical Microenvironment for Regenerative Medicine and Tissue Engineering Applications
Bibliographic record
Abstract
A stem cell in its microenvironment is subjected to a myriad of soluble chemical cues and mechanical forces that act in concert to orchestrate cell fate. Intuitively, many of these soluble and biophysical factors have been the focus of intense study to successfully influence and direct cell differentiation in vitro. Human pluripotent stem cells (hPSCs) have been of considerable interest in these studies due to their great promise for regenerative medicine. Culturing and directing differentiation of hPSCs, however, is currently extremely labor-intensive and lacks the efficiency required to generate large populations of clinical-grade cells. Improved efficiency may come from efforts to understand how the cell biophysical signals can complement biochemical signals to regulate cell pluripotency and direct differentiation. In this concise review, we explore hPSC mechanobiology and how the hPSC biophysical microenvironment can be manipulated to maintain and differentiate hPSCs into functional cell types for regenerative medicine and tissue engineering applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".