Mesenchymal stem cell mechanobiology and emerging experimental platforms
Bibliographic record
Abstract
Experimental control over progenitor cell lineage specification can be achieved by modulating properties of the cell's microenvironment. These include physical properties of the cell adhesion substrate, such as rigidity, topography and deformation owing to dynamic mechanical forces. Multipotent mesenchymal stem cells (MSCs) generate contractile forces to sense and remodel their extracellular microenvironments and thereby obtain information that directs broad aspects of MSC function, including lineage specification. Various physical factors are important regulators of MSC function, but improved understanding of MSC mechanobiology requires novel experimental platforms. Engineers are bridging this gap by developing tools to control mechanical factors with improved precision and throughput, thereby enabling biological investigation of mechanics-driven MSC function. In this review, we introduce MSC mechanobiology and review emerging cell culture platforms that enable new insights into mechanobiological control of MSCs. Our main goals are to provide engineers and microtechnology developers with an up-to-date description of MSC mechanobiology that is relevant to the design of experimental platforms and to introduce biologists to these emerging platforms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".