Combinatorial Screen of Dynamic Mechanical Stimuli for Predictive Control of MSC Mechano-Responsiveness
Bibliographic record
Abstract
Abstract Mechanobiological-based control of mesenchymal stromal cells (MSCs) to aid in the engineering and regeneration of load-bearing tissues requires systematic investigations of specific dynamic mechanical stimulation protocols. Using deformable membrane microdevice arrays paired with combinatorial experimental design and modeling, we systematically probed the individual and integrative effects of mechanical stimulation parameters (strain magnitude (STRAIN), rate at which strain is changed (RATE) and duty period (DUTY)) on myofibrogenesis and matrix production of MSCs in 3D hydrogels. These functions were found to be dominantly influenced by a novel and higher-order interactive effect between STRAIN and DUTY. Empirical models based on our combinatorial cue-response data predicted an optimal loading regime in which STRAIN and DUTY were increased synchronously over time, which was validated to most effectively promote MSC matrix production. These findings inform the design of loading regimes for MSC-based engineered tissues and validate a broadly applicable approach to probe multifactorial regulating effects of microenvironmental and mechanobiological cues.
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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 | 0.001 | 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".