Effect of mechanical strain on the pluripotency of murine embryonic stem cells seeded in a collagen‐I scaffold
Bibliographic record
Abstract
The use of embryonic stem cells (ESC) in regenerative medicine is restricted due to the possibility of tumorigenicity after inefficient or incomplete differentiation. Studies from our group, and others, suggest that mechanical stimuli may have a suppressive effect on the pluripotency/tumorigenesis of murine ESC (mESC). Furthermore, we have demonstrated that mESC seeded in a type I collagen scaffold, and transplanted into a murine bone fracture model, demonstrated repair without tumor formation. However, it remains unknown if mechanical factors were involved in blocking tumorigenicity of the mESC. Therefore, the aims of the current study were: (i) to characterize the mechanical environment within the transplanted construct (mESC-Col I) in an in vivo murine fracture model using computational analyses; and (ii) to reproduce this mechanical environment in vitro to elucidate the role of these mechanical factors on mESC pluripotent gene expression. It was predicted that the mESC-Col I construct was subjected to an average octahedral shear strain of ∼3.8% and a compressive strain of ∼3.1% within the fracture in vivo when the murine tibia was subjected to an axial compression load of 4 N (1 Hz). When a similar strain environment was replicated experimentally in vitro, the expression patterns of marker genes for pluripotency (Oct 4, Sox 2, Nanog, Rex 1, and oncogene ERas) were significantly down-regulated. This suggests that the local micro-mechanical environment within the fracture site in vivo may be involved in regulating stem cell fate after transplantation, and that these physical factors should be considered when developing regenerative medicine strategies. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:799-807, 2018.
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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 | 0.000 | 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".