Robust and Precise Wounding and Analysis of Engineered Contractile Tissues
Bibliographic record
Abstract
Fibrous tissue gap closure is a critically important process initiated in response to traumatic injury. Recent three-dimensional (3D) bioengineered models capture cellular details of this process, including wound retraction and closure, but have high failure rates, are labor-intensive, and require considerable expertise to develop and implement with tools that are typically not available in standard wet laboratories. Here, we develop a simple and effective 3D-printed wounding platform to reliably create and puncture arrays of prestressed tissues and monitor subsequent wound dynamics. We demonstrate the ability to create a range of wound sizes in a contractile collagen/fibroblast tissue, within 125 μm of the desired target location, with high degrees of circularity. Wounds exhibit an initial expansion due to tissue prestress, and sufficiently small wounds close completely within 24 h, while larger wounds initially closed much more rapidly, but did not complete the closure process. Simulating the dynamics of tissue retraction with a viscoplastic finite element model indicates a temporary elevation of circumferential stresses around the wound edge. Finally, to determine whether active wounding and retraction of the tissue significantly affect closure rates, we compared active puncture of prestressed tissue with passive removal of a structure that prevents closure, and found that active wounding and retraction substantially accelerated wound closure when compared with the passive case. Taken together, our findings support the role of active tissue mechanics in wound closure arising from an initial retraction of the tissue. More broadly, these findings demonstrate the utility of the platform and methodology developed here in further understanding the mechanobiological basis for wound closure. Impact Statement In vitro models to study wound formation and closure in prestressed tissue are typically challenging to implement. This work provides an easily accessible approach to produce and analyze wounds in arrays of contractile tissues that recapitulate critical features of wound retraction and closure in animal models. The specific modeling and experiments results presented here suggest that mechanobiology effects arising from wound retraction in viscoplastic extracellular matrices could play an important role in driving wound closure.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".