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Record W2996404693 · doi:10.1089/ten.tec.2019.0123

Robust and Precise Wounding and Analysis of Engineered Contractile Tissues

2019· article· en· W2996404693 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTissue Engineering Part C Methods · 2019
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsWound closureProcess (computing)Biomedical engineeringClosure (psychology)Wound healingTissue engineeringFibroblastComputer scienceSurgeryMaterials scienceAnatomyCell biologyChemistryMedicineBiologyIn vitro

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.044
GPT teacher head0.365
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it