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Record W4402729724 · doi:10.1089/ten.tec.2024.0228

Design of an Innovative Method for Measuring the Contractile Behavior of Engineered Tissues

2024· article· en· W4402729724 on OpenAlexaffabout
Étienne Savard, Brice Magne, Carolyne Simard‐Bisson, Christian Martel, Danielle Larouche, Robert Gauvin, Véronique Moulin, Lucie Germain

Bibliographic record

VenueTissue Engineering Part C Methods · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular Mechanics and Interactions
Canadian institutionsUniversité LavalCentre hospitalier de l'Université Laval
Fundersnot available
KeywordsBiomedical engineeringTissue engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Hypertrophic scarring is a common complication in severely burned patients who undergo autologous skin grafting. Meshed skin grafts tend to contract during wound healing, increasing the risk of pathological scarring. Although various technologies have been used to study cellular contraction, current methods for measuring contractile forces at the tissue level are limited and do not replicate the complexity of native tissues. Self-assembled skin substitutes (SASSs) were developed at the “Centre de recherche en organogénèse expérimentale de l’Université Laval/LOEX” and are used as permanent full-thickness skin grafts. The autologous skin substitutes are produced using the self-assembly method, allowing the cultured cells to produce their extracellular matrix leading to a tissue-engineered substitute resembling the native skin. The level of contraction of the SASSs during the fabrication process is patient-dependent. Thus, because of its architecture and composition, SASS is an interesting model to study skin contraction in vitro. Unfortunately, standard measurement methods are unsuited for SASS contraction assessment, mainly due to incompatibilities between the SASS manufacturing process and the current contraction force measurement methods. Here, we present an innovative contraction measurement method specifically designed to quantify the contractile behavior of tissue-engineered substitutes, without disrupting the protocol of production. The method uses C-shape anchoring frames that close at different speeds and magnitudes according to the tissue contractile behavior. A finite element analysis model is then used to associate the frame deformation to a contractile force amplitude. This article shows that the method can be used to measure the contraction force of tissues produced with cells displaying different contractile properties, such as primary skin fibroblasts and myofibroblasts. It can also be used to study the effects of cell culture conditions on tissue contraction, such as serum concentration. This protocol can be easily and affordably applied and tuned to many regenerative medicine applications or contraction-related pathological studies. Impact StatementThe protocol presented in this article is a new and simple method to quantify contraction forces present in tissue-engineered substitutes. Using finite element analysis, it allows for the measurement of a contraction force rather than a surface reduction as usually provided by other tissue contraction measurement methods. The results shown are in correlation with the current literature relevant to tissue contraction. It can be easily implemented, and hence, this method will open up new avenues to study tissue contraction of living substitutes engineered with various cell types and to optimize culture conditions.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.046
GPT teacher head0.381
Teacher spread0.335 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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