Tissue‐engineered extracellular matrices for 3D tissue modeling and clinical applications (82.2)
Bibliographic record
Abstract
Extracellular matrices (ECMs) are a key component of the tissue structure and influence cell responses and organ functionality. At the LOEX centre from Laval University, the self‐assembly approach for tissue engineering was designed to elaborate complex living substitutes based on the ability of cells to produce and organize their own extracellular matrices. Various tissues have been produced such as skin, cornea, blood vessels, etc without exogenous ECM or scaffolds. These natural ECMs favor the preservation of stem cells, facilitate vascularization and innervation, and provides adequate mechanical properties to these living three‐dimensional constructs. Moreover, this technique allows the fabrication of autologous substitutes. Tissue‐engineered skin, comprising a dermis and an epidermis, provides a permanent autologous graft for severely burned patients. No significant contraction was observed in vivo after grafting these tissue‐engineered skin substitutes. The presence of the ECMs promoted a good healing and suppleness after grafting. Thus, natural ECMs can be produced in vitro to generate tissue‐engineered substitutes for in vitro studies and clinical applications. Supported by the Canadian Institutes for Health Research (CIHR), Fonds de Recherche du Québec en Santé (FRQS), and the Cell and Tissue Therapy Network of the FRQS. LG is holder of the Chair on Stem Cells and Tissue Engineering.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".