A Cost‐Effective Culture System for the In Vitro Assembly, Maturation, and Stimulation of Advanced Multilayered Multiculture Tubular Tissue Models
Bibliographic record
Abstract
The development of tubular engineered tissues is a challenging research area aiming to provide tissue substitutes but also in vitro models to test drugs, medical devices, and even to study physiological and pathological processes. In this work, the design, fabrication, and validation of an original cost-effective tubular multilayered-tissue culture system (TMCS) are reported. By exploiting cellularized collagen gel as scaffold, a simple moulding technique and an endothelialization step on a rotating system, TMCS allowed to easily prepare in 48 h, trilayered arterial wall models with finely organized cellular composition and to mature them for 2 weeks without any need of manipulation. Multilayered constructs incorporating different combinations of vascular cells are compared in terms of cell organization and viscoelastic mechanical properties demonstrating that cells always progressively aligned parallel to the longitudinal direction. Also, fibroblast compacted less the collagen matrix and appeared crucial in term of maturation/deposition of elastic extracellular matrix. Preliminary studies under shear stress stimulation upon connection with a flow bioreactor are successfully conducted without damaging the endothelial monolayer. Altogether, the TMCS herein developed, thanks to its versatility and multiple functionalities, holds great promise for vascular tissue engineering applications, but also for other tubular tissues such as trachea or oesophagus.
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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".