The acellular matrix (ACM) for bladder tissue engineering: A quantitative magnetic resonance imaging study
Bibliographic record
Abstract
Bladder acellular matrices (ACMs) derived from natural tissue are gaining increasing attention for their role in tissue engineering and regeneration. Unlike conventional scaffolds based on biodegradable polymers or gels, ACMs possess native biomechanical and many acquired biologic properties. Efforts to optimize ACM-based scaffolds are ongoing and would be greatly assisted by a noninvasive means to characterize scaffold properties and monitor interaction with cells. MRI is well suited to this role, but research with MRI for scaffold characterization has been limited. This study presents initial results from quantitative MRI measurements for bladder ACM characterization and investigates the effects of incorporating hyaluronic acid, a natural biomaterial useful in tissue-engineering and regeneration. Measured MR relaxation times (T(1), T(2)) and diffusion coefficient were consistent with increased water uptake and glycosaminoglycan content observed on biochemistry in hyaluronic acid ACMs. Multicomponent MRI provided greater specificity, with diffusion data showing an acellular environment and T(2) components distinguishing the separate effects of increased glycosaminoglycans and hydration. These results suggest that quantitative MRI may provide useful information on matrix composition and structure, which is valuable in guiding further development using bladder ACMs for organ regeneration and in strategies involving the use of hyaluronic acid.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".