Decellularized extracellular matrix biomaterials for regenerative therapies: Advances, challenges and clinical prospects
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.
Bibliographic record
Abstract
Tissue engineering and regenerative medicine have shown potential in the repair and regeneration of tissues and organs via the use of engineered biomaterials and scaffolds. However, current constructs face limitations in replicating the intricate native microenvironment and achieving optimal regenerative capacity and functional recovery. To address these challenges, the utilization of decellularized tissues and cell-derived extracellular matrix (ECM) has emerged as a promising approach. These biocompatible and bioactive biomaterials can be engineered into porous scaffolds and grafts that mimic the structural and compositional aspects of the native tissue or organ microenvironment, both in vitro and in vivo. Bioactive dECM materials provide a unique tissue-specific microenvironment that can regulate and guide cellular processes, thereby enhancing regenerative therapies. In this review, we explore the emerging frontiers of decellularized tissue-derived and cell-derived biomaterials and bio-inks in the field of tissue engineering and regenerative medicine. We discuss the need for further improvements in decellularization methods and techniques to retain structural, biological, and physicochemical characteristics of the dECM products in a way to mimic native tissues and organs. This article underscores the potential of dECM biomaterials to stimulate in situ tissue repair through chemotactic effects for the development of growth factor and cell-free tissue engineering strategies. The article also identifies the challenges and opportunities in developing sterilization and preservation methods applicable for decellularized biomaterials and grafts and their translation into clinical products.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it