Extracellular Matrix‐Surrogate Advanced Functional Composite Biomaterials for Tissue Repair and Regeneration
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
Native tissues, comprising multiple cell types and extracellular matrix components, are inherently composites. Mimicking the intricate structure, functionality, and dynamic properties of native composite tissues represents a significant frontier in biomaterials science and tissue engineering research. Biomimetic composite biomaterials combine the benefits of different components, such as polymers, ceramics, metals, and biomolecules, to create tissue-template materials that closely simulate the structure and functionality of native tissues. While the design of composite biomaterials and their in vitro testing are frequently reviewed, there is a considerable gap in whole animal studies that provides insight into the progress toward clinical translation. Herein, we provide an insightful critical review of advanced composite biomaterials applicable in several tissues. The incorporation of bioactive cues and signaling molecules into composite biomaterials to mimic the native microenvironment is discussed. Strategies for the spatiotemporal release of growth factors, cytokines, and extracellular matrix proteins are elucidated, highlighting their role in guiding cellular behavior, promoting tissue regeneration, and modulating immune responses. Advanced composite biomaterials design challenges, such as achieving optimal mechanical properties, improving long-term stability, and integrating multifunctionality into composite biomaterials and future directions, are discussed. We believe that this manuscript provides the reader with a timely perspective on composite biomaterials.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 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