Matrix bound nanovesicles: A great promise for TERM in less than a decade of research
Bibliographic record
Abstract
• Matrix bound nanovesicles (MBVs) were recently discovered as a type of extracellular vesicle adhered to the ECM. • A detailed and current overview of the characteristics, potential and applications of MBVs is provided. • MBVs carry a rich cargo and have specific biological roles, such as immunomodulation. • MBVs have shown to mitigate inflammation in pathological conditions such as rheumatoid arthritis. • MBVs show great potential as novel biomaterials for regenerative medicine. Decellularized extracellular matrix materials have been widely studied for tissue engineering and regenerative medicine (TERM) applications, largely because of their intrinsic bioactivity and immunomodulatory potentials. These properties confer decellularized extracellular matrix biomaterials a biological advantage over other biomaterials, especially synthetic ones, leading to several successful applications in TERM. While the complex composition of decellularized materials is well known and thought to play a role in providing the regenerative advantage, the fine mechanisms laying behind their bioactivity and immunomodulation were not fully understood yet. In the last decade, researchers have discovered a novel component in decellularized extracellular matrix materials: the matrix bound nanovesicle (MBV). This newly described type of extracellular vesicle is characterized by a tight relation to the extracellular matrix, differently from other liquid phase vesicles, and presents a unique tissue specific cargo, thought to be secreted by cells for specific cell signalling purposes. Although other extracellular vesicles subtypes have been extensively studied in past years, MBVs are different in many ways, making this research field noticeably young. Major bioactivity and immune modulating ability are key features of MBVs that were evident right from the first research works. However, to understand how MBVs can recapitulate and confer decellularized biomaterials with their signature biological performance, they are being characterized in depth. In particular, their rich and varied cargo is being explored, which has shown to play a fundamental role in MBVs’ biological potential. This discovery not only revolutionized the look on decellularized extracellular matrix materials, but it also opened the way for research on a novel type of biomaterial, with plenty potential in therapeutical and regenerative applications. This review presents in detail what has been discovered up to now on MBVs, their properties, biological roles, and potential in TERM.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".