MESENCHYMAL STEM CELL-DERIVED EXTRACELLULAR VESICLE MIMETICS AS OSTEOINDUCTIVE MEDIATORS: AN IN VITRO INVESTIGATION
Bibliographic record
Abstract
Mesenchymal stem cells-derived extracellular vesicles (MSC-EVs) have great promise in the field of orthopaedic nanomedicine due to their regenerative, as well as immunomodulatory and anti-inflammatory properties. Researchers are interested in harnessing these biologically sourced nanovesicles as powerful therapeutic tools with intrinsic bioactivity to help treat various orthopaedic diseases and defects. Recently, a new class of EV mimetics has emerged known as nanoghosts (NGs). These vesicles are derived from the plasma membrane of ghost cells, thus inheriting the surface functionalities and characteristics of the parent cell while at the same time allowing for a more standardized and reproducible production and significantly greater yield when compared to EVs. This study aims to investigate and compare the osteoinductive potential of MSC-EVs and MSC-NGs in vitro as novel tools in the field of bone tissue engineering and nanomedicine. To carry out this investigation, MSC-EVs were isolated from serum-free MSC conditioned media through differential ultracentrifugation. The remaining cells were treated with hypotonic buffer to produce MSC-ghosts that were then homogenized and serially extruded through 400 and 200 nm polycarbonate membranes to form the MSC-NGs. The concentration, size distribution, zeta potential, and protein content of the isolated nanoparticles were assessed. Afterwards, MSCs were treated with either MSC-EVs or MSC-NGs under osteogenic conditions, and their differentiation was assessed through secreted ALP assay, qPCR, and Alizarin Red mineralization staining. Isolation of MSC-EVs and MSC-NGs was successful, with relatively similar mean diameter size and colloidal stability. No effect on MSC viability and metabolic activity was observed with either treatment. Both MSC-EV and MSC-NG groups had enhanced osteogenic outcomes compared to the control; however, a trend was observed that suggests MSC-NGs as better osteoinductive mediators compared to MSC-EVs. Acknowledgements: The authors would like to acknowledge Canada Research Chair – Tier 1 in Regenerative Medicine and Nanomedicine, CHRP, and McGill's Faculty of Dental Medicine and Oral Health Sciences for their financial support.
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 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) | 0.000 | 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) | 0.000 | 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".