Mesenchymal Stromal Cell Immunology for Efficient and Safe Treatment of Osteoarthritis
Bibliographic record
Abstract
for possible clinical application. They possess a unique immunological profile and regulatory machinery that underline their therapeutic effects. They also have the capacity to sense the changes within the tissue environment to display the adequate response. Indeed, there is a close interaction between MSCs and the host cells. Accordingly, MSCs demonstrate encouraging results for a variety of diseases including OA. However, their effectiveness needs to be improved. In this review, we selected to discuss the importance of the immunological features of MSCs, including the type of transplantation and the immune and blood compatibility. It is important to consider MSC immune evasive rather than immune privileged. We also highlighted some of the actions/mechanisms that are displayed during tissue healing including the response of MSCs to injury signals, their interaction with the immune system, and the impact of their lifespan. Finally, we briefly summarized the results of clinical studies reporting on the application of MSCs for the treatment of OA. The research field of MSCs is inspiring and innovative but requires more knowledge about the immunobiological properties of these cells. A better understanding of these features will be key for developing a safe and efficient medicinal product for clinical use in OA.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".