Mesenchymal Stromal Cell-Based Therapy: New Perspectives and Challenges
Bibliographic record
Abstract
Stem cells have been the focus of intense research opening up new possibilities for the treatment of various diseases. Mesenchymal stromal cells (MSCs) are multipotent cells with relevant immunomodulatory properties and are thus considered as a promising new strategy for immune disease management. To enhance their efficiency, several issues related to both MSC biology and functions are needed to be identified and, most importantly, well clarified. The sources from which MSCs are isolated are diverse and might affect their properties. Both clinicians and scientists need to handle a phenotypic-characterized population of MSCs, particularly regarding their immunological profile. Moreover, it is now recognized that the tissue-reparative effects of MSCs are based on their immunomodulatory functions that are activated following a priming/licensing step. Thus, finding the best ways to pre-conditionate MSCs before their injection will strengthen their activity potential. Finally, soluble elements derived from MSC-secretome, including extracellular vesicles (EVs), have been proposed as a cell-free alternative tool for therapeutic medicine. Collectively, these features have to be considered and developed to ensure the efficiency and safety of MSC-based therapy. By participating to this Special Issue "Mesenchymal Stem/Stromal Cells in Immunity and Disease", your valuable contribution will certainly enrich the content and discussion related to the thematic of MSCs.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".