Mesenchymal stem cells and their use as cell replacement therapy and disease modelling tool
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
Introduction Mechanisms of immunological tolerance MSCs and cell replacement strategies Clinical applications based on MSCs immune modulatory properties: overview of ongoing clinical trials MSCs as a model to study cell transformation and disease Concluding remarks Abstract Mesenchymal stem cells (MSCs) from adult somatic tissues may differentiate in vitro and in vivo into multiple mesodermal tissues including bone, cartilage, adipose tissue, tendon, ligament or even muscle. MSCs preferentially home to damaged tissues where they exert their therapeutic potential. A striking feature of the MSCs is their low inherent immunogenicity as they induce little, if any, proliferation of allogeneic lymphocytes and antigen‐presenting cells. Instead, MSCs appear to be immunosuppressive in vitro . Their multi‐lineage differentiation potential coupled to their immuno‐privileged properties is being exploited worldwide for both autologous and allo‐geneic cell replacement strategies. Here, we introduce the readers to the biology of MSCs and the mechanisms underlying immune tolerance. We then outline potential cell replacement strategies and clinical applications based on the MSCs immunological properties. Ongoing clinical trials for graft‐versus‐host‐disease, haematopoietic recovery after co‐transplantation of MSCs along with haematopoietic stem cells and tissue repair are discussed. Finally, we review the emerging area based on the use of MSCs as a target cell subset for either spontaneous or induced neoplastic transformation and, for modelling non‐haematological mesenchymal cancers such as sarcomas.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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