Unleashing the Potential: Exploring the Application and Mechanism of Mesenchymal Stem Cells in Autoimmune Diseases
Bibliographic record
Abstract
Autoimmune diseases (AIDs) occur when the immune system mistakenly attacks the body's own antigens. Traditionally, these conditions are treated with nonspecific immunosuppressive therapies, including corticosteroids, immunosuppressants, biological agents, and human immunoglobulins. However, these treatments often fail to achieve optimal outcomes, especially for patients with severe cases. Mesenchymal stem cells (MSCs) present a promising alternative due to their robust self-renewal capabilities and multidirectional differentiation potential. MSCs are easily accessible, exhibit low immunogenicity, and can help reduce graft rejection. MSCs can inhibit T cell proliferation, reduce proinflammatory T cells, inhibit B cell differentiation, induce macrophage polarization towards the anti-inflammatory M2 phenotype, and suppress activity of natural killer (NK) cells and dendritic cells (DCs). Additionally, MSCs can regulate T cells, macrophages, and fibroblast-like synoviocytes (FLS) by releasing microRNA (miRNA) through exosomes or extracellular vesicles (EVs), thus providing therapeutic benefits for various diseases. Numerous clinical trials have highlighted the therapeutic benefits of MSCs in treating various AIDs, leading to increased interest in MSC transplantation. This review summarizes the current applications and mechanisms of action of MSCs in the treatment of AIDs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".