Bibliographic record
Abstract
PURPOSE OF REVIEW: Mesenchymal stromal cells (MSCs) are widely utilized in preclinical and clinical studies, with over 1500 clinical trials, including applications in Covid-19 treatment. This review consolidates recent advances in understanding MSC biology, mechanisms of action, and clinical utility. RECENT FINDINGS: This review discusses recent progress made in understanding MSC biology, including immunomodulatory mechanisms mediated by microRNAs and long noncoding RNAs. Clinically, MSC therapies have shown promise in treating conditions like Covid-19-associated ARDS and several MSC therapeutic products have been approved. Single-cell analyses have shed light on MSC heterogeneity, revealing tissue-specific and conserved subpopulations influenced by the extracellular matrix. The FDA's updated recommendations on potency assays emphasize a holistic approach to quality control, reinforcing the need for a universal reference standard to improve reproducibility and clinical outcomes. In addition, to better understand their limited success in randomized clinical trials, we highlight the importance of a universal reference standard for MSC potency. SUMMARY: MSCs offer significant therapeutic potential, but addressing challenges in heterogeneity and potency standardization is essential. Advances in understanding their immune properties and clinical applications provide opportunities to refine and expand their use in regenerative medicine.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".