Therapeutic Mesenchymal Stem/Stromal Cells: Value, Challenges and Optimization
Bibliographic record
Abstract
Cellular therapy aims to replace damaged resident cells by restoring cellular and molecular environments suitable for tissue repair and regeneration. Among several candidates, mesenchymal stem/stromal cells (MSCs) represent a critical component of stromal niches known to be involved in tissue homeostasis. In vitro , MSCs appear as fibroblast-like plastic adherent cells regardless of the tissue source. The therapeutic value of MSCs is being explored in several conditions, including immunological, inflammatory and degenerative diseases, as well as cancer. An improved understanding of their origin and function would facilitate their clinical use. The stemness of MSCs is still debated and requires further study. Several terms have been used to designate MSCs, although consensual nomenclature has yet to be determined. The presence of distinct markers may facilitate the identification and isolation of specific subpopulations of MSCs. Regarding their therapeutic properties, the mechanisms underlying their immune and trophic effects imply the secretion of various mediators rather than direct cellular contact. These mediators can be packaged in extracellular vesicles, thus paving the way to exploit therapeutic cell-free products derived from MSCs. Of importance, the function of MSCs and their secretome are significantly sensitive to their environment. Several features, such as culture conditions, delivery method, therapeutic dose and the immunobiology of MSCs, may influence their clinical outcomes. In this review, we will summarize recent findings related to MSC properties. We will also discuss the main preclinical and clinical challenges that may influence the therapeutic value of MSCs and discuss some optimization strategies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".