The Therapeutic Potential of Mesenchymal Stromal Cells for Regenerative Medicine: Current Knowledge and Future Understandings
Bibliographic record
Abstract
In recent decades, research on the therapeutic potential of progenitor cells has advanced considerably. Among progenitor cells, mesenchymal stromal cells (MSCs) have attracted significant interest and have proven to be a promising tool for regenerative medicine. MSCs are isolated from various anatomical sites, including bone marrow, adipose tissue, and umbilical cord. Advances in separation, culture, and expansion techniques for MSCs have enabled their large-scale therapeutic application. This progress accompanied by the rapid improvement of transplantation practices has enhanced the utilization of MSCs in regenerative medicine. During tissue healing, MSCs may exhibit several therapeutic functions to support the repair and regeneration of injured tissue. The process underlying these effects likely involves the migration and homing of MSCs, as well as their immunotropic functions. The direct differentiation of MSCs as a cell replacement therapeutic mechanism is discussed. The fate and behavior of MSCs are further regulated by their microenvironment, which may consequently influence their repair potential. A paracrine pathway based on the release of different messengers, including regulatory factors, chemokines, cytokines, growth factors, and nucleic acids that can be secreted or packaged into extracellular vesicles, is also implicated in the therapeutic properties of MSCs. In this review, we will discuss relevant outcomes regarding the properties and roles of MSCs during tissue repair and regeneration. We will critically examine the influence of the local microenvironment, especially immunological and inflammatory signals, as well as the mechanisms underlying these therapeutic effects. Importantly, we will describe the interactions of local progenitor and immune cells with MSCs and their modulation during tissue injury. We will also highlight the crucial role of paracrine pathways, including the role of extracellular vesicles, in this healing process. Moreover, we will discuss the therapeutic potential of MSCs and MSC-derived extracellular vesicles in the treatment of COVID-19 (coronavirus disease 2019) patients. Overall, this review will provide a better understanding of MSC-based therapies as a novel immunoregenerative strategy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".