Current Advanced Therapies Based on Human Mesenchymal Stem Cells for Skin Diseases
Bibliographic record
Abstract
Skin disease may be related with immunological disorders, external aggressions, or genetic conditions. Injuries or cutaneous diseases such as wounds, burns, psoriasis, and scleroderma among others are common pathologies in dermatology, and in some cases, conventional treatments are ineffective. In recent years, advanced therapies using human mesenchymal stem cells (hMSCs) from different sources has emerged as a promising strategy for the treatment of many pathologies. Due to their properties; regenerative, immunomodulatory and differentiation capacities, they could be applied for the treatment of cutaneous diseases. In this review, a total of thirteen types of hMSCs used as advanced therapy have been analyzed, considering the last 5 years (2015–2020). The most investigated types were those isolated from umbilical cord blood (hUCB-MSCs), adipose tissue (hAT-MSCs) and bone marrow (hBM-MSCs). The most studied diseases were wounds and ulcers, burns and psoriasis. At preclinical level, in vivo studies with mice and rats were the main animal models used, and a wide range of types of hMSCs were used. Clinical studies analyzed revealed that cell therapy by intravenous administration was the advanced therapy preferred except in the case of wounds and burns where tissue engineering was also reported. Although in most of the clinical trials reviewed results have not been posted yet, safety was high and only local slight adverse events (mild nausea or abdominal pain) were reported. In terms of effectiveness, it was difficult to compare the results due to the different doses administered and variables measured, but in general, percentage of wound’s size reduction was higher than 80% in wounds, Psoriasis Area and Severity Index and Severity Scoring for Atopic Dermatitis were significantly reduced, for scleroderma, parameters such as Modified Rodnan skin score (MRSC) or European Scleroderma Study Group activity index reported an improvement of the disease and for hypertrophic scars, Vancouver Scar Scale (VSS) score was decreased after applying these therapies. On balance, hMSCs used for the treatment of cutaneous diseases is a promising strategy, however, the different experimental designs and endpoints stablished in each study, makes necessary more research to find the best way to treat each patient and disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.005 | 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".