Stem cell secretome as a mechanism for restoring hair loss due to stress, particularly alopecia areata: narrative review
Bibliographic record
Abstract
BACKGROUND: Living organisms are continuously exposed to multiple internal and external stimuli which may influence their emotional, psychological, and physical behaviors. Stress can modify brain structures, reduces functional memory and results in many diseases such as skin disorders like acne, psoriasis, telogen effluvium, and alopecia areata. In this review, we aim to discuss the effect of secretome on treating alopecia, especially alopecia areata. We will shed the light on the mechanism of action of the secretome in the recovery of hair loss and this by reviewing all reported in vitro and in vivo literature. MAIN BODY: Hair loss has been widely known to be enhanced by stressful events. Alopecia areata is one of the skin disorders which can be highly induced by neurogenic stress especially if the patient has a predisposed genetic background. This condition is an autoimmune disease where stress in this case activates the immune response to attack the body itself leading to hair cycle destruction. The currently available treatments include medicines, laser therapy, phototherapy, and alternative medicine therapies with little or no satisfactory results. Regenerative medicine is a new era in medicine showing promising results in treating many medical conditions including Alopecia. The therapeutic effects of stem cells are due to their paracrine and trophic effects which are due to their secretions (secretome). CONCLUSION: Stem cells should be more used as an alternative to conventional therapies due to their positive outcomes. More clinical trials on humans should be done to maximize the dose needed and type of stem cells that must be used to treat alopecia areata.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".