Systematic review of exosome treatment in hair restoration: Preliminary evidence, safety, and future directions
Bibliographic record
Abstract
BACKGROUND: Exosomes are small extracellular vesicles with potential roles in modulating the hair growth cycle and are an emerging therapy for patients with alopecia. In recent years, researchers have made significant progress in deciphering the network of cellular interactions and signaling pathways mediated by the transfer of exosomes. This has opened the door to a wide range of potential therapeutic applications with an increasing focus on its application in precision medicine. AIM: To evaluate current published evidence, both preclinical and clinical, on the use of exosomes for hair restoration. METHODS: In January 2023, a systematic search was conducted using PubMed, Embase, and the Cochrane Library. Records were identified, screened, and assessed for eligibility as per the PRISMA guideline. RESULTS: We identified 16 studies (15 preclinical and 1 clinical) showing varying degrees of efficacy using exosomes derived from sources including adipose-derived stem cells (ADSCs) and dermal papilla cells (DPCs). Applications of exosomes isolated from ADSCs (ADSC-Exo) and DPCs have shown early promising results in preclinical studies corroborated by results obtained from different model systems. Topical ADSC-Exo has been tried successfully in 39 androgenetic alopecia patients demonstrating significant increases in hair density and thickness. No significant adverse reactions associated with exosome treatment have been reported thus far. CONCLUSIONS: Although current clinical evidence supporting the use of exosome treatment is limited, there is a growing body of evidence suggesting its therapeutic potential. Further studies are warranted to define its mechanism of action, optimize its delivery and efficacy, and to address important safety concerns.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".