ArticlePlastic and reconstructive surgery. Global open2024
Autologous Stem Cell-derived Therapies for Androgenetic Alopecia: A Systematic Review of Randomized Control Trials on Efficacy, Safety, and Outcomes.
Article in Plastic and reconstructive surgery. Global open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Potential Treatment of Steroid-Resistant Alopecia Areata Using Allogeneic Exosomes Derived From Adipose Tissue Mesenchymal Stromal Cells: A Case Report.Clinical case reports · 2026Article
- Advances in Medicalized Hair Loss Solutions: A Review of Current Clinical Practices and Regenerative Medicine-Based Protocols with Focus on Off-Label Injectable Treatments.Journal of clinical medicine · 2026Review
- Emerging pharmacotherapies and regenerative solutions for promoting hair growth for androgenetic alopecia.Frontiers in pharmacology · 2026Review
- Advanced medical treatments for hair loss.Cell transplantationReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Androgenic alopecia (AGA), a prevalent and extensively studied condition characterized by hair loss, presents a significant global issue for both men and women. Stem cell therapy has emerged as a promising therapeutic approach for AGA due to its regenerative and immunomodulatory properties. The primary objective of this systematic review was to assess the current literature on the efficacy and safety of cellular and acellular stem cell-derived therapies in the management of AGA. Methods: A computerized literature search was conducted in ClinicalTrials.gov, PubMed, and Cochrane Library in October 2023. The online screening process was performed by three independent reviewers with the Covidence tool. The protocol was reported using the Preferred Reporting Items for Systematic Review and Meta-Analyses, and it was registered at the International Prospective Register of Systematic Reviews of the National Institute for Health Research. Results: The search yielded 53 articles from 2013 to 2023. Twelve randomized controlled trials were included. Stem cells and their derivatives were isolated from human adipose tissue, hair follicles, bone marrow, umbilical cord blood, and exfoliated deciduous teeth. These trials showed that stem cell-derived treatments can promote hair regeneration and density. Conclusions: Both cellular and acellular stem cell-based therapies are safe and effective in improving hair regeneration and density in AGA patients. Although the outcomes may be temporary in some cases, regenerative treatments may become useful adjuncts in combination with traditional methods of hair transplantation. Future research should focus on protocol optimization to enhance long-term patient outcomes.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.