Evidence map›Paper›PMID 41656280›Full record

ReviewStem cell research & therapy2026

Stem cell-based therapies for alopecia areata: a narrative review.

Aiping Fan, Mingjuan Liu, Jun Li

Abstract readReview
In one paragraph

Review in Stem cell research & therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Aiping FanDepartment of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Mingjuan LiuDepartment of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Jun LiDepartment of Dermatology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China. lijun35@hotmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alopecia Areata (AA) is a chronic inflammatory disorder characterized by non-scarring, patchy hair loss that may progress to the entire scalp (alopecia totalis) or body (alopecia universalis), significantly impairing patients' quality of life and psychological health. Although the exact pathogenesis of AA remains unclear, current evidence suggests that the breakdown of hair follicle immune privilege (IP) and subsequent autoimmune-mediated follicular attack play a pivotal role. Conventional therapeutic modalities, including corticosteroid and Janus kinase (JAK) inhibitors, are often limited by suboptimal efficacy in severe cases and high relapse rates following treatment cessation. In recent years, stem cell-based therapy has emerged as a novel treatment for AA, showing therapeutic potential through multiple mechanisms. Preliminary clinical trials have indicated significant efficacy in promoting hair regrowth among AA patients. However, comprehensive evaluation of long-term safety and therapeutic efficacy remains imperative. This review article aims to give a comprehensive overview of the recent advances in stem cell-based therapies for AA and explore their underlying mechanisms and clinical application prospects, hoping to provide a framework and reference for future research and clinical practice.

Indexed as

Alopecia AreataStem CellsStem Cell TransplantationAnimalsHair FollicleHumansAlopecia areataHair regenerationMesenchymal stem cellsStem cell therapy

Identifiers

PMID41656280
PMCPMC12983639

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.