Evidence map›Paper›PMID 41122868›Full record

ReviewCell transplantation

Advanced medical treatments for hair loss.

Kwanrudee Kaewmungkun, Somruethai Kaisang, Nattida Yokhaphachon, Rangsun Parnpai

Abstract readReview
In one paragraph

Review in Cell transplantation. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

4 authors.

Kwanrudee KaewmungkunEmbryo Technology and Stem Cell Research Center, School of Biotechnology, Suranaree University of Technology, Nakhon Ratchasima, Thailand.ORCID 0009-0008-6421-4813
Somruethai KaisangMEDEZE Research and Development, MEDEZE Group Public Company Limited, Nakhon Pathom, Thailand.
Nattida YokhaphachonMEDEZE Research and Development, MEDEZE Group Public Company Limited, Nakhon Pathom, Thailand.
Rangsun ParnpaiEmbryo Technology and Stem Cell Research Center, School of Biotechnology, Suranaree University of Technology, Nakhon Ratchasima, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patients with hair-loss experience stress and a loss of confidence. Several factors contribute to hair loss, including aging, androgen hormone effects, genetics, and diseases. Normally, physicians treat hair loss using medications such as minoxidil and finasteride. However, these treatments are associated with side effects and some limitations. Currently, hair transplantation is the most efficient technique; however, it requires the use of numerous hair grafts. Stem cell and hair follicle cell (HFC) treatments have been developed for hair-loss treatment. Previously, stem cell and HFC therapies have been shown to promote hair development, prolong the anagen phase, and increase hair diameter and density. In addition, growth factors and extracellular vesicles (EVs) are important for prolonging the anagen phase, improving hair follicle health, and reducing dihydrotestosterone effects. Furthermore, gene therapy and tissue engineering have also been developed as treatments for hair loss. In this review, we discuss (i) the basic knowledge and molecular signaling relevant to hair regeneration and (ii) advanced technology medical treatments for hair loss (stem cells, HFCs, growth factor, EVs, gene therapy, and tissue engineers).

Indexed as

AlopeciaAnimalsGenetic TherapyHair FollicleHumansStem CellsStem Cell TransplantationTissue Engineeringextracellular vesicleshair follicle cellshair loss treatmentstem cellstissue engineer

Identifiers

PMID41122868
PMCPMC12553935

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.