Evidence map›Paper›PMID 39236901›Full record

ArticleThe Journal of investigative dermatology2025

Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging.

Jasson Makkar, Jorge Flores, Mason Matich, Tommy T Duong, Sean M Thompson, Yiqing Du, Isabelle Busch, Quan M Phan, Qing Wang, Kristen Delevich and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of investigative dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. A Comprehensive Review of GWASs of Human Hair Traits.The Journal of investigative dermatology · 2025
    Review
  3. Article
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

13 authors.

Jasson MakkarSchool of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
Jorge FloresSchool of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
Mason MatichSchool of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
Tommy T DuongSchool of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
Sean M ThompsonSchool of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
Yiqing DuSchool of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
Isabelle BuschSchool of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
Quan M PhanSchool of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
Qing WangDepartment of Integrative Physiology and Neuroscience, College of Veterinary Medicine, Washington State University, Pullman, Washington, USA.
Kristen DelevichDepartment of Integrative Physiology and Neuroscience, College of Veterinary Medicine, Washington State University, Pullman, Washington, USA; Center for Reproductive Biology, College of Veterinary Medicine, Washington State University, Pullman, Washington, USA.
Liam Broughton-NeiswangerWashington Animal Disease Diagnostic Laboratory, College of Veterinary Medicine, Washington State University, Pullman, Washington, USA.
Iwona M DriskellSchool of Molecular Biosciences, Washington State University, Pullman, Washington, USA.
Ryan R DriskellSchool of Molecular Biosciences, Washington State University, Pullman, Washington, USA; Center for Reproductive Biology, College of Veterinary Medicine, Washington State University, Pullman, Washington, USA. Electronic address: ryan.driskell@wsu.edu.

Funding

TRAINING IN BIOTECHNOLOGY: EMPHASIS ON PROTEIN CHEMISTRYT32GM008336 · NIGMS · WASHINGTON STATE UNIVERSITY · PI CALL, DOUGLAS R. · 1989 to 2023
$8.9M
Investigating the role of Lef1 in fibroblast lineages to induce skin regeneration and reverse agingR01AR078743 · NIAMS · WASHINGTON STATE UNIVERSITY · PI DRISKELL, RYAN · 2021 to 2025
$1.6M
NIAMS NIH HHS R01 AR078743NIGMS NIH HHS T32 GM008336
6 · The paper itself

Abstract

Hair quality is an important indicator of health in humans and other animals. Current approaches to assess hair quality are generally nonquantitative or are low throughput owing to technical limitations of splitting hairs. We developed a deep learning-based computer vision approach for the high-throughput quantification of individual hair fibers at a high resolution. Our innovative computer vision tool can distinguish and extract overlapping fibers for quantification of multivariate features, including length, width, and color, to generate single-hair phenomes of diverse conditions across the lifespan of mice. Using our tool, we explored the effects of hormone signaling, genetic modifications, and aging on hair follicle output. Our analyses revealed hair phenotypes resultant of endocrinological, developmental, and aging-related alterations in the fur coats of mice. These results demonstrate the efficacy of our deep hair phenomics tool for characterizing factors that modulate the hair follicle and developing, to our knowledge, previously unreported diagnostic methods for detecting disease through the hair fiber. Finally, we have generated a searchable, interactive web tool for the exploration of our hair fiber data at skinregeneration.org.

Indexed as

AgingDeep LearningHairHair FolliclePhenomicsAnimalsHumansMicePhenotypeAgingAIDevelopmentEndocrinologyHair

Identifiers

PMID39236901
PMCPMC11873809

What OpenQuestion holds

Textmetadata
LicenceTDM
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.