Evidence map›Paper›PMID 40463861›Full record

ArticleJournal of inflammation research2025

Single-Nucleus and Bulk RNA Sequencing Reveals the Involvement of Natural Killer and CD8

Haijing Fu, Wumei Zhao, Leiwei Jiang, Shijun Shan

Abstract read
In one paragraph

Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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.

Haijing FuDepartment of Dermatology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361000, People's Republic of China.
Wumei ZhaoDepartment of Dermatology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361000, People's Republic of China.
Leiwei JiangDepartment of Dermatology, Guizhou Provincial People's Hospital, Guiyang City, Guizhou Province, People's Republic of China.
Shijun ShanDepartment of Dermatology, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361000, People's Republic of China.ORCID 0000-0003-2339-5466

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Androgenetic alopecia (AGA) is the most common type of androgen-associated hair loss. Emerging evidence highlights inflammation as a critical mediator in follicular miniaturization and disease progression. This investigation systematically explores inflammatory mechanisms in AGA through comprehensive analysis of hair follicles transcriptional profiles combined with cellular heterogeneity. Methods: Matched follicular specimens were procured from AGA patients: occipital non-balding units (controls) versus frontal alopecic zones (experimental). Bulk RNA-sequencing was conducted on Norwood-Hamilton grade 3-5 AGA scalp tissues to delineate inflammatory signatures. Subsequent single-nucleus RNA sequencing (snRNA-seq) of grade 5 specimens resolved cellular heterogeneity. Immune subsets (NK/CD8 Results: Bulk RNA-sequencing of AGA hair follicles revealed heightened inflammatory signatures in grade 5 patients compared to grade 3-4 counterparts. To dissect cellular heterogeneity, we systematically investigated the dynamic changes of immune cells in hair follicles of AGA patients using snRNA-seq technology for the first time. The result showed that grade 5 AGA hair follicles, identifying significant enrichment of natural killer (NK) and CD8 Conclusion: These findings collectively implicate NK and CD8

Indexed as

androgenetic alopeciaCD8+ T cellsinflammation and snRNA-seqnatural killer cells

Identifiers

PMID40463861
PMCPMC12132621

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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.