Evidence map›Paper›PMID 39436402›Full record

ArticleJournal of anatomy2025

Three-dimensional visualization of uterine nerve fiber distribution using fluorescence micro-optical sectioning tomography (fMOST): A pilot study.

Yonglan Deng, Yirong Zhang, Fei Gao, Yifeng Lin, Pei Li, Yanjuan Qiu, Lin Yang

Abstract read
In one paragraph

Article in Journal of anatomy, 2025. 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.

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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

7 authors.

Yonglan DengGuizhou Province Liupanshui People's Hospital, Liupanshui, China.
Yirong ZhangGuizhou Province Liupanshui People's Hospital, Liupanshui, China.
Fei GaoGuizhou Province Liupanshui People's Hospital, Liupanshui, China.
Yifeng LinZhuhai Campus of Zunyi Medical University, Zunyi, China.
Pei LiGuizhou Province Liupanshui People's Hospital, Liupanshui, China.
Yanjuan QiuChinese Academy of Medical Sciences, Institute of Pharmaceutical Sciences, Thousands of Mirror Image (Beijing) Technology Co, Beijing, China.
Lin YangZhuhai Campus of Zunyi Medical University, Zunyi, China.ORCID 0000-0003-1372-8677

Funding

National Natural Science Foundation of China 82260456; 81960419Science and Technology Planning Project of Shenzen Municipality JCYJ20230807140559047
6 · The paper itself

Abstract

STUDY

objectiveThis study aimed to observe and quantitatively analyze the morphology and distribution of uterine nerve fibers using fluorescence micro-optical sectioning tomography (fMOST). The goal was to provide an accurate morphological reference for pathological evaluations of uterine nerves. MEASUREMENTS AND MAIN

resultsUsing fMOST technique, we observed and analyzed the distribution of nerve fibers within the uterus. Our findings revealed a radial dispersion of nerve fibers radiating from myometrium to endometrium. The cervix uteri region exhibited a high density of nerve fibers, displaying terminations in a flower spray pattern. In contrast, nerve fibers in corpus uteri were comparatively sparse. However, we identified a unique "vine-like" pattern of a single nerve fiber extending from myometrium to endometrial layer in areas with concentrated nerves.

conclusionsThe fMOST technique is able to effectively elucidate the morphology and distribution of uterine nerve fibers. This method enables three-dimensional visualization of nerves in myometrium and offers a novel approach to observe the pathological changes in uterine nerves.

Indexed as

Imaging, Three-DimensionalNerve FibersTomography, OpticalUterusFemaleHumansMyometriumPilot Projectsfluorescence micro‐optical sectioning tomographynerve fibersuterus

Identifiers

PMID39436402
PMCPMC11996715

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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.