Evidence map›Paper›PMID 38168640›Full record

ArticleCell research2024

Mapping of individual sensory nerve axons from digits to spinal cord with the transparent embedding solvent system.

Yating Yi, Youqi Li, Shiwen Zhang, Yi Men, Yuhong Wang, Dian Jing, Jiayi Ding, Qingjie Zhu, Zexi Chen, Xingjun Chen and 10 more

Open access · hybridAbstract read
In one paragraph

Article in Cell research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
24.5field-weighted citation impact, top 1% of its field
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

27 citing papers in PubMed, 37 citations in OpenAlex.

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  11. Intrinsic neuronal network organization of a mammalian peripheral nervous system.Proceedings of the National Academy of Sciences of the United States of America · 2025
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  19. Near-Freezing-Temperature Golgi Neuronal Staining for X-ray Imaging of Human Brain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors at 8 institutions in 2 countries.

Yating Yi *State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
Youqi Li *Chinese Institute for Brain Research, Beijing, China.
Shiwen Zhang *State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
Yi Men *State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
Yuhong WangWest China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
Dian JingDepartment of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine; College of Stomatology, Shanghai Jiao Tong University; National Center for Stomatology; National Clinical Research Center for Oral Diseases; Shanghai Key Laboratory of Stomatology; Shanghai Research Institute of Stomatology, Shanghai, China.
Jiayi DingChinese Institute for Brain Research, Beijing, China.
Qingjie ZhuChinese Institute for Brain Research, Beijing, China.
Zexi ChenChinese Institute for Brain Research, Beijing, China.
Xingjun ChenChinese Institute for Brain Research, Beijing, China.
Jun-Liszt LiChinese Institute for Brain Research, Beijing, China.
Yilong WangChinese Institute for Brain Research, Beijing, China.
Jun WangState Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
Hanchuan PengSEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu, China.
Li ZhangChinese Institute for Brain Research, Beijing, China.ORCID 0000-0002-0698-0754
Wenjing Luo341 Hunnewell St Needham, MA, USA.
Jian Q FengTexas A&M University, College of Dentistry, Dallas, TX, USA.
Yongwen HeQujing Medical College, Qujing, Yunnan, China. heyongwen2@sina.com.ORCID 0000-0001-6621-4616
Woo-Ping GeChinese Institute for Brain Research, Beijing, China. woopingge@cibr.ac.cn.ORCID 0000-0001-8219-1055
Hu ZhaoChinese Institute for Brain Research, Beijing, China. zhaohu@cibr.ac.cn.ORCID 0000-0002-2961-0086
Chinese Institute for Brain Research · CNSichuan University · CNCapital Medical University · CNPeking University · CNQujing Normal University · CNShanghai Jiao Tong University · CNSoutheast University · CNTexas A&M University · US

Funding

National Natural Science Foundation of China (National Science Foundation of China) 82101059
6 · The paper itself

Abstract

Achieving uniform optical resolution for a large tissue sample is a major challenge for deep imaging. For conventional tissue clearing methods, loss of resolution and quality in deep regions is inevitable due to limited transparency. Here we describe the Transparent Embedding Solvent System (TESOS) method, which combines tissue clearing, transparent embedding, sectioning and block-face imaging. We used TESOS to acquire volumetric images of uniform resolution for an adult mouse whole-body sample. The TESOS method is highly versatile and can be combined with different microscopy systems to achieve uniformly high resolution. With a light sheet microscope, we imaged the whole body of an adult mouse, including skin, at a uniform 0.8 × 0.8 × 3.5 μm

Indexed as

AxonsImaging, Three-DimensionalAnimalsMicePeripheral Nervous SystemSolventsSpinal CordSolvents

Identifiers

PMID38168640
PMCPMC10837210
OpenAlexW4390519222

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.