Evidence map›Paper›PMID 34146232›Full record

ArticleNeuroscience bulletin2021

Simultaneous 3D Visualization of the Microvascular and Neural Network in Mouse Spinal Cord Using Synchrotron Radiation Micro-Computed Tomography.

Liyuan Jiang, Chengjun Li, Miao Li, Xianzhen Yin, Tianding Wu, Chunyue Duan, Yong Cao, Hongbin Lu, Jianzhong Hu

Open access · hybridAbstract read
In one paragraph

Article in Neuroscience bulletin, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.0field-weighted citation impact, top 9% 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

8 citing papers in PubMed, 18 citations in OpenAlex.

  1. IShuffleNet-PCNN hybrid classifier within an MResU-Net-based framework for spinal cord injury detection and classification using CT images.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
    Article
  2. Article
  3. Improved deep joint model with tuned improved LeNet for spinal cord segmentation and injury detection using CT images.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
    Article
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  6. Review
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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

9 authors at 3 institutions in 1 country.

Liyuan JiangDepartment of Spine Surgery, Xiangya Hospital, Central South University, Changsha, 410008, China.
Chengjun LiDepartment of Spine Surgery, Xiangya Hospital, Central South University, Changsha, 410008, China.
Miao LiDepartment of Spine Surgery, Xiangya Hospital, Central South University, Changsha, 410008, China.
Xianzhen YinCenter for Drug Delivery Systems, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
Tianding WuDepartment of Spine Surgery, Xiangya Hospital, Central South University, Changsha, 410008, China.
Chunyue DuanDepartment of Spine Surgery, Xiangya Hospital, Central South University, Changsha, 410008, China.
Yong CaoDepartment of Spine Surgery, Xiangya Hospital, Central South University, Changsha, 410008, China. caoyong1912@163.com.
Hongbin LuKey Laboratory of Organ Injury, Aging and Regenerative Medicine of Hunan Province, Changsha, 410008, China.
Jianzhong HuDepartment of Spine Surgery, Xiangya Hospital, Central South University, Changsha, 410008, China. jianzhonghu@hotmail.com.
Central South University · CNXiangya Hospital Central South University · CNShanghai Institute of Materia Medica · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Effective methods for visualizing neurovascular morphology are essential for understanding the normal spinal cord and the morphological alterations associated with diseases. However, ideal techniques for simultaneously imaging neurovascular structure in a broad region of a specimen are still lacking. In this study, we combined Golgi staining with angiography and synchrotron radiation micro-computed tomography (SRμCT) to visualize the 3D neurovascular network in the mouse spinal cord. Using our method, the 3D neurons, nerve fibers, and vasculature in a broad region could be visualized in the same image at cellular resolution without destructive sectioning. Besides, we found that the 3D morphology of neurons, nerve fiber tracts, and vasculature visualized by SRμCT were highly consistent with that visualized using the histological method. Moreover, the 3D neurovascular structure could be quantitatively evaluated by the combined methodology. The method shown here will be useful in fundamental neuroscience studies.

Indexed as

Imaging, Three-DimensionalSynchrotronsAnimalsMiceNeural Networks, ComputerSpinal CordX-Ray Microtomography3DHigh-resolutionNeurovascularSpinal cordSrμct

Identifiers

PMID34146232
PMCPMC8490558
OpenAlexW3172395611

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.