Evidence map›Paper›PMID 35936500›Full record

ArticleFrontiers in cellular neuroscience2022

Visual three-dimensional spatial distribution of motor neurons innervating superficial limb muscles in mice.

Zhidan Qi, Shuai Han, Shen Wang, Xinyi Gu, Jin Deng, Chen Huang, Xiaofeng Yin

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cellular neuroscience, 2022. 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
1.3field-weighted citation impact, top 20% 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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. 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

7 authors at 3 institutions in 1 country.

Zhidan QiDepartment of Orthopedics and Trauma, Peking University People's Hospital, Beijing, China.
Shuai HanDepartment of Orthopaedics, Peking University Third Hospital, Beijing, China.
Shen WangDepartment of Orthopedics and Trauma, Peking University People's Hospital, Beijing, China.
Xinyi GuDepartment of Orthopedics and Trauma, Peking University People's Hospital, Beijing, China.
Jin DengDepartment of Orthopedics and Trauma, Peking University People's Hospital, Beijing, China.
Chen HuangDepartment of Orthopedics and Trauma, Peking University People's Hospital, Beijing, China.
Xiaofeng YinDepartment of Orthopedics and Trauma, Peking University People's Hospital, Beijing, China.
Peking University · CNPeking University People's Hospital · CNPeking University Third Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coordination of motor function in the spinal cord depends on selective connections between distinct classes of motor neurons and their target muscles. However, knowledge regarding the anatomical connections between the superficial limb skeletal muscles and the motor neurons that innervate them is limited. In this study, with a combination of the multiple retrograde tracing method with 3DISCO clearing, we explored the spatial distribution of different motor neuron pools targeting specific superficial muscles of the forelimbs or hindlimbs in mouse spinal cords, which were dominated by the radial, median, ulnar, or sciatic nerve. This study reveals the precise interrelationship among different motor neuron pools innervating limb muscles under the same space and time. The data will help to further understand the neural loop and muscular motor coordination.

Indexed as

3D imagingmotor neuronoptical clearingretrograde tracingskeletal musclespinal cord

Identifiers

PMID35936500
PMCPMC9354668
OpenAlexW4286488815

What OpenQuestion holds

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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.