Evidence map›Paper›PMID 33505458›Full record

ReviewNeural plasticity2021

The Mechanisms of Peripheral Nerve Preconditioning Injury on Promoting Axonal Regeneration.

Xiaoyan Yang, Ruixuan Liu, Ying Xu, XiangYu Ma, Bing Zhou

Abstract readReview
In one paragraph

Review in Neural plasticity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–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

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. [Progress of electrical stimulation to promote peripheral nerve regeneration].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2024
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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

5 authors.

Xiaoyan YangBeijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China.
Ruixuan LiuBeijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China.
Ying XuBeijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China.
XiangYu MaBeijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China.
Bing ZhouBeijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China.ORCID 0000-0002-7718-2311

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Two major factors contribute to the failure of axonal regrowth in the central nervous system (CNS), namely, the neuronal intrinsic regenerative capacity and the extrinsic local inhibitory microenvironments. However, a preconditioning peripheral nerve lesion could substantially enhance the regeneration of central axons following a subsequent spinal cord injury. In the present review, we summarize the molecular mechanisms of the preconditioning injury effect on promoting axonal regeneration. The injury signal transduction resulting from preconditioning peripheral nerve injury regulates the RAG expression to enhance axonal regeneration. Importantly, preconditioning peripheral nerve injury triggers interactions between neurons and nonneuronal cells to amplify and maintain their effects. Additionally, the preconditioning injury impacts mitochondria, protein, and lipid synthesis. All these coordinated changes endow axonal regeneration.

Indexed as

AnimalsAxonsHumansMitochondriaNerve RegenerationNeuronsPeripheral Nerve InjuriesSpinal Cord Injuries

Identifiers

PMID33505458
PMCPMC7806370

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.