Evidence map›Paper›PMID 40688669›Full record

ReviewMaterials today. Bio2025

Biological applications of graphene-based nanomaterials in neurodegenerative diseases.

Rong-Kun Zhu, Jun Shi, Hong-Jian Zhou, Ling Ge, Wen-Hao Yin, Hui Zeng, Xiong-Wei Wang

Abstract readReview
In one paragraph

Review in Materials today. Bio, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
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.

Rong-Kun ZhuDepartment of Neurosurgery, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, 200090, China.
Jun ShiDepartment of Neurosurgery, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, 200090, China.
Hong-Jian ZhouDepartment of Neurosurgery, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, 200090, China.
Ling GeDepartment of Neurosurgery, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, 200090, China.
Wen-Hao YinDepartment of Neurosurgery, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, 200090, China.
Hui ZengDepartment of Neurology, Yangpu Hospital of Traditional Chinese Medicine, Shanghai, 200090, China.
Xiong-Wei WangDepartment of Neurosurgery, Yangpu Hospital, School of Medicine, Tongji University, Shanghai, 200090, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurodegenerative diseases (NDDs) have become a major challenge in global public health due to neurotoxicity caused by progressive neuronal degeneration and abnormal protein aggregation, which has attracted widespread attention. Graphene-based nanomaterials provide innovative solutions for the early diagnosis and precise treatment of NDDs by virtue of their ultra-high conductivity, large specific surface area and multifunctional properties. In this paper, we systematically discuss the key applications of these materials in the diagnosis and treatment of NDDs, and deeply analyze the technological breakthroughs and clinical translation challenges. The core of this paper is to illustrate that graphene-based nanomaterials are expected to reshape the paradigm of NDDs diagnosis and treatment through cross-scale technological innovations, promoting the synergistic development of early diagnosis, personalized treatment and real-time monitoring, and providing a transformative strategy for overcoming NDDs.

Indexed as

Alzheimer's diseaseAmyotrophic lateral sclerosisGraphene-based nanomaterialsNeurodegenerative diseasesParkinson's disease

Identifiers

PMID40688669
PMCPMC12275944

What OpenQuestion holds

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LicenceCC BY
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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.