Evidence map›Paper›PMID 35029088›Full record

ArticleJournal of Zhejiang University. Science. B2022

Biocompatible chitosan/polyethylene glycol/multi-walled carbon nanotube composite scaffolds for neural tissue engineering.

Shengbo Sang, Rong Cheng, Yanyan Cao, Yayun Yan, Zhizhong Shen, Yajing Zhao, Yanqing Han

Open access · greenAbstract read
In one paragraph

Article in Journal of Zhejiang University. Science. B, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
3.6field-weighted citation impact, top 6% 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

15 citing papers in PubMed, 38 citations in OpenAlex.

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  15. Principles of Biocompatible Nanotube Design for High-Performance Biomaterials.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnology
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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

7 authors at 4 institutions in 1 country.

Shengbo SangShanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception, College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China. sunboa-sang@tyut.edu.cn.
Rong ChengShanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception, College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.
Yanyan CaoShanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception, College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.
Yayun YanShanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception, College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.
Zhizhong ShenShanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception, College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.
Yajing ZhaoShanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception, College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.
Yanqing HanDepartment of Neurology, Shanxi Provincial Cardiovascular Hospital, Taiyuan 030024, China.
Taiyuan University of Technology · CNHebei North University · CNShanxi Cardiovascular Hospital · CNShanxi University · CN

Funding

the National Natural Science Foundation of China 51975400 and 62031022the Shanxi Provincial Key Medical Scientific Research Project 2020XM06
6 · The paper itself

Abstract

Carbon nanotube (CNT) composite materials are very attractive for use in neural tissue engineering and biosensor coatings. CNT scaffolds are excellent mimics of extracellular matrix due to their hydrophilicity, viscosity, and biocompatibility. CNTs can also impart conductivity to other insulating materials, improve mechanical stability, guide neuronal cell behavior, and trigger axon regeneration. The performance of chitosan (CS)/polyethylene glycol (PEG) composite scaffolds could be optimized by introducing multi-walled CNTs (MWCNTs). CS/PEG/CNT composite scaffolds with CNT content of 1%, 3%, and 5% (1%=0.01 g/mL) were prepared by freeze-drying. Their physical and chemical properties and biocompatibility were evaluated. Scanning electron microscopy (SEM) showed that the composite scaffolds had a highly connected porous structure. Transmission electron microscope (TEM) and Raman spectroscopy proved that the CNTs were well dispersed in the CS/PEG matrix and combined with the CS/PEG nanofiber bundles. MWCNTs enhanced the elastic modulus of the scaffold. The porosity of the scaffolds ranged from 83% to 96%. They reached a stable water swelling state within 24 h, and swelling decreased with increasing MWCNT concentration. The electrical conductivity and cell adhesion rate of the scaffolds increased with increasing MWCNT content. Immunofluorescence showed that rat pheochromocytoma (PC12) cells grown in the scaffolds had characteristics similar to nerve cells. We measured changes in the expression of nerve cell markers by quantitative real-time polymerase chain reaction (qRT-PCR), and found that PC12 cells cultured in the scaffolds expressed growth-associated protein 43 (GAP43), nerve growth factor receptor (NGFR), and class III β‍-tubulin (TUBB3) proteins. Preliminary research showed that the prepared CS/PEG/CNT scaffold has good biocompatibility and can be further applied to neural tissue engineering research.

Indexed as

ChitosanNanotubes, CarbonAnimalsAxonsBiocompatible MaterialsNerve RegenerationPolyethylene GlycolsPorosityRatsTissue EngineeringTissue ScaffoldsBiocompatible MaterialsChitosanNanotubes, CarbonPolyethylene GlycolsBiocompatibilityCell-scaffoldMulti-walled carbon nanotube (MWCNT)PC12 cells

Identifiers

PMID35029088
PMCPMC8758934
OpenAlexW4206794798

What OpenQuestion holds

Textmetadata
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.