1.Enhancing chondrogenic differentiation in precartilaginous stem cells with 620 nm red light
Kunpeng LI ; Tao XU ; Yu DU ; Chen GONG ; Fei PENG ; Anmin CHEN ; Fengjin GUO
Chinese Journal of Physical Medicine and Rehabilitation 2012;34(3):172-176
Objective To investigate the effect of 620 nm red light on chondrogenic differentiation in rat precartilaginous stem cells (PSCs). Methods Rats' PSCs were isolated and purified using magnetically activated cell sorting and cultured in vitro.The PSCs were exposed once to 620 nm wavelength red light from a light-emitting diode (LED) with an irradiation energy of 0.5 J/cm2,1 J/cm2,2 J/cm2 or 4 J/cm2.Any effect was confirmed by Alcian blue staining,immunohistochemistry and observing histomorphological changes under a light microscope,as well as detection using a reverse transcription polymerase chain reaction (RT-PCR). Results After being induced for 14 d,the PSCs exhibited polygonal and round shapes. Alcian blue and type Ⅱ collagen immunohistoehemistry staining showed positive results,but the control group had no significant change.RT-PCR showed that the mRNA expression of Sox9 and type Ⅱ collagen increased significantly compared with the control group. Conclusion Low energy 620 nm red light can enhance chondrogenic differentiation in PSCs significantly.
2.Construction of recombinant adenovirus and mediated reported gene expression in the guinea pig cochlea.
Yingpeng LIU ; Guopeng WANG ; Anmin SHEN ; Jianting WANG ; Pei CHEN ; Zeweng LI ; Shusheng GONG
Journal of Clinical Otorhinolaryngology Head and Neck Surgery 2007;21(16):748-751
OBJECTIVE:
To purify P0 protein from guinea pig's inner ear by preparative SDS-PAGE and study the possible role it may play in the etiology of autoimmune inner ear disease.
METHOD:
A mixture of membraneous proteins of inner ear was separated by preparative SDS-PAGE. The corresponding band at 30kd was cut and electrically eluted. The protein collected was identified by analytical SDS-PAGE and Western blot assay. A group of 20 guinea pigs were immunized with P0 protein emulsified in complete Freund's adjuvant, another 10 guinea pigs were immunized with complete Freund 's adjuvant only as control. The guinea pigs' hearing thresholds, serum IgG level and morphological changes in the inner ear were investigated. The distribution of P0 protein in the cochlear was detected by immunohistochemical technique.
RESULT:
The purity of the protein was demonstrated by a single band at the 30 kD site in SDS-PAGE, which was identified as P0 protein by western blot analysis assay. About 17.5% P0-immunized guinea pigs showed increased hearing thresholds, elevated IgG level (F =6.48, P <0. 01), as well as a decreased number of spiral ganglion cells and inflammatory cell infiltration in the cochlear nerve region. The P0 protein is distributed in the cochlear nerve and spiral ganglion only.
CONCLUSION
P0 protein from guinea pig's inner ear can be successfully purified by preparative SDS-PAGE and an animal model of experimental autoimmune inner ear disease induced by P0 protein is successfully established.
Adenoviridae
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genetics
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Animals
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Cochlea
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metabolism
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Disease Models, Animal
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Electrophoresis, Polyacrylamide Gel
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Gene Expression
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Gene Transfer Techniques
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Genes, Homeobox
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Genes, Reporter
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Genetic Vectors
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Guinea Pigs
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Myelin P0 Protein
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isolation & purification
3.Methods and application status of neurofeedback training in promoting rehabilitation of neuropsychiatric disorders
Miheng YUAN ; Peng DING ; Wenya NAN ; Anmin GONG ; Yunfa FU
Chinese Journal of Neuromedicine 2022;21(9):956-963
Neurofeedback training (NFT) is an important neuromodulation method that can produce certain plasticity in the central nervous system, and is expected to be an effective physical intervention method for rehabilitation of mental disorders. So far, there have been many studies on NFT promoting the rehabilitation of common mental disorders in the literature. For different mental disorders, there are different NFT training programs with different intervention effects and mechanisms. This paper focuses on the application of NFT in the rehabilitation of mental disorders, introduces the concept, process and common solutions of NFT, and focuses on the methods and applications of NFT to promote the rehabilitation of common mental disorders, and finally discusses the faced problems and future research trends, aiming to provide references for innovative research and clinical application of NFT to promote the rehabilitation of mental disorders.
4.Methods and application status of neurofeedback training in promoting rehabilitation of neuropsychiatric disorders
Miheng YUAN ; Peng DING ; Wenya NAN ; Anmin GONG ; Yunfa FU
Chinese Journal of Neuromedicine 2022;21(9):956-963
Neurofeedback training (NFT) is an important neuromodulation method that can produce certain plasticity in the central nervous system, and is expected to be an effective physical intervention method for rehabilitation of mental disorders. So far, there have been many studies on NFT promoting the rehabilitation of common mental disorders in the literature. For different mental disorders, there are different NFT training programs with different intervention effects and mechanisms. This paper focuses on the application of NFT in the rehabilitation of mental disorders, introduces the concept, process and common solutions of NFT, and focuses on the methods and applications of NFT to promote the rehabilitation of common mental disorders, and finally discusses the faced problems and future research trends, aiming to provide references for innovative research and clinical application of NFT to promote the rehabilitation of mental disorders.
5.Human factors engineering of brain-computer interface and its applications: Human-centered brain-computer interface design and evaluation methodology.
Xiaotong LU ; Peng DING ; Siyu LI ; Anmin GONG ; Lei ZHAO ; Qian QIAN ; Lei SU ; Yunfa FU
Journal of Biomedical Engineering 2021;38(2):210-223
Brain-computer interface (BCI) is a revolutionizing human-computer Interaction, which is developing towards the direction of intelligent brain-computer interaction and brain-computer intelligent integration. However, the practical application of BCI is facing great challenges. The maturity of BCI technology has not yet reached the needs of users. The traditional design method of BCI needs to be improved. It is necessary to pay attention to BCI human factors engineering, which plays an important role in narrowing the gap between research and practical application, but it has not attracted enough attention and has not been specifically discussed in depth. Aiming at BCI human factors engineering, this article expounds the design requirements (from users), design ideas, objectives and methods, as well as evaluation indexes of BCI with the human-centred-design. BCI human factors engineering is expected to make BCI system design under different use conditions more in line with human characteristics, abilities and needs, improve the user satisfaction of BCI system, enhance the user experience of BCI system, improve the intelligence of BCI, and make BCI move towards practical application.
Brain
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Brain-Computer Interfaces
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Electroencephalography
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Ergonomics
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Humans
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User-Computer Interface
6.Key technologies for intelligent brain-computer interaction based on magnetoencephalography.
Haotian XU ; Anmin GONG ; Peng DING ; Jiangong LUO ; Chao CHEN ; Yunfa FU
Journal of Biomedical Engineering 2022;39(1):198-206
Brain-computer interaction (BCI) is a transformative human-computer interaction, which aims to bypass the peripheral nerve and muscle system and directly convert the perception, imagery or thinking activities of cranial nerves into actions for further improving the quality of human life. Magnetoencephalogram (MEG) measures the magnetic field generated by the electrical activity of neurons. It has the unique advantages of non-contact measurement, high temporal and spatial resolution, and convenient preparation. It is a new BCI driving signal. MEG-BCI research has important brain science significance and potential application value. So far, few documents have elaborated the key technical issues involved in MEG-BCI. Therefore, this paper focuses on the key technologies of MEG-BCI, and details the signal acquisition technology involved in the practical MEG-BCI system, the design of the MEG-BCI experimental paradigm, the MEG signal analysis and decoding key technology, MEG-BCI neurofeedback technology and its intelligent method. Finally, this paper also discusses the existing problems and future development trends of MEG-BCI. It is hoped that this paper will provide more useful ideas for MEG-BCI innovation research.
Brain/physiology*
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Brain-Computer Interfaces
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Electroencephalography
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Humans
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Imagery, Psychotherapy
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Magnetoencephalography
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Technology
7.Applications, industrial transformation and commercial value of brain-computer interface technology.
Jiangong LUO ; Peng DING ; Anmin GONG ; Guixin TIAN ; Haotian XU ; Lei ZHAO ; Yunfa FU
Journal of Biomedical Engineering 2022;39(2):405-415
Brain-computer interface (BCI) is a revolutionary human-computer interaction technology, which includes both BCI that can output instructions directly from the brain to external devices or machines without relying on the peripheral nerve and muscle system, and BCI that bypasses the peripheral nerve and muscle system and inputs electrical, magnetic, acoustic and optical stimuli or neural feedback directly to the brain from external devices or machines. With the development of BCI technology, it has potential application not only in medical field, but also in non-medical fields, such as education, military, finance, entertainment, smart home and so on. At present, there is little literature on the relevant application of BCI technology, the current situation of BCI industrialization at home and abroad and its commercial value. Therefore, this paper expounds and discusses the above contents, which are expected to provide valuable information for the public and organizations, BCI researchers, BCI industry translators and salespeople, and improve the cognitive level of BCI technology, further promote the application and industrial transformation of BCI technology and enhance the commercial value of BCI, so as to serve mankind better.
Brain/physiology*
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Brain-Computer Interfaces
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Electroencephalography
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Humans
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Technology
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User-Computer Interface
8.Neurofeedback technology based on functional near infrared spectroscopy imaging and its applications.
Mengqi LI ; Anmin GONG ; Wenya NAN ; Bojun XU ; Peng DING ; Yunfa FU
Journal of Biomedical Engineering 2022;39(5):1041-1049
Neurofeedback (NF) technology based on electroencephalogram (EEG) data or functional magnetic resonance imaging (fMRI) has been widely studied and applied. In contrast, functional near infrared spectroscopy (fNIRS) has become a new technique in NF research in recent years. fNIRS is a neuroimaging technology based on hemodynamics, which has the advantages of low cost, good portability and high spatial resolution, and is more suitable for use in natural environments. At present, there is a lack of comprehensive review on fNIRS-NF technology (fNIRS-NF) in China. In order to provide a reference for the research of fNIRS-NF technology, this paper first describes the principle, key technologies and applications of fNIRS-NF, and focuses on the application of fNIRS-NF. Finally, the future development trend of fNIRS-NF is prospected and summarized. In conclusion, this paper summarizes fNIRS-NF technology and its application, and concludes that fNIRS-NF technology has potential practicability in neurological diseases and related fields. fNIRS can be used as a good method for NF training. This paper is expected to provide reference information for the development of fNIRS-NF technology.
Neurofeedback/methods*
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Spectroscopy, Near-Infrared/methods*
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Brain/diagnostic imaging*
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Magnetic Resonance Imaging
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Technology
9.Ethics considerations on brain-computer interface technology.
Zhe ZHANG ; Xu ZHAO ; Yixin MA ; Peng DING ; Wenya NAN ; Anmin GONG ; Yunfa FU
Journal of Biomedical Engineering 2023;40(2):358-364
The development and potential application of brain-computer interface (BCI) technology is closely related to the human brain, so that the ethical regulation of BCI has become an important issue attracting the consideration of society. Existing literatures have discussed the ethical norms of BCI technology from the perspectives of non-BCI developers and scientific ethics, while few discussions have been launched from the perspective of BCI developers. Therefore, there is a great need to study and discuss the ethical norms of BCI technology from the perspective of BCI developers. In this paper, we present the user-centered and non-harmful BCI technology ethics, and then discuss and look forward on them. This paper argues that human beings can cope with the ethical issues arising from BCI technology, and as BCI technology develops, its ethical norms will be improved continuously. It is expected that this paper can provide thoughts and references for the formulation of ethical norms related to BCI technology.
Humans
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Brain-Computer Interfaces
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Technology
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Brain
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User-Computer Interface
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Electroencephalography
10.Execution, assessment and improvement methods of motor imagery for brain-computer interface.
Guixin TIAN ; Junjie CHEN ; Peng DING ; Anmin GONG ; Fan WANG ; Jiangong LUO ; Yiyang DONG ; Lei ZHAO ; Caiping DANG ; Yunfa FU
Journal of Biomedical Engineering 2021;38(3):434-446
Motor imagery (MI) is an important paradigm of driving brain computer interface (BCI). However, MI is not easy to control or acquire, and the performance of MI-BCI depends heavily on the performance of the subjects' MI. Therefore, the correct execution of MI mental activities, ability evaluation and improvement methods play important and even critical roles in the improvement and application of MI-BCI system's performance. However, in the research and development of MI-BCI, the existing researches mainly focus on the decoding algorithm of MI, but do not pay enough attention to the above three aspects of MI mental activities. In this paper, these problems of MI-BCI are discussed in detail, and it is pointed out that the subjects tend to use visual motor imagery as kinesthetic motor imagery. In the future, we need to develop some objective, quantitatively visualized MI ability evaluation methods, and develop some effective and less time-consumption training methods to improve MI ability. It is also necessary to solve the differences and commonness of MI problems between and within individuals and MI-BCI illiteracy to a certain extent.
Algorithms
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Brain-Computer Interfaces
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Electroencephalography
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Humans
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Imagery, Psychotherapy
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Imagination