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Gou L, Liu YY, Lin C, Zhang G, Gao J, Zhu YP, Guo X, Lu XX, Ma ZG. [Etiologies of extreme thrombocytosis in children: a retrospective study]. Zhonghua Xue Ye Xue Za Zhi 2023; 44:344-346. [PMID: 37357007 DOI: 10.3760/cma.j.issn.0253-2727.2023.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Affiliation(s)
- L Gou
- West China Second University Hospital, Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Chengdu 610041, China
| | - Y Y Liu
- West China Second University Hospital, Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Chengdu 610041, China
| | - C Lin
- West China Second University Hospital, Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Chengdu 610041, China
| | - G Zhang
- West China Second University Hospital, Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Chengdu 610041, China
| | - J Gao
- West China Second University Hospital, Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Chengdu 610041, China
| | - Y P Zhu
- West China Second University Hospital, Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Chengdu 610041, China
| | - X Guo
- West China Second University Hospital, Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Chengdu 610041, China
| | - X X Lu
- West China Second University Hospital, Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Chengdu 610041, China
| | - Z G Ma
- West China Second University Hospital, Key Laboratory of Obstetric & Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education, Chengdu 610041, China
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2
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Hahn JD, Gao L, Boesen T, Gou L, Hintiryan H, Dong HW. Macroscale connections of the mouse lateral preoptic area and anterior lateral hypothalamic area. J Comp Neurol 2022; 530:2254-2285. [PMID: 35579973 PMCID: PMC9283274 DOI: 10.1002/cne.25331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 04/05/2022] [Accepted: 04/07/2022] [Indexed: 11/25/2022]
Abstract
The macroscale neuronal connections of the lateral preoptic area (LPO) and the caudally adjacent lateral hypothalamic area anterior region (LHAa) were investigated in mice by anterograde and retrograde axonal tracing. Both hypothalamic regions are highly and diversely connected, with connections to >200 gray matter regions spanning the forebrain, midbrain, and rhombicbrain. Intrahypothalamic connections predominate, followed by connections with the cerebral cortex and cerebral nuclei. A similar overall pattern of LPO and LHAa connections contrasts with substantial differences between their input and output connections. Strongest connections include outputs to the lateral habenula, medial septal and diagonal band nuclei, and inputs from rostral and caudal lateral septal nuclei; however, numerous additional robust connections were also observed. The results are discussed in relation to a current model for the mammalian forebrain network that associates LPO and LHAa with a range of functional roles, including reward prediction, innate survival behaviors (including integrated somatomotor and physiological control), and affect. The present data suggest a broad and intricate role for LPO and LHAa in behavioral control, similar in that regard to previously investigated LHA regions, contributing to the finely tuned sensory‐motor integration that is necessary for behavioral guidance supporting survival and reproduction.
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Affiliation(s)
- Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, California, USA
| | - Lei Gao
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Tyler Boesen
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Lin Gou
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Houri Hintiryan
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
| | - Hong-Wei Dong
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA
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3
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Foster NN, Barry J, Korobkova L, Garcia L, Gao L, Becerra M, Sherafat Y, Peng B, Li X, Choi JH, Gou L, Zingg B, Azam S, Lo D, Khanjani N, Zhang B, Stanis J, Bowman I, Cotter K, Cao C, Yamashita S, Tugangui A, Li A, Jiang T, Jia X, Feng Z, Aquino S, Mun HS, Zhu M, Santarelli A, Benavidez NL, Song M, Dan G, Fayzullina M, Ustrell S, Boesen T, Johnson DL, Xu H, Bienkowski MS, Yang XW, Gong H, Levine MS, Wickersham I, Luo Q, Hahn JD, Lim BK, Zhang LI, Cepeda C, Hintiryan H, Dong HW. The mouse cortico-basal ganglia-thalamic network. Nature 2021; 598:188-194. [PMID: 34616074 PMCID: PMC8494639 DOI: 10.1038/s41586-021-03993-3] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/03/2021] [Indexed: 12/05/2022]
Abstract
The cortico–basal ganglia–thalamo–cortical loop is one of the fundamental network motifs in the brain. Revealing its structural and functional organization is critical to understanding cognition, sensorimotor behaviour, and the natural history of many neurological and neuropsychiatric disorders. Classically, this network is conceptualized to contain three information channels: motor, limbic and associative1–4. Yet this three-channel view cannot explain the myriad functions of the basal ganglia. We previously subdivided the dorsal striatum into 29 functional domains on the basis of the topography of inputs from the entire cortex5. Here we map the multi-synaptic output pathways of these striatal domains through the globus pallidus external part (GPe), substantia nigra reticular part (SNr), thalamic nuclei and cortex. Accordingly, we identify 14 SNr and 36 GPe domains and a direct cortico-SNr projection. The striatonigral direct pathway displays a greater convergence of striatal inputs than the more parallel striatopallidal indirect pathway, although direct and indirect pathways originating from the same striatal domain ultimately converge onto the same postsynaptic SNr neurons. Following the SNr outputs, we delineate six domains in the parafascicular and ventromedial thalamic nuclei. Subsequently, we identify six parallel cortico–basal ganglia–thalamic subnetworks that sequentially transduce specific subsets of cortical information through every elemental node of the cortico–basal ganglia–thalamic loop. Thalamic domains relay this output back to the originating corticostriatal neurons of each subnetwork in a bona fide closed loop. Mesoscale connectomic mapping of the cortico–basal ganglia–thalamic network reveals key architectural and information processing features.
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Affiliation(s)
- Nicholas N Foster
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. .,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| | - Joshua Barry
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Laura Korobkova
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Luis Garcia
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Lei Gao
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marlene Becerra
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yasmine Sherafat
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Bo Peng
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Jun-Hyeok Choi
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Lin Gou
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Brian Zingg
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sana Azam
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Darrick Lo
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Neda Khanjani
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Bin Zhang
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jim Stanis
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ian Bowman
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kaelan Cotter
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chunru Cao
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Seita Yamashita
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Amanda Tugangui
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, China
| | - Tao Jiang
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Xueyan Jia
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Zhao Feng
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Sarvia Aquino
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hyun-Seung Mun
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Muye Zhu
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Anthony Santarelli
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Nora L Benavidez
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Monica Song
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Gordon Dan
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marina Fayzullina
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarah Ustrell
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Tyler Boesen
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - David L Johnson
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hanpeng Xu
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Michael S Bienkowski
- Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - X William Yang
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience, Los Angeles, CA, USA
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, China
| | - Michael S Levine
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Ian Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China.,School of Biomedical Engineering, Hainan University, Haikou, China
| | - Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Byung Kook Lim
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Li I Zhang
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Carlos Cepeda
- Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Houri Hintiryan
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hong-Wei Dong
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. .,Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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4
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Muñoz-Castañeda R, Zingg B, Matho KS, Chen X, Wang Q, Foster NN, Li A, Narasimhan A, Hirokawa KE, Huo B, Bannerjee S, Korobkova L, Park CS, Park YG, Bienkowski MS, Chon U, Wheeler DW, Li X, Wang Y, Naeemi M, Xie P, Liu L, Kelly K, An X, Attili SM, Bowman I, Bludova A, Cetin A, Ding L, Drewes R, D'Orazi F, Elowsky C, Fischer S, Galbavy W, Gao L, Gillis J, Groblewski PA, Gou L, Hahn JD, Hatfield JT, Hintiryan H, Huang JJ, Kondo H, Kuang X, Lesnar P, Li X, Li Y, Lin M, Lo D, Mizrachi J, Mok S, Nicovich PR, Palaniswamy R, Palmer J, Qi X, Shen E, Sun YC, Tao HW, Wakemen W, Wang Y, Yao S, Yuan J, Zhan H, Zhu M, Ng L, Zhang LI, Lim BK, Hawrylycz M, Gong H, Gee JC, Kim Y, Chung K, Yang XW, Peng H, Luo Q, Mitra PP, Zador AM, Zeng H, Ascoli GA, Josh Huang Z, Osten P, Harris JA, Dong HW. Cellular anatomy of the mouse primary motor cortex. Nature 2021; 598:159-166. [PMID: 34616071 PMCID: PMC8494646 DOI: 10.1038/s41586-021-03970-w] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 08/27/2021] [Indexed: 12/24/2022]
Abstract
An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input-output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture.
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Affiliation(s)
| | - Brian Zingg
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Xiaoyin Chen
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Quanxin Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nicholas N Foster
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | | | - Karla E Hirokawa
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | - Bingxing Huo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Laura Korobkova
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Chris Sin Park
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Young-Gyun Park
- Institute for Medical Engineering and Science, Department of Chemical Engineering, Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Michael S Bienkowski
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Uree Chon
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA
| | - Diek W Wheeler
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Yun Wang
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Peng Xie
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Lijuan Liu
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Kathleen Kelly
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xu An
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Sarojini M Attili
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Ian Bowman
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Ali Cetin
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Liya Ding
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Rhonda Drewes
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Corey Elowsky
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | | | - Lei Gao
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Jesse Gillis
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Lin Gou
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Joshua T Hatfield
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Houri Hintiryan
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Junxiang Jason Huang
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hideki Kondo
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xiuli Kuang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | - Xu Li
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Yaoyao Li
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Mengkuan Lin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Darrick Lo
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | | | - Philip R Nicovich
- Allen Institute for Brain Science, Seattle, WA, USA
- Cajal Neuroscience, Seattle, WA, USA
| | | | - Jason Palmer
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xiaoli Qi
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Elise Shen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Yu-Chi Sun
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Huizhong W Tao
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Yimin Wang
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Huiqing Zhan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Muye Zhu
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Li I Zhang
- Center for Neural Circuits and Sensory Processing Disorders, Zilkha Neurogenetics Institute (ZNI), Department of Physiology and Neuroscience, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Byung Kook Lim
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
- Division of Biological Science, Neurobiology section, University of California San Diego, San Diego, CA, USA
| | | | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - James C Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA
| | - Kwanghun Chung
- Institute for Medical Engineering and Science, Department of Chemical Engineering, Picower Institute for Learning and Memory, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - X William Yang
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Hanchuan Peng
- SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Partha P Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures and Plasticity, Bioengineering Department and Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.
| | - Z Josh Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA.
| | - Pavel Osten
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA, USA.
- Cajal Neuroscience, Seattle, WA, USA.
| | - Hong-Wei Dong
- UCLA Brain Research and Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
- USC Stevens Neuroimaging and Informatics Institute (INI), Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA.
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5
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Callaway EM, Dong HW, Ecker JR, Hawrylycz MJ, Huang ZJ, Lein ES, Ngai J, Osten P, Ren B, Tolias AS, White O, Zeng H, Zhuang X, Ascoli GA, Behrens MM, Chun J, Feng G, Gee JC, Ghosh SS, Halchenko YO, Hertzano R, Lim BK, Martone ME, Ng L, Pachter L, Ropelewski AJ, Tickle TL, Yang XW, Zhang K, Bakken TE, Berens P, Daigle TL, Harris JA, Jorstad NL, Kalmbach BE, Kobak D, Li YE, Liu H, Matho KS, Mukamel EA, Naeemi M, Scala F, Tan P, Ting JT, Xie F, Zhang M, Zhang Z, Zhou J, Zingg B, Armand E, Yao Z, Bertagnolli D, Casper T, Crichton K, Dee N, Diep D, Ding SL, Dong W, Dougherty EL, Fong O, Goldman M, Goldy J, Hodge RD, Hu L, Keene CD, Krienen FM, Kroll M, Lake BB, Lathia K, Linnarsson S, Liu CS, Macosko EZ, McCarroll SA, McMillen D, Nadaf NM, Nguyen TN, Palmer CR, Pham T, Plongthongkum N, Reed NM, Regev A, Rimorin C, Romanow WJ, Savoia S, Siletti K, Smith K, Sulc J, Tasic B, Tieu M, Torkelson A, Tung H, van Velthoven CTJ, Vanderburg CR, Yanny AM, Fang R, Hou X, Lucero JD, Osteen JK, Pinto-Duarte A, Poirion O, Preissl S, Wang X, Aldridge AI, Bartlett A, Boggeman L, O’Connor C, Castanon RG, Chen H, Fitzpatrick C, Luo C, Nery JR, Nunn M, Rivkin AC, Tian W, Dominguez B, Ito-Cole T, Jacobs M, Jin X, Lee CT, Lee KF, Miyazaki PA, Pang Y, Rashid M, Smith JB, Vu M, Williams E, Biancalani T, Booeshaghi AS, Crow M, Dudoit S, Fischer S, Gillis J, Hu Q, Kharchenko PV, Niu SY, Ntranos V, Purdom E, Risso D, de Bézieux HR, Somasundaram S, Street K, Svensson V, Vaishnav ED, Van den Berge K, Welch JD, An X, Bateup HS, Bowman I, Chance RK, Foster NN, Galbavy W, Gong H, Gou L, Hatfield JT, Hintiryan H, Hirokawa KE, Kim G, Kramer DJ, Li A, Li X, Luo Q, Muñoz-Castañeda R, Stafford DA, Feng Z, Jia X, Jiang S, Jiang T, Kuang X, Larsen R, Lesnar P, Li Y, Li Y, Liu L, Peng H, Qu L, Ren M, Ruan Z, Shen E, Song Y, Wakeman W, Wang P, Wang Y, Wang Y, Yin L, Yuan J, Zhao S, Zhao X, Narasimhan A, Palaniswamy R, Banerjee S, Ding L, Huilgol D, Huo B, Kuo HC, Laturnus S, Li X, Mitra PP, Mizrachi J, Wang Q, Xie P, Xiong F, Yu Y, Eichhorn SW, Berg J, Bernabucci M, Bernaerts Y, Cadwell CR, Castro JR, Dalley R, Hartmanis L, Horwitz GD, Jiang X, Ko AL, Miranda E, Mulherkar S, Nicovich PR, Owen SF, Sandberg R, Sorensen SA, Tan ZH, Allen S, Hockemeyer D, Lee AY, Veldman MB, Adkins RS, Ament SA, Bravo HC, Carter R, Chatterjee A, Colantuoni C, Crabtree J, Creasy H, Felix V, Giglio M, Herb BR, Kancherla J, Mahurkar A, McCracken C, Nickel L, Olley D, Orvis J, Schor M, Hood G, Dichter B, Grauer M, Helba B, Bandrowski A, Barkas N, Carlin B, D’Orazi FD, Degatano K, Gillespie TH, Khajouei F, Konwar K, Thompson C, Kelly K, Mok S, Sunkin S. A multimodal cell census and atlas of the mammalian primary motor cortex. Nature 2021; 598:86-102. [PMID: 34616075 PMCID: PMC8494634 DOI: 10.1038/s41586-021-03950-0] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Accepted: 08/25/2021] [Indexed: 12/14/2022]
Abstract
Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1-5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.
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6
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Zhang B, Hu L, Zhang J, Wu H, Li W, Gou L, Liu H. Insulin growth factor-1 enhances proliferation and inhibits apoptosis of neural progenitor cells by phosphorylation of Akt/mTOR/p70S6K molecules and triggering intrinsic apoptosis signaling pathway. Cell Tissue Bank 2021; 23:459-472. [PMID: 34494222 DOI: 10.1007/s10561-021-09956-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 09/02/2021] [Indexed: 02/05/2023]
Abstract
Neural progenitor cells (NPCs) transplantation is known as a potential strategy for treating spinal cord injury (SCI). This study aimed to investigate effects of insulin growth factor-1 (IGF-I) on NPCs proliferation and clarify associated mechanisms. NPCs isolated from T8-T10 segmental spinal cord tissues of rats were cultured and identification. Then, lentivirus packing plasmids containing IGF-I was constructed and used for NPCs infection. Cell proliferation was evaluated by detecting 5-Bromodeoxyuridine (BrdU) expression in NPCs, cell differentiation was detected using double-labeling immunofluorescence staining while cell apoptosis was detected using TUNEL assay. In addition, the signal expression of Akt/mTOR/p70S6K in NPCs cells were investigated using immunofluorescence staining and western blot assay. The experimental group was defined as pCMV-IGF-I group, while the negative control group was defined as pCMV-LacZ group. Cells infected with pCMV-IGF-I lentivirus followed by addition of 100 mg/ml rapamycin were defined as pCMV-IGF-I + Rapa group. NPCs were successfully isolated, identified and cultured. IGF-I overexpression significantly inhibited cell apoptosis and enhanced cell migration. Akt/mTOR/ p70S6K signaling cascade was proved to be present in NPCs, IGF-I overexpression significantly activated Akt/mTOR/p70S6K signaling cascade, while rapamycin addition inhibited its expression. Also, the activated Akt/mTOR/p70S6K signal cascade induced by IGF-I significantly enhanced BrdU expression and inhibited cell apoptosis, and promoted the differentiation of NPC into the neuronal system. However, the rapamycin addition inhibited the cell response induced by IGF-I overexpression. IGF-I overexpression could enhance cell proliferation, inhibit cell apoptosis and promote their differentiation into neuronal systems by activating Akt/mTOR/p70S6K signaling cascade in vitro, indicating that the Akt/mTOR/p70S6K signaling cascade may be the potentially mechanism for the endogenous repair and remodeling of spinal cord after injury.
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Affiliation(s)
- Bo Zhang
- Department of Orthopaedic Surgery, Nanchong Central Hospital, the Second Clinical Medical College of North Sichuan Medical College, Nanchong, 637000, Sichuan, China
| | - Lingyun Hu
- Department of Orthopaedic Surgery, Nanchong Central Hospital, the Second Clinical Medical College of North Sichuan Medical College, Nanchong, 637000, Sichuan, China.,Department of Orthopedic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Jianying Zhang
- Department of Radiology, Nanchong Central Hospital, the Second Clinical Medical College of North Sichuan Medical College, Nanchong, Sichuan, 637000, China
| | - Hui Wu
- Department of Orthopaedic Surgery, Nanchong Central Hospital, the Second Clinical Medical College of North Sichuan Medical College, Nanchong, 637000, Sichuan, China
| | - Wei Li
- Department of Orthopaedic Surgery, Nanchong Central Hospital, the Second Clinical Medical College of North Sichuan Medical College, Nanchong, 637000, Sichuan, China
| | - Lin Gou
- Department of Orthopaedic Surgery, Nanchong Central Hospital, the Second Clinical Medical College of North Sichuan Medical College, Nanchong, 637000, Sichuan, China
| | - Hao Liu
- Department of Orthopedic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China.
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7
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Benavidez NL, Bienkowski MS, Zhu M, Garcia LH, Fayzullina M, Gao L, Bowman I, Gou L, Khanjani N, Cotter KR, Korobkova L, Becerra M, Cao C, Song MY, Zhang B, Yamashita S, Tugangui AJ, Zingg B, Rose K, Lo D, Foster NN, Boesen T, Mun HS, Aquino S, Wickersham IR, Ascoli GA, Hintiryan H, Dong HW. Organization of the inputs and outputs of the mouse superior colliculus. Nat Commun 2021; 12:4004. [PMID: 34183678 PMCID: PMC8239028 DOI: 10.1038/s41467-021-24241-2] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/02/2021] [Indexed: 11/16/2022] Open
Abstract
The superior colliculus (SC) receives diverse and robust cortical inputs to drive a range of cognitive and sensorimotor behaviors. However, it remains unclear how descending cortical input arising from higher-order associative areas coordinate with SC sensorimotor networks to influence its outputs. Here, we construct a comprehensive map of all cortico-tectal projections and identify four collicular zones with differential cortical inputs: medial (SC.m), centromedial (SC.cm), centrolateral (SC.cl) and lateral (SC.l). Further, we delineate the distinctive brain-wide input/output organization of each collicular zone, assemble multiple parallel cortico-tecto-thalamic subnetworks, and identify the somatotopic map in the SC that displays distinguishable spatial properties from the somatotopic maps in the neocortex and basal ganglia. Finally, we characterize interactions between those cortico-tecto-thalamic and cortico-basal ganglia-thalamic subnetworks. This study provides a structural basis for understanding how SC is involved in integrating different sensory modalities, translating sensory information to motor command, and coordinating different actions in goal-directed behaviors.
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Affiliation(s)
- Nora L Benavidez
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael S Bienkowski
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Muye Zhu
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Luis H Garcia
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Marina Fayzullina
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Lei Gao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Ian Bowman
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Lin Gou
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Neda Khanjani
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Kaelan R Cotter
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Laura Korobkova
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marlene Becerra
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chunru Cao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Monica Y Song
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Bin Zhang
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Seita Yamashita
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Amanda J Tugangui
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Brian Zingg
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Kasey Rose
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
| | - Darrick Lo
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Nicholas N Foster
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Tyler Boesen
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Hyun-Seung Mun
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Sarvia Aquino
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ian R Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Giorgio A Ascoli
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Houri Hintiryan
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Hong-Wei Dong
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
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8
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Hintiryan H, Bowman I, Johnson DL, Korobkova L, Zhu M, Khanjani N, Gou L, Gao L, Yamashita S, Bienkowski MS, Garcia L, Foster NN, Benavidez NL, Song MY, Lo D, Cotter KR, Becerra M, Aquino S, Cao C, Cabeen RP, Stanis J, Fayzullina M, Ustrell SA, Boesen T, Tugangui AJ, Zhang ZG, Peng B, Fanselow MS, Golshani P, Hahn JD, Wickersham IR, Ascoli GA, Zhang LI, Dong HW. Connectivity characterization of the mouse basolateral amygdalar complex. Nat Commun 2021; 12:2859. [PMID: 34001873 PMCID: PMC8129205 DOI: 10.1038/s41467-021-22915-5] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 03/25/2021] [Indexed: 11/08/2022] Open
Abstract
The basolateral amygdalar complex (BLA) is implicated in behaviors ranging from fear acquisition to addiction. Optogenetic methods have enabled the association of circuit-specific functions to uniquely connected BLA cell types. Thus, a systematic and detailed connectivity profile of BLA projection neurons to inform granular, cell type-specific interrogations is warranted. Here, we apply machine-learning based computational and informatics analysis techniques to the results of circuit-tracing experiments to create a foundational, comprehensive BLA connectivity map. The analyses identify three distinct domains within the anterior BLA (BLAa) that house target-specific projection neurons with distinguishable morphological features. We identify brain-wide targets of projection neurons in the three BLAa domains, as well as in the posterior BLA, ventral BLA, posterior basomedial, and lateral amygdalar nuclei. Inputs to each nucleus also are identified via retrograde tracing. The data suggests that connectionally unique, domain-specific BLAa neurons are associated with distinct behavior networks.
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Affiliation(s)
- Houri Hintiryan
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
| | - Ian Bowman
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - David L Johnson
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Laura Korobkova
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Muye Zhu
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Neda Khanjani
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Lin Gou
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Lei Gao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Seita Yamashita
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Michael S Bienkowski
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Luis Garcia
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Nicholas N Foster
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Nora L Benavidez
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Monica Y Song
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Darrick Lo
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Kaelan R Cotter
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Marlene Becerra
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarvia Aquino
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chunru Cao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Ryan P Cabeen
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jim Stanis
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marina Fayzullina
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarah A Ustrell
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Tyler Boesen
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Amanda J Tugangui
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Zheng-Gang Zhang
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Physiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Bo Peng
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Michael S Fanselow
- Brain Research Institute, Department of Psychology, University of California, Los Angeles, CA, USA
| | - Peyman Golshani
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- West Los Angeles Veterans Administration Medical Center, Los Angeles, CA, USA
| | - Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Ian R Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Giorgio A Ascoli
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Li I Zhang
- Center for Neural Circuitry & Sensory Processing Disorders, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hong-Wei Dong
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
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9
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Peng L, Li A, Liu S, Sun H, Zheng M, Zhou J, Zhang J, Zhang X, Zhou Q, Zhong W, Yang X, Tu H, Su J, Yan H, Gou L, Gao H, Wu Y. P85.02 NGS could not Replace FISH Regarding to MET Amplification as an Optimal Biomarker. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.01.1224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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10
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Abstract
Chronic pain is a significant public health problem with emotional and disabling factors, which may not completely respond to current medical treatments such as opioids. The systematic review and meta-analysis aimed to examine the effectiveness and safety of MBCT for patients with chronic pain. Database searches of PubMed, Medline, EMBASE, the Cochrane Library, PsycINFO, Web of Science, Scopus and CINAHL up to 15 October 2019. Included studies assessed with the Cochrane risk-of-bias tool. Eight RCTs involved 433 patients, including chronic low back pain, fibromyalgia, migraine, rheumatoid arthritis and mix etiology. MBCT intervention demonstrated a short-term improvement on depression mood [standardized mean difference -0.72; 95% confidence interval = -1.22 to -0.22, p = 0.005] compared with usual care and was associated with short-term improvement in mindfulness compared with non-MBCT [SMD 0.51; 95% CI = 0.01 to 1.01, p = 0.04]. Between-group differences in pain intensity, pain inference and pain acceptance were not significant at short- or long-term follow-up. Compared to active treatments, MBCT intervention not found significant differences in either short- or long-term outcomes. MBCT showed short-term efficacious on depressed mood and mindfulness of chronic pain patients. Longer follow-ups, large sample and rigorous RCTs that can be best understand remaining uncertainties needed.
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Affiliation(s)
- Ju-Hong Pei
- School of Nursing, Lanzhou University, Lanzhou, Gansu, People's Republic of China
| | - Tong Ma
- Department of Nursing, Lanzhou University Second Hospital, Lanzhou, Gansu, People's Republic of China
| | - Rui-Lin Nan
- Department of Spine Minimally Invasive Orthopedics, Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou, Gansu, People's Republic of China
| | - Hai-Xia Chen
- School of Nursing, Lanzhou University, Lanzhou, Gansu, People's Republic of China
| | - Ya-Bin Zhang
- School of Nursing, Lanzhou University, Lanzhou, Gansu, People's Republic of China
| | - Lin Gou
- School of Nursing, Lanzhou University, Lanzhou, Gansu, People's Republic of China
| | - Xin-Man Dou
- School of Nursing, Lanzhou University, Lanzhou, Gansu, People's Republic of China.,Department of EICU, Lanzhou University Second Hospital, Lanzhou, Gansu, People's Republic of China
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11
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Cao M, Gou L, Chen Y, Huang M. 241P Germline genetic features of Chinese patients with breast cancer. Ann Oncol 2020. [DOI: 10.1016/j.annonc.2020.08.362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
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12
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Tang Z, Zhou C, Cai Y, Tang Y, Sun W, Yao H, Zheng T, Chen H, Xiao Y, Shan Z, Bu T, Wang X, Huang L, Gou L. Purification, characterization and antioxidant activities in vitro of polysaccharides from Amaranthus hybridus L. PeerJ 2020; 8:e9077. [PMID: 32391207 PMCID: PMC7195838 DOI: 10.7717/peerj.9077] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 04/07/2020] [Indexed: 11/29/2022] Open
Abstract
Background Amaranthus hybridus L. is an annual, erect or less commonly ascending herb that is a member of the Amaranthaceae family. Polysaccharides extracted from traditional Chinese medicines may be effective substances with antioxidant activity. Methods In this study, we isolated crude polysaccharides from A. hybridus (AHP-M) using microwave-assisted extraction. Then, the AHP-M was purified by chromatography with DEAE-32 cellulose, and two fractions, AHP-M-1 and AHP-M-2, were obtained. The structural characteristics of AHP-M-1 and AHP-M-2 were investigated, and their antioxidant activities were analyzed in vitro. Results We found that the monosaccharide composition of AHP-M-1 was different from that of AHP-M-2. The molecular weights of AHP-M-1 and AHP-M-2 were 77.625 kDa and 93.325 kDa, respectively. The results showed that the antioxidant activity of AHP-M-2 was better than that of AHP-M-1. For AHP-M-2, the DPPH radical scavenging rate at a concentration of 2 mg/mL was 78.87%, the hydroxyl radical scavenging rate was 39.34%, the superoxide anion radical scavenging rate was 80.2%, and the reduction ability of Fe3+ was approximately 0.90. The total antioxidant capacity per milligram of AHP-M-2 was 6.42, which was higher than that of Vitamin C (Vc). Conclusion The in vitro test indicated that AHP-M-1 and AHP-M-2 have good antioxidant activity, demonstrating that A. hybridus L. polysaccharide has immense potential as a natural antioxidants.
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Affiliation(s)
- Zizhong Tang
- College of Life Sciences, Sichuan Agricultural University, Yaan, China
| | - Caixia Zhou
- College of Life Sciences, Sichuan Agricultural University, Yaan, China
| | - Yi Cai
- College of Life Sciences, Sichuan Agricultural University, Yaan, China
| | - Yujia Tang
- College of Life Sciences, Sichuan Agricultural University, Yaan, China
| | - Wenjun Sun
- College of Life Sciences, Sichuan Agricultural University, Yaan, China
| | - Huipeng Yao
- College of Life Sciences, Sichuan Agricultural University, Yaan, China
| | - Tianrun Zheng
- College of Life Sciences, Sichuan Agricultural University, Yaan, China
| | - Hui Chen
- College of Life Sciences, Sichuan Agricultural University, Yaan, China
| | - Yirong Xiao
- Sichuan Agricultural University Hospital, Yaan, China
| | - Zhi Shan
- College of Life Sciences, Sichuan Agricultural University, Yaan, China
| | - Tongliang Bu
- College of Life Sciences, Sichuan Agricultural University, Yaan, China
| | - Xiaoli Wang
- College of Life Sciences, Sichuan Agricultural University, Yaan, China
| | - Lin Huang
- Triticeae Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Lin Gou
- College of Life Sciences, Sichuan Agricultural University, Yaan, China
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13
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Abstract
Regenerative dentistry is an emerging field of medicine involving stem cell technology, tissue engineering and dental science. It exploits biological mechanisms to regenerate damaged oral tissues and restore their functions. Platelet‐rich plasma (PRP) is a biological product that is defined as the portion of plasma fraction of autologous blood with a platelet concentration above that of the original whole blood. A super‐mixture of key cytokines and growth factors is present in platelet granules. Thus, the application of PRP has gained unprecedented attention in regenerative medicine. The rationale underlies the utilization of PRP is that it acts as a biomaterial to deliver critical growth factors and cytokines from platelet granules to the targeted area, thus promoting regeneration in a variety of tissues. Based on enhanced understanding of cell signalling and growth factor biology, researchers have begun to use PRP treatment as a novel method to regenerate damaged tissues, including liver, bone, cartilage, tendon and dental pulp. To enable better understanding of the regenerative effects of PRP in dentistry, this review describes different methods of preparation and application of this biological product, and provides detailed explanations of the controversies and future prospects related to the use of PRP in dental regenerative medicine.
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Affiliation(s)
- J Xu
- Shenzhen Longgang Institute of Stomatology, Shenzhen, Guangdong, China.,Department of Otolaryngology, Longgang E.N.T. Hospital & Shenzhen Key Laboratory of E.N.T., Institute of E.N.T, Shenzhen, Guangdong, China
| | - L Gou
- Center for Genetic Medicine, Xuzhou Maternity and Child Health Care Hospital, Xuzhou, Jiangsu, China
| | - P Zhang
- Shenzhen Longgang Institute of Stomatology, Shenzhen, Guangdong, China.,Department of Otolaryngology, Longgang E.N.T. Hospital & Shenzhen Key Laboratory of E.N.T., Institute of E.N.T, Shenzhen, Guangdong, China
| | - H Li
- Shenzhen Longgang Institute of Stomatology, Shenzhen, Guangdong, China.,Department of Otolaryngology, Longgang E.N.T. Hospital & Shenzhen Key Laboratory of E.N.T., Institute of E.N.T, Shenzhen, Guangdong, China
| | - S Qiu
- Department of Otolaryngology, Longgang E.N.T. Hospital & Shenzhen Key Laboratory of E.N.T., Institute of E.N.T, Shenzhen, Guangdong, China
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14
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Bienkowski MS, Benavidez NL, Wu K, Gou L, Becerra M, Dong H. Cover Image, Volume 527, Issue 9. J Comp Neurol 2019. [DOI: 10.1002/cne.24686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Michael S. Bienkowski
- USC Stevens Neuroimaging and Informatics Institute, Center for Integrative ConnectomicsKeck School of Medicine of University of Southern California Los Angeles California
| | - Nora L. Benavidez
- USC Stevens Neuroimaging and Informatics Institute, Center for Integrative ConnectomicsKeck School of Medicine of University of Southern California Los Angeles California
| | - Kevin Wu
- USC Stevens Neuroimaging and Informatics Institute, Center for Integrative ConnectomicsKeck School of Medicine of University of Southern California Los Angeles California
| | - Lin Gou
- USC Stevens Neuroimaging and Informatics Institute, Center for Integrative ConnectomicsKeck School of Medicine of University of Southern California Los Angeles California
| | - Marlene Becerra
- USC Stevens Neuroimaging and Informatics Institute, Center for Integrative ConnectomicsKeck School of Medicine of University of Southern California Los Angeles California
| | - Hong‐Wei Dong
- USC Stevens Neuroimaging and Informatics Institute, Center for Integrative ConnectomicsKeck School of Medicine of University of Southern California Los Angeles California
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15
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Bienkowski MS, Benavidez NL, Wu K, Gou L, Becerra M, Dong HW. Extrastriate connectivity of the mouse dorsal lateral geniculate thalamic nucleus. J Comp Neurol 2019; 527:1419-1442. [PMID: 30620046 DOI: 10.1002/cne.24627] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 11/14/2018] [Accepted: 12/10/2018] [Indexed: 12/12/2022]
Abstract
The mammalian visual system is one of the most well-studied brain systems. Visual information from the retina is relayed to the dorsal lateral geniculate nucleus of the thalamus (LGd). The LGd then projects topographically to primary visual cortex (VISp) to mediate visual perception. In this view, the VISp is a critical network hub where visual information must traverse LGd-VISp circuits to reach higher order "extrastriate" visual cortices, which surround the VISp on its medial and lateral borders. However, decades of conflicting reports in a variety of mammals support or refute the existence of extrastriate LGd connections that can bypass the VISp. Here, we provide evidence of bidirectional extrastriate connectivity with the mouse LGd. Using small, discrete coinjections of anterograde and retrograde tracers within the thalamus and cortex, our cross-validated approach identified bidirectional connectivity between LGd and extrastriate visual cortices. We find robust reciprocal connectivity of the medial extrastriate regions with LGd neurons distributed along the "ventral strip" border with the intergeniculate leaflet. In contrast, LGd input to lateral extrastriate regions is sparse, but lateral extrastriate regions return stronger descending projections to localized LGd areas. We show further evidence that axons from lateral extrastriate regions can overlap onto medial extrastriate-projecting LGd neurons in the ventral strip, providing a putative subcortical LGd pathway for communication between medial and lateral extrastriate regions. Overall, our findings support the existence of extrastriate LGd circuits and provide novel understanding of LGd organization in rodent visual system.
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Affiliation(s)
- Michael S Bienkowski
- USC Stevens Neuroimaging and Informatics Institute, Center for Integrative Connectomics, Keck School of Medicine of University of Southern California, Los Angeles, California
| | - Nora L Benavidez
- USC Stevens Neuroimaging and Informatics Institute, Center for Integrative Connectomics, Keck School of Medicine of University of Southern California, Los Angeles, California
| | - Kevin Wu
- USC Stevens Neuroimaging and Informatics Institute, Center for Integrative Connectomics, Keck School of Medicine of University of Southern California, Los Angeles, California
| | - Lin Gou
- USC Stevens Neuroimaging and Informatics Institute, Center for Integrative Connectomics, Keck School of Medicine of University of Southern California, Los Angeles, California
| | - Marlene Becerra
- USC Stevens Neuroimaging and Informatics Institute, Center for Integrative Connectomics, Keck School of Medicine of University of Southern California, Los Angeles, California
| | - Hong-Wei Dong
- USC Stevens Neuroimaging and Informatics Institute, Center for Integrative Connectomics, Keck School of Medicine of University of Southern California, Los Angeles, California
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16
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Li Y, Gou L, Bai Y, Luo X, Li W, Li L. Clinical effect of two anterior approaches to adjacent two-segment cervical spondylotic myelopathy. Biomed Res 2018. [DOI: 10.4066/biomedicalresearch.29-17-3472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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17
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Tang Z, Jin W, Sun R, Liao Y, Zhen T, Chen H, Wu Q, Gou L, Li C. Improved thermostability and enzyme activity of a recombinant phyA mutant phytase from Aspergillus niger N25 by directed evolution and site-directed mutagenesis. Enzyme Microb Technol 2018; 108:74-81. [DOI: 10.1016/j.enzmictec.2017.09.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Revised: 09/04/2017] [Accepted: 09/22/2017] [Indexed: 12/23/2022]
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18
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Gou L, Lee J, Yang JM, Park YD, Zhou HM, Zhan Y, Lü ZR. Inhibition of tyrosinase by fumaric acid: Integration of inhibition kinetics with computational docking simulations. Int J Biol Macromol 2017; 105:1663-1669. [DOI: 10.1016/j.ijbiomac.2016.12.013] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2016] [Revised: 11/30/2016] [Accepted: 12/05/2016] [Indexed: 10/20/2022]
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19
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Gou L, Lee J, Hao H, Park YD, Zhan Y, Lü ZR. The effect of oxaloacetic acid on tyrosinase activity and structure: Integration of inhibition kinetics with docking simulation. Int J Biol Macromol 2017; 101:59-66. [DOI: 10.1016/j.ijbiomac.2017.03.073] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 03/09/2017] [Accepted: 03/14/2017] [Indexed: 01/26/2023]
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20
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Gou L, Tang E, Huang X. Myoepithelial carcinoma of the salivary glands: clinicopathologic features, evaluation of intratumoral microvessel density and analysis of treatment outcomes of 14 cases. Int J Oral Maxillofac Surg 2015. [DOI: 10.1016/j.ijom.2015.08.097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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21
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Yang M, Shi SG, Liu W, Zhang M, Gou L, Kang YX, Liu JJ. Phenotypic variation and diversity of Magnolia sprengeri Pamp. in native habitat. Genet Mol Res 2015; 14:6495-508. [PMID: 26125854 DOI: 10.4238/2015.june.12.2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
The population of Magnolia sprengeri individuals deceased drastically in the late 20th century because of the widespread harvest for traditional Chinese medicinal recipes. In this study, the levels of phenotypic variation and the genetic structure of 2 populations of M. sprengeri were estimated. The phenotypic variation of M. sprengeri characteristics was nonsynchronous, with a coefficient of variation for 37 characters from 9.55-35.87%. The variance stabilizing transformation value ranged from 0.034-52.344%. The variation contribution within the population was greater than the contribution among the population; the among-population rate was 2.864%, while the within-population rate was 15.849%; values of repeatability for among-population and within-population were 0.430 and 0.098, respectively. This indicates that more variation arose from within-population and that population repeatability was much greater than individual repeatability. Variation in the flower organ was greater than that in the leaf organ; this means that vegetative variation was more stable than reproductive variation. Variation in the southern population was greater than that in the northern population.
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Affiliation(s)
- M Yang
- Northwest A&F University, Yangling, Shaanxi, China
| | - S G Shi
- Northwest A&F University, Yangling, Shaanxi, China
| | - W Liu
- Northwest A&F University, Yangling, Shaanxi, China
| | - M Zhang
- Northwest A&F University, Yangling, Shaanxi, China
| | - L Gou
- Northwest A&F University, Yangling, Shaanxi, China
| | - Y X Kang
- Northwest A&F University, Yangling, Shaanxi, China
| | - J J Liu
- Northwest A&F University, Yangling, Shaanxi, China
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22
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Liu R, Wang Z, Gou L, Xu H. A cortical astrocyte subpopulation inhibits axon growth in vitro and in vivo. Mol Med Rep 2015; 12:2598-606. [PMID: 25936767 PMCID: PMC4464481 DOI: 10.3892/mmr.2015.3702] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Accepted: 02/19/2015] [Indexed: 12/28/2022] Open
Abstract
Astrocytes are the most heterogeneous and predominant glial cell type in the central nervous system. However, the functional significance of this heterogeneity remains to be elucidated. Following injury, damaged astrocytes inhibit axonal regeneration in vivo and in vitro. Cultured primary astrocytes are commonly considered good supportive substrates for neuron attachment and axon regeneration. However, it is not known whether different populations of cells in the heterogeneous astrocyte culture affect neuron behavior in the same way. In the present study, the effect of astrocyte heterogeneity on neuronal attachment and neurite outgrowth was examined using an in vitro and in vivo co-culture system. In vitro, neonatal cortical astrocytes were co-cultured with purified dorsal root ganglia (DRG) neurons and astrocyte growth morphology, neuron attachment and neurite growth were evaluated. The results demonstrated that the heterogeneous astrocyte cells showed two different types of growth pattern, typical and atypical. Typical astrocytes were supportive to neuron attachment and neurite growth, which was consistent with previous studies, whereas atypical astrocytes inhibited neuron attachment and neurite growth. These inhibitory astrocytes exhibited a special growth pattern with various shapes and sizes, a high cell density, few oligodendrocytes on the top layer and occupied a smaller growth area compared with typical astrocytes. Neurites extended freely on typical supportive astrocyte populations, however, moved away when they reached atypical astrocyte growth pattern. Neurons growing on the atypical astrocyte pattern demonstrated minimal neurite outgrowth and these neurites had a dystrophic appearance, however, neuronal survival was unaffected. Immunocytochemistry studies demonstrated that these atypical inhibitory astrocytes were glial fibrillary acidic protein (GFAP) positive cells. The existence of inhibitory astrocyte subpopulations in normal astrocytes reflects the complexity of the function of astrocyte populations. In vivo, DRG neurons in grey matter did not show neurite growth, while DRG neurons survived and showed robust axon outgrowth along the corpus callosum. In conclusion, further studies on this new type of inhibitory astrocyte subpopulation may deepen our understanding of the complex biology of astrocytes.
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Affiliation(s)
- Rui Liu
- Department of Physiotherapy and Rehabilitation, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi 710038, P.R. China
| | - Zhe Wang
- Unit of Spinal Surgery, Department of Orthopedic Surgery, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi 710032, P.R. China
| | - Lin Gou
- Laboratory of Neuroimaging, Department of Neurology, UCLA School of Medicine, Los Angeles, CA 90002, USA
| | - Hanpeng Xu
- Unit of Spinal Surgery, Department of Orthopedic Surgery, Xijing Hospital, The Fourth Military Medical University, Xi'an, Shaanxi 710032, P.R. China
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Gou L, Wu Y, Yang J, Zhang X. Targeting C-Met Overexpression for Acquired Resistance to Egfr Tkis. Ann Oncol 2014. [DOI: 10.1093/annonc/mdu349.58] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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24
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Zingg B, Hintiryan H, Gou L, Song MY, Bay M, Bienkowski MS, Foster NN, Yamashita S, Bowman I, Toga AW, Dong HW. Neural networks of the mouse neocortex. Cell 2014; 156:1096-111. [PMID: 24581503 DOI: 10.1016/j.cell.2014.02.023] [Citation(s) in RCA: 490] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2013] [Revised: 01/25/2014] [Accepted: 02/10/2014] [Indexed: 10/25/2022]
Abstract
Numerous studies have examined the neuronal inputs and outputs of many areas within the mammalian cerebral cortex, but how these areas are organized into neural networks that communicate across the entire cortex is unclear. Over 600 labeled neuronal pathways acquired from tracer injections placed across the entire mouse neocortex enabled us to generate a cortical connectivity atlas. A total of 240 intracortical connections were manually reconstructed within a common neuroanatomic framework, forming a cortico-cortical connectivity map that facilitates comparison of connections from different cortical targets. Connectivity matrices were generated to provide an overview of all intracortical connections and subnetwork clusterings. The connectivity matrices and cortical map revealed that the entire cortex is organized into four somatic sensorimotor, two medial, and two lateral subnetworks that display unique topologies and can interact through select cortical areas. Together, these data provide a resource that can be used to further investigate cortical networks and their corresponding functions.
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Affiliation(s)
- Brian Zingg
- Zilkha Neurogenetic Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Houri Hintiryan
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Lin Gou
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Monica Y Song
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Maxwell Bay
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Michael S Bienkowski
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Nicholas N Foster
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Seita Yamashita
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Ian Bowman
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Arthur W Toga
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA; Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA
| | - Hong-Wei Dong
- Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA; Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA 90032, USA.
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25
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Liu R, Xu H, Wang G, Li J, Gou L, Zhang L, Miao J, Li Z. Extraocular muscle characteristics related to myasthenia gravis susceptibility. PLoS One 2013; 8:e55611. [PMID: 23409007 PMCID: PMC3568149 DOI: 10.1371/journal.pone.0055611] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2012] [Accepted: 01/02/2013] [Indexed: 11/30/2022] Open
Abstract
Background The pathogenesis of extraocular muscle (EOM) weakness in myasthenia gravis might involve a mechanism specific to the EOM. The aim of this study was to investigate characteristics of the EOM related to its susceptibility to myasthenia gravis. Methods Female F344 rats and female Sprague-Dawley rats were assigned to experimental and control groups. The experimental group received injection with Ringer solution containing monoclonal antibody against the acetylcholine receptor (AChR), mAb35 (0.25 mg/kg), to induce experimental autoimmune myasthenia gravis, and the control group received injection with Ringer solution alone. Three muscles were analyzed: EOM, diaphragm, and tibialis anterior. Tissues were examined by light microscopy, fluorescence histochemistry, and transmission electron microscopy. Western blot analysis was used to assess marker expression and ELISA analysis was used to quantify creatine kinase levels. Microarray assay was conducted to detect differentially expressed genes. Results In the experimental group, the EOM showed a simpler neuromuscular junction (NMJ) structure compared to the other muscles; the NMJ had fewer synaptic folds, showed a lesser amount of AChR, and the endplate was wider compared to the other muscles. Results of microarray assay showed differential expression of 54 genes in the EOM between the experimental and control groups. Conclusion Various EOM characteristics appear to be related to the increased susceptibility of the EOM and the mechanism of EOM weakness in myasthenia gravis.
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Affiliation(s)
- Rui Liu
- Department of Geratology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, P. R. China
| | - Hanpeng Xu
- LONI, Department of Neurology, UCLA, Los Angeles, California, United States of America
| | - Guiping Wang
- Department of Neurosurgery, 208th Hospital of PLA, Changchun, Jilin Province, P. R. China
| | - Jie Li
- Department of Endocrinology, 451 Hospital of PLA,Xi'an, Shaanxi Province, P. R. China
| | - Lin Gou
- LONI, Department of Neurology, UCLA, Los Angeles, California, United States of America
| | - Lihua Zhang
- Department of Geratology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, P. R. China
| | - Jianting Miao
- Department of Neurology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, P. R. China
- * E-mail: (ZL); (JM)
| | - Zhuyi Li
- Department of Neurology, Tangdu Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, P. R. China
- * E-mail: (ZL); (JM)
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Fu X, Li S, Jia G, Gou L, Tian X, Sun L, Ling X, Lan N, Yin X, Ma R, Liu L, Liu Y. Protective effect of the nitric oxide pathway in L-citrulline renal ischaemia-reperfusion injury in rats. Folia Biol (Praha) 2013; 59:225-232. [PMID: 24485304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
To observe the protective effects of L-citrulline on the renal I/R injury and elucidate the mechanisms involved, 48 rats were randomized into eight groups: Group 1: sham operated; Group 2: I/R (45 min renal ischaemia and 24 h reperfusion); Group 3: I/R + L-citrulline (300 mg/kg, i.g.); Group 4: I/R + L-citrulline (600 mg/kg, i.g.); Group 5: I/R + L-citrulline (900 mg/kg, i.g.); Group 6: I/R + normal saline (NS, i.g.); Group 7: I/R + N sup ω nitro-L-arginine ester (L-NAME, 20 mg/kg, i.p.); Group 8: I/R + L-citrulline (900 mg/kg, i.g.) + L-NAME (20 mg/ kg, i.p.). At the end of the reperfusion period, serum was collected and the kidneys underwent histological and biochemical examinations. Our results showed that pre-treatment with L-citrulline (300, 600, and 900 mg/kg) significantly ameliorated the renal injury caused by I/R. Moreover, L-citrulline prevented induction of lipid peroxidation and increased the activity of superoxide dismutase and the levels of glutathione and nitric oxide. The I/R-induced decreases in total nitric oxide synthase activity, inducible nitric oxide activity, constitutive nitric oxide activity and endothelial nitric oxide protein expression in the renal cortex were significantly prevented. However, the L-citrulline-mediated protection was significantly antagonized by co-administration of L-NAME. These results suggested that L-citrulline administration exhibited significant protection against renal I/R injury. This protective effect, at least in part, via up-regulation of the endothelial nitric oxide protein expression and constitutive nitric oxide synthase activity, maintained production of nitric oxide at the basal level.
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Affiliation(s)
- X Fu
- School of Pharmacy, Xuzhou Medical College, Nanjing University, Nanjing, P. R. China
| | - S Li
- School of Pharmacy, Xuzhou Medical College, Nanjing University, Nanjing, P. R. China
| | - G Jia
- School of Pharmacy, Xuzhou Medical College, Nanjing University, Nanjing, P. R. China
| | - L Gou
- School of Pharmacy, Xuzhou Medical College, Nanjing University, Nanjing, P. R. China
| | - X Tian
- School of Pharmacy, Xuzhou Medical College, Nanjing University, Nanjing, P. R. China
| | - L Sun
- School of Pharmacy, Xuzhou Medical College, Nanjing University, Nanjing, P. R. China
| | - X Ling
- School of Pharmacy, Xuzhou Medical College, Nanjing University, Nanjing, P. R. China
| | - N Lan
- School of Pharmacy, Xuzhou Medical College, Nanjing University, Nanjing, P. R. China
| | - X Yin
- School of Pharmacy, Xuzhou Medical College, Nanjing University, Nanjing, P. R. China
| | - R Ma
- School of Environment, Nanjing University, Nanjing, P. R. China
| | - L Liu
- Xuzhou Environmental Monitoring Station, Xuzhou, P. R. China, Nanjing University, Nanjing, P. R. China
| | - Y Liu
- School of Pharmacy, Xuzhou Medical College, Nanjing University, Nanjing, P. R. China
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Zhao SM, Fu FL, Gou L, Wang HG, He G, Li WC. Cloning and truncation modification of trehalose-6-phosphate synthase gene from Selaginella pulvinata. Gene 2013; 512:414-21. [DOI: 10.1016/j.gene.2012.09.052] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2012] [Revised: 09/06/2012] [Accepted: 09/21/2012] [Indexed: 01/25/2023]
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Gou L, Zhang HX, Fan XY, Li DL. Synthesis, crystal structure, and luminescent property of [Zn2(Ox)3]H2L · 4H2O (L = 2,2′-(1,4-butanediyl-bis(1H-benzimidazole)). RUSS J COORD CHEM+ 2012. [DOI: 10.1134/s1070328412080052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Hintiryan H, Gou L, Zingg B, Yamashita S, Lyden HM, Song MY, Grewal AK, Zhang X, Toga AW, Dong HW. Comprehensive connectivity of the mouse main olfactory bulb: analysis and online digital atlas. Front Neuroanat 2012; 6:30. [PMID: 22891053 PMCID: PMC3412993 DOI: 10.3389/fnana.2012.00030] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2012] [Accepted: 07/19/2012] [Indexed: 11/24/2022] Open
Abstract
We introduce the first open resource for mouse olfactory connectivity data produced as part of the Mouse Connectome Project (MCP) at UCLA. The MCP aims to assemble a whole-brain connectivity atlas for the C57Bl/6J mouse using a double coinjection tracing method. Each coinjection consists of one anterograde and one retrograde tracer, which affords the advantage of simultaneously identifying efferent and afferent pathways and directly identifying reciprocal connectivity of injection sites. The systematic application of double coinjections potentially reveals interaction stations between injections and allows for the study of connectivity at the network level. To facilitate use of the data, raw images are made publicly accessible through our online interactive visualization tool, the iConnectome, where users can view and annotate the high-resolution, multi-fluorescent connectivity data (www.MouseConnectome.org). Systematic double coinjections were made into different regions of the main olfactory bulb (MOB) and data from 18 MOB cases (~72 pathways; 36 efferent/36 afferent) currently are available to view in iConnectome within their corresponding atlas level and their own bright-field cytoarchitectural background. Additional MOB injections and injections of the accessory olfactory bulb (AOB), anterior olfactory nucleus (AON), and other olfactory cortical areas gradually will be made available. Analysis of connections from different regions of the MOB revealed a novel, topographically arranged MOB projection roadmap, demonstrated disparate MOB connectivity with anterior versus posterior piriform cortical area (PIR), and exposed some novel aspects of well-established cortical olfactory projections.
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Affiliation(s)
- Houri Hintiryan
- Laboratory of Neuro Imaging, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles Los Angeles, CA, USA
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Biag J, Huang Y, Gou L, Hintiryan H, Askarinam A, Hahn JD, Toga AW, Dong HW. Cyto- and chemoarchitecture of the hypothalamic paraventricular nucleus in the C57BL/6J male mouse: A study of immunostaining and multiple fluorescent tract tracing. J Comp Neurol 2012. [DOI: 10.1002/cne.23002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Biag J, Huang Y, Gou L, Hintiryan H, Askarinam A, Hahn JD, Toga AW, Dong HW. Cyto- and chemoarchitecture of the hypothalamic paraventricular nucleus in the C57BL/6J male mouse: a study of immunostaining and multiple fluorescent tract tracing. J Comp Neurol 2012; 520:6-33. [PMID: 21674499 PMCID: PMC4104804 DOI: 10.1002/cne.22698] [Citation(s) in RCA: 148] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The paraventricular nucleus of the hypothalamus (PVH) plays a critical role in the regulation of autonomic, neuroendocrine, and behavioral activities. This understanding has come from extensive characterization of the PVH in rats, and for this mammalian species we now have a robust model of basic PVH neuroanatomy and function. However, in mice, whose use as a model research animal has burgeoned with the increasing sophistication of tools for genetic manipulation, a comparable level of PVH characterization has not been achieved. To address this, we employed a variety of fluorescent tract tracing and immunostaining techniques in several different combinations to determine the neuronal connections and cyto- and chemoarchitecture of the PVH in the commonly used C57BL/6J male mouse. Our findings reveal a distinct organization in the mouse PVH that is substantially different from the PVH of male rats. The differences are particularly evident with respect to the spatial relations of two principal neuroendocrine divisions (magnocellular and parvicellular) and three descending preautonomic populations in the PVH. We discuss these data in relation to what is known about PVH function and provide the work as a resource for further studies of the neuronal architecture and function of the mouse PVH.
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Affiliation(s)
- Jonathan Biag
- Laboratory of Neuroimaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
| | - Yi Huang
- Laboratory of Neuroimaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
| | - Lin Gou
- Laboratory of Neuroimaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
| | - Houri Hintiryan
- Laboratory of Neuroimaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
| | - Asal Askarinam
- Laboratory of Neuroimaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
| | - Joel D. Hahn
- Brain Architecture Center, University of Southern California, Los Angeles, California 90089-2520
| | - Arthur W. Toga
- Laboratory of Neuroimaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
| | - Hong-Wei Dong
- Laboratory of Neuroimaging, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
- Brain Research Institute, David Geffen School of Medicine at UCLA, Los Angeles, California 90095-7334
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Abstract
In the central nervous system of all mammals, severed axons after injury are unable to regenerate to their original targets and functional recovery is very poor. The failure of axon regeneration is a combined result of several factors including the hostile glial cell environment, inhibitory myelin related molecules and decreased intrinsic neuron regenerative capacity. Astrocytes are the most predominant glial cell type in central nervous system and play important role in axon functions under physiology and pathology conditions. Contrast to the homologous oligodendrocytes, astrocytes are a heterogeneous cell population composed by different astrocyte subpopulations with diverse morphologies and gene expression. The functional significance of this heterogeneity, such as their influences on axon growth, is largely unknown. To study the glial cell, especially the function of astrocyte heterogeneity in neuron behavior, we established a new method by co-culturing high purified dorsal root ganglia neurons with glial cells obtained from the rat cortex. By this technique, we were able to directly compare neuron adhesion and axon growth on different astrocytes subpopulations under the same condition. In this report, we give the detailed protocol of this method for astrocytes isolation and culture, dorsal root ganglia neurons isolation and purification, and the co-culture of DRG neurons with astrocytes. This method could also be extended to other brain regions to study cellular or regional specific interaction between neurons and glial cells.
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Affiliation(s)
- Han-Peng Xu
- Department of Neurosurgery, Cedars Sinai Medical Center, UCLA, USA.
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Gou L, Lorenz H, Robl S, Leonhard K, Schaber K, Seidel-Morgenstern A. Integrierter Prozess zur Trennung chiraler Systeme mit Verbindungsbildung. CHEM-ING-TECH 2010. [DOI: 10.1002/cite.201050380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Zhang P, Wang CT, Yan F, Gou L, Tong AP, Cai F, Li Q, Deng HX, Wei YQ. Prokaryotic expression of a novel mouse pro-apoptosis protein PNAS-4 and application of its polyclonal antibodies. Braz J Med Biol Res 2009; 41:504-11. [PMID: 18622494 DOI: 10.1590/s0100-879x2008000600012] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2008] [Accepted: 05/26/2008] [Indexed: 02/05/2023] Open
Abstract
Mouse PNAS-4 (mPNAS-4) has 96% identity with human PNAS-4 (hPNAS-4) in primary sequence and has been reported to be involved in the apoptotic response to DNA damage. However, there have been no studies reported of the biological functions of mPNAS-4. In studies conducted by our group (unpublished data), it was interesting to note that overexpression of mPNAS-4 promoted apoptotic death in Lewis lung carcinoma cells (LL2) and colon carcinoma cells (CT26) of mice both in vitro and in vivo. In our studies, mPNAS-4 was cloned into the pGEX-6P-1 vector with GST tag at N-terminal in Escherichia coli strain BL21(DE3). The soluble and insoluble expression of recombinant protein mPNAS-4 (rmPNAS-4) was temperature-dependent. The majority of rmPNAS-4 was insoluble at 37 degrees C, while it was almost exclusively expressed in soluble form at 20 degrees C. The soluble rmPNAS-4 was purified by one-step affinity purification, using a glutathione Sepharose 4B column. The rmPNAS-4 protein was further identified by electrospray ionization-mass spectrometry analysis. The search parameters of the parent and fragment mass error tolerance were set at 0.1 and 0.05 kDa, respectively, and the sequence coverage of search result was 28%. The purified rmPNAS-4 was further used as immunogen to raise polyclonal antibodies in New Zealand white rabbit, which were suitable to detect both the recombinant and the endogenous mPNAS-4 in mouse brain tissue and LL2 cells after immunoblotting and/or immunostaining. The purified rmPNAS-4 and our prepared anti-mPNAS-4 polyclonal antibodies may provide useful tools for future biological function studies for mPNAS.
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Affiliation(s)
- P Zhang
- State Key Laboratory of Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, China
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Gou L, Lü ZR, Park D, Oh SH, Shi L, Park SJ, Bhak J, Park YD, Ren ZL, Zou F. The Effect of Histidine Residue Modification on Tyrosinase Activity and Conformation: Inhibition Kinetics and Computational Prediction. J Biomol Struct Dyn 2008; 26:395-402. [DOI: 10.1080/07391102.2008.10507254] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Feng LN, Chen WX, Cong R, Gou L. Therapeutic effects of eustachian tube surfactant in barotitis media in guinea pigs. Aviat Space Environ Med 2003; 74:707-10. [PMID: 12862323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
BACKGROUND Previous research has shown that the eustachian tube (ET) in animals and humans is lined with a substance that lowers surface tension and thus facilitates the opening of the eustachian tube and aeration of the middle ear. The aims of the present study were to observe the role of eustachian tube surfactant (ETS) on the opening of the ET and to explore the therapeutic effect of natural and artificial ETS on barotitis media (BOM). METHODS BOM was successfully established in 50 guinea pigs by simulated ascent in an altitude chamber. Subsets of the affected ears were treated by flushing with natural ETS, artificial ETS, artificial phospholipid, or saline. The effects were evaluated by measuring eustachian tube pressure opening level (POL). Other animals with BOM were treated with artificial ETS on one side and saline in the other, after which the clinical signs were observed. RESULTS The POL of the saline group remained unchanged. Natural ETS decreased the POL from 11.98 to 6.11 kPa (p < 0.01); artificial ETS reduced the POL from 11.91 to 6.67 kPa (p < 0.01); there was no significant difference between the two treatments. Artificial phospholipid was less effective, decreasing POL from 11.86 to 8.61 kPa (p < 0.05). Clinical observations showed that after 1 wk of treatment with artificial ETS, the congestion in the tympanic membrane was alleviated, the hearing threshold improved, and the effusion in tympanic cavity diminished. CONCLUSION Artificial ETS was as effective as natural ETS in facilitating the opening of eustachian tube and had definite therapeutic effects on BOM in this model.
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Affiliation(s)
- Li-Ning Feng
- Department of Clinical Aerospace Medicine, Faculty of Aerospace Medicine, The Fourth Military Medical University, Xi'an, PR China.
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Feng L, Chen W, Cong R, Zheng G, Gou L, Guo Q. [An experimental study on the therapeutic effects of eustachian tube surfactant in barotitis media]. Lin Chuang Er Bi Yan Hou Ke Za Zhi 2002; 16:613-5. [PMID: 15515553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/01/2023]
Abstract
OBJECTIVE To observe the effect of surfactant on eustachian tube (ET) on the opening of ET as well as it's therapeutic role in barotitis media (BM). METHOD 50 guinea pigs were successfully established as BM models by stimulated ascending in altitude chamber. Parts of the models were treated with by middle ear flushing with nature ETS, artificial ETS, artificial phospholipid and saline, after which the eustachian tube pressure opening level (POL) of each group was tested. Others were injected with 1 ml artificial ETS in on side of the middle ear, and 1 ml of saline in the other served as control. RESULT Natural ETS decreased the POL from 11.98 to 6.11 kPa (P < 0.01); Artificial ETS reduced the POL from 11.91 to 6.67 kPa (P < 0.01), there were no significant differences between the two groups. Artificial phospholipid decreased the POL from 11.86 to 8.61 kPa (P < 0.05), which was not as effective as natural ETS. While the POL of saline group remained unchanged. After one week of artificial ETS treatment, the congestion in drum membrane alleviated, the hearing threshold of ETS group improved and the effusion in tympanic cavity lessened. CONCLUSION The results suggest that artificial ETS is as effective as nature ETS to facilitates the opening of eustachian tube. Artificial ETS may exert therapeutic effects on BM.
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Affiliation(s)
- Lining Feng
- Department of Clinical Medicine, Faculty of Aerospace medicine, Fourth Military Medical University, Xi'an 710032
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Qiu P, Zhang Z, Jiang Y, Gou Q, Wang B, Gou L, Chen J. [Effect of infrasound on ultrastructure and permeability of rat's blood-retinal barrier]. Zhonghua Yan Ke Za Zhi 2002; 38:499-501. [PMID: 12410992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
Abstract
OBJECTIVE To investigate the possible effect of infrasound on the ultra-structure and permeability of rat's blood-retinal barrier (BRB). METHODS Ultra-structural changes of BRB were observed through the injection of lanthanum nitrate (La), which was used as a tracer to demonstrate the breakdown of the BRB, into blood vessels. Fifteen mature male rats divided into 5 groups were exposed to infrasound at a 8 Hz frequency, 130 dB sound pressure level in a pressure chamber especially designed for the experiment for 0, 1, 7, 14, 21 days, respectively. RESULTS Under the action of infrasound, along with the prolongation of exposure, the damage of BRB was severer and severer. On the 1st day, there was no significant change in La leakage. On the 7th day, La diffused in the interphotoreceptor space at nuclear level. On the 14th day, La granules could be seen in the space of nervous cells. Finally, on the 21st day, La was found between synapses, synapses and nerve cells, as well as between the nerve cells and supporting cells, then sometimes reached vitreous body. Under the electron microscope, there were no significant morphological changes, but changes related to metabolism, such as edematous mitochondria, dilated rough endoplasmic reticula, precipitation of glycogen grandules, widening of perinuclear space, etc. CONCLUSIONS The results thus suggest that the exposure to infrasound cause the breakdown of rat's blood-retinal barrier and visual impairment.
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Affiliation(s)
- Ping Qiu
- Department of Clinical Aerospace Medicine, Faculty of Aviation Medicine, Guangzhou 510515, China.
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Miller GA, Gou L, Narayanan V, Scranton AB. Modeling of photobleaching for the photoinitiation of thick polymerization systems. ACTA ACUST UNITED AC 2002. [DOI: 10.1002/pola.10162] [Citation(s) in RCA: 93] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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