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Du Y, Niu J, Xing Y, Li B, Calhoun VD. Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade. Schizophr Bull 2024:sbae110. [PMID: 38982882 DOI: 10.1093/schbul/sbae110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years. STUDY DESIGN The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade. STUDY RESULTS Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders. CONCLUSIONS We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.
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Affiliation(s)
- Yuhui Du
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ju Niu
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Ying Xing
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Bang Li
- School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China
| | - Vince D Calhoun
- The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, 30303, GA, USA
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Mao S, Huang X, Chen R, Zhang C, Diao Y, Li Z, Wang Q, Tang S, Guo S. STW-MD: a novel spatio-temporal weighting and multi-step decision tree method for considering spatial heterogeneity in brain gene expression data. Brief Bioinform 2024; 25:bbae051. [PMID: 38385881 PMCID: PMC10883420 DOI: 10.1093/bib/bbae051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 02/23/2024] Open
Abstract
Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of brain development or abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development.
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Affiliation(s)
- Shanjun Mao
- Department of Statistics, Hunan University, Shijiachong Road, Changsha 410000, China
| | - Xiao Huang
- Department of Statistics, Hunan University, Shijiachong Road, Changsha 410000, China
| | - Runjiu Chen
- Department of Statistics, Hunan University, Shijiachong Road, Changsha 410000, China
| | - Chenyang Zhang
- Department of Statistics, Hunan University, Shijiachong Road, Changsha 410000, China
| | - Yizhu Diao
- Department of Statistics, Hunan University, Shijiachong Road, Changsha 410000, China
| | - Zongjin Li
- Central University of Finance and Economics
| | - Qingzhe Wang
- Shanghai Institute for Advanced Studies, University of Science and Technology of China
| | - Shan Tang
- Department of Statistics, Hunan University, Shijiachong Road, Changsha 410000, China
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Lushan Road, Changsha 410000, China
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Gu YW, Fan JW, Zhào H, Zhao SW, Liu XF, Yu R, Ren L, Wang X, Yin H, Cui LB. Imaging Transcriptomics of the Brain for Schizophrenia. ALPHA PSYCHIATRY 2024; 25:9-14. [PMID: 38799487 PMCID: PMC11114239 DOI: 10.5152/alphapsychiatry.2024.231369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/04/2023] [Indexed: 05/29/2024]
Abstract
Schizophrenia is a severe mental disorder with a neurodevelopmental origin. Although schizophrenia results from changes in the brain, the underlying biological mechanisms are unknown. Transcriptomics studies quantitative expression changes or qualitative changes of all genes and isoforms, providing a more meaningful biological insight. Magnetic resonance imaging (MRI) techniques play roles in revealing brain structure and function. We give a narrative focused review on the current transcriptome combined with MRI studies related to schizophrenia and summarize the research methodology and content of these studies to identify the research commonalities as well as the implications for future research, in an attempt to provide new insights into the mechanism, clinical diagnosis, and treatments of schizophrenia.
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Affiliation(s)
- Yue-Wen Gu
- Department of Clinical Psychology, Air Force Medical University, Xi’an, China
- Department of Radiology, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Jing-Wen Fan
- Department of Clinical Psychology, Air Force Medical University, Xi’an, China
- Department of Radiology, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Hóngyi Zhào
- Department of Neurology, NO. 984 Hospital of PLA, Beijing, China
| | - Shu-Wan Zhao
- Department of Clinical Psychology, Air Force Medical University, Xi’an, China
- Department of Radiology, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Xiao-Fan Liu
- Department of Clinical Psychology, Air Force Medical University, Xi’an, China
| | - Renqiang Yu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lei Ren
- Department of Clinical Psychology, Air Force Medical University, Xi’an, China
| | - Xinjiang Wang
- Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Air Force Medical University, Xi’an, China
- Department of Radiology, Xi’an People’s Hospital (Xi’an Fourth Hospital), Xi’an, China
| | - Long-Biao Cui
- Department of Clinical Psychology, Air Force Medical University, Xi’an, China
- Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China
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Chai C, Ding H, Du X, Xie Y, Man W, Zhang Y, Ji Y, Liang M, Zhang B, Ning Y, Zhuo C, Yu C, Qin W. Dissociation between neuroanatomical and symptomatic subtypes in schizophrenia. Eur Psychiatry 2023; 66:e78. [PMID: 37702075 PMCID: PMC10594537 DOI: 10.1192/j.eurpsy.2023.2446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 05/20/2023] [Accepted: 08/01/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND Schizophrenia is a complex and heterogeneous syndrome with high clinical and biological stratification. Identifying distinctive subtypes can improve diagnostic accuracy and help precise therapy. A key challenge for schizophrenia subtyping is understanding the subtype-specific biological underpinnings of clinical heterogeneity. This study aimed to investigate if the machine learning (ML)-based neuroanatomical and symptomatic subtypes of schizophrenia are associated. METHODS A total of 314 schizophrenia patients and 257 healthy controls from four sites were recruited. Gray matter volume (GMV) and Positive and Negative Syndrome Scale (PANSS) scores were employed to recognize schizophrenia neuroanatomical and symptomatic subtypes using K-means and hierarchical methods, respectively. RESULTS Patients with ML-based neuroanatomical subtype-1 had focally increased GMV, and subtype-2 had widespread reduced GMV than the healthy controls based on either K-means or Hierarchical methods. In contrast, patients with symptomatic subtype-1 had severe PANSS scores than subtype-2. No differences in PANSS scores were shown between the two neuroanatomical subtypes; similarly, no GMV differences were found between the two symptomatic subtypes. Cohen's Kappa test further demonstrated an apparent dissociation between the ML-based neuroanatomical and symptomatic subtypes (P > 0.05). The dissociation patterns were validated in four independent sites with diverse disease progressions (chronic vs. first episodes) and ancestors (Chinese vs. Western). CONCLUSIONS These findings revealed a replicable dissociation between ML-based neuroanatomical and symptomatic subtypes of schizophrenia, which provides a new viewpoint toward understanding the heterogeneity of schizophrenia.
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Affiliation(s)
- Chao Chai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Hao Ding
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Xiaotong Du
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Weiqi Man
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Yu Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yi Ji
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Bin Zhang
- Department of Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yuping Ning
- Department of Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Chuanjun Zhuo
- Department of Psychiatry, Tianjin Fourth Center Hospital, Tianjin, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- School of Medical Imaging, Tianjin Medical University, Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
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Xu F, Zhang H. The application of cognitive behavioral therapy in patients with schizophrenia: A review. Medicine (Baltimore) 2023; 102:e34827. [PMID: 37565853 PMCID: PMC10419479 DOI: 10.1097/md.0000000000034827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 07/28/2023] [Indexed: 08/12/2023] Open
Abstract
The aim of this review is to explore the clinical nursing application of cognitive behavioral therapy (CBT) in patients with schizophrenia. A literature search was conducted using the CINAHL and MEDLINE databases. The database search occurred during the month of December 2022. This article comprehensively summarizes the theoretical basis of CBT in improving schizophrenia in clinical nursing, its application in managing symptoms and improving social function, as well as research progress in this field. There are still inconsistencies in the research results on CBT, but overall, psychological intervention combined with drug treatment is more effective than conventional treatment alone. If social function training can be added at the same time, it is believed that it will have better effects on clinical treatment and can maintain long-lasting effectiveness. Only in this way can patients truly understand and recognize the disease, improve treatment compliance, and ultimately achieve the goal of improving prognosis and quality of life.
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Affiliation(s)
- Feifei Xu
- School of Psychology, Zhejiang Normal University, Jin Hua, China
| | - Hang Zhang
- School of Humanities and International Education Exchange, Anhui University of Chinese Medicine, HeFei, China
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Fan JW, Gu YW, Wang DB, Liu XF, Zhao SW, Li X, Li B, Yin H, Wu WJ, Cui LB. Transcriptomics and magnetic resonance imaging in major psychiatric disorders. Front Psychiatry 2023; 14:1185471. [PMID: 37383618 PMCID: PMC10296768 DOI: 10.3389/fpsyt.2023.1185471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/16/2023] [Indexed: 06/30/2023] Open
Abstract
Major psychiatric disorders create a significant public health burden, and mental disorders such as major depressive disorder, bipolar disorder, and schizophrenia are major contributors to the national disease burden. The search for biomarkers has been a leading endeavor in the field of biological psychiatry in recent decades. And the application of cross-scale and multi-omics approaches combining genes and imaging in major psychiatric studies has facilitated the elucidation of gene-related pathogenesis and the exploration of potential biomarkers. In this article, we summarize the results of using combined transcriptomics and magnetic resonance imaging to understand structural and functional brain changes associated with major psychiatric disorders in the last decade, demonstrating the neurobiological mechanisms of genetically related structural and functional brain alterations in multiple directions, and providing new avenues for the development of quantifiable objective biomarkers, as well as clinical diagnostic and prognostic indicators.
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Affiliation(s)
- Jing-Wen Fan
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yue-Wen Gu
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Dong-Bao Wang
- Schizophrenia Imaging Lab, Fourth Military Medical University, Xi’an, China
| | - Xiao-Fan Liu
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
| | - Shu-Wan Zhao
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Xiao Li
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Baojuan Li
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
- Department of Radiology, Xi'an People's Hospital (Xi'an Fourth Hospital), Xi'an, China
| | - Wen-Jun Wu
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Long-Biao Cui
- Department of Clinical Psychology, Fourth Military Medical University, Xi'an, China
- Schizophrenia Imaging Lab, Fourth Military Medical University, Xi’an, China
- Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
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7
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Chen Y, Cao H, Liu S, Zhang B, Zhao G, Zhang Z, Li S, Li H, Yu X, Deng H. Brain Structure Measurements Predict Individualized Treatment Outcome of 12-Week Antipsychotic Monotherapies in First-episode Schizophrenia. Schizophr Bull 2023; 49:697-705. [PMID: 37010371 PMCID: PMC10154710 DOI: 10.1093/schbul/sbad043] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2023]
Abstract
BACKGROUND AND HYPOTHESIS Early prediction of treatment response to antipsychotics in schizophrenia remains a challenge in clinical practice. This study aimed to investigate if brain morphometries including gray matter volume and cortical thickness could serve as potential predictive biomarkers in first-episode schizophrenia. STUDY DESIGN Sixty-eight drug-naïve first-episode patients underwent baseline structural MRI scans and were subsequently randomized to receive a single antipsychotic throughout the first 12 weeks. Assessments for symptoms and social functioning were conducted by eight "core symptoms" selected from the Positive and Negative Syndrome Scale (PANSS-8) and the Personal and Social performance scale (PSP) multiple times during follow-ups. Treatment outcome was evaluated as subject-specific slope coefficients for PANSS-8 and PSP scores using linear mixed model. LASSO regression model were conducted to examine the performance of baseline gray matter volume and cortical thickness in prediction of individualized treatment outcome. STUDY RESULTS The study showed that individual brain morphometries at baseline, especially the orbitofrontal, temporal and parietal cortex, pallidum and amygdala, significantly predicted 12-week treatment outcome of PANSS-8 (r[predicted vs observed] = 0.49, P = .001) and PSP (r[predicted vs observed] = 0.40, P = .003) in first-episode schizophrenia. Moreover, the gray matter volume performed better than cortical thickness in the prediction the symptom changes (P = .034), while cortical thickness outperformed gray matter volume in the prediction of outcome of social functioning (P = .029). CONCLUSIONS These findings provide initial evidence that brain morphometry have potential to be used as prognostic predictors for antipsychotic response in patients, encouraging the future investigation of the translational value of these measures in precision psychiatry.
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Affiliation(s)
- Ying Chen
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
- Hope Recovery and Rehabilitation Center, West China Hospital of Sichuan University, Chengdu, China
| | - Hengyi Cao
- Center for Psychiatric Neuroscience, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Shanming Liu
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Bo Zhang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | | | - Zhuoqiu Zhang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Shuiying Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Haiming Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xin Yu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China
| | - Hong Deng
- Hope Recovery and Rehabilitation Center, West China Hospital of Sichuan University, Chengdu, China
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China
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Levchenko A, Nurgaliev T, Kanapin A, Samsonova A, Gainetdinov RR. Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders. Heliyon 2020; 6:e03990. [PMID: 32462093 PMCID: PMC7240336 DOI: 10.1016/j.heliyon.2020.e03990] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 10/31/2019] [Accepted: 05/12/2020] [Indexed: 12/13/2022] Open
Abstract
A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mental illness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category. In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a next step, a perspective on the path personalized psychiatry may take in the future is given, paying particular attention to machine learning algorithms that can be used with the goal of handling multidimensional datasets.
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Affiliation(s)
- Anastasia Levchenko
- Theodosius Dobzhansky Center for Genome Bioinformatics, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Timur Nurgaliev
- Institute of Translational Biomedicine, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Alexander Kanapin
- Theodosius Dobzhansky Center for Genome Bioinformatics, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Anastasia Samsonova
- Theodosius Dobzhansky Center for Genome Bioinformatics, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
| | - Raul R. Gainetdinov
- Institute of Translational Biomedicine, Saint Petersburg State University, 7/9 Universitetskaya nab., Saint Petersburg, 199034, Russia
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