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Zhang Y, Zhang X, Zhang H, Liu A, Liu CC. Low-rank latent matrix-factor prediction modeling for generalized high-dimensional matrix-variate regression. Stat Med 2023; 42:3616-3635. [PMID: 37314066 DOI: 10.1002/sim.9821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 04/19/2023] [Accepted: 06/01/2023] [Indexed: 06/15/2023]
Abstract
Motivated by diagnosing the COVID-19 disease using two-dimensional (2D) image biomarkers from computed tomography (CT) scans, we propose a novel latent matrix-factor regression model to predict responses that may come from an exponential distribution family, where covariates include high-dimensional matrix-variate biomarkers. A latent generalized matrix regression (LaGMaR) is formulated, where the latent predictor is a low-dimensional matrix factor score extracted from the low-rank signal of the matrix variate through a cutting-edge matrix factor model. Unlike the general spirit of penalizing vectorization plus the necessity of tuning parameters in the literature, instead, our prediction modeling in LaGMaR conducts dimension reduction that respects the geometric characteristic of intrinsic 2D structure of the matrix covariate and thus avoids iteration. This greatly relieves the computation burden, and meanwhile maintains structural information so that the latent matrix factor feature can perfectly replace the intractable matrix-variate owing to high-dimensionality. The estimation procedure of LaGMaR is subtly derived by transforming the bilinear form matrix factor model onto a high-dimensional vector factor model, so that the method of principle components can be applied. We establish bilinear-form consistency of the estimated matrix coefficient of the latent predictor and consistency of prediction. The proposed approach can be implemented conveniently. Through simulation experiments, the prediction capability of LaGMaR is shown to outperform some existing penalized methods under diverse scenarios of generalized matrix regressions. Through the application to a real COVID-19 dataset, the proposed approach is shown to predict efficiently the COVID-19.
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Affiliation(s)
- Yuzhe Zhang
- School of Management, University of Science and Technology of China, Hefei, Anhui, China
| | - Xu Zhang
- School of Mathematical Sciences, South China Normal University, Guangzhou, Guangdong, China
| | - Hong Zhang
- School of Management, University of Science and Technology of China, Hefei, Anhui, China
| | - Aiyi Liu
- National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA
| | - Catherine C Liu
- Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR
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Tan T, Xu Z, Gao C, Shen T, Li L, Chen Z, Chen L, Xu M, Chen B, Liu J, Zhang Z, Yuan Y. Influence and interaction of resting state functional magnetic resonance and tryptophan hydroxylase-2 methylation on short-term antidepressant drug response. BMC Psychiatry 2022; 22:218. [PMID: 35337298 PMCID: PMC8957120 DOI: 10.1186/s12888-022-03860-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 03/11/2022] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Most antidepressants have been developed on the basis of the monoamine deficiency hypothesis of depression, in which neuronal serotonin (5-HT) plays a key role. 5-HT biosynthesis is regulated by the rate-limiting enzyme tryptophan hydroxylase-2 (TPH2). TPH2 methylation is correlated with antidepressant effects. Resting-state functional MRI (rs-fMRI) is applied for detecting abnormal brain functional activity in patients with different antidepressant effects. We will investigate the effect of the interaction between rs-fMRI and TPH2 DNA methylation on the early antidepressant effects. METHODS A total of 300 patients with major depressive disorder (MDD) and 100 healthy controls (HCs) were enrolled, of which 60 patients with MDD were subjected to rs-fMRI. Antidepressant responses was assessed by a 50% reduction in 17-item Hamilton Rating Scale for Depression (HAMD-17) scores at baseline and after two weeks of medication. The RESTPlus software in MATLAB was used to analyze the rs-fMRI data. The amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), fractional ALFF (fALFF), and functional connectivity (FC) were used, and the above results were used as regions of interest (ROIs) to extract the average value of brain ROIs regions in the RESTPlus software. Generalized linear model analysis was performed to analyze the association between abnormal activity found in rs-fMRI and the effect of TPH2 DNA methylation on antidepressant responses. RESULTS Two hundred ninety-one patients with MDD and 100 HCs were included in the methylation statistical analysis, of which 57 patients were included in the further rs-fMRI analysis (3 patients were excluded due to excessive head movement). 57 patients were divided into the responder group (n = 36) and the non-responder group (n = 21). Rs-fMRI results showed that the ALFF of the left inferior frontal gyrus (IFG) was significantly different between the two groups. The results showed that TPH2-1-43 methylation interacted with ALFF of left IFG to affect the antidepressant responses (p = 0.041, false discovery rate (FDR) corrected p = 0.149). CONCLUSIONS Our study demonstrated that the differences in the ALFF of left IFG between the two groups and its association with TPH2 methylation affect short-term antidepressant drug responses.
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Affiliation(s)
- Tingting Tan
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,grid.263826.b0000 0004 1761 0489Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Zhi Xu
- Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009, People's Republic of China. .,Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009, People's Republic of China.
| | - Chenjie Gao
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,grid.263826.b0000 0004 1761 0489Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Tian Shen
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,grid.89957.3a0000 0000 9255 8984Department of Psychiatric Rehabilitation, Wuxi Mental Health Center, Nanjing Medical University, WuXi, 214123 People’s Republic of China
| | - Lei Li
- grid.263826.b0000 0004 1761 0489School of Medicine, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Zimu Chen
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,grid.263826.b0000 0004 1761 0489Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Lei Chen
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,Department of Psychology and Psychiatry, School of Medicine, Jinling Hospital, Nanjing University, Nanjing, 210018 People’s Republic of China
| | - Min Xu
- grid.263826.b0000 0004 1761 0489Department of Anatomy, Medical School, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Bingwei Chen
- grid.263826.b0000 0004 1761 0489Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Jiacheng Liu
- grid.452290.80000 0004 1760 6316Department of Nuclear Medicine, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Zhijun Zhang
- grid.452290.80000 0004 1760 6316Department of Neurology, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China
| | - Yonggui Yuan
- grid.452290.80000 0004 1760 6316Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, 210009 People’s Republic of China ,grid.263826.b0000 0004 1761 0489Key Laboratory of Developmental Genes and Human Diseases, Ministry of Education, School of Medicine, Southeast University, Nanjing, 210009 People’s Republic of China
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Jiang B, Raftery AE, Steele RJ, Wang N. Balancing Inferential Integrity and Disclosure Risk Via Model Targeted Masking and Multiple Imputation. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2021.1909597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Bei Jiang
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada
| | | | - Russell J. Steele
- Department of Mathematics and Statistics, McGill University, Montreal, QC, Canada
| | - Naisyin Wang
- Department of Statistics, University of Michigan, Ann Arbor, MI
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Matraszek-Gawron R, Chwil M, Terlecka P, Skoczylas MM. Recent Studies on Anti-Depressant Bioactive Substances in Selected Species from the Genera Hemerocallis and Gladiolus: A Systematic Review. Pharmaceuticals (Basel) 2019; 12:ph12040172. [PMID: 31775329 PMCID: PMC6958339 DOI: 10.3390/ph12040172] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 11/04/2019] [Accepted: 11/22/2019] [Indexed: 12/14/2022] Open
Abstract
Herbal therapy is a potential alternative applied to pharmacological alleviation of depression symptoms and treatment of this disorder, which is predicted by the World Health Organization (WHO) to be the most serious health problem worldwide over the next several years. It has been well documented that many herbs with psychotropic effects have far fewer side effects than a variety of pharmaceutical agents used by psychiatrists for the treatment of depression. This systematic review presents literature data on the antidepressant activity of representatives of the genera Hemerocallis (H. fulva and H. citrina Baroni, family Xanthorrhoeaceae) and Gladiolus (G. dalenii, family Iridaceae) and on biologically active compounds and their mechanisms of action to consider the application of herbal preparations supporting the treatment of depression.
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Affiliation(s)
- Renata Matraszek-Gawron
- Department of Botany and Plant Physiology, University of Life Sciences in Lublin, 15 Akademicka Street, 20-950 Lublin, Poland;
| | - Mirosława Chwil
- Department of Botany and Plant Physiology, University of Life Sciences in Lublin, 15 Akademicka Street, 20-950 Lublin, Poland;
- Correspondence: ; Tel.: +48-81-445-66-24
| | - Paulina Terlecka
- Chair and Department of Pneumology, Oncology and Allergology, Medical University of Lublin, 8 Jaczewskiego Street, 20-090 Lublin, Poland;
| | - Michał M. Skoczylas
- Department of Diagnostic Imaging and Interventional Radiology, Pomeranian Medical University in Szczecin, 1 Unii Lubelskiej Street, 71-252 Szczecin, Poland;
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