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Newby D, Orgeta V, Marshall CR, Lourida I, Albertyn CP, Tamburin S, Raymont V, Veldsman M, Koychev I, Bauermeister S, Weisman D, Foote IF, Bucholc M, Leist AK, Tang EYH, Tai XY, Llewellyn DJ, Ranson JM. Artificial intelligence for dementia prevention. Alzheimers Dement 2023; 19:5952-5969. [PMID: 37837420 PMCID: PMC10843720 DOI: 10.1002/alz.13463] [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: 04/12/2023] [Revised: 08/01/2023] [Accepted: 08/07/2023] [Indexed: 10/16/2023]
Abstract
INTRODUCTION A wide range of modifiable risk factors for dementia have been identified. Considerable debate remains about these risk factors, possible interactions between them or with genetic risk, and causality, and how they can help in clinical trial recruitment and drug development. Artificial intelligence (AI) and machine learning (ML) may refine understanding. METHODS ML approaches are being developed in dementia prevention. We discuss exemplar uses and evaluate the current applications and limitations in the dementia prevention field. RESULTS Risk-profiling tools may help identify high-risk populations for clinical trials; however, their performance needs improvement. New risk-profiling and trial-recruitment tools underpinned by ML models may be effective in reducing costs and improving future trials. ML can inform drug-repurposing efforts and prioritization of disease-modifying therapeutics. DISCUSSION ML is not yet widely used but has considerable potential to enhance precision in dementia prevention. HIGHLIGHTS Artificial intelligence (AI) is not widely used in the dementia prevention field. Risk-profiling tools are not used in clinical practice. Causal insights are needed to understand risk factors over the lifespan. AI will help personalize risk-management tools for dementia prevention. AI could target specific patient groups that will benefit most for clinical trials.
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Affiliation(s)
- Danielle Newby
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Vasiliki Orgeta
- Division of Psychiatry, University College London, London, W1T 7BN, UK
| | - Charles R Marshall
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, E1 4NS, UK
- Department of Neurology, Royal London Hospital, London, E1 1BB, UK
| | - Ilianna Lourida
- Population Health Sciences Institute, Newcastle University, Newcastle, NE2 4AX, UK
- University of Exeter Medical School, Exeter, EX1 2HZ, UK
| | - Christopher P Albertyn
- Department of Old Age Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, 37129, Italy
| | - Vanessa Raymont
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Michele Veldsman
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, UK
- Department of Experimental Psychology, University of Oxford, Oxford, OX2 6GG, UK
| | - Ivan Koychev
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - Sarah Bauermeister
- University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK
| | - David Weisman
- Abington Neurological Associates, Abington, PA 19001, USA
| | - Isabelle F Foote
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, E1 4NS, UK
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO 80309, USA
| | - Magda Bucholc
- Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, BT48 7JL, UK
| | - Anja K Leist
- Institute for Research on Socio-Economic Inequality (IRSEI), Department of Social Sciences, University of Luxembourg, L-4365, Luxembourg
| | - Eugene Y H Tang
- Population Health Sciences Institute, Newcastle University, Newcastle, NE2 4AX, UK
| | - Xin You Tai
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, OX3 9DU, UK
- Division of Clinical Neurology, John Radcliffe Hospital, Oxford University Hospitals Trust, Oxford, OX3 9DU, UK
| | | | - David J. Llewellyn
- University of Exeter Medical School, Exeter, EX1 2HZ, UK
- The Alan Turing Institute, London, NW1 2DB, UK
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Leyder E, Suresh P, Jun R, Overbey K, Banerjee T, Melnikova T, Savonenko A. Depression-related phenotypes at early stages of Aβ and tau accumulation in inducible Alzheimer's disease mouse model: Task-oriented and concept-driven interpretations. Behav Brain Res 2023; 438:114187. [PMID: 36343696 DOI: 10.1016/j.bbr.2022.114187] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 10/16/2022] [Accepted: 10/30/2022] [Indexed: 11/06/2022]
Abstract
Depression is highly prevalent in Alzheimer Disease (AD); however, there is paucity of studies that focus specifically on the assessment of depression-relevant phenotypes in AD mouse models. Conditional doxycycline-dependent transgenic mouse models reproducing amyloidosis (TetOffAPPsi) and/or tau (TetOffTauP301L) pathology starting at middle age (6 months) were used in this study. As AD patients can experience depressive symptoms relatively early in disease, testing was conducted at early, pre-pathology stages of Aβ and/or tau accumulation (starting from 45 days of transgenes expression). Tau-related differences were detected in the Novelty Suppressed Feeding task (NSF), whereas APP-related differences were observed predominantly in measures of the Open Field (OF) and Forced Swim tasks (FST). Effects of combined production of Aβ and tau were detected in immobility during the 1st half of the Tail Suspension task (TST). These data demonstrate that results from different tasks are difficult to reconcile using task/variable-centered interpretations in which a single task/variable is assigned an ad-hoc meaning relevant to depression. An alternative, concept-oriented, approach is based on multiple variables/tests, with an understanding of their possible inter-dependence and utilization of statistical approaches that handle correlated data sets. The existence of strong correlations within and between some of the tasks supported utilization of factor analyses (FA). FA explained a similar amount of variability across the genotypes (∼80%) and identified two factors stable across genotypes and representing motor activity and anxiety measures in OF. In contrast, variables related to FST, TST, and NSFT did not demonstrate a structure of factor loadings that would support the existence of a single integral factor of "depressive state" measured by these tasks. In addition, factor loadings varied between genotypes, indicating that genotype-specific between-task correlations need to be considered for interpretations of findings in any single task. In general, this study demonstrates that utilization of multiple tasks to characterize behavioral phenotypes, an approach that is finally gaining more widespread adoption, requires a step of data integration across different behavioral tests for appropriate interpretations.
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Affiliation(s)
- Erica Leyder
- Department of Pathology, The Johns Hopkins University School of Medicine, 558 Ross Research Building, 720 Rutland Avenue, Baltimore, MD 21205, USA
| | - Prakul Suresh
- Department of Pathology, The Johns Hopkins University School of Medicine, 558 Ross Research Building, 720 Rutland Avenue, Baltimore, MD 21205, USA
| | - Rachel Jun
- Department of Pathology, The Johns Hopkins University School of Medicine, 558 Ross Research Building, 720 Rutland Avenue, Baltimore, MD 21205, USA
| | - Katherine Overbey
- Department of Pathology, The Johns Hopkins University School of Medicine, 558 Ross Research Building, 720 Rutland Avenue, Baltimore, MD 21205, USA
| | - Tirtho Banerjee
- Department of Pathology, The Johns Hopkins University School of Medicine, 558 Ross Research Building, 720 Rutland Avenue, Baltimore, MD 21205, USA
| | - Tatiana Melnikova
- Department of Pathology, The Johns Hopkins University School of Medicine, 558 Ross Research Building, 720 Rutland Avenue, Baltimore, MD 21205, USA.
| | - Alena Savonenko
- Department of Pathology, The Johns Hopkins University School of Medicine, 558 Ross Research Building, 720 Rutland Avenue, Baltimore, MD 21205, USA
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Ribaldi F, Rolandi E, Vaccaro R, Colombo M, Battista Frisoni G, Guaita A. The clinical heterogeneity of subjective cognitive decline: a data-driven approach on a population-based sample. Age Ageing 2022; 51:6770075. [PMID: 36273347 DOI: 10.1093/ageing/afac209] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND subjective cognitive decline (SCD) refers to the subjective experience of cognitive decline in the absence of detectable cognitive impairment. SCD has been largely studied as a risk condition for cognitive decline. Empirical observations suggest that persons with SCD are heterogeneous, including individuals with early Alzheimer's disease and others with psychological vulnerabilities and/or physical comorbidity. The semiology of SCD is still in its infancy, and the features predicting cognitive decline are poorly defined. The present study aims to identify subgroups of SCD using a data-driven approach and study their clinical evolution across 8 years. METHODS the study population is the InveCe.Ab population-based cohort, including cognitively unimpaired people aged 70-74 years and followed for 8 years. Hierarchical cluster analysis (HCA) was carried out to identify distinct SCD subgroups based on nine clinical and cognitive features. Longitudinal changes by baseline SCD status were estimated using linear mixed models for cognitive decline and Cox proportional-hazard model for all-cause dementia risk. RESULTS out of 956 individuals, 513 were female (54%); and the mean age was 72.1 (SD = 1.3), education was 7.2 (3.3), and 370 (39%) reported cognitive complaints (SCD). The HCA resulted in two clusters (SCD1 and SCD2). SCD2 were less educated and had more comorbidities, cardiovascular risk and depressive symptoms than SCD1 and controls. SCD2 presented steeper cognitive decline (Mini-Mental State Examination; β = -0.31) and increased all-cause dementia risk (hazard-ratio = 3.4). CONCLUSIONS at the population level, basic clinical information can differentiate individuals with SCD at higher risk of developing dementia, underlining the heterogeneous nature of this population even in a sample selected for a narrow age range, in a specific geographic area.
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Affiliation(s)
- Federica Ribaldi
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland.,Department of Rehabilitation and Geriatrics, Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Elena Rolandi
- "Golgi Cenci" Foundation, Corso San Martino 10, Abbiategrasso 20081, Italy.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia 27100, Italy
| | - Roberta Vaccaro
- "Golgi Cenci" Foundation, Corso San Martino 10, Abbiategrasso 20081, Italy
| | - Mauro Colombo
- "Golgi Cenci" Foundation, Corso San Martino 10, Abbiategrasso 20081, Italy
| | - Giovanni Battista Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE), University of Geneva, Geneva, Switzerland.,Department of Rehabilitation and Geriatrics, Geneva Memory Center, Geneva University Hospitals, Geneva, Switzerland
| | - Antonio Guaita
- "Golgi Cenci" Foundation, Corso San Martino 10, Abbiategrasso 20081, Italy
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The default mode network, depression and Alzheimer's disease. Int Psychogeriatr 2022; 34:675-678. [PMID: 35918182 DOI: 10.1017/s1041610222000539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Piras F, Banaj N, Porcari DE, Piras F, Spalletta G. Later life depression as risk factor for developing dementia: epidemiological evidence, predictive models, preventive strategies and future trends. Minerva Med 2021; 112:456-466. [PMID: 34056888 DOI: 10.23736/s0026-4806.21.07571-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Current investigations in pre-symptomatic dementia have suggested that depressive mood, a treatable condition, may play an important role in the development of the disorder. However, whether depression in adulthood constitute a risk factor, or a prodrome of dementia remains unclear. A major implication in such dispute is the analytic framework used to identify putative risk factors. Indeed, if evaluated in the years immediately prior to dementia diagnosis the association between depression and dementia may reflect depressive symptoms as a prodrome of yet-undiagnosed dementia. Unfortunately, long term prospective cohort investigations, reaching back into the preclinical phase of dementia are sparse. Here, we have surveyed high-quality evidence (systematic reviews and meta-analyses) on the association between depressive symptoms and increased odds of dementia. Meta-analytic findings are also presented and discussed regarding depression as a prodromal stage of dementia, or a consequence of underlying neurodegenerative processes. Additionally, the potential confounding effect of several variables on the risk association between depression and dementia, an aspect hardly investigated, is discussed. While early onset late-life depression - defined as starting before 60 years of age - increases the odds of developing dementia in predisposed subjects, late-onset depression appears to be a prodrome and a clear accelerating factor for cognitive deterioration. Since it is increasingly important to consider the potential of preemptive approaches to decrease the impact of dementia, evidence on potentially effective preventive strategies targeting depression as a risk factor, and next steps in further research are presented as concluding remarks.
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Affiliation(s)
- Federica Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Nerisa Banaj
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Desirée E Porcari
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Gianfranco Spalletta
- Laboratory of Neuropsychiatry, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy - .,Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
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Huan Z, Mei Z, Na H, Xinxin M, Yaping W, Ling L, Lei W, Kejin Z, Yanan L. lncRNA MIR155HG Alleviates Depression-Like Behaviors in Mice by Regulating the miR-155/BDNF Axis. Neurochem Res 2021; 46:935-944. [PMID: 33511575 DOI: 10.1007/s11064-021-03234-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 12/27/2020] [Accepted: 01/06/2021] [Indexed: 12/14/2022]
Abstract
Depression is one of most common psychiatric disorders, and the detailed molecular mechanism remains to be fully elucidated. Brain-derived neurotrophic factor (BDNF) is a critical neurotrophic factor that is decreased and closely involved in the development of depression. Noncoding RNAs are central regulators of cellular activities that modulate target genes. However, the roles of long noncoding RNA (lncRNA) MIR155HG and miRNA-155 (miR-155) in the pathophysiology of depression are unclear. In the present study, we aimed to explore the effects of lncRNA MIR155HG and miR-155 on the development of depression and uncover the underlying molecular mechanism. Real-time quantitative polymerase chain reaction was used to examine the expression of MIR155HG and miR-155. Western blotting was applied to measure the expression of BDNF. A luciferase reporter assay was utilized to determine the regulatory relationship between MIR155HG and miR-155. Our current work found that lncRNA MIR155HG and BDNF levels decreased while miR-155 levels increased in the hippocampal region of CUMS (chronic unpredictable mild stress) mice, a well-accepted mouse model of depression. Moreover, MIR155HG rescued while miR-155 exacerbated the depression-like behaviors of CUMS mice. Through bioinformatics analysis and luciferase reporter assays, we found that MIR155HG directly bound to and negatively modulated the expression of miR-155. Moreover, increased miR-155 was found to repress the expression of BDNF, a critical neurotrophic factor that has been reported to alleviate the depression-like behaviors of CUMS mice. Our present study revealed that lncRNA MIR155HG protected CUMS mice by regulating the miR-155/BDNF axis. Our study aimed to understand the pathophysiology of depression and provided potential therapeutic targets to diagnose and treat depression.
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Affiliation(s)
- Zhang Huan
- Department of Psychology and Psychiatry, The Second Affiliated Hospital of Xi'an Jiaotong University College of Medicine, Xian, 710004, China
| | - Zhu Mei
- Department of Psychology and Psychiatry, The Second Affiliated Hospital of Xi'an Jiaotong University College of Medicine, Xian, 710004, China
| | - Huang Na
- Core Research Laboratory, The Second Affiliated Hospital, College of Medicine, Jiaotong University, Xi'an, 710004, Shaanxi, China
| | - Ma Xinxin
- Department of Psychology and Psychiatry, The Second Affiliated Hospital of Xi'an Jiaotong University College of Medicine, Xian, 710004, China
| | - Wang Yaping
- Department of Psychology and Psychiatry, The Second Affiliated Hospital of Xi'an Jiaotong University College of Medicine, Xian, 710004, China
| | - Liu Ling
- Department of Psychology and Psychiatry, The Second Affiliated Hospital of Xi'an Jiaotong University College of Medicine, Xian, 710004, China
| | - Wang Lei
- Department of Psychology and Psychiatry, The Second Affiliated Hospital of Xi'an Jiaotong University College of Medicine, Xian, 710004, China
| | - Zhang Kejin
- School of Medicine, Northwest University, Xi'an, 710069, China
| | - Liu Yanan
- Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University College of Medicine, Xian, 710069, China.
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Santabárbara J, Bueno-Notivol J, Lipnicki DM, de la Cámara C, López-Antón R, Lobo A, Gracia-García P. A Novel Score for Predicting Alzheimer's Disease Risk from Late Life Psychopathological and Health Risk Factors. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:1802. [PMID: 33673250 PMCID: PMC7918511 DOI: 10.3390/ijerph18041802] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Revised: 02/01/2021] [Accepted: 02/08/2021] [Indexed: 11/28/2022]
Abstract
With the increasing size of the aging population, dementia risk reduction has become a main public health concern. Dementia risk models or indices may help to identify individuals in the community at high risk to develop dementia. We have aimed to develop a novel dementia risk index focused on the late-life (65 years or more) population, that addresses risk factors for Alzheimer's disease (AD) easily identifiable at primary care settings. These risk factors include some shown to be associated with the risk of AD but not featured in existing indices, such as hearing loss and anxiety. Our index is also the first to account for the competing risk of death. The Zaragoza Dementia and Depression Project (ZARADEMP) Alzheimer Dementia Risk Score predicts an individual´s risk of developing AD within 5 years. The probability of late onset AD significantly increases in those with risk scores between 21 and 28 and, furthermore, is almost 4-fold higher for those with risk scores of 29 or higher. Our index may provide a practical instrument to identify subjects at high risk of AD and to design preventive strategies targeting the contributing risk factors.
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Affiliation(s)
- Javier Santabárbara
- Department of Preventive Medicine and Public Health, Universidad de Zaragoza, 50001 Zaragoza, Spain;
- Instituto de Investigación Sanitaria de Aragón (IIS Aragón), 50001 Zaragoza, Spain; (C.d.l.C.); (R.L.-A.); (A.L.); (P.G.-G.)
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Ministry of Science and Innovation, 28029 Madrid, Spain
| | - Juan Bueno-Notivol
- Psychiatry Service, Hospital Universitario Miguel Servet, 50009 Zaragoza, Spain
| | - Darren M. Lipnicki
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales Medicine, 2052 Randwick, Australia;
| | - Concepción de la Cámara
- Instituto de Investigación Sanitaria de Aragón (IIS Aragón), 50001 Zaragoza, Spain; (C.d.l.C.); (R.L.-A.); (A.L.); (P.G.-G.)
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Ministry of Science and Innovation, 28029 Madrid, Spain
- Psychiatry Service, Hospital Clínico Universitario Lozano Blesa, 50009 Zaragoza, Spain
- Department of Medicine and Psychiatry, Universidad de Zaragoza, 50001 Zaragoza, Spain
| | - Raúl López-Antón
- Instituto de Investigación Sanitaria de Aragón (IIS Aragón), 50001 Zaragoza, Spain; (C.d.l.C.); (R.L.-A.); (A.L.); (P.G.-G.)
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Ministry of Science and Innovation, 28029 Madrid, Spain
- Department of Psychology and Sociology, Universidad de Zaragoza, 50001 Zaragoza, Spain
| | - Antonio Lobo
- Instituto de Investigación Sanitaria de Aragón (IIS Aragón), 50001 Zaragoza, Spain; (C.d.l.C.); (R.L.-A.); (A.L.); (P.G.-G.)
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Ministry of Science and Innovation, 28029 Madrid, Spain
- Department of Medicine and Psychiatry, Universidad de Zaragoza, 50001 Zaragoza, Spain
| | - Patricia Gracia-García
- Instituto de Investigación Sanitaria de Aragón (IIS Aragón), 50001 Zaragoza, Spain; (C.d.l.C.); (R.L.-A.); (A.L.); (P.G.-G.)
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Ministry of Science and Innovation, 28029 Madrid, Spain
- Psychiatry Service, Hospital Universitario Miguel Servet, 50009 Zaragoza, Spain
- Department of Medicine and Psychiatry, Universidad de Zaragoza, 50001 Zaragoza, Spain
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