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Sánchez-Ortí JV, Correa-Ghisays P, Balanzá-Martínez V, Selva-Vera G, Vila-Francés J, Magdalena-Benedito R, San-Martin C, Victor VM, Escribano-Lopez I, Hernandez-Mijares A, Vivas-Lalinde J, Crespo-Facorro B, Tabarés-Seisdedos R. Inflammation and lipid metabolism as potential biomarkers of memory impairment across type 2 diabetes mellitus and severe mental disorders. Prog Neuropsychopharmacol Biol Psychiatry 2023; 127:110817. [PMID: 37327846 DOI: 10.1016/j.pnpbp.2023.110817] [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] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/20/2023] [Accepted: 06/12/2023] [Indexed: 06/18/2023]
Abstract
INTRODUCTION Neurocognitive impairment is a transdiagnostic feature across several psychiatric and cardiometabolic conditions. The relationship between inflammatory and lipid metabolism biomarkers and memory performance is not fully understood. This study aimed to identify peripheral biomarkers suitable to signal memory decline from a transdiagnostic and longitudinal perspective. METHODS Peripheral blood biomarkers of inflammation, oxidative stress and lipid metabolism were assessed twice over a 1-year period in 165 individuals, including 30 with schizophrenia (SZ), 42 with bipolar disorder (BD), 35 with major depressive disorder (MDD), 30 with type 2 diabetes mellitus (T2DM), and 28 healthy controls (HCs). Participants were stratified by memory performance quartiles, taking as a reference their global memory score (GMS) at baseline, into categories of high memory (H; n = 40), medium to high memory (MH; n = 43), medium to low memory (ML; n = 38) and low memory (L; n = 44). Exploratory and confirmatory factorial analysis, mixed one-way analysis of covariance and discriminatory analyses were performed. RESULTS L group was significantly associated with higher levels of tumor necrosis factor-alpha (TNF-α) and lower levels of apolipoprotein A1 (Apo-A1) compared to those from the MH and H groups (p < 0.05; η2p = 0.06-0.09), with small to moderate effect sizes. Moreover, the combination of interleukin-6 (IL-6), TNF-α, c-reactive protein (CRP), Apo-A1 and Apo-B compounded the transdiagnostic model that best discriminated between groups with different degrees of memory impairment (χ2 = 11.9-49.3, p < 0.05-0.0001). CONCLUSIONS Inflammation and lipid metabolism seem to be associated with memory across T2DM and severe mental illnesses (SMI). A panel of biomarkers may be a useful approach to identify individuals at greater risk of neurocognitive impairment. These findings may have a potential translational utility for early intervention and advance precision medicine in these disorders.
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Affiliation(s)
- Joan Vicent Sánchez-Ortí
- INCLIVA - Health Research Institute, Valencia, Spain; TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain; Faculty of Psychology, University of Valencia, Valencia, Spain
| | - Patricia Correa-Ghisays
- INCLIVA - Health Research Institute, Valencia, Spain; Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain; TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain; Faculty of Psychology, University of Valencia, Valencia, Spain; Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain.
| | - Vicent Balanzá-Martínez
- INCLIVA - Health Research Institute, Valencia, Spain; Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain; TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain; Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain; Mental Health Unit of Catarroja, Valencia, Spain.
| | - Gabriel Selva-Vera
- INCLIVA - Health Research Institute, Valencia, Spain; Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain; TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain; Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
| | - Joan Vila-Francés
- IDAL - Intelligent Data Analysis Laboratory, University of Valencia, Valencia, Spain
| | | | - Constanza San-Martin
- Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain; TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain; Department of Physiotherapy, University of Valencia, Valencia, Spain
| | - Víctor M Victor
- Service of Endocrinology and Nutrition, University Hospital Dr. Peset, Spain; Foundation for the Promotion of Health and Biomedical Research in the Valencian Region (FISABIO), Valencia, Spain; Department of Physiology, University of Valencia, Valencia, Spain
| | | | | | | | - Benedicto Crespo-Facorro
- Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain; Department of Psychiatry, Faculty of Medicine, University of Sevilla, HU Virgen del Rocío IBIS, Spain
| | - Rafael Tabarés-Seisdedos
- INCLIVA - Health Research Institute, Valencia, Spain; Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain; TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain; Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
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Casañ RR, García-Vidal E, Grimaldi D, Carrasco-Farré C, Vaquer-Estalrich F, Vila-Francés J. Online polarization and cross-fertilization in multi-cleavage societies: the case of Spain. Soc Netw Anal Min 2022; 12:79. [PMID: 35855845 PMCID: PMC9281267 DOI: 10.1007/s13278-022-00909-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 06/16/2022] [Accepted: 06/20/2022] [Indexed: 11/12/2022]
Abstract
The impact of the social media (SM) has been seen on the one hand as the cause of large exacerbation of negative messages, responsible for massively harmful societal phenomenon against democracies. On the other hand, recent studies have begun to look at how these online channels were able to provide a new impulse in human communication. The novelty of our work resides on analysing several axes of polarizations related to different societal topics. We believe our approach to reflect a more complex society, differing from the recent literature, which has considered a unique left–right dichotomic cleavage. Our methodology consists of extracting topics from the priority themes of the SM debate, using BERT language processing techniques and TF-IDF model. Our results show situation of social media interactions in a multidimensional space does exist. We highlight how social media behaviours, polarization and cross-fertilization differ as upon concrete topics. We argue therefore the ‘mega-identity partisanship’ which differentiate US online users in two different spaces cannot be extended for the rest of countries taking as first evidence the case of Spain. Further research should extend our conclusions for a possible generalization.
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Correa-Ghisays P, Vicent Sánchez-Ortí J, Balanzá-Martínez V, Fuentes-Durá I, Martinez-Aran A, Ruiz-Bolo L, Correa-Estrada P, Ruiz-Ruiz JC, Selva-Vera G, Vila-Francés J, Macias Saint-Gerons D, San-Martín C, Ayesa-Arriola R, Tabarés-Seisdedos R. MICEmi: A method to identify cognitive endophenotypes of mental illnesses. Eur Psychiatry 2022; 65:e85. [PMID: 36440538 PMCID: PMC9807453 DOI: 10.1192/j.eurpsy.2022.2348] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Characterizing neurocognitive endophenotypes of mental illnesses (MIs) could be useful for identifying at-risk individuals, increasing early diagnosis, improving disease subtyping, and proposing therapeutic strategies to reduce the negative effects of the symptoms, in addition to serving as a scientific basis to unravel the physiopathology of the disease. However, a standardized algorithm to determine cognitive endophenotypes has not yet been developed. The main objective of this study was to present a method for the identification of endophenotypes in MI research. METHODS For this purpose, a 14-expert working group used a scoping review methodology and designed a method that includes a scoring template with five criteria and indicators, a strategy for their verification, and a decision tree. CONCLUSIONS This work is ongoing since it is necessary to obtain external validation of the applicability of the method in future research.
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Affiliation(s)
- Patricia Correa-Ghisays
- Center for Biomedical Research in Mental Health Network (CIBERSAM), ISCIII, Madrid, Spain.,Department of Personality, Evaluation and Psychological Treatment, Faculty of Psychology, University of Valencia, Valencia, Spain.,INCLIVA Biomedical Research Institute, Valencia, Spain.,TMAP Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, Department of Medicine, University of Valencia, Valencia, Spain
| | - Joan Vicent Sánchez-Ortí
- Department of Personality, Evaluation and Psychological Treatment, Faculty of Psychology, University of Valencia, Valencia, Spain.,INCLIVA Biomedical Research Institute, Valencia, Spain.,TMAP Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, Department of Medicine, University of Valencia, Valencia, Spain
| | - Vicent Balanzá-Martínez
- Center for Biomedical Research in Mental Health Network (CIBERSAM), ISCIII, Madrid, Spain.,INCLIVA Biomedical Research Institute, Valencia, Spain.,TMAP Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, Department of Medicine, University of Valencia, Valencia, Spain.,Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
| | - Inmaculada Fuentes-Durá
- Center for Biomedical Research in Mental Health Network (CIBERSAM), ISCIII, Madrid, Spain.,Department of Personality, Evaluation and Psychological Treatment, Faculty of Psychology, University of Valencia, Valencia, Spain.,INCLIVA Biomedical Research Institute, Valencia, Spain.,TMAP Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, Department of Medicine, University of Valencia, Valencia, Spain
| | - Anabel Martinez-Aran
- Center for Biomedical Research in Mental Health Network (CIBERSAM), ISCIII, Madrid, Spain.,Bipolar Disorders Unit, Neurosciences Institute, Hospital Clínic de Barcelona, IDIBAPS, Universitat de Barcelona, Catalonia, Spain
| | - Lara Ruiz-Bolo
- Department of Personality, Evaluation and Psychological Treatment, Faculty of Psychology, University of Valencia, Valencia, Spain
| | | | - Juan Carlos Ruiz-Ruiz
- Department of Personality, Evaluation and Psychological Treatment, Faculty of Psychology, University of Valencia, Valencia, Spain
| | - Gabriel Selva-Vera
- Center for Biomedical Research in Mental Health Network (CIBERSAM), ISCIII, Madrid, Spain.,INCLIVA Biomedical Research Institute, Valencia, Spain.,TMAP Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, Department of Medicine, University of Valencia, Valencia, Spain.,Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
| | - Joan Vila-Francés
- Intelligent Data Analysis Laboratory (IDAL), University of Valencia, Spain
| | - Diego Macias Saint-Gerons
- Center for Biomedical Research in Mental Health Network (CIBERSAM), ISCIII, Madrid, Spain.,INCLIVA Biomedical Research Institute, Valencia, Spain.,TMAP Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, Department of Medicine, University of Valencia, Valencia, Spain
| | - Constanza San-Martín
- Center for Biomedical Research in Mental Health Network (CIBERSAM), ISCIII, Madrid, Spain.,INCLIVA Biomedical Research Institute, Valencia, Spain.,TMAP Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, Department of Medicine, University of Valencia, Valencia, Spain.,Department of Physiotherapy, University of Valencia, Valencia, Spain
| | - Rosa Ayesa-Arriola
- Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Rafael Tabarés-Seisdedos
- Center for Biomedical Research in Mental Health Network (CIBERSAM), ISCIII, Madrid, Spain.,INCLIVA Biomedical Research Institute, Valencia, Spain.,TMAP Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, Department of Medicine, University of Valencia, Valencia, Spain.,Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
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Sánchez-Ortí JV, Balanzá-Martínez V, Correa-Ghisays P, Selva-Vera G, Vila-Francés J, Magdalena-Benedito R, San-Martin C, Victor VM, Escribano-Lopez I, Hernández-Mijares A, Vivas-Lalinde J, Crespo-Facorro B, Tabarés-Seisdedos R. Specific metabolic syndrome components predict cognition and social functioning in people with type 2 diabetes mellitus and severe mental disorders. Acta Psychiatr Scand 2022; 146:215-226. [PMID: 35359023 DOI: 10.1111/acps.13433] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Obesity and metabolic diseases such as metabolic syndrome (MetS) are more prevalent in people with type 2 diabetes mellitus (T2DM), major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ). MetS components might be associated with neurocognitive and functional impairments in these individuals. The predictive and discriminatory validity of MetS and its components regarding those outcomes were assessed from prospective and transdiagnostic perspectives. METHODS Metabolic syndrome components and neurocognitive and social functioning were assessed in 165 subjects, including 30 with SZ, 42 with BD, 35 with MDD, 30 with T2DM, and 28 healthy controls (HCs). A posteriori, individuals were classified into two groups. The MetS group consisted of those who met at least three of the following criteria: abdominal obesity (AO), elevated triglycerides (TG), reduced high-density lipoprotein cholesterol (HDL), elevated blood pressure (BP), and elevated fasting glucose (FPG); the remaining participants comprised the No-MetS group. Mixed one-way analysis of covariance and linear and binary logistic regression analyses were performed. RESULTS Cognitive impairment was significantly greater in the MetS group (n = 82) than in the No-MetS group (n = 83), with small effect sizes (p < 0.05; η²p = 0.02 - 0.03). In both groups, the most robust associations between MetS components and neurocognitive and social functioning were observed with TG and FPG (p < 0.05). There was also evidence for a significant relationship between cognition and BP in the MetS group (p < 0.05). The combination of TG, FPG, elevated systolic BP and HDL best classified individuals with greater cognitive impairment (p < 0.001), and TG was the most accurate (p < 0.0001). CONCLUSIONS Specific MetS components are significantly associated with cognitive impairment across somatic and psychiatric disorders. Our findings provide further evidence on the summative effect of MetS components to predict cognition and social functioning and allow the identification of individuals with worse outcomes. Transdiagnostic, lifestyle-based therapeutic interventions targeted at that group hold the potential to improve health outcomes.
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Affiliation(s)
- Joan Vicent Sánchez-Ortí
- INCLIVA - Biomedical Research Institute, Valencia, Spain.,TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain.,Faculty of Psychology, University of Valencia, Valencia, Spain
| | - Vicent Balanzá-Martínez
- INCLIVA - Biomedical Research Institute, Valencia, Spain.,TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain.,Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain.,Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain.,Mental Health Unit of Catarroja, Valencia, Spain
| | - Patricia Correa-Ghisays
- INCLIVA - Biomedical Research Institute, Valencia, Spain.,TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain.,Faculty of Psychology, University of Valencia, Valencia, Spain.,Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain.,Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
| | - Gabriel Selva-Vera
- INCLIVA - Biomedical Research Institute, Valencia, Spain.,TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain.,Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain.,Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
| | - Joan Vila-Francés
- IDAL - Intelligent Data Analysis Laboratory, University of Valencia, Valencia, Spain
| | | | - Constanza San-Martin
- TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain.,Department of Physiotherapy, University of Valencia, Valencia, Spain
| | - Víctor M Victor
- Service of Endocrinology and Nutrition, University Hospital Dr. Peset, Valencia, Spain.,Foundation for the Promotion of Health and Biomedical Research in the Valencian Region (FISABIO), Valencia, Spain.,Department of Physiology, University of Valencia, Valencia, Spain
| | - Irene Escribano-Lopez
- Service of Endocrinology and Nutrition, University Hospital Dr. Peset, Valencia, Spain
| | | | | | - Benedicto Crespo-Facorro
- TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain.,Department of Psychiatry, Faculty of Medicine, University of Sevilla, HU Virgen del Rocío IBIS, Sevilla, Spain
| | - Rafael Tabarés-Seisdedos
- INCLIVA - Biomedical Research Institute, Valencia, Spain.,TMAP - Evaluation Unit in Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain.,Center for Biomedical Research in Mental Health Network (CIBERSAM), Health Institute, Carlos III, Madrid, Spain.,Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
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Ferrer-Sánchez A, Bagan J, Vila-Francés J, Magdalena-Benedito R, Bagan-Debon L. Prediction of the risk of cancer and the grade of dysplasia in leukoplakia lesions using deep learning. Oral Oncol 2022; 132:105967. [PMID: 35763911 DOI: 10.1016/j.oraloncology.2022.105967] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 05/19/2022] [Accepted: 06/07/2022] [Indexed: 10/17/2022]
Abstract
OBJECTIVES To estimate the probability of malignancy of an oral leukoplakia lesion using Deep Learning, in terms of evolution to cancer and high-risk dysplasia. MATERIALS AND METHODS A total of 261 oral leukoplakia lesions with a mean of 5.5 years follow-up were analysed from standard digital photographs. A deep learning pipeline composed by a U-Net based segmentation of the lesion followed by a multi-task CNN classifier was used to predict the malignant transformation and the risk of dysplasia of the lesion. An explainability heatmap is constructed using LIME in order to interpret the decision of the model for each output. RESULTS A Dice coefficient of 0.561 was achieved on the segmentation task. For the prediction of a malignant transformation, the model provided a sensitivity of 1 with a specificity of 0.692. For the prediction of high-risk dysplasia, the model achieved a specificity of 0.740 and a sensitivity of 0.928. CONCLUSION The proposed model using deep learning can be a helpful tool for predicting the possible malignant evolution of oral leukoplakias. The generated heatmap provides a high confidence on the output of the model and enables its interpretability.
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Affiliation(s)
- Antonio Ferrer-Sánchez
- Intelligent Data Analysis Laboratory (IDAL), School of Engineering, University of Valencia, Spain
| | - Jose Bagan
- Professor of Oral Medicine, University of Valencia. Chairman service of Stomatology and Maxillofacial Surgery. University General Hospital, Valencia, Spain.
| | - Joan Vila-Francés
- Intelligent Data Analysis Laboratory (IDAL), School of Engineering, University of Valencia, Spain
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Garés-Caballer M, Sánchez-Ortí JV, Correa-Ghisays P, Balanzá-Martínez V, Selva-Vera G, Vila-Francés J, Magdalena-Benedito R, San-Martin C, Victor VM, Escribano-Lopez I, Hernandez-Mijares A, Vivas-Lalinde J, Vieta E, Leza JC, Tabarés-Seisdedos R. Immune–Inflammatory Biomarkers Predict Cognition and Social Functioning in Patients With Type 2 Diabetes Mellitus, Major Depressive Disorder, Bipolar Disorder, and Schizophrenia: A 1-Year Follow-Up Study. Front Neurol 2022; 13:883927. [PMID: 35720107 PMCID: PMC9201031 DOI: 10.3389/fneur.2022.883927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 02/25/2022] [Accepted: 04/19/2022] [Indexed: 11/30/2022] Open
Abstract
Background Systemic, low-grade immune–inflammatory activity, together with social and neurocognitive performance deficits are a transdiagnostic trait of people suffering from type 2 diabetes mellitus (T2DM) and severe mental illnesses (SMIs), such as schizophrenia (SZ), major depressive disorder (MDD), and bipolar disorder (BD). We aimed to determine if immune–inflammatory mediators were significantly altered in people with SMIs or T2DM compared with healthy controls (HC) and whether these biomarkers could help predict their cognition and social functioning 1 year after assessment. Methods We performed a prospective, 1-year follow-up cohort study with 165 participants at baseline (TB), including 30 with SZ, 42 with BD, 35 with MDD, 30 with T2DM, and 28 HC; and 125 at 1-year follow-up (TY), and determined executive domain (ED), global social functioning score (GSFS), and peripheral blood immune–inflammatory and oxidative stress biomarkers. Results Participants with SMIs and T2DM showed increased peripheral levels of inflammatory markers, such as interleukin-10 (p < 0.01; η2p = 0.07) and tumor necrosis factor-α (p < 0.05; η2p = 0.08); and oxidative stress biomarkers, such as reactive oxygen species (ROS) (p < 0.05; η2p = 0.07) and mitochondrial ROS (p < 0.01; η2p = 0.08). The different combinations of the exposed biomarkers anticipated 46–57.3% of the total ED and 23.8–35.7% of GSFS for the participants with SMIs. Limitations Participants' treatment, as usual, was continued without no specific interventions; thus, it was difficult to anticipate substantial changes related to the psychopharmacological pattern. Conclusion People with SMIs show significantly increased levels of peripheral immune–inflammatory biomarkers, which may contribute to the neurocognitive and social deficits observed in SMIs, T2DM, and other diseases with systemic immune–inflammatory activation of chronic development. These parameters could help identify the subset of patients who could benefit from immune–inflammatory modulator strategies to ameliorate their functional outcomes.
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Affiliation(s)
- Marta Garés-Caballer
- Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
| | - Joan Vicent Sánchez-Ortí
- INCLIVA—Health Research Institute, Valencia, Spain
- TMAP—Evaluation Unit of Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain
- Faculty of Psychology and Speech Therapy, University of Valencia, Valencia, Spain
| | - Patricia Correa-Ghisays
- INCLIVA—Health Research Institute, Valencia, Spain
- TMAP—Evaluation Unit of Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain
- Faculty of Psychology and Speech Therapy, University of Valencia, Valencia, Spain
- Center for Biomedical Research in Mental Health Network (CIBERSAM), Institute of Health Carlos III, Madrid, Spain
| | - Vicent Balanzá-Martínez
- Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
- INCLIVA—Health Research Institute, Valencia, Spain
- TMAP—Evaluation Unit of Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain
- Center for Biomedical Research in Mental Health Network (CIBERSAM), Institute of Health Carlos III, Madrid, Spain
- Mental Health Unit of Catarroja, Valencia, Spain
| | - Gabriel Selva-Vera
- Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
- INCLIVA—Health Research Institute, Valencia, Spain
- TMAP—Evaluation Unit of Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain
- Center for Biomedical Research in Mental Health Network (CIBERSAM), Institute of Health Carlos III, Madrid, Spain
| | - Joan Vila-Francés
- IDAL—Intelligent Data Analysis Laboratory, University of Valencia, Valencia, Spain
| | | | - Constanza San-Martin
- INCLIVA—Health Research Institute, Valencia, Spain
- TMAP—Evaluation Unit of Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain
- Department of Physiotherapy, University of Valencia, Valencia, Spain
| | - Victor M. Victor
- Service of Endocrinology and Nutrition, University Hospital Dr. Peset, Valencia, Spain
- Foundation for the Promotion of Health and Biomedical Research in the Valencian Region (FISABIO), Valencia, Spain
- Department of Physiology, University of Valencia, Valencia, Spain
| | - Irene Escribano-Lopez
- Service of Endocrinology and Nutrition, University Hospital Dr. Peset, Valencia, Spain
| | | | | | - Eduard Vieta
- Center for Biomedical Research in Mental Health Network (CIBERSAM), Institute of Health Carlos III, Madrid, Spain
- Barcelona Bipolar and Depressive Disorders Unit, Institute of Neurosciences, Hospital Clínic of Barcelona, University of Barcelona, IDIBAPS, Catalonia, Spain
| | - Juan C. Leza
- Center for Biomedical Research in Mental Health Network (CIBERSAM), Institute of Health Carlos III, Madrid, Spain
- Department of Pharmacology and Toxicology, Faculty of Medicine, Complutense University of Madrid, Madrid, Spain
| | - Rafael Tabarés-Seisdedos
- Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
- INCLIVA—Health Research Institute, Valencia, Spain
- TMAP—Evaluation Unit of Personal Autonomy, Dependency and Serious Mental Disorders, University of Valencia, Valencia, Spain
- Center for Biomedical Research in Mental Health Network (CIBERSAM), Institute of Health Carlos III, Madrid, Spain
- *Correspondence: Rafael Tabarés-Seisdedos
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Correa-Ghisays P, Sánchez-Ortí JV, Balanzá-Martínez V, Selva-Vera G, Vila-Francés J, Magdalena-Benedito R, Victor VM, Escribano-López I, Hernández-Mijares A, Vivas-Lalinde J, San-Martín C, Crespo-Facorro B, Tabarés-Seisdedos R. Transdiagnostic neurocognitive deficits in patients with type 2 diabetes mellitus, major depressive disorder, bipolar disorder, and schizophrenia: A 1-year follow-up study. J Affect Disord 2022; 300:99-108. [PMID: 34965401 DOI: 10.1016/j.jad.2021.12.074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 11/05/2021] [Accepted: 12/19/2021] [Indexed: 10/19/2022]
Abstract
BACKGROUND Neurocognition impairments are critical factors in patients with major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ), and also in those with somatic diseases such as type 2 diabetes mellitus (T2DM). Intriguingly, these severe mental illnesses are associated with an increased co-occurrence of diabetes (direct comorbidity). This study sought to investigate the neurocognition and social functioning across T2DM, MDD, BD, and SZ using a transdiagnostic and longitudinal approach. METHODS A total of 165 participants, including 30 with SZ, 42 with BD, 35 with MDD, 30 with T2DM, and 28 healthy controls (HC), were assessed twice at a 1-year interval using a comprehensive, integrated test battery on neuropsychological and social functioning. RESULTS Common neurocognitive impairments in somatic and psychiatric disorders were identified, including deficits in short-term memory and cognitive reserve (p < 0.01, η²p=0.08-0.31). Social functioning impairments were observed in almost all the disorders (p < 0.0001; η²p=0.29-0.49). Transdiagnostic deficits remained stable across the 1-year follow-up (p < 0.001; η²p=0.13-0.43) and could accurately differentiate individuals with somatic and psychiatric disorders (χ² = 48.0, p < 0.0001). LIMITATIONS The initial sample size was small, and high experimental mortality was observed after follow-up for one year. CONCLUSIONS This longitudinal study provides evidence of some possible overlap in neurocognition deficits across somatic and psychiatric diagnostic categories, such as T2DM, MDD, BD, and SZ, which have high comorbidity. This overlap may be a result of shared genetic and environmental etiological factors. The findings open promising avenues for research on transdiagnostic phenotypes of neurocognition in these disorders, in addition to their biological bases.
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Affiliation(s)
- Patricia Correa-Ghisays
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid 28029, Spain; TMAP - Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, University of Valencia, Valencia 46010, Spain; INCLIVA - Health Research Institute, Valencia 46010, Spain; Faculty of Psychology, University of Valencia, Valencia 46010, Spain; Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Blasco-Ibáñez 17, Valencia 46010, Spain
| | - Joan Vicent Sánchez-Ortí
- TMAP - Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, University of Valencia, Valencia 46010, Spain; INCLIVA - Health Research Institute, Valencia 46010, Spain; Faculty of Psychology, University of Valencia, Valencia 46010, Spain
| | - Vicent Balanzá-Martínez
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid 28029, Spain; TMAP - Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, University of Valencia, Valencia 46010, Spain; INCLIVA - Health Research Institute, Valencia 46010, Spain; Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Blasco-Ibáñez 17, Valencia 46010, Spain; Unitat de Salut Mental de Catarroja, Valencia 46470, Spain
| | - Gabriel Selva-Vera
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid 28029, Spain; TMAP - Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, University of Valencia, Valencia 46010, Spain; INCLIVA - Health Research Institute, Valencia 46010, Spain; Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Blasco-Ibáñez 17, Valencia 46010, Spain
| | - Joan Vila-Francés
- IDAL - Intelligent Data Analysis Laboratory, University of Valencia, Valencia 46100, Spain
| | | | - Victor M Victor
- Service of Endocrinology and Nutrition, University Hospital Doctor Peset, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region (FISABIO), Valencia 46017, Spain; Department of Physiology, University of Valencia, Valencia 46010, Spain
| | - Irene Escribano-López
- Service of Endocrinology and Nutrition, University Hospital Doctor Peset, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region (FISABIO), Valencia 46017, Spain
| | - Antonio Hernández-Mijares
- Service of Endocrinology and Nutrition, University Hospital Doctor Peset, Foundation for the Promotion of Health and Biomedical Research in the Valencian Region (FISABIO), Valencia 46017, Spain; Department of Medicine, University of Valencia, Valencia 46010, Spain
| | | | - Constanza San-Martín
- TMAP - Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, University of Valencia, Valencia 46010, Spain; Departament of Physioterapy, University of Valencia, Valencia 46010, Spain
| | - Benedicto Crespo-Facorro
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid 28029, Spain; Hospital Universitario Virgen del Roció-IBIS- University of Sevilla, Sevilla, Spain
| | - Rafael Tabarés-Seisdedos
- Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid 28029, Spain; TMAP - Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, University of Valencia, Valencia 46010, Spain; INCLIVA - Health Research Institute, Valencia 46010, Spain; Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Blasco-Ibáñez 17, Valencia 46010, Spain.
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8
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Ruiz JC, Fuentes-Durá I, López-Gilberte M, Dasí C, Pardo-García C, Fuentes-Durán MC, Pérez-González F, Salmeron L, Soldevila-Matías P, Vila-Francés J, Balanza-Martínez V. Public stigma profile toward mental disorders across different university degrees in the University of Valencia (Spain). Front Psychiatry 2022; 13:951894. [PMID: 36032229 PMCID: PMC9411748 DOI: 10.3389/fpsyt.2022.951894] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 07/27/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND A large proportion of studies carried out in recent years in different populations have shown that stigma toward mental disorders is highly prevalent. In the present study we conducted a comprehensive assessment of stigma to describe and compare stigma toward mental disorders in students enrolled in five different university degrees. METHODS Three hundred and twenty-five students from the University of Valencia (Spain), attending the second term of their first-degree courses in the faculties of medicine, psychology, teaching, economics, and data science participated in this cross-sectional study. Stigma was measured using: the Reported and Intended Behavior Scale (RIBS), the Scale of Community Attitudes toward Mental Illness (CAMI), the Attribution Questionnaire (AQ-27), and the Knowledge about Mental Illness test (KMI). RESULTS We found different patterns of stigma according to gender, the fact of knowing or living with a person with mental disorders and the university degree studied. Overall, women show fewer stigmatizing attitudes than men but similar stereotypes and prejudice toward people with mental disorders. However, the pattern of results across degrees is more complex. Overall, students of medicine, psychology and teaching showed fewer stigmatizing attitudes than students of economics and data science but differences between degrees were more subtle in stereotypes and prejudice toward people with mental disorders. CONCLUSION Our study suggests the existence of different profiles of stigma in relation to mental disorders in university students. These profiles varied in relation with the degree being studied, gender and already knowing or living with a person with mental disorders.
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Affiliation(s)
- Juan C Ruiz
- Faculty of Psychology, University of Valencia, Valencia, Spain
| | - Inmaculada Fuentes-Durá
- Faculty of Psychology, University of Valencia, Valencia, Spain.,Center for Biomedical Research in Mental Health Network (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain.,INCLIVA Biomedical Research Institute, Valencia, Spain
| | | | - Carmen Dasí
- Faculty of Psychology, University of Valencia, Valencia, Spain
| | | | | | | | | | | | - Joan Vila-Francés
- Intelligent Data Analysis Laboratory (IDAL), University of Valencia, Valencia, Spain
| | - Vicent Balanza-Martínez
- Center for Biomedical Research in Mental Health Network (CIBERSAM), Instituto de Salud Carlos III (ISCIII), Madrid, Spain.,Department of Medicine, University of Valencia, Valencia, Spain
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9
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Ribera C, Vidal-Rubio SL, Romeu-Climent JE, Vila-Francés J, Van Rheenen TE, Balanzá-Martínez V. Cognitive impairment and consumption of mental healthcare resources in outpatients with bipolar disorder. J Psychiatr Res 2021; 138:535-540. [PMID: 33990024 DOI: 10.1016/j.jpsychires.2021.05.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 04/27/2021] [Accepted: 05/01/2021] [Indexed: 11/16/2022]
Abstract
Cognitive dysfunction is a major predictor of functional outcomes, and loss of occupational functioning is usually linked with a higher cost of illness. However, the association between cognitive impairment and consumption of health resources has not been studied in bipolar disorder to date. This study aims to examine this relationship. This is an observational, retrospective study of a representative sample of euthymic outpatients between 18 and 55 years, fulfilling DSM 5 criteria for bipolar disorder and recruited at a catchment area in Spain. Cognitive performance was screened with the Spanish version of the Screen for Cognitive Impairment in Psychiatry (SCIP-S), and several variables of health resources consumption during the previous year were registered. A total of 72 patients were assessed. Cognitive impairment according to the SCIP-S was significantly associated with the number of scheduled clinical appointments (p < 0.005) and hospital admissions (p < 0.04) but not with other health resources consumption variables. These results need to be interpreted with caution given that neither a control group nor a comprehensive, objective neuropsychological battery were used. However, despite these limitations, this study shows that in euthymic outpatients with bipolar disorder, those with suspected cognitive impairment had consumed a higher number of health resources over the previous year. These preliminary results may foster similar studies on the relationship between mental healthcare resource use and cognitive dysfunction in bipolar disorder and other psychiatric disorders.
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Affiliation(s)
- Carlos Ribera
- Department of Mental Health, La Ribera University Hospital, Carretera Corbera Km 1 s/n 46600, Alzira, Valencia, Spain
| | - Sonia Ll Vidal-Rubio
- Department of Mental Health, La Ribera University Hospital, Carretera Corbera Km 1 s/n 46600, Alzira, Valencia, Spain
| | - Jose E Romeu-Climent
- Department of Mental Health, La Ribera University Hospital, Carretera Corbera Km 1 s/n 46600, Alzira, Valencia, Spain
| | - Joan Vila-Francés
- Intelligent Data Analysis Laboratory (IDAL) University of Valencia, Avenida Universitat s/n 46100, Burjassot, Valencia, Spain
| | - Tamsyn E Van Rheenen
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Level 3, Alan Gilbert Building, 161 Barry St, Carlton, VIC, 3053, Australia; Faculty of Health, Arts and Design, School of Health Sciences, Center for Mental Health, Swinburne University, Level 3, Alan Gilbert Building, 161 Barry St, Carlton, VIC, 3053, Australia
| | - Vicent Balanzá-Martínez
- Teaching Unit of Psychiatry, Department of Medicine, University of Valencia, Valencia, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), ISCIII., Avenida Blasco Ibáñez 15, 46010, Valencia, Spain.
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10
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Aliño-Dies M, Sánchez-Ortí JV, Correa-Ghisays P, Balanzá-Martínez V, Vila-Francés J, Selva-Vera G, Correa-Estrada P, Forés-Martos J, San-Martín Valenzuela C, Monfort-Pañego M, Ayesa-Arriola R, Ruiz-Veguilla M, Crespo-Facorro B, Tabarés-Seisdedos R. Grip Strength, Neurocognition, and Social Functioning in People WithType-2 Diabetes Mellitus, Major Depressive Disorder, Bipolar Disorder, and Schizophrenia. Front Psychol 2020; 11:525231. [PMID: 33324271 PMCID: PMC7723830 DOI: 10.3389/fpsyg.2020.525231] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [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: 01/08/2020] [Accepted: 10/30/2020] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Frailty is a common syndrome among older adults and patients with several comorbidities. Grip strength (GS) is a representative parameter of frailty because it is a valid indicator of current and long-term physical conditions in the general population and patients with severe mental illnesses (SMIs). Physical and cognitive capacities of people with SMIs are usually impaired; however, their relationship with frailty or social functioning have not been studied to date. The current study aimed to determine if GS is a valid predictor of changes in cognitive performance and social functioning in patients with type-2 diabetes mellitus and SMIs. METHODS Assessments of social functioning, cognitive performance, and GS (measured with an electronic dynamometer) were conducted in 30 outpatients with type 2 diabetes mellitus, 35 with major depressive disorder, 42 with bipolar disorder, 30 with schizophrenia, and 28 healthy controls, twice during 1-year, follow-up period. Descriptive analyses were conducted using a one-way analysis of variance for continuous variables and the chi-squared test for categorical variables. Differences between groups for the motor, cognitive, and social variables at T1 and T2 were assessed using a one-way analysis of covariance, with sex and age as co-variates (p < 0.01). To test the predictive capacity of GS at baseline to explain the variance in cognitive performance and social functioning at T2, a linear regression analysis was performed (p < 0.05). RESULTS Predictive relationships were found among GS when implicated with clinical, cognitive, and social variables. These relationships explained changes in cognitive performance after one year of follow-up; the variability percentage was 67.7%, in patients with type-2 diabetes mellitus and 89.1% in patients with schizophrenia. Baseline GS along with other variables, also predicted changes in social functioning in major depressive disorder, bipolar disorder, and schizophrenia, with variability percentages of 67.3, 36, and 59%, respectively. CONCLUSION GS combined with other variables significantly predicted changes in cognitive performance and social functioning in people with SMIs or type-2 diabetes mellitus. Interventions aimed to improve the overall physical conditions of patients who have poor GS could be a therapeutic option that confers positive effects on cognitive performance and social functioning.
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Affiliation(s)
- María Aliño-Dies
- Department of Medicine, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain
| | - Joan Vicent Sánchez-Ortí
- Department of Medicine, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain
- Faculty of Psychology, University of Valencia, Valencia, Spain
- TMAP – Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, University of Valencia, Valencia, Spain
| | - Patricia Correa-Ghisays
- Faculty of Psychology, University of Valencia, Valencia, Spain
- TMAP – Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, University of Valencia, Valencia, Spain
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- INCLIVA Health Research Institute, Valencia, Spain
| | - Vicent Balanzá-Martínez
- Department of Medicine, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain
- TMAP – Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, University of Valencia, Valencia, Spain
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
| | - Joan Vila-Francés
- IDAL – Intelligent Data Analysis Laboratory, University of Valencia, Valencia, Spain
| | - Gabriel Selva-Vera
- Department of Medicine, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain
- TMAP – Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, University of Valencia, Valencia, Spain
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- INCLIVA Health Research Institute, Valencia, Spain
| | | | - Jaume Forés-Martos
- Department of Medicine, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain
- TMAP – Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, University of Valencia, Valencia, Spain
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- INCLIVA Health Research Institute, Valencia, Spain
| | - Constanza San-Martín Valenzuela
- TMAP – Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, University of Valencia, Valencia, Spain
- Department of Physiotherapy, Faculty of Physiotherapy, University of Valencia, Valencia, Spain
| | - Manuel Monfort-Pañego
- Department of Physical Education Teacher Training, University of Valencia, Valencia, Spain
| | - Rosa Ayesa-Arriola
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Department of Psychiatry, Marqués de Valdecilla University Hospital, IDIVAL, School of Medicine, University of Cantabria, Santander, Spain
| | - Miguel Ruiz-Veguilla
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Hospital Universitario Virgen del Roció-IBIS, University of Sevilla, Seville, Spain
| | - Benedicto Crespo-Facorro
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- Hospital Universitario Virgen del Roció-IBIS, University of Sevilla, Seville, Spain
| | - Rafael Tabarés-Seisdedos
- Department of Medicine, Faculty of Medicine and Dentistry, University of Valencia, Valencia, Spain
- TMAP – Unidad de Evaluación en Autonomía Personal, Dependencia y Trastornos Mentales Graves, University of Valencia, Valencia, Spain
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain
- INCLIVA Health Research Institute, Valencia, Spain
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11
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Correa-Ghisays P, Sánchez-Ortí JV, Ayesa-Arriola R, Setién-Suero E, Balanzá-Martínez V, Selva-Vera G, Ruiz-Ruiz JC, Vila-Francés J, Martinez-Aran A, Vivas-Lalinde J, Conforte-Molina C, San-Martín C, Martínez-Pérez C, Fuentes-Durá I, Crespo-Facorro B, Tabarés-Seisdedos R. Visual memory dysfunction as a neurocognitive endophenotype in bipolar disorder patients and their unaffected relatives. Evidence from a 5-year follow-up Valencia study. J Affect Disord 2019; 257:31-37. [PMID: 31299402 DOI: 10.1016/j.jad.2019.06.059] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 06/26/2019] [Accepted: 06/30/2019] [Indexed: 11/29/2022]
Abstract
BACKGROUND Scarce research has focused on Visual Memory (VM) deficits as a possible neurocognitive endophenotype of bipolar disorder (BD). The main aim of this longitudinal, family study with healthy controls was to explore whether VM dysfunction represents a neurocognitive endophenotype of BD. METHODS Assessment of VM by Rey-Osterrieth Complex Figure Test (ROCF) was carried out on a sample of 317 subjects, including 140 patients with BD, 60 unaffected first-degree relatives (BD-Rel), and 117 genetically-unrelated healthy controls (HC), on three occasions over a 5-year period (T1, T2, and T3). BD-Rel group scores were analyzed only at T1 and T2. RESULTS Performance of BD patients was significantly worse than the HC group (p < 0.01). Performance of BD-Rel was also significantly different from HC scores at T1 (p < 0.01) and T2 (p = 0.05), and showed an intermediate profile between the BD and HC groups. Only among BD patients, there were significant differences according to sex, with females performing worse than males (p = 0.03). Regarding other variables, education represented significant differences only in average scores of BD-Rel group (p = 0.01). LIMITATIONS Important attrition in BD-Rel group over time was detected, which precluded analysis at T3. CONCLUSIONS BD patients show significant deficits in VM that remain stable over time, even after controlling sociodemographic and clinical variables. Unaffected relatives also show stable deficits in VM. Accordingly, the deficit in VM could be considered a potential endophenotype of BD, which in turn may be useful as a predictor of the evolution of the disease. Further studies are needed to confirm these findings.
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Affiliation(s)
- Patricia Correa-Ghisays
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain; Faculty of Psychology, University of Valencia, Valencia, Spain; INCLIVA Health Research Institute, Valencia, Spain; TMAP Unidad de evaluación en autonomía personal, dependencia y trastornos mentales graves, Department of Medicine, University of Valencia, Valencia, Spain
| | - Joan Vicent Sánchez-Ortí
- Faculty of Psychology, University of Valencia, Valencia, Spain; TMAP Unidad de evaluación en autonomía personal, dependencia y trastornos mentales graves, Department of Medicine, University of Valencia, Valencia, Spain
| | - Rosa Ayesa-Arriola
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain; University Hospital Marqués de Valdecilla. Department of Psychiatry, IDIVAL, Santander, Spain; Department of Psychiatry, IDIVAL, School of Medicine, Marqués de Valdecilla University Hospital, University of Cantabria, Santander, Spain
| | - Esther Setién-Suero
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain; University Hospital Marqués de Valdecilla. Department of Psychiatry, IDIVAL, Santander, Spain; Department of Psychiatry, IDIVAL, School of Medicine, Marqués de Valdecilla University Hospital, University of Cantabria, Santander, Spain
| | - Vicent Balanzá-Martínez
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain; TMAP Unidad de evaluación en autonomía personal, dependencia y trastornos mentales graves, Department of Medicine, University of Valencia, Valencia, Spain; Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
| | - Gabriel Selva-Vera
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain; INCLIVA Health Research Institute, Valencia, Spain; TMAP Unidad de evaluación en autonomía personal, dependencia y trastornos mentales graves, Department of Medicine, University of Valencia, Valencia, Spain; Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain
| | | | - Joan Vila-Francés
- IDAL - Intelligent Data Analysis Laboratory, University of Valencia, Valencia, Spain
| | - Anabel Martinez-Aran
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain; Bipolar Disorders Unit, Neurosciences Institute, Hospital Clínic de Barcelona, IDIBAPS, Universitat de Barcelona, Catalonia, Spain
| | | | | | - Constanza San-Martín
- TMAP Unidad de evaluación en autonomía personal, dependencia y trastornos mentales graves, Department of Medicine, University of Valencia, Valencia, Spain; Departament of Physioterapiy, University of Valencia, Valencia, Spain
| | | | | | - Benedicto Crespo-Facorro
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain; Hospital Universitario Virgen del Rocio, Universidad de Sevilla, Spain
| | - Rafael Tabarés-Seisdedos
- Centro Investigación Biomédica en Red de Salud Mental, CIBERSAM, Madrid, Spain; INCLIVA Health Research Institute, Valencia, Spain; TMAP Unidad de evaluación en autonomía personal, dependencia y trastornos mentales graves, Department of Medicine, University of Valencia, Valencia, Spain; Teaching Unit of Psychiatry and Psychological Medicine, Department of Medicine, University of Valencia, Valencia, Spain.
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12
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Urueña López A, Mateo F, Navío-Marco J, Martínez-Martínez JM, Gómez-Sanchís J, Vila-Francés J, José Serrano-López A. Analysis of computer user behavior, security incidents and fraud using Self-Organizing Maps. Comput Secur 2019. [DOI: 10.1016/j.cose.2019.01.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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13
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Vidal-Rubio SL, Balanzá-Martínez V, Cuenca M, Vila-Francés J, Vieta E, Romeu JE. Duration of euthymia and predominant polarity in bipolar disorder. J Affect Disord 2018; 241:356-359. [PMID: 30144718 DOI: 10.1016/j.jad.2018.08.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Revised: 05/23/2018] [Accepted: 08/01/2018] [Indexed: 01/10/2023]
Abstract
BACKGROUND The concept of Predominant Polarity (PP) provides relevant information for clinical practice and has been widely described as course specifier for Bipolar Disorder (BD), however it has not been incorporated in DSM-5 yet. A descriptive study was conducted to identify clinical patterns associated with PP in outpatients attending a Mental Health Unit. METHODS Clinical and socio-demographic characteristics were assessed from a sample of 118 euthymic outpatients fulfilling DSM 5 criteria for BDI or II recruited at a catchment area. According to their PP, patients were divided into three subgroups: depressive (DPP; 39.0%), manic (MPP; 32.2%) or indeterminate (IPP; 28.8%). Subgroups of PP were compared regarding a comprehensive set of demographic and clinical features. RESULTS PP subgroups significantly differed in duration of euthymia, measured in months since the last episode (p < 0.04), with MMP patients showing longer periods (42.4 months) than those with DPP and IPP (18.6 and 18.1 months, respectively). Moreover, history of seasonal pattern was significantly higher in the DPP group compared with the PPM group (p < 0.001). There were no significant correlations between PP and type of last episode, length of illness, number of previous admissions, history of psychotic symptoms, or number of suicide attempts. LIMITATIONS Cross sectional design, relatively modest sample size. CONCLUSIONS Our study showed similar results to previous literature regarding distribution of predominant polarity. The association found between PP and duration of euthymia represents a novel finding which awaits confirmation and adds further support to the usefulness of PP in clinical practice.
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Affiliation(s)
- Sonia Ll Vidal-Rubio
- Servicio de Psiquiatría, Departamento de Salud de la Ribera, Av. Santos Patronos 24, 8°, 29(a), 46.600 Alzira, Valencia, Spain.
| | - Vicent Balanzá-Martínez
- Teaching Unit of Psychiatry, Department of Medicine, University of Valencia, Valencia, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), ISCIII, Madrid, Spain
| | - María Cuenca
- Dirección de Investigación y Docencia, Departamento de Salud de La Ribera. Valencia, Spain; Universidad Católica de Valencia San Vicente Mártir. Valencia. Spain
| | - Joan Vila-Francés
- IDAL - Intelligent Data Analysis laboratory, University of Valencia, Valencia, Spain
| | - Eduard Vieta
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), ISCIII, Madrid, Spain; Bipolar Unit, Hospital Clinic, Institute of Neuroscience, University of Barcelona, IDIBAPS, Barcelona, Spain
| | - José E Romeu
- Servicio de Psiquiatría, Departamento de Salud de la Ribera, Av. Santos Patronos 24, 8°, 29(a), 46.600 Alzira, Valencia, Spain
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Escandell-Montero P, Lorente D, Martínez-Martínez JM, Soria-Olivas E, Vila-Francés J, Martín-Guerrero JD. Online fitted policy iteration based on extreme learning machines. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2016.03.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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15
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Cano A, Holland C, Vila-Francés J, Alexis B, James B, Castro A, Soria-Olivas E, Santos NC, Cunha P, Sousa N, O’Connell MD, Feeney J, Kenny RA. Somatometric and clinical cardiovascular risk factors in midlife and older women. A tale of four European countries. Maturitas 2015. [DOI: 10.1016/j.maturitas.2015.02.348] [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: 10/23/2022]
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16
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Cano A, Vila-Francés J, Castro A, Soria-Olivas E. Self-organising maps for the analysis of data from big cohorts. The case of the Spanish CARMEN cohort. Maturitas 2015. [DOI: 10.1016/j.maturitas.2015.02.399] [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: 10/23/2022]
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17
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Escandell-Montero P, Chermisi M, Martínez-Martínez JM, Gómez-Sanchis J, Barbieri C, Soria-Olivas E, Mari F, Vila-Francés J, Stopper A, Gatti E, Martín-Guerrero JD. Optimization of anemia treatment in hemodialysis patients via reinforcement learning. Artif Intell Med 2014; 62:47-60. [PMID: 25091172 DOI: 10.1016/j.artmed.2014.07.004] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [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: 11/03/2013] [Revised: 06/23/2014] [Accepted: 07/11/2014] [Indexed: 10/25/2022]
Abstract
OBJECTIVE Anemia is a frequent comorbidity in hemodialysis patients that can be successfully treated by administering erythropoiesis-stimulating agents (ESAs). ESAs dosing is currently based on clinical protocols that often do not account for the high inter- and intra-individual variability in the patient's response. As a result, the hemoglobin level of some patients oscillates around the target range, which is associated with multiple risks and side-effects. This work proposes a methodology based on reinforcement learning (RL) to optimize ESA therapy. METHODS RL is a data-driven approach for solving sequential decision-making problems that are formulated as Markov decision processes (MDPs). Computing optimal drug administration strategies for chronic diseases is a sequential decision-making problem in which the goal is to find the best sequence of drug doses. MDPs are particularly suitable for modeling these problems due to their ability to capture the uncertainty associated with the outcome of the treatment and the stochastic nature of the underlying process. The RL algorithm employed in the proposed methodology is fitted Q iteration, which stands out for its ability to make an efficient use of data. RESULTS The experiments reported here are based on a computational model that describes the effect of ESAs on the hemoglobin level. The performance of the proposed method is evaluated and compared with the well-known Q-learning algorithm and with a standard protocol. Simulation results show that the performance of Q-learning is substantially lower than FQI and the protocol. When comparing FQI and the protocol, FQI achieves an increment of 27.6% in the proportion of patients that are within the targeted range of hemoglobin during the period of treatment. In addition, the quantity of drug needed is reduced by 5.13%, which indicates a more efficient use of ESAs. CONCLUSION Although prospective validation is required, promising results demonstrate the potential of RL to become an alternative to current protocols.
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Affiliation(s)
- Pablo Escandell-Montero
- Intelligent Data Analysis Laboratory, University of Valencia, Av. de la Universidad, s/n, 46100 Burjassot (Valencia), Spain.
| | - Milena Chermisi
- Healthcare and Business Advanced Modeling, Fresenius Medical Care, Else-Kröner-Strasse 1, 61352 Bad Homburg, Germany
| | - José M Martínez-Martínez
- Intelligent Data Analysis Laboratory, University of Valencia, Av. de la Universidad, s/n, 46100 Burjassot (Valencia), Spain
| | - Juan Gómez-Sanchis
- Intelligent Data Analysis Laboratory, University of Valencia, Av. de la Universidad, s/n, 46100 Burjassot (Valencia), Spain
| | - Carlo Barbieri
- Healthcare and Business Advanced Modeling, Fresenius Medical Care, Else-Kröner-Strasse 1, 61352 Bad Homburg, Germany
| | - Emilio Soria-Olivas
- Intelligent Data Analysis Laboratory, University of Valencia, Av. de la Universidad, s/n, 46100 Burjassot (Valencia), Spain
| | - Flavio Mari
- Healthcare and Business Advanced Modeling, Fresenius Medical Care, Else-Kröner-Strasse 1, 61352 Bad Homburg, Germany
| | - Joan Vila-Francés
- Intelligent Data Analysis Laboratory, University of Valencia, Av. de la Universidad, s/n, 46100 Burjassot (Valencia), Spain
| | - Andrea Stopper
- Healthcare and Business Advanced Modeling, Fresenius Medical Care, Else-Kröner-Strasse 1, 61352 Bad Homburg, Germany
| | - Emanuele Gatti
- Healthcare and Business Advanced Modeling, Fresenius Medical Care, Else-Kröner-Strasse 1, 61352 Bad Homburg, Germany; Centre for Biomedical Technology at Danube, University of Krems, Dr.-Karl-Dorrek-Strasse 30, 3500 Krems, Austria
| | - José D Martín-Guerrero
- Intelligent Data Analysis Laboratory, University of Valencia, Av. de la Universidad, s/n, 46100 Burjassot (Valencia), Spain
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Caravaca J, Soria-Olivas E, Bataller M, Serrano AJ, Such-Miquel L, Vila-Francés J, Guerrero JF. Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation. Comput Biol Med 2014; 45:1-7. [DOI: 10.1016/j.compbiomed.2013.11.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2013] [Revised: 11/13/2013] [Accepted: 11/18/2013] [Indexed: 11/25/2022]
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Soria-Olivas E, Gómez-Sanchis J, Martín JD, Vila-Francés J, Martínez M, Magdalena JR, Serrano AJ. BELM: Bayesian extreme learning machine. ACTA ACUST UNITED AC 2011; 22:505-9. [PMID: 21257373 DOI: 10.1109/tnn.2010.2103956] [Citation(s) in RCA: 108] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The theory of extreme learning machine (ELM) has become very popular on the last few years. ELM is a new approach for learning the parameters of the hidden layers of a multilayer neural network (as the multilayer perceptron or the radial basis function neural network). Its main advantage is the lower computational cost, which is especially relevant when dealing with many patterns defined in a high-dimensional space. This brief proposes a bayesian approach to ELM, which presents some advantages over other approaches: it allows the introduction of a priori knowledge; obtains the confidence intervals (CIs) without the need of applying methods that are computationally intensive, e.g., bootstrap; and presents high generalization capabilities. Bayesian ELM is benchmarked against classical ELM in several artificial and real datasets that are widely used for the evaluation of machine learning algorithms. Achieved results show that the proposed approach produces a competitive accuracy with some additional advantages, namely, automatic production of CIs, reduction of probability of model overfitting, and use of a priori knowledge.
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Affiliation(s)
- Emilio Soria-Olivas
- Digital Signal Processing Group, Department of Electronic Engineering, ETSE, University of Valencia, Burjassot 46100, Spain.
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Vila-Francés J, Calpe-Maravilla J, Gómez-Chova L, Amorós-López J. Design of a configurable multispectral imaging system based on an AOTF. IEEE Trans Ultrason Ferroelectr Freq Control 2011; 58:259-262. [PMID: 21244996 DOI: 10.1109/tuffc.2011.1795] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
In this paper, we present a configurable multispectral imaging system based on an acousto-optic tunable filter (AOTF). Typically, AOTFs are used to filter a single wavelength at a time, but thanks to the use of a versatile sweeping frequency generator implemented with a direct digital synthesizer, the imager may capture a configurable spectral range. Experimental results show a good spectral and imaging response of the system for spectral bandwidth up to a 50 nm.
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Soria-Olivas E, Martín-Guerrero J, Serrano-López A, Calpe-Maravilla J, Vila-Francés J, Camps-Valls G. Efficient pruning of multilayer perceptrons using a fuzzy sigmoid activation function. Neurocomputing 2006. [DOI: 10.1016/j.neucom.2005.04.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [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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