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Llera-Oyola J, Carceller H, Andreu Z, Hidalgo MR, Soler-Sáez I, Gordillo F, Gómez-Cabañes B, Roson B, de la Iglesia-Vayá M, Mancuso R, Guerini FR, Mizokami A, García-García F. The role of microRNAs in understanding sex-based differences in Alzheimer's disease. Biol Sex Differ 2024; 15:13. [PMID: 38297404 PMCID: PMC10832236 DOI: 10.1186/s13293-024-00588-1] [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] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/23/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND The incidence of Alzheimer's disease (AD)-the most frequent cause of dementia-is expected to increase as life expectancies rise across the globe. While sex-based differences in AD have previously been described, there remain uncertainties regarding any association between sex and disease-associated molecular mechanisms. Studying sex-specific expression profiles of regulatory factors such as microRNAs (miRNAs) could contribute to more accurate disease diagnosis and treatment. METHODS A systematic review identified six studies of microRNA expression in AD patients that incorporated information regarding the biological sex of samples in the Gene Expression Omnibus repository. A differential microRNA expression analysis was performed, considering disease status and patient sex. Subsequently, results were integrated within a meta-analysis methodology, with a functional enrichment of meta-analysis results establishing an association between altered miRNA expression and relevant Gene Ontology terms. RESULTS Meta-analyses of miRNA expression profiles in blood samples revealed the alteration of sixteen miRNAs in female and 22 miRNAs in male AD patients. We discovered nine miRNAs commonly overexpressed in both sexes, suggesting a shared miRNA dysregulation profile. Functional enrichment results based on miRNA profiles revealed sex-based differences in biological processes; most affected processes related to ubiquitination, regulation of different kinase activities, and apoptotic processes in males, but RNA splicing and translation in females. Meta-analyses of miRNA expression profiles in brain samples revealed the alteration of six miRNAs in female and four miRNAs in male AD patients. We observed a single underexpressed miRNA in female and male AD patients (hsa-miR-767-5p); however, the functional enrichment analysis for brain samples did not reveal any specifically affected biological process. CONCLUSIONS Sex-specific meta-analyses supported the detection of differentially expressed miRNAs in female and male AD patients, highlighting the relevance of sex-based information in biomedical data. Further studies on miRNA regulation in AD patients should meet the criteria for comparability and standardization of information.
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Affiliation(s)
- Jaime Llera-Oyola
- Computational Biomedicine Laboratory, Príncipe Felipe Research Center (CIPF), C/ Eduardo Primo Yúfera, 3, 46012, Valencia, Spain
- Carlos Simon Foundation-INCLIVA Instituto de Investigación Sanitaria, Valencia, Spain
| | - Héctor Carceller
- Neurobiology Unit, Program in Neurosciences and Institute of Biotechnology and Biomedicine (BIOTECMED), Universitat de València, Burjassot, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spanish National Network for Research in Mental Health, Madrid, Spain
- Joint Unit in Biomedical Imaging FISABIO-CIPF, Foundation for the Promotion of Health and Biomedical Research of Valencia Region, València, Spain
| | - Zoraida Andreu
- Foundation Valencian Institute of Oncology (FIVO), 46009, Valencia, Spain
| | - Marta R Hidalgo
- Computational Biomedicine Laboratory, Príncipe Felipe Research Center (CIPF), C/ Eduardo Primo Yúfera, 3, 46012, Valencia, Spain
| | - Irene Soler-Sáez
- Computational Biomedicine Laboratory, Príncipe Felipe Research Center (CIPF), C/ Eduardo Primo Yúfera, 3, 46012, Valencia, Spain
| | - Fernando Gordillo
- Computational Biomedicine Laboratory, Príncipe Felipe Research Center (CIPF), C/ Eduardo Primo Yúfera, 3, 46012, Valencia, Spain
| | - Borja Gómez-Cabañes
- Computational Biomedicine Laboratory, Príncipe Felipe Research Center (CIPF), C/ Eduardo Primo Yúfera, 3, 46012, Valencia, Spain
| | - Beatriz Roson
- Carlos Simon Foundation-INCLIVA Instituto de Investigación Sanitaria, Valencia, Spain
| | - Maria de la Iglesia-Vayá
- Joint Unit in Biomedical Imaging FISABIO-CIPF, Foundation for the Promotion of Health and Biomedical Research of Valencia Region, València, Spain
| | - Roberta Mancuso
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | | | - Akiko Mizokami
- Oral Health/Brain Health/Total Health (OBT) Research Center, Faculty of Dental Science, Kyushu University, Fukuoka, Japan
| | - Francisco García-García
- Computational Biomedicine Laboratory, Príncipe Felipe Research Center (CIPF), C/ Eduardo Primo Yúfera, 3, 46012, Valencia, Spain.
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Jiménez-Murillo D, Castro-Ospina AE, Duque-Muñoz L, Martínez-Vargas JD, Suárez-Revelo JX, Vélez-Arango JM, de la Iglesia-Vayá M. Automatic Detection of Focal Cortical Dysplasia Using MRI: A Systematic Review. Sensors (Basel) 2023; 23:7072. [PMID: 37631608 PMCID: PMC10458261 DOI: 10.3390/s23167072] [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] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/02/2023] [Accepted: 08/07/2023] [Indexed: 08/27/2023]
Abstract
Focal cortical dysplasia (FCD) is a congenital brain malformation that is closely associated with epilepsy. Early and accurate diagnosis is essential for effectively treating and managing FCD. Magnetic resonance imaging (MRI)-one of the most commonly used non-invasive neuroimaging methods for evaluating the structure of the brain-is often implemented along with automatic methods to diagnose FCD. In this review, we define three categories for FCD identification based on MRI: visual, semi-automatic, and fully automatic methods. By conducting a systematic review following the PRISMA statement, we identified 65 relevant papers that have contributed to our understanding of automatic FCD identification techniques. The results of this review present a comprehensive overview of the current state-of-the-art in the field of automatic FCD identification and highlight the progress made and challenges ahead in developing reliable, efficient methods for automatic FCD diagnosis using MRI images. Future developments in this area will most likely lead to the integration of these automatic identification tools into medical image-viewing software, providing neurologists and radiologists with enhanced diagnostic capabilities. Moreover, new MRI sequences and higher-field-strength scanners will offer improved resolution and anatomical detail for precise FCD characterization. This review summarizes the current state of automatic FCD identification, thereby contributing to a deeper understanding and the advancement of FCD diagnosis and management.
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Affiliation(s)
- David Jiménez-Murillo
- Grupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia; (D.J.-M.); (L.D.-M.)
| | - Andrés Eduardo Castro-Ospina
- Grupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia; (D.J.-M.); (L.D.-M.)
| | - Leonardo Duque-Muñoz
- Grupo de investigación Máquinas Inteligentes y Reconocimiento de Patrones, Instituto Tecnológico Metropolitano, Medellín 050013, Colombia; (D.J.-M.); (L.D.-M.)
| | | | - Jazmín Ximena Suárez-Revelo
- Grupo de Investigación en Imágenes Médicas SURA, Ayudas Diagnósticas SURA, Carrera 48 # 26-50, Piso 2, Medellín 050021, Colombia; (J.X.S.-R.); (J.M.V.-A.)
| | - Jorge Mario Vélez-Arango
- Grupo de Investigación en Imágenes Médicas SURA, Ayudas Diagnósticas SURA, Carrera 48 # 26-50, Piso 2, Medellín 050021, Colombia; (J.X.S.-R.); (J.M.V.-A.)
| | - Maria de la Iglesia-Vayá
- Biomedical Imaging Unit FISABIO-CIPF, Foundation for the Promotion of the Research in Healthcare and Biomedicine (FISABIO), Avda. de Catalunya, 21, 46020 Valencia, Spain;
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM-G23), 28029 Madrid, Spain
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Sáenz-Gamboa JJ, Domenech J, Alonso-Manjarrés A, Gómez JA, de la Iglesia-Vayá M. Automatic semantic segmentation of the lumbar spine: Clinical applicability in a multi-parametric and multi-center study on magnetic resonance images. Artif Intell Med 2023; 140:102559. [PMID: 37210154 DOI: 10.1016/j.artmed.2023.102559] [Citation(s) in RCA: 2] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 04/14/2023] [Accepted: 04/18/2023] [Indexed: 05/22/2023]
Abstract
Significant difficulties in medical image segmentation include the high variability of images caused by their origin (multi-center), the acquisition protocols (multi-parametric), the variability of human anatomy, illness severity, the effect of age and gender, and notable other factors. This work addresses problems associated with the automatic semantic segmentation of lumbar spine magnetic resonance images using convolutional neural networks. We aimed to assign a class label to each pixel of an image, with classes defined by radiologists corresponding to structural elements such as vertebrae, intervertebral discs, nerves, blood vessels, and other tissues. The proposed network topologies represent variants of the U-Net architecture, and we used several complementary blocks to define the variants: three types of convolutional blocks, spatial attention models, deep supervision, and multilevel feature extractor. Here, we describe the topologies and analyze the results of the neural network designs that obtained the most accurate segmentation. Several proposed designs outperform the standard U-Net used as a baseline, primarily when used in ensembles, where the outputs of multiple neural networks are combined according to different strategies.
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Affiliation(s)
- Jhon Jairo Sáenz-Gamboa
- FISABIO-CIPF Joint Research Unit in Biomedical Imaging, Fundaciò per al Foment de la Investigaciò Sanitària i Biomèdica (FISABIO), Av. de Catalunya 21, 46020 València, Spain.
| | - Julio Domenech
- Orthopedic Surgery Department, Hospital Arnau de Vilanova, Carrer de San Clemente s/n, 46015, València, Spain
| | - Antonio Alonso-Manjarrés
- Radiology Department, Hospital Arnau de Vilanova, Carrer de San Clemente s/n, 46015, València, Spain
| | - Jon A Gómez
- Pattern Recognition and Human Language Technology research center, Universitat Politècnica de València, Camí de Vera, s/n, 46022, València, Spain
| | - Maria de la Iglesia-Vayá
- FISABIO-CIPF Joint Research Unit in Biomedical Imaging, Fundaciò per al Foment de la Investigaciò Sanitària i Biomèdica (FISABIO), Av. de Catalunya 21, 46020 València, Spain; Regional ministry of Universal Health and Public Health in Valencia, Carrer de Misser Mascó 31, 46010 València, Spain.
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Bustos A, Pertusa A, Salinas JM, de la Iglesia-Vayá M. PadChest: A large chest x-ray image dataset with multi-label annotated reports. Med Image Anal 2020; 66:101797. [PMID: 32877839 DOI: 10.1016/j.media.2020.101797] [Citation(s) in RCA: 143] [Impact Index Per Article: 35.8] [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: 01/23/2019] [Revised: 07/24/2020] [Accepted: 07/24/2020] [Indexed: 10/23/2022]
Abstract
We present a labeled large-scale, high resolution chest x-ray dataset for the automated exploration of medical images along with their associated reports. This dataset includes more than 160,000 images obtained from 67,000 patients that were interpreted and reported by radiologists at San Juan Hospital (Spain) from 2009 to 2017, covering six different position views and additional information on image acquisition and patient demography. The reports were labeled with 174 different radiographic findings, 19 differential diagnoses and 104 anatomic locations organized as a hierarchical taxonomy and mapped onto standard Unified Medical Language System (UMLS) terminology. Of these reports, 27% were manually annotated by trained physicians and the remaining set was labeled using a supervised method based on a recurrent neural network with attention mechanisms. The labels generated were then validated in an independent test set achieving a 0.93 Micro-F1 score. To the best of our knowledge, this is one of the largest public chest x-ray databases suitable for training supervised models concerning radiographs, and the first to contain radiographic reports in Spanish. The PadChest dataset can be downloaded from http://bimcv.cipf.es/bimcv-projects/padchest/.
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Affiliation(s)
- Aurelia Bustos
- Department of Software and Computing Systems, University Institute for Computing Research, University of Alicante, Spain.
| | - Antonio Pertusa
- Department of Software and Computing Systems, University Institute for Computing Research, University of Alicante, Spain.
| | - Jose-Maria Salinas
- Department of Health Informatics, Hospital Universitario San Juan de Alicante, Spain.
| | - Maria de la Iglesia-Vayá
- Foundation for the Promotion of the Research in Healthcare and Biomedicine (FISABIO), Valencia, Spain.
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Proal E, Alvarez-Segura M, de la Iglesia-Vayá M, Martí-Bonmatí L, Castellanos FX. [Functional cerebral activity in a state of rest: connectivity networks]. Rev Neurol 2011; 52 Suppl 1:S3-S10. [PMID: 21365601 PMCID: PMC4418791] [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] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Functional connectivity can be measured during task-based functional magnetic resonance imaging (fMRI), or in the absence of specific stimuli or tasks. In either case, the study of low frequency fluctuations in the BOLD signal reveals patterns of synchronization which delineate the intrinsic functional architecture of the brain. The scientific community now has available shared resources to accelerate the exploitation of resting state fMRI with the objectives of improving diagnostic methods and leading to better treatments grounded in neuroscience. Fomenting a collaborative scientific culture will accelerate our understanding of the underlying phenonmemna. Recently, the Spanish Resting State Network (SRSN) has joined this collaborative effort by creating a setting to facilitate collaboration among the various neuroscience research groups working in Spanish (http://www.nitrc.org/projects/srsn).
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Affiliation(s)
- Erika Proal
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, Child Study Center, NYU Langone Medical Center, New York, NY USA
- Unitat Recerca en Neurociència Cognitiva, Universitat Autònoma de Barcelona, España
| | - Mar Alvarez-Segura
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, Child Study Center, NYU Langone Medical Center, New York, NY USA
- Fundación Alicia Koplowitz
| | | | - Luis Martí-Bonmatí
- Servicio de Radiología, Hospital Quirón de Valencia, España
- Área de Imagen Médica, Hospital Universitario y Politécnico La Fe de Valencia, España
| | - F. Xavier Castellanos
- Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience, Child Study Center, NYU Langone Medical Center, New York, NY USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
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