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1. Using MR Physics for Domain Generalisation and Super-Resolution

Author

Borges, Pedro, Fernandez, Virginia, Tudosiu, Petru Daniel, Nachev, Parashkev, Ourselin, Sebastien, Cardoso, M. Jorge, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Fernandez, Virginia, editor, Wolterink, Jelmer M., editor, Wiesner, David, editor, Remedios, Samuel, editor, Zuo, Lianrui, editor, and Casamitjana, Adrià, editor

Published
2025
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2. A 3D generative model of pathological multi-modal MR images and segmentations

Author

Fernandez, Virginia, Pinaya, Walter Hugo Lopez, Borges, Pedro, Graham, Mark S., Vercauteren, Tom, and Cardoso, M. Jorge

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Generative modelling and synthetic data can be a surrogate for real medical imaging datasets, whose scarcity and difficulty to share can be a nuisance when delivering accurate deep learning models for healthcare applications. In recent years, there has been an increased interest in using these models for data augmentation and synthetic data sharing, using architectures such as generative adversarial networks (GANs) or diffusion models (DMs). Nonetheless, the application of synthetic data to tasks such as 3D magnetic resonance imaging (MRI) segmentation remains limited due to the lack of labels associated with the generated images. Moreover, many of the proposed generative MRI models lack the ability to generate arbitrary modalities due to the absence of explicit contrast conditioning. These limitations prevent the user from adjusting the contrast and content of the images and obtaining more generalisable data for training task-specific models. In this work, we propose brainSPADE3D, a 3D generative model for brain MRI and associated segmentations, where the user can condition on specific pathological phenotypes and contrasts. The proposed joint imaging-segmentation generative model is shown to generate high-fidelity synthetic images and associated segmentations, with the ability to combine pathologies. We demonstrate how the model can alleviate issues with segmentation model performance when unexpected pathologies are present in the data., Comment: Accepted for publication at the 2023 Deep Generative Models (DGM4MICCAI) MICCAI workshop (Vancouver, Canada)

Published
2023
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4. Generative AI for Medical Imaging: extending the MONAI Framework

Author

Pinaya, Walter H. L., Graham, Mark S., Kerfoot, Eric, Tudosiu, Petru-Daniel, Dafflon, Jessica, Fernandez, Virginia, Sanchez, Pedro, Wolleb, Julia, da Costa, Pedro F., Patel, Ashay, Chung, Hyungjin, Zhao, Can, Peng, Wei, Liu, Zelong, Mei, Xueyan, Lucena, Oeslle, Ye, Jong Chul, Tsaftaris, Sotirios A., Dogra, Prerna, Feng, Andrew, Modat, Marc, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M. Jorge

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perform an array of diverse applications, such as anomaly detection, image-to-image translation, denoising, and MRI reconstruction. However, due to the complexity of these models, their implementation and reproducibility can be difficult. This complexity can hinder progress, act as a use barrier, and dissuade the comparison of new methods with existing works. In this study, we present MONAI Generative Models, a freely available open-source platform that allows researchers and developers to easily train, evaluate, and deploy generative models and related applications. Our platform reproduces state-of-art studies in a standardised way involving different architectures (such as diffusion models, autoregressive transformers, and GANs), and provides pre-trained models for the community. We have implemented these models in a generalisable fashion, illustrating that their results can be extended to 2D or 3D scenarios, including medical images with different modalities (like CT, MRI, and X-Ray data) and from different anatomical areas. Finally, we adopt a modular and extensible approach, ensuring long-term maintainability and the extension of current applications for future features.

Published
2023

5. Privacy Distillation: Reducing Re-identification Risk of Multimodal Diffusion Models

Author

Fernandez, Virginia, Sanchez, Pedro, Pinaya, Walter Hugo Lopez, Jacenków, Grzegorz, Tsaftaris, Sotirios A., and Cardoso, Jorge

Subjects
Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition
Abstract

Knowledge distillation in neural networks refers to compressing a large model or dataset into a smaller version of itself. We introduce Privacy Distillation, a framework that allows a text-to-image generative model to teach another model without exposing it to identifiable data. Here, we are interested in the privacy issue faced by a data provider who wishes to share their data via a multimodal generative model. A question that immediately arises is ``How can a data provider ensure that the generative model is not leaking identifiable information about a patient?''. Our solution consists of (1) training a first diffusion model on real data (2) generating a synthetic dataset using this model and filtering it to exclude images with a re-identifiability risk (3) training a second diffusion model on the filtered synthetic data only. We showcase that datasets sampled from models trained with privacy distillation can effectively reduce re-identification risk whilst maintaining downstream performance.

Published
2023

6. Transfer Learning for Fine-grained Classification Using Semi-supervised Learning and Visual Transformers

Author

Lagunas, Manuel, Impata, Brayan, Martinez, Victor, Fernandez, Virginia, Georgakis, Christos, Braun, Sofia, and Bertrand, Felipe

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Fine-grained classification is a challenging task that involves identifying subtle differences between objects within the same category. This task is particularly challenging in scenarios where data is scarce. Visual transformers (ViT) have recently emerged as a powerful tool for image classification, due to their ability to learn highly expressive representations of visual data using self-attention mechanisms. In this work, we explore Semi-ViT, a ViT model fine tuned using semi-supervised learning techniques, suitable for situations where we have lack of annotated data. This is particularly common in e-commerce, where images are readily available but labels are noisy, nonexistent, or expensive to obtain. Our results demonstrate that Semi-ViT outperforms traditional convolutional neural networks (CNN) and ViTs, even when fine-tuned with limited annotated data. These findings indicate that Semi-ViTs hold significant promise for applications that require precise and fine-grained classification of visual data., Comment: 6 pages, 1 figure, 3 tables

Published
2023

8. Can segmentation models be trained with fully synthetically generated data?

Author

Fernandez, Virginia, Pinaya, Walter Hugo Lopez, Borges, Pedro, Tudosiu, Petru-Daniel, Graham, Mark S, Vercauteren, Tom, and Cardoso, M Jorge

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In order to achieve good performance and generalisability, medical image segmentation models should be trained on sizeable datasets with sufficient variability. Due to ethics and governance restrictions, and the costs associated with labelling data, scientific development is often stifled, with models trained and tested on limited data. Data augmentation is often used to artificially increase the variability in the data distribution and improve model generalisability. Recent works have explored deep generative models for image synthesis, as such an approach would enable the generation of an effectively infinite amount of varied data, addressing the generalisability and data access problems. However, many proposed solutions limit the user's control over what is generated. In this work, we propose brainSPADE, a model which combines a synthetic diffusion-based label generator with a semantic image generator. Our model can produce fully synthetic brain labels on-demand, with or without pathology of interest, and then generate a corresponding MRI image of an arbitrary guided style. Experiments show that brainSPADE synthetic data can be used to train segmentation models with performance comparable to that of models trained on real data., Comment: 12 pages, 2 (+2 App.) figures, 3 tables. Accepted at Simulation and Synthesis in Medical Imaging workshop (MICCAI 2022)

Published
2022
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9. Brain Imaging Generation with Latent Diffusion Models

Author

Pinaya, Walter H. L., Tudosiu, Petru-Daniel, Dafflon, Jessica, da Costa, Pedro F, Fernandez, Virginia, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, M. Jorge

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Quantitative Biology - Quantitative Methods
Abstract

Deep neural networks have brought remarkable breakthroughs in medical image analysis. However, due to their data-hungry nature, the modest dataset sizes in medical imaging projects might be hindering their full potential. Generating synthetic data provides a promising alternative, allowing to complement training datasets and conducting medical image research at a larger scale. Diffusion models recently have caught the attention of the computer vision community by producing photorealistic synthetic images. In this study, we explore using Latent Diffusion Models to generate synthetic images from high-resolution 3D brain images. We used T1w MRI images from the UK Biobank dataset (N=31,740) to train our models to learn about the probabilistic distribution of brain images, conditioned on covariables, such as age, sex, and brain structure volumes. We found that our models created realistic data, and we could use the conditioning variables to control the data generation effectively. Besides that, we created a synthetic dataset with 100,000 brain images and made it openly available to the scientific community., Comment: 10 pages, 3 figures, Accepted in the Deep Generative Models workshop @ MICCAI 2022

Published
2022

10. Morphology-preserving Autoregressive 3D Generative Modelling of the Brain

Author

Tudosiu, Petru-Daniel, Pinaya, Walter Hugo Lopez, Graham, Mark S., Borges, Pedro, Fernandez, Virginia, Yang, Dai, Appleyard, Jeremy, Novati, Guido, Mehra, Disha, Vella, Mike, Nachev, Parashkev, Ourselin, Sebastien, and Cardoso, Jorge

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, 68T99 (Primary) 92C55 (Secondary), I.2.1, J.3
Abstract

Human anatomy, morphology, and associated diseases can be studied using medical imaging data. However, access to medical imaging data is restricted by governance and privacy concerns, data ownership, and the cost of acquisition, thus limiting our ability to understand the human body. A possible solution to this issue is the creation of a model able to learn and then generate synthetic images of the human body conditioned on specific characteristics of relevance (e.g., age, sex, and disease status). Deep generative models, in the form of neural networks, have been recently used to create synthetic 2D images of natural scenes. Still, the ability to produce high-resolution 3D volumetric imaging data with correct anatomical morphology has been hampered by data scarcity and algorithmic and computational limitations. This work proposes a generative model that can be scaled to produce anatomically correct, high-resolution, and realistic images of the human brain, with the necessary quality to allow further downstream analyses. The ability to generate a potentially unlimited amount of data not only enables large-scale studies of human anatomy and pathology without jeopardizing patient privacy, but also significantly advances research in the field of anomaly detection, modality synthesis, learning under limited data, and fair and ethical AI. Code and trained models are available at: https://github.com/AmigoLab/SynthAnatomy., Comment: 13 pages, 3 figures, 2 tables, accepted at SASHIMI MICCAI 2022

Published
2022

11. A 3D Generative Model of Pathological Multi-modal MR Images and Segmentations

Author

Fernandez, Virginia, Pinaya, Walter Hugo Lopez, Borges, Pedro, Graham, Mark S., Vercauteren, Tom, Cardoso, M. Jorge, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mukhopadhyay, Anirban, editor, Oksuz, Ilkay, editor, Engelhardt, Sandy, editor, Zhu, Dajiang, editor, and Yuan, Yixuan, editor

Published
2024
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12. Privacy Distillation: Reducing Re-identification Risk of Diffusion Models

Author

Fernandez, Virginia, Sanchez, Pedro, Pinaya, Walter Hugo Lopez, Jacenków, Grzegorz, Tsaftaris, Sotirios A., Cardoso, M. Jorge, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mukhopadhyay, Anirban, editor, Oksuz, Ilkay, editor, Engelhardt, Sandy, editor, Zhu, Dajiang, editor, and Yuan, Yixuan, editor

Published
2024
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15. Unsupervised Heteromodal Physics-Informed Representation of MRI Data: Tackling Data Harmonisation, Imputation and Domain Shift

Author

Borges, Pedro, Fernandez, Virginia, Tudosiu, Petru Daniel, Nachev, Parashkev, Ourselin, Sebastien, Cardoso, M. Jorge, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wolterink, Jelmer M., editor, Svoboda, David, editor, Zhao, Can, editor, and Fernandez, Virginia, editor

Published
2023
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23. Asthma management and control in children, adolescents, and adults in 25 countries: a Global Asthma Network Phase I cross-sectional study

Author

Ahmetaj, Luljeta N., Ajeagah, Gideon A., Alkhayer, Ghroob, Alomary, Shaker A., Ambriz-Moreno, Maria J., Arias-Cruz, Alfredo, Awasthi, Shally, Badellino, Hector, Behniafard, Nasrin, Bercedo-Sanz, Alberto, Brożek, Grzegorz, Bucaliu-Ismajli, Ibadete, Cabrera-Aguilar, Angela, Chinratanapisit, Sasawan, Del-Río-Navarro, Blanca E., Douros, Kostas, El Sadig, Hana, Escalante-Dominguez, Alberto J., Falade, Adegoke G., Gacaferri-Lumezi, Besa, García-Almaráz, Roberto, Garcia-Muñoz, Rosa, Ghashi, Valbona, Ghoshal, Aloke G., González-Díaz, Carlos, Hana-Lleshi, Leonora, Hernández-Mondragón, Luis O., Huang, Jing-Long, Jiménez-González, Carlos A., Juan-Pineda, M. Ángeles, Kochar, Sanjay K., Kuzmicheva, Kseniiay, Linares-Zapien, Francisco J., Lokaj-Berisha, Violeta, López-Silvarrey, Angel, Lozano-Sáenz, José S., Mahesh, Padukudru A., Mallol, Javier, Martinez-Torres, Antonia E., Masekela, Refiloe, Mérida-Palacio, J. Valente, Mohammad, Yousser, Moreno-Gardea, Héctor L., Navarrete-Rodriguez, Elsy M., Ndikum, Achiri E., Noor, Magde, Ochoa-Lopez, Georgina, Pajaziti, Laura, Pellegrini-Belinchon, Javier, Perez-Fernández, Virginia, Priftis, Kostas, Ramos-García, Beatriz C., Ranasinghe, Jagath C., Robertson, Steve, Rodriguez-Perez, Noel, Rutter, Charlotte E., Sacre-Hazouri, José A., Salvi, Sundeep, Sanchez, Javier F., Sánchez, José F., Sanchez-Coronel, Maria G., Saucedo-Ramirez, Omar J., Singh, Meenu, Singh, Nishtha, Singh, Virendra, Sinha, Sanjeev, Sit, Niranjan, Sosa-Ferrari, Suyapa M., Soto-Martínez, Manuel E., Urrutia-Pereira, Marylin, Yeh, Kuo-Wei, Zar, Heather J., Zhjeqi, Valbona, García-Marcos, Luis, Chiang, Chen-Yuan, Asher, M Innes, Marks, Guy B, El Sony, Asma, Bissell, Karen, Ellwood, Eamon, Ellwood, Philippa, Pearce, Neil, Strachan, David P, Mortimer, Kevin, and Morales, Eva

Published
2023
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24. Clinical and Molecular Features of Malignant Pleural Effusion in Non-Small Cell Lung Cancer (NSCLC) of a Caucasian Population.

Author

Lojo-Rodríguez, Irene, Botana-Rial, Maribel, González-Montaos, Almudena, Leiro-Fernández, Virginia, González-Piñeiro, Ana, Ramos-Hernández, Cristina, and Fernández-Villar, Alberto

Subjects
NON-small-cell lung carcinoma, LUNG cancer, UNIVARIATE analysis, SMOKING, CANCER diagnosis, PLEURAL effusions
Abstract

Background and Objectives: The diversity of patients with malignant pleural effusion (MPE) due to non-small cell lung cancer (NSCLC) as well as the variability in mutations makes it essential to improve molecular characterization. Objective: Describe clinical, pathological, and molecular characteristics MPE in a Caucasian population. Materials and Methods: Retrospective study of patients with NSCLC diagnosis who had undergone a molecular study from 1 January 2018–31 December 2022. Univariate analysis was performed to compare patient characteristics between the group with and without MPE and molecular biomarkers. Results: A total of 400 patients were included; 53% presented any biomarker and 29% had MPE.PDL1, which was the most frequent. EGFR mutation was associated with women (OR:3.873) and lack of smoking (OR:5.105), but not with MPE. Patients with pleural effusion were older and had lower ECOG. There was no significant difference in the presence of any biomarker. We also did not find an association between the presence of specific mutations and MPE (22.4% vs. 18%, p = 0.2), or PDL1 expression (31.9% vs. 35.9%, p = 0.3). Being younger constituted a protective factor for the presence of MPE (OR:0.962; 95% CI 0.939–0.985, p = 0.002), as well as ECOG ≤ 1 (OR:0.539; 95% CI 0.322–0.902, p = 0.01). Conclusions: This is the first study that describes the clinical, pathological, and molecular characteristics of MPE patients due to NSCLC in a Caucasian population. Although overall we did not find significant differences in the molecular profile between patients with MPE and without effusion, EGFR mutation was associated with a tendency towards pleural progression. [ABSTRACT FROM AUTHOR]

Published
2024
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25. [Translated article] Biological Biomarkers in Respiratory Diseases

Author

García-Río, Francisco, Alcázar-Navarrete, Bernardino, Castillo-Villegas, Diego, Cilloniz, Catia, García-Ortega, Alberto, Leiro-Fernández, Virginia, Lojo-Rodriguez, Irene, Padilla-Galo, Alicia, Quezada-Loaiza, Carlos A., Rodriguez-Portal, Jose Antonio, Sánchez-de-la-Torre, Manuel, Sibila, Oriol, and Martínez-García, Miguel A.

Published
2022
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26. Biomarcadores biológicos en las enfermedades respiratorias

Author

García-Río, Francisco, Alcázar-Navarrete, Bernardino, Castillo-Villegas, Diego, Cilloniz, Catia, García-Ortega, Alberto, Leiro-Fernández, Virginia, Lojo-Rodriguez, Irene, Padilla-Galo, Alicia, Quezada-Loaiza, Carlos A., Rodriguez-Portal, Jose Antonio, Sánchez-de-la-Torre, Manuel, Sibila, Oriol, and Martínez-García, Miguel A.

Published
2022
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28. Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3

Author

Pahlevan, Nima, Smith, Brandon, Alikas, Krista, Anstee, Janet, Barbosa, Claudio, Binding, Caren, Bresciani, Mariano, Cremella, Bruno, Giardino, Claudia, Gurlin, Daniela, Fernandez, Virginia, Jamet, Cédric, Kangro, Kersti, Lehmann, Moritz K., Loisel, Hubert, Matsushita, Bunkei, Hà, Nguyên, Olmanson, Leif, Potvin, Geneviève, Simis, Stefan G.H., VanderWoude, Andrea, Vantrepotte, Vincent, and Ruiz-Verdù, Antonio

Published
2022
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29. Different Faces of Idiopathic Pulmonary Fibrosis With Preserved Forced Vital Capacity

Author

Bermudo, Guadalupe, Suarez-Cuartin, Guillermo, Rivera-Ortega, Pilar, Rodriguez-Portal, Jose Antonio, Sauleda, Jaume, Nuñez, Belen, Castillo, Diego, Aburto, Myriam, Portillo, Karina, Balcells, Eva, Badenes-Bonet, Diana, Valenzuela, Claudia, Fernandez-Fabrellas, Estrella, González-Budiño, Teresa, Cano, Esteban, Acosta, Orlando, Leiro-Fernández, Virginia, Romero, Ana, Planas-Cerezales, Lurdes, Villar, Ana, Moreno, Amalia, Laporta, Rosalia, Vicens-Zygmunt, Vanesa, Shull, Jessica, Franquet, Tomàs, Luburich, Patricio, and Molina-Molina, Maria

Published
2022
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30. Gene regulatory landscape of cerebral cortex folding

Author

Singh, Aditi, primary, Del-Valle-Anton, Lucia, additional, de Juan Romero, Camino, additional, Zhang, Ziyi, additional, Ortuño, Eduardo Fernández, additional, Mahesh, Arun, additional, Espinós, Alexandre, additional, Soler, Rafael, additional, Cárdenas, Adrián, additional, Fernández, Virginia, additional, Lusby, Ryan, additional, Tiwari, Vijay K., additional, and Borrell, Víctor, additional

Published
2024
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31. Outcomes and clinical characteristics of the compassionate use of plitidepsin in COVID-19 patients with solid tumours, haematological malignancies or anti-CD20 antibody treatment

Author

Aguareles, Jose, primary, Villares Fernández, Paula, additional, Forné, Carles, additional, Martí-Ballesteros, Eva María, additional, Pradillo Fernández, Virginia, additional, Sotres-Fernandez, Gabriel, additional, de la Fuente-Burguera, Adolfo, additional, Navarro-San Francisco, Carolina, additional, Buzón-Martín, Luis Miguel, additional, García-Delangue, Teresa, additional, Aiello, Francesco Tommaso, additional, Carnevali-Ruiz, Daniel, additional, Lloris, Raquel, additional, Luepke-Estefan, Xavier Erik, additional, López-Martín, José Antonio, additional, Jimeno, José María, additional, García-Casas, Ana, additional, and Guisado-Vasco, Pablo, additional

Published
2024
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33. Circulating levels of calprotectin, a signature of neutrophil activation in prediction of severe respiratory failure in COVID-19 patients: a multicenter, prospective study (CalCov study)

Author

García de Guadiana-Romualdo, Luis, Rodríguez Rojas, Carlos, Morell-García, Daniel, Andaluz-Ojeda, David, Rodríguez Mulero, María Dolores, Rodríguez-Borja, Enrique, Ballesteros-Vizoso, Antonieta, Calvo, María Dolores, Albert-Botella, Lourdes, Pozo Giráldez, Adela, Llompart-Alabern, Isabel, Bolado Jiménez, Cristina, Hernández Olivo, Marta, Fernández Presa, Lucía, Ortega-Pérez, Juan, Fora Romero, María José, Campos-Rodríguez, Valerio, Ballester Férriz, Andrea, Guiu-Martí, Alexandra M., Pinilla Arribas, Leyre, Galindo Martínez, María, Puerto-Lara, Edwin R., López Tarazaga, Ana Belen, Ros Braquehais, María Salomé, Nogales Martín, Leonor, Juez Santamaría, Celia, Trapiello Fernández, Wysalli, Espinilla Fernández, Virginia, Havelka, Aleksandra, and Albaladejo-Otón, María Dolores

Published
2022
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37. Worldwide trends in the burden of asthma symptoms in school-aged children: Global Asthma Network Phase I cross-sectional study

Author

Bissell, Karen, Chiang, Chen-Yuan, Ellwood, Eamon, Ellwood, Philippa, Marks, Guy B, Masekela, Refiloe, Morales, Eva, Mortimer, Kevin, Pearce, Neil, Strachan, David, Martinez-Torres, Antonela, Pérez-Fernández, Virginia, Robertson, Stephen, Rutter, Charlotte, Silverwood, Richard, Mallol, Javier, Soto-Martínez, Manuel, Cabrera Aguilar, Angelita, Douros, Konstantinos, Sabir, Mohammed, Singh, Meenu, Singh, Virendra, Sukumaran, Thevaruparambil Unny, Awasthi, Shally, Kabra, Sushil Kumar, Salvi, Sundeep, García-Almaráz, Roberto, Mérida-Palacio, J. Valente, Del Río Navarro, Blanca E, González-Díaz, Sandra Nora, Navarrete-Rodriguez, Elsy Maureen, Sánchez, José Félix, Falade, Adegoke G, Zar, Heather J, López-Silvarrey Varela, Angel, González Díaz, Carlos, Nour, Magde, Dib, Gazal, Mohammad, Yousser, Huang, Jing-Long, Chinratanapisit, Sasawan, Soto-Quirós, Manuel E, Vichyanond, Pakit, Aguilar, Pedro, Barba, Sergio, Kumar, Lata, Sharma, S K, Linares-Zapién, Francisco J, Onadeko, Babatunde O, Musa, Omer Abdel Aziz, Aguirre, Viviana, Baeza-Bacab, Manuel, Mohammad, Samira, Cortez, Eliana, Gratziou, Christina H, Chopra, Kamlesh, Hanumante, Neeta Milind, Nelson, Hugo, Rubio, Alfonso Delgado, Hsieh, Kue-Hsiung, Shah, Jayant, Asher, M Innes, Rutter, Charlotte E, El Sony, Asma, García-Marcos, Luis, Robertson, Steven, Silverwood, Richard J, and Strachan, David P

Published
2021
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41. Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Author

Aznar Muñoz, Esther, Gil Divasson, Pedro, González Muñiz, Patricia, Muñoz Aguirre, Clara, Díaz Menéndez, Marta, de la Calle Prieto, Fernando, Arsuaga Vicente, Marta, Trigo Esteban, Elena, Pérez Valero, Ignacio, de Miguel Buckley, Rosa, Cadiñaños Loidi, Julen, Diaz Pollan, Beatriz, Martín Carbonero, Luz, Ramos Ramos, Juan Carlos, Loeches Yagüe, Belén, Montejano Sánchez, Rocío, González García, Juan, García Rodríguez, Julio, Berenguer, Juan, Ramírez, Margarita, Gutiérrez, Isabel, Tejerina, Francisco, Aldámiz-Echevarría, Teresa, Díez, Cristina, Fanciulli, Chiara, Pérez-Latorre, Leire, Pinilla, Blanca, López, Juan Carlos, Such Diaz, Ana, Álvaro Alonso, Elena, Torres Macho, Juan, Cuevas Tascon, Guillermo, Jiménez González de Buitrago, Eva, Brañas Baztán, Fátima, Valencia de la Rosa, Jorge, Pérez Butragueño, Mario, Fernández Jiménez, Inés, Muñiz Nicolás, Gemma, Sepúlveda Berrocal, Antonia, Gato Díez, Alberto, Toledano Sierra, María Pilar, García Butenegro, María Paz, Peláez Ballesta, Ana Isabel, Morcillo Rodríguez, Elena, Fernández Romero, Isidoro, Peláez Ballesta, Cristina, Guirado Torrecillas, María Isabel, Goikoetxea Agirre, Josune, Bereciartua Bastarrica, Elena, Guio Carrión, Laura, Rodríguez Álvarez, Regino, Ibarrola Hierro, Marta, Pérez Hernández, Isabel A., Pérez Zapata, Inés, Román Soto, Sergio, Kallouchi, Mohamed, Domínguez Vicent, Juan Ramón, Silvariño Fernández, Rafae, Ugalde Espiñeira, Jon, Sanjuan López, Ainhoa, García Martínez, Silvia, Temprano Gogenola, Mikel, Asensi, Víctor, Suárez, Silvia, Suárez, Lucia, Yllera, Carmen, Rivas-Carmenado, María, Romero-Palacios, Alberto, Ruiz Aragón, Jesús, Jiménez Aguilar, Patricia, Fernández Ávila, Ma Luisa, Castilla Ortiz, Rosario, Alende Castro, Vanesa, Pérez García, Cristina, Fernández Morales, Marta, Valle Feijoo Begoña Rodríguez Ferreira, María Lorena María, Gómez-Junyent, Joan, Villar-García, Judit, López-Montesinos, Inmaculada, Arrieta-Aldea, Itziar, Rial-Villavecchia, Abora, García Vázquez, Elisa, Roura Piloto, Aychel Elena, Moral Escudero, Encarnación, Hernández Torres, Alicia, Albendín Iglesias, Helena, Vinuesa García, David, Martínez Montes, Clara, De la Hera Fernández, Francisco Javier, Anguita Santos, Francisco, Ruiz Sancho, Andrés, Díaz de Brito Fernández, Vicens, Sanmarti Vilamala, Montserrat, España Cueto, Sergio, Molina Morant, Daniel, González-Cuevas, Araceli, Chara Cervantes, Joel Elías, Policarpo Torres, Guillem, Ortega Montoliu, Meritxell, Angerri Nadal, Mònica, De Genover Gil, Ariadna, Patera, Eleni, Godoy Lorenzo, Rita, Zioga, Evangelia Anna María, Isern Fernández, Virginia, Sabbagh Fajardo, Carlos Enrique, Ferrer Ribera, Ana, Bea Serrano, Carlos, Oltra Sempere, Rosa, Vela Bernal, Sara, Albiol Viñals, Paloma, Pedromingo Kus, Miguel, Garcinuño, María Ángeles, Fiorante, Silvana, Pérez Pinto, Sergio, de la Vega, Alexandra, Fariñas Álvarez, María Carmen, González Rico, Claudia, Arnaiz de las Revillas, Francisco, Giménez, Teresa, Calvo, Jorge, Meije Castillo, Yolanda, Duarte Borges, Alejandra, Pareja Coca, Júlia, Clemente Presas, Mercedes, Sanz Salvador, Xavier, Pérez Rodríguez, Ma Teresa, Sousa, Adrián, Pérez González, Alexandre, Longueira, Rebeca, Araujo, Alejandro, Alonso Martínez, Blanca, García Escudero, Laura, Lidia Kamel Rey, Sara, Roa Alonso, David, Avilés Parra, Juan Pablo, Pelegrín Senent, Iván, Rouco Esteves Marques, Rosana, Raich Montiu, Laia, Souto Higueras, Jessica, Gálvez Bobadilla, Manuel Alejandro, Parra Ruiz, Jorge, Ramos Sesma, Violeta, Velasco Fuentes, Sara, García Pereña, Laura, Lluna Carrascosa, Alfonso, Gilaberte Reyzábal, Sergio, Liébana Gómez, Mónica, Salillas Hernando, Juan, Serrano Martínez, Alberto, Torralba González de Suso, Miguel, Martínez Martín, Patricia, Rábago Lorite, Isabel, González-Ruano Pérez, Patricia, Pérez-Monte Mínguez, Beatriz, García Flores, Ángeles, Comas Casanova, Pere, Martín Plata, Andrea, Santana Báez, Sergio Manuel, Sanz Peláez, Oscar, Mohamed Ramírez, Karim, Robaina Bordón, José María, Vílchez Rueda, Helem Haydeé, Riera Jaume, Melchor, Mut Ramon, Gemma, Gavalda Manso, Meritxell, Planas Bibiloni, Lluis, Castelo Corral, Laura, Ramos Merino, Lucía, Sánchez Vidal, Efrén, Rodríguez Mayo, María, Míguez Rey, Enrique, García de Lomas Guerrero, José M., De la Torre Lima, Javier, Correa Ruiz, Ana, Fernández Sánchez, Fernando, Jiménez-García, Nicolás, Sierra-Monzón, José Luis, Gracia-Tello, Borja, Hernández-Bonaga, María, Pellejero, Galadriel, Asín-Corrochano, Marta, Boix Palop, Lucia, Calbo, Esther, Badía, Cristina, Dietl, Beatriz, Lucía, Gómez, Domínguez-Castellano, Ángel, Ríos-Villegas, María José, del Toro, María D., Palacios Baena, Zaira R., Salamanca-Rivera, Elena, Marín, Elena, Almadana, Virginia, Pérez-Galera, Salvador, González-Iglesias, Luisa, Abelenda-Alonso, Gabriela, Álvarez-Pouso, Claudia, Escrihuela, Francesc, Gudiol, Carlota, Lorenzo-Esteller, Laia, Niubó, Jordi, Podzamczer, Daniel, Pujol, Miquel, Rombauts, Alexander, Salvert Lletí, Miguel, Gil Sánchez, Ricardo, Jiménez Escrig, Marta, Parra Gómez, Laura, Tasias Pitarch, Mariona, Navarro Vilasaró, Marta, Machado Sicilia, María Luisa, Gomila Grange, Aina, Calzado Isbert, Sonia, Carrasco Antón, Nerea, Petkova-Saiz, Elizabet, Cabello Úbeda, Alfonso, Górgolas Hernández-Mora, Miguel, Sánchez-Pernaute, Olga, Dueñas Gutiérrez, Carlos, Martin Guerra, Javier, Castrodeza Sanz, José Javier, Fernández Espinilla, Virginia, Rodríguez Fernández, Laura, González-Moreno, Juan, Villoslada Gelabert, Aroa, Ribot Sanso, María Antonia, Fernández-Baca, María Victoria, Hernández Milian, Almudena, Morán Rodríguez, Miguel Ángel, Ortiz de Zárate Ibarra, Zuriñe, Portu Zapirain, José Joaquin, Saez de Adana Arroniz, Ester, Gainzarain Arana, Juan Carlos, Meca Birlanga, Olga, del Amor Espín, Ma Jesús, Viqueira González, Montserrat, García García, Josefina, Martínez Madrid, Onofre, Bernal Morell, Enrique, Alcaraz, Antonia, Muñoz, Ángeles, Pina, Ignacio, de la Rosa, Vicente, Caínzos Romero, Tamara, Sánchez Trigo, Sabela, Mariño Callejo, Ana Isabel, Álvarez Díaz, Hortensia, Valcarce Pardeiro, Nieves, Sánchez Serrano, Adriana, Piñar Cabezos, Diana, García Villalba, Eva Pilar, Aguayo Jiménez, Carmen, Ruíz Campuzano, María, Naranjo Velasco, Virginia, Santos Peña, Marta, Mora Delgado, Juan, Sevilla Moreno, Israel, Lojo Cruz, Cristina, Kortajarena Urkola, Xabier, Iribarren Loyarte, José Antonio, Bustinduy Odriozola, María Jesús, Ibarguren Pinilla, Maialen, Álvarez Rodríguez, Ignacio, Martínez Marcos, Francisco Javier, Rodríguez Gómez, Francisco Javier, Asschert Agüero, Isabel, Muñoz Beamud, Francisco, Ruiz Reina, Antonio José, Llenas-García, Jara, González-Cuello, Inmaculada, Hellín-Valiente, Elena, Martínez Birlanga, Esther, Tafalla Torres, José Manuel, Calderón Parra, Jorge, Escudero López, Gabriela, Gutiérrez Martín, Isabel, Andrés Eisenhofer, Ane, García Prieto, Sonia, Álvarez Franco, Raquel, Roger Zapata, Daniel, Martínez Cifre, Blanca, Aranda Rife, Elena, Martín Rubio, Irene, Barbosa Ventura, André, Garrido, Javier, Gonzalo, Concepción, Piñero, Iván, de la Cruz Felipe, Nieves, Talavera García, Eva, Lamata Subero, Marta, Mendoza Roy, Paula, García de Carlos, María Soledad, Lajusticia Aisa, Justo, Arteche Eguizabal, Lorea, Urrutia Losada, Ainhoa, Domingo Echaburu, Saioa, Cuadros Tito, Pedro Ángel, Orbe Narváez, Gurutz, Liébana Martos, Ma del Carmen, Roldán Fontana, Carolina, Herrero Rodríguez, Carmen, Duro Ruiz, Gaspar, Pérez Parra, Santiago, Mera Fidalgo, Arantzazu, Hortos Alsina, Miquel, Alberich Conesa, Ana, Bladé Vidal, Lourdes, Merchante Gutiérrez, Nicolás, León Jiménez, Eva, Espíndola Gómez, Reinaldo, Erostarbe Gallardo, María, Martínez Pérez-Crespo, Pedro, Cisneros, José Miguel, Aguilar-Guisado, Manuela, Aldabó, Teresa, Bueno, Claudio, Cordero-Matía, Elisa, Escoresca, Ana, Infante, Carmen, Guillermo, Martín, Salto, Sonsoles, Gioia, Francesca, Vizcarra, Pilar, Fortún Abete, Jesús, Martín Dávila, Pilar, Moreno Guillén, Santiago, Oteo Revuelta, José A., García-García, Concepción, Santibañez Sáenz, Paula, Cervera Acedo, Cristina, Azcona Gutiérrez, José M., Reguera Iglesias, José María, Plata Ciezar, Antonio, Valiente de Santis, Lucia, Sobrino Diaz, Beatriz, Ruiz Mesa, Juan Diego, Rodríguez-Baño, Jesús, Pachón, Jerónimo, Carratalà, Jordi, Ryan, Pablo, Jarrín, Inmaculada, Yllescas, María, and Arribas, José Ramón

Published
2021
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46. Multiple parallel cell lineages in the developing mammalian cerebral cortex

Author

Del-Valle-Anton, Lucia, primary, Amin, Salma, additional, Cimino, Daniela, additional, Neuhaus, Florian, additional, Dvoretskova, Elena, additional, Fernández, Virginia, additional, Babal, Yigit K., additional, Garcia-Frigola, Cristina, additional, Prieto-Colomina, Anna, additional, Murcia-Ramón, Raquel, additional, Nomura, Yuki, additional, Cárdenas, Adrián, additional, Feng, Chao, additional, Moreno-Bravo, Juan Antonio, additional, Götz, Magdalena, additional, Mayer, Christian, additional, and Borrell, Víctor, additional

Published
2024
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50. Multiple parallel cell lineages in the developing mammalian cerebral cortex

Author

Ministerio de Ciencia e Innovación (España), European Commission, European Research Council, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Fundación Tatiana Pérez de Guzmán el Bueno, Ministerio de Economía y Competitividad (España), Valle-Anton, Lucia del, Amin, Salma, Cimino, Daniela, Neuhaus, Florian, Dvoretskova, Elena, Fernández, Virginia, Babal, Yigit K., García-Frigola, Cristina, Prieto-Colomina, Anna, Murcia-Ramón, Raquel, Nomura, Yuki, Cárdenas, Adrián, Feng, Chao, Moreno-Bravo, Juan Antonio, Götz, Magdalena, Mayer, Christian, Borrell, Víctor, Ministerio de Ciencia e Innovación (España), European Commission, European Research Council, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Fundación Tatiana Pérez de Guzmán el Bueno, Ministerio de Economía y Competitividad (España), Valle-Anton, Lucia del, Amin, Salma, Cimino, Daniela, Neuhaus, Florian, Dvoretskova, Elena, Fernández, Virginia, Babal, Yigit K., García-Frigola, Cristina, Prieto-Colomina, Anna, Murcia-Ramón, Raquel, Nomura, Yuki, Cárdenas, Adrián, Feng, Chao, Moreno-Bravo, Juan Antonio, Götz, Magdalena, Mayer, Christian, and Borrell, Víctor

Abstract

Cortical neurogenesis follows a simple lineage: apical radial glia cells (RGCs) generate basal progenitors, and these produce neurons. How this occurs in species with expanded germinal zones and a folded cortex, such as human, remains unclear. We used single-cell RNA sequencing from individual cortical germinal zones in ferret and barcoded lineage tracking to determine the molecular diversity of progenitor cells and their lineages. We identified multiple RGC classes that initiate parallel lineages, converging onto a common class of newborn neuron. Parallel RGC classes and transcriptomic trajectories were repeated across germinal zones and conserved in ferret and human, but not in mouse. Neurons followed parallel differentiation trajectories in the gyrus and sulcus, with different expressions of human cortical malformation genes. Progenitor cell lineage multiplicity is conserved in the folded mammalian cerebral cortex.

Published
2024

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