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2,913 results on '"Carbayo A"'

1. Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data

Author

Parida, Abhijeet, Capellán-Martín, Daniel, Jiang, Zhifan, Tapp, Austin, Liu, Xinyang, Anwar, Syed Muhammad, Ledesma-Carbayo, María J., and Linguraru, Marius George

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

Gliomas, a kind of brain tumor characterized by high mortality, present substantial diagnostic challenges in low- and middle-income countries, particularly in Sub-Saharan Africa. This paper introduces a novel approach to glioma segmentation using transfer learning to address challenges in resource-limited regions with minimal and low-quality MRI data. We leverage pre-trained deep learning models, nnU-Net and MedNeXt, and apply a stratified fine-tuning strategy using the BraTS2023-Adult-Glioma and BraTS-Africa datasets. Our method exploits radiomic analysis to create stratified training folds, model training on a large brain tumor dataset, and transfer learning to the Sub-Saharan context. A weighted model ensembling strategy and adaptive post-processing are employed to enhance segmentation accuracy. The evaluation of our proposed method on unseen validation cases on the BraTS-Africa 2024 task resulted in lesion-wise mean Dice scores of 0.870, 0.865, and 0.926, for enhancing tumor, tumor core, and whole tumor regions and was ranked first for the challenge. Our approach highlights the ability of integrated machine-learning techniques to bridge the gap between the medical imaging capabilities of resource-limited countries and established developed regions. By tailoring our methods to a target population's specific needs and constraints, we aim to enhance diagnostic capabilities in isolated environments. Our findings underscore the importance of approaches like local data integration and stratification refinement to address healthcare disparities, ensure practical applicability, and enhance impact. A dockerized version of the BraTS-Africa 2024 winning algorithm is available at https://hub.docker.com/r/aparida12/brats-ssa-2024 ., Comment: 10 pages, 3 figures, 3 tables. BraTS-Africa 2024 winning algorithm presented at MICCAI BraTS 2024

Published
2024

2. Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation

Author

Jiang, Zhifan, Capellán-Martín, Daniel, Parida, Abhijeet, Tapp, Austin, Liu, Xinyang, Ledesma-Carbayo, María J., Anwar, Syed Muhammad, and Linguraru, Marius George

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

Accurate and automatic segmentation of brain tumors in multi-parametric magnetic resonance imaging (mpMRI) is essential for quantitative measurements, which play an increasingly important role in clinical diagnosis and prognosis. The International Brain Tumor Segmentation (BraTS) Challenge 2024 offers a unique benchmarking opportunity, including various types of brain tumors in both adult and pediatric populations, such as pediatric brain tumors (PED), meningiomas (MEN-RT) and brain metastases (MET), among others. Compared to previous editions, BraTS 2024 has implemented changes to substantially increase clinical relevance, such as refined tumor regions for evaluation. We propose a deep learning-based ensemble approach that integrates state-of-the-art segmentation models. Additionally, we introduce innovative, adaptive pre- and post-processing techniques that employ MRI-based radiomic analyses to differentiate tumor subtypes. Given the heterogeneous nature of the tumors present in the BraTS datasets, this approach enhances the precision and generalizability of segmentation models. On the final testing sets, our method achieved mean lesion-wise Dice similarity coefficients of 0.926, 0.801, and 0.688 for the whole tumor in PED, MEN-RT, and MET, respectively. These results demonstrate the effectiveness of our approach in improving segmentation performance and generalizability for various brain tumor types. The source code of our implementation is available at https://github.com/Precision-Medical-Imaging-Group/HOPE-Segmenter-Kids. Additionally, an open-source web-application is accessible at https://segmenter.hope4kids.io/ which uses the docker container aparida12/brats-peds-2024:v20240913 ., Comment: 11 pages, 4 figures, 3 tables. This paper was accepted at MICCAI-BraTS 2024

Published
2024

3. Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging

Author

Capellán-Martín, Daniel, Jiang, Zhifan, Parida, Abhijeet, Liu, Xinyang, Lam, Van, Nisar, Hareem, Tapp, Austin, Elsharkawi, Sarah, Ledesma-Carbayo, Maria J., Anwar, Syed Muhammad, and Linguraru, Marius George

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

Segmenting brain tumors in multi-parametric magnetic resonance imaging enables performing quantitative analysis in support of clinical trials and personalized patient care. This analysis provides the potential to impact clinical decision-making processes, including diagnosis and prognosis. In 2023, the well-established Brain Tumor Segmentation (BraTS) challenge presented a substantial expansion with eight tasks and 4,500 brain tumor cases. In this paper, we present a deep learning-based ensemble strategy that is evaluated for newly included tumor cases in three tasks: pediatric brain tumors (PED), intracranial meningioma (MEN), and brain metastases (MET). In particular, we ensemble outputs from state-of-the-art nnU-Net and Swin UNETR models on a region-wise basis. Furthermore, we implemented a targeted post-processing strategy based on a cross-validated threshold search to improve the segmentation results for tumor sub-regions. The evaluation of our proposed method on unseen test cases for the three tasks resulted in lesion-wise Dice scores for PED: 0.653, 0.809, 0.826; MEN: 0.876, 0.867, 0.849; and MET: 0.555, 0.6, 0.58; for the enhancing tumor, tumor core, and whole tumor, respectively. Our method was ranked first for PED, third for MEN, and fourth for MET, respectively., Comment: 11 pages, 6 figures, 2 tables; This method ranked 1st, 3rd and 4th for BraTS2023 PED, MEN, and MET, respectively. This paper was accepted at MICCAI 2023's BrainLes Workshop

Published
2024

4. Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration

Author

Parida, Abhijeet, Alomar, Antonia, Jiang, Zhifan, Roshanitabrizi, Pooneh, Tapp, Austin, Ledesma-Carbayo, Maria, Xu, Ziyue, Anwar, Syed Muhammed, Linguraru, Marius George, and Roth, Holger R.

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

Deploying deep learning-based imaging tools across various clinical sites poses significant challenges due to inherent domain shifts and regulatory hurdles associated with site-specific fine-tuning. For histopathology, stain normalization techniques can mitigate discrepancies, but they often fall short of eliminating inter-site variations. Therefore, we present Data Alchemy, an explainable stain normalization method combined with test time data calibration via a template learning framework to overcome barriers in cross-site analysis. Data Alchemy handles shifts inherent to multi-site data and minimizes them without needing to change the weights of the normalization or classifier networks. Our approach extends to unseen sites in various clinical settings where data domain discrepancies are unknown. Extensive experiments highlight the efficacy of our framework in tumor classification in hematoxylin and eosin-stained patches. Our explainable normalization method boosts classification tasks' area under the precision-recall curve(AUPR) by 0.165, 0.545 to 0.710. Additionally, Data Alchemy further reduces the multisite classification domain gap, by improving the 0.710 AUPR an additional 0.142, elevating classification performance further to 0.852, from 0.545. Our Data Alchemy framework can popularize precision medicine with minimal operational overhead by allowing for the seamless integration of pre-trained deep learning-based clinical tools across multiple sites., Comment: accepted to Machine Learning in Medical Imaging (MLMI 2024)

Published
2024

7. Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration

Author

Parida, Abhijeet, Alomar, Antonia, Jiang, Zhifan, Roshanitabrizi, Pooneh, Tapp, Austin, Ledesma-Carbayo, María J., Xu, Ziyue, Anwar, Syed Muhammed, Linguraru, Marius George, Roth, Holger R., 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, Xu, Xuanang, editor, Cui, Zhiming, editor, Rekik, Islem, editor, Ouyang, Xi, editor, and Sun, Kaicong, editor

Published
2025
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8. Fetal Body Parts Segmentation Using Volumetric MRI Reconstructions

Author

Alarcón-Gil, Pedro Pablo, Alfano, Felicia, Uus, Alena, Ledesma-Carbayo, María Jesús, Cordero-Grande, Lucilio, 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, Link-Sourani, Daphna, editor, Abaci Turk, Esra, editor, Macgowan, Christopher, editor, Hutter, Jana, editor, Melbourne, Andrew, editor, and Licandro, Roxane, editor

Published
2025
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9. DiCoM -- Diverse Concept Modeling towards Enhancing Generalizability in Chest X-Ray Studies

Author

Parida, Abhijeet, Capellan-Martin, Daniel, Atito, Sara, Awais, Muhammad, Ledesma-Carbayo, Maria J., Linguraru, Marius G., and Anwar, Syed Muhammad

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

Chest X-Ray (CXR) is a widely used clinical imaging modality and has a pivotal role in the diagnosis and prognosis of various lung and heart related conditions. Conventional automated clinical diagnostic tool design strategies relying on radiology reads and supervised learning, entail the cumbersome requirement of high quality annotated training data. To address this challenge, self-supervised pre-training has proven to outperform supervised pre-training in numerous downstream vision tasks, representing a significant breakthrough in the field. However, medical imaging pre-training significantly differs from pre-training with natural images (e.g., ImageNet) due to unique attributes of clinical images. In this context, we introduce Diverse Concept Modeling (DiCoM), a novel self-supervised training paradigm that leverages a student teacher framework for learning diverse concepts and hence effective representation of the CXR data. Hence, expanding beyond merely modeling a single primary label within an image, instead, effectively harnessing the information from all the concepts inherent in the CXR. The pre-trained model is subsequently fine-tuned to address diverse domain-specific tasks. Our proposed paradigm consistently demonstrates robust performance across multiple downstream tasks on multiple datasets, highlighting the success and generalizability of the pre-training strategy. To establish the efficacy of our methods we analyze both the power of learned representations and the speed of convergence (SoC) of our models. For diverse data and tasks, DiCoM is able to achieve in most cases better results compared to other state-of-the-art pre-training strategies. This when combined with the higher SoC and generalization capabilities positions DiCoM to be established as a foundation model for CXRs, a widely used imaging modality.

Published
2024

10. Zero-Shot Pediatric Tuberculosis Detection in Chest X-Rays using Self-Supervised Learning

Author

Capellán-Martín, Daniel, Parida, Abhijeet, Gómez-Valverde, Juan J., Sanchez-Jacob, Ramon, Roshanitabrizi, Pooneh, Linguraru, Marius G., Ledesma-Carbayo, María J., and Anwar, Syed M.

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

Tuberculosis (TB) remains a significant global health challenge, with pediatric cases posing a major concern. The World Health Organization (WHO) advocates for chest X-rays (CXRs) for TB screening. However, visual interpretation by radiologists can be subjective, time-consuming and prone to error, especially in pediatric TB. Artificial intelligence (AI)-driven computer-aided detection (CAD) tools, especially those utilizing deep learning, show promise in enhancing lung disease detection. However, challenges include data scarcity and lack of generalizability. In this context, we propose a novel self-supervised paradigm leveraging Vision Transformers (ViT) for improved TB detection in CXR, enabling zero-shot pediatric TB detection. We demonstrate improvements in TB detection performance ($\sim$12.7% and $\sim$13.4% top AUC/AUPR gains in adults and children, respectively) when conducting self-supervised pre-training when compared to fully-supervised (i.e., non pre-trained) ViT models, achieving top performances of 0.959 AUC and 0.962 AUPR in adult TB detection, and 0.697 AUC and 0.607 AUPR in zero-shot pediatric TB detection. As a result, this work demonstrates that self-supervised learning on adult CXRs effectively extends to challenging downstream tasks such as pediatric TB detection, where data are scarce., Comment: 5 pages, 3 figures, 2 tables. This paper has been accepted at IEEE ISBI 2024

Published
2024
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11. Concordance in the estimation of tumor percentage in non-small cell lung cancer using digital pathology

Author

Carretero-Barrio, Irene, Pijuan, Lara, Illarramendi, Adrián, Curto, Daniel, López-Ríos, Fernando, Estébanez-Gallo, Ángel, Castellvi, Josep, Granados-Aparici, Sofía, Compañ-Quilis, Desamparados, Noguera, Rosa, Esteban-Rodríguez, Isabel, Sánchez-Güerri, Ignacio, Ramos-Guerra, Ana Delia, Ortuño, Juan Enrique, Garrido, Pilar, Ledesma-Carbayo, María Jesús, Benito, Amparo, and Palacios, José

Published
2024
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13. A Lightweight, Rapid and Efficient Deep Convolutional Network for Chest X-Ray Tuberculosis Detection

Author

Capellán-Martín, Daniel, Gómez-Valverde, Juan J., Bermejo-Peláez, David, and Ledesma-Carbayo, María J.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, 68T07, I.2.1, I.4.9, I.4.m, J.3
Abstract

Tuberculosis (TB) is still recognized as one of the leading causes of death worldwide. Recent advances in deep learning (DL) have shown to enhance radiologists' ability to interpret chest X-ray (CXR) images accurately and with fewer errors, leading to a better diagnosis of this disease. However, little work has been done to develop models capable of diagnosing TB that offer good performance while being efficient, fast and computationally inexpensive. In this work, we propose LightTBNet, a novel lightweight, fast and efficient deep convolutional network specially customized to detect TB from CXR images. Using a total of 800 frontal CXR images from two publicly available datasets, our solution yielded an accuracy, F1 and area under the ROC curve (AUC) of 0.906, 0.907 and 0.961, respectively, on an independent test subset. The proposed model demonstrates outstanding performance while delivering a rapid prediction, with minimal computational and memory requirements, making it highly suitable for deployment in handheld devices that can be used in low-resource areas with high TB prevalence. Code publicly available at https://github.com/dani-capellan/LightTBNet., Comment: 5 pages, 3 figures, 3 tables. This paper has been accepted at ISBI 2023

Published
2023
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14. Plasma extracellular vesicle tau and TDP-43 as diagnostic biomarkers in FTD and ALS

Author

Chatterjee, Madhurima, Özdemir, Selcuk, Fritz, Christian, Möbius, Wiebke, Kleineidam, Luca, Mandelkow, Eckhard, Biernat, Jacek, Doğdu, Cem, Peters, Oliver, Cosma, Nicoleta Carmen, Wang, Xiao, Schneider, Luisa-Sophia, Priller, Josef, Spruth, Eike, Kühn, Andrea A., Krause, Patricia, Klockgether, Thomas, Vogt, Ina R., Kimmich, Okka, Spottke, Annika, Hoffmann, Daniel C., Fliessbach, Klaus, Miklitz, Carolin, McCormick, Cornelia, Weydt, Patrick, Falkenburger, Björn, Brandt, Moritz, Guenther, René, Dinter, Elisabeth, Wiltfang, Jens, Hansen, Niels, Bähr, Mathias, Zerr, Inga, Flöel, Agnes, Nestor, Peter J., Düzel, Emrah, Glanz, Wenzel, Incesoy, Enise, Bürger, Katharina, Janowitz, Daniel, Perneczky, Robert, Rauchmann, Boris S., Hopfner, Franziska, Wagemann, Olivia, Levin, Johannes, Teipel, Stefan, Kilimann, Ingo, Goerss, Doreen, Prudlo, Johannes, Gasser, Thomas, Brockmann, Kathrin, Mengel, David, Zimmermann, Milan, Synofzik, Matthis, Wilke, Carlo, Selma-González, Judit, Turon-Sans, Janina, Santos-Santos, Miguel Angel, Alcolea, Daniel, Rubio-Guerra, Sara, Fortea, Juan, Carbayo, Álvaro, Lleó, Alberto, Rojas-García, Ricardo, Illán-Gala, Ignacio, Wagner, Michael, Frommann, Ingo, Roeske, Sandra, Bertram, Lucas, Heneka, Michael T., Brosseron, Frederic, Ramirez, Alfredo, Schmid, Matthias, Beschorner, Rudi, Halle, Annett, Herms, Jochen, Neumann, Manuela, Barthélemy, Nicolas R., Bateman, Randall J., Rizzu, Patrizia, Heutink, Peter, Dols-Icardo, Oriol, Höglinger, Günter, Hermann, Andreas, and Schneider, Anja

Published
2024
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16. Prognostic models for depression and post-traumatic stress disorder symptoms following traumatic brain injury: a CENTER-TBI study

Author

Kelly Jones, Antonio Belli, Suzanne Polinder, Emmanuel Stamatakis, Hester F Lingsma, Dick Tibboel, Ronny Beer, Raimund Helbok, Horia Ples, Ari Ercole, Peter Cameron, Marek Czosnyka, Catherine McMahon, Peter Vajkoczy, Paul Dark, Alexandra Brazinova, Marek Majdan, Rahul Raj, William Stewart, Ewout W Steyerberg, Giuseppe Citerio, Nino Stocchetti, Alessia Vargiolu, Hester Lingsma, Helen Dawes, Benoit Misset, Faye Johnson, David van Klaveren, Simon Stanworth, Fiona Lecky, Peter Smielewski, Alice Theadom, Peter Bragge, David Nelson, János Sándor, Paul M Vespa, Olli Tenovuo, Olav Røise, Wim Van Hecke, Vincent Degos, David Menon, Bart Depreitere, Audny Anke, Rolf Rossaint, Mark Coburn, Victor Volovici, Daan Nieboer, Jonathan Rosand, Nada Andelic, Hans Clusmann, Emma Donoghue, Jonathan Coles, stefan jankowski, Jonathan Rhodes, Guy Williams, Rolf Lefering, Mathieu van der Jagt, Daniel Kondziella, Noah D Silverberg, Arturo Chieregato, Sophie Richter, Endre Czeiter, Stefan Winzeck, Jan Verheyden, András Büki, Benjamin Gravesteijn, Evgenios Kornaropoulos, Virginia Newcombe, Lindsay Wilson, Lindsay Horton, Amra Covic, Dominique Van Praag, Cecilia Åkerlund, Krisztina Amrein, Lasse Andreassen, Anna Antoni, Gérard Audibert, Philippe Azouvi, Ronald Bartels, Pál Barzó, Romuald Beauvais, Bo-Michael Bellander, Habib Benali, Maurizio Berardino, Luigi Beretta, Morten Blaabjerg, Vibeke Brinck, Joanne Brooker, Camilla Brorsson, Monika Bullinger, Manuel Cabeleira, Alessio Caccioppola, Emiliana Calappi, Guillermo Carbayo Lozano, Marco Carbonara, Simona Cavallo, Giorgio Chevallard, Marta Correia, Nicola Curry, Véronique De Keyser, Hugo den Boogert, Đula Đilvesi, Abhishek Dixit, Jens Dreier, Patrick Esser, Erzsébet Ezer, Martin Fabricius, Kelly Foks, Shirin Frisvold, Alex Furmanov, Pablo Gagliardo, Damien Galanaud, Dashiell Gantner, Guoyi Gao, Pradeep George, Alexandre Ghuysen, Lelde Giga, Jagoš Golubovic Ben Glocker, Johannes Gratz, Francesca Grossi, Deepak Gupta, Iain Haitsma, Eirik Helseth, Jilske Huijben, Bram Jacobs, Mike Jarrett, Mladen Karan, Erwin Kompanje, Noémi Kovács, Ana Kowark, Alfonso Lagares, Linda Lanyon, Steven Laureys, Didier Ledoux, Valerie Legrand, Aurelie Lejeune, Leon Levi, Roger Lightfoot, Marc Maegele, Alex Manara, Geoffrey Manley, Costanza Martino, Hugues Maréchal, Julia Mattern, Béla Melegh, Tomas Menovsky, Ana Mikolic, Visakh Muraleedharan, Lynnette Murray, Ancuta Negru, József Nyirádi, Otesile Olubukola, Matej Oresic, Fabrizio Ortolano, Aarno Palotie, Paul M Parizel, Natascha Perera, Vincent Perlbarg, Paolo Persona, Wilco Peul, Matti Pirinen, Inigo Pomposo, Louis Puybasset, Andreea Radoi, Arminas Ragauskas, Malinka Rambadagalla, Sylvia Richardson, Samuli Ripatti, Saulius Rocka, Cecilie Roe, Christina Rosenlund, Guy Rosenthal, Sandra Rossi, Juan Sahuquillo, Oliver Sakowitz, Nadine Schäfer, Silke Schmidt, Herbert Schoechl, Guus Schoonman, Elisabeth Schwendenwein, Charlie Sewalt, Toril Skandsen, Abayomi Sorinola, Robert Stevens, Nina Sundström, Riikka Takala, Viktória Tamás, Tomas Tamosuitis, Mark Steven Taylor, Matt Thomas, Marjolein Timmers, Christos Tolias, Tony Trapani, Shirley Vallance, Egils Valeinis, Zoltán Vámos, Emmanuel Vega, Kimberley Velt, Anne Vik, Rimantas Vilcinis, Daphne Voormolen, Petar Vulekovic, Kevin K W Wang, Eveline Wiegers, Stefan Wolf, Zhihui Yang, Peter Ylén, Alexander Younsi, Frederick A Zeiler, Veronika Zelinkova, Agate Ziverte, Tommaso Zoerle, Joukje van der Naalt, Braden Te Ao, Caroline van Heugten, Francesco Della Corte, Ernest Van Veen, Andrew IR Maas, Andreas Unterberg, Thomas A van Essen, Mathilde Jost, Shuyuan Shi, Maria Luisa Azzolini, Maria Rosa Calvi, Jamie D Cooper, Claire Dahyot Fizelier, Guy Loup Dulière, Valery L Feigin, Pedro A Gomez, Russell L Gruen, Juanita A Haagsma, Peter J Hutchinson, Ji yao Jiang, Angelos G Kolias, Lars Owe Koskinen, Andrew I Z Maas, Ana M Castaño León, Jean François Payen, Anna Piippo Karjalainen, Dana Pisica, Jussi PPosti, Isabel Retel Helmrich, Jeffrey VRosenfeld, Daniel Rueckert Martin Rusnák, Renan SanchezPorras, Rico Frederik Schou, Cristina Maria Tudora, Gregory Van der Steen, Jeroen T J M van Dijck, Thijs Vande Vyvere, Roel P J van Wijk, and Nicole von Steinbüchel

Subjects
Psychiatry, RC435-571
Abstract

Background Traumatic brain injury (TBI) is associated with an increased risk of major depressive disorder (MDD) and post-traumatic stress disorder (PTSD). We aimed to identify predictors and develop models for the prediction of depression and PTSD symptoms at 6 months post-TBI.Methods We analysed data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury study. We used linear regression to model the relationship between predictors and depression (Patient Health Questionnaire-9) and PTSD symptoms (PTSD Checklist for Diagnostic and Statistical Manual for Mental Health Disorders Fifth Edition). Predictors were selected based on Akaike’s Information Criterion. Additionally, we fitted logistic models for the endpoints ‘probable MDD’ and ‘probable PTSD’. We also examined the incremental prognostic value of 2–3 weeks of symptoms.Results We included 2163 adults (76% Glasgow Coma Scale=13–15). Depending on the scoring criteria, 7–18% screened positive for probable MDD and about 10% for probable PTSD. For both outcomes, the selected models included psychiatric history, employment status, sex, injury cause, alcohol intoxication and total injury severity; and for depression symptoms also preinjury health and education. The performance of the models was modest (proportion of explained variance=R2 8% and 7% for depression and PTSD, respectively). Symptoms assessed at 2–3 weeks had a large incremental prognostic value (delta R2=0.25, 95% CI 0.24 to 0.26 for depression symptoms; delta R2=0.30, 95% CI 0.29 to 0.31 for PTSD).Conclusion Preinjury characteristics, such as psychiatric history and unemployment, and injury characteristics, such as violent injury cause, can increase the risk of mental health problems after TBI. The identification of patients at risk should be guided by early screening of mental health.

Published
2025
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17. Genetic vulnerability and adverse mental health outcomes following mild traumatic brain injury: a meta-analysis of CENTER-TBI and TRACK-TBI cohortsResearch in context

Author

Mart Kals, Lindsay Wilson, Daniel F. Levey, Livia Parodi, Ewout W. Steyerberg, Sylvia Richardson, Feng He, Xiaoying Sun, Sonia Jain, Aarno Palotie, Samuli Ripatti, Jonathan Rosand, Geoff T. Manley, Andrew I.R. Maas, Murray B. Stein, David K. Menon, Cecilia Ackerlund, Hadie Adams, Krisztina Amrein, Nada Andelic, Lasse Andreassen, Audny Anke, Anna Antoni, Gérard Audibert, Philippe Azouvi, Maria Luisa Azzolini, Ronald Bartels, Pál Barzó, Romuald Beauvais, Ronny Beer, Bo-Michael Bellander, Antonio Belli, Habib Benali, Maurizio Berardino, Luigi Beretta, Morten Blaabjerg, Peter Bragge, Alexandra Brazinova, Vibeke Brinck, Joanne Brooker, Camilla Brorsson, Andras Buki, Monika Bullinger, Manuel Cabeleira, Alessio Caccioppola, Emiliana Calappi, Maria Rosa Calvi, Peter Cameron, Guillermo Carbayo Lozano, Marco Carbonara, Ana M. Castaño-León, Simona Cavallo, Giorgio Chevallard, Arturo Chieregato, Giuseppe Citerio, Hans Clusmann, Mark Steven Coburn, Jonathan Coles, Jamie D. Cooper, Marta Correia, Amra Čović, Nicola Curry, Endre Czeiter, Marek Czosnyka, Claire Dahyot-Fizelier, Paul Dark, Helen Dawes, Véronique De Keyser, Vincent Degos, Francesco Della Corte, Hugo den Boogert, Bart Depreitere, Đula Đilvesi, Abhishek Dixit, Emma Donoghue, Jens Dreier, Guy-Loup Dulière, Ari Ercole, Patrick Esser, Erzsébet Ezer, Martin Fabricius, Valery L. Feigin, Kelly Foks, Shirin Frisvold, Alex Furmanov, Pablo Gagliardo, Damien Galanaud, Dashiell Gantner, Guoyi Gao, Pradeep George, Alexandre Ghuysen, Lelde Giga, Ben Glocker, Jagoš Golubović, Pedro A. Gomez, Johannes Gratz, Benjamin Gravesteijn, Francesca Grossi, Russell L. Gruen, Deepak Gupta, Juanita A. Haagsma, Iain Haitsma, Raimund Helbok, Eirik Helseth, Lindsay Horton, Jilske Huijben, Peter J. Hutchinson, Bram Jacobs, Stefan Jankowski, Mike Jarrett, Ji-yao Jiang, Faye Johnson, Kelly Jones, Mladen Karan, Angelos G. Kolias, Erwin Kompanje, Daniel Kondziella, Lars-Owe Koskinen, Noémi Kovács, Ana Kowark, Alfonso Lagares, Linda Lanyon, Steven Laureys, Fiona Lecky, Didier Ledoux, Rolf Lefering, Valerie Legrand, Aurelie Lejeune, Leon Levi, Roger Lightfoot, Hester Lingsma, Marc Maegele, Marek Majdan, Alex Manara, Hugues Maréchal, Costanza Martino, Julia Mattern, Charles McFadyen, Catherine McMahon, Béla Melegh, Tomas Menovsky, Ana Mikolic, Benoit Misset, Visakh Muraleedharan, Lynnette Murray, Ancuta Negru, David Nelson, Virginia Newcombe, Daan Nieboer, József Nyirádi, Matej Oresic, Fabrizio Ortolano, Olubukola Otesile, Paul M. Parizel, Jean-François Payen, Natascha Perera, Vincent Perlbarg, Paolo Persona, Wilco Peul, Anna Piippo-Karjalainen, Matti Pirinen, Dana Pisica, Horia Ples, Suzanne Polinder, Inigo Pomposo, Jussi P. Posti, Louis Puybasset, Andreea Rădoi, Arminas Ragauskas, Rahul Raj, Malinka Rambadagalla, Veronika Rehorčíková, Isabel Retel Helmrich, Jonathan Rhodes, Sophie Richter, Saulius Rocka, Cecilie Roe, Olav Roise, Jeffrey Rosenfeld, Christina Rosenlund, Guy Rosenthal, Rolf Rossaint, Sandra Rossi, Daniel Rueckert, Martin Rusnák, Juan Sahuquillo, Oliver Sakowitz, Renan Sanchez-Porras, Janos Sandor, Nadine Schäfer, Silke Schmidt, Herbert Schoechl, Guus Schoonman, Rico Frederik Schou, Elisabeth Schwendenwein, Charlie Sewalt, Ranjit D. Singh, Toril Skandsen, Peter Smielewski, Abayomi Sorinola, Emmanuel Stamatakis, Simon Stanworth, Robert Stevens, William Stewart, Nino Stocchetti, Nina Sundström, Riikka Takala, Viktória Tamás, Tomas Tamosuitis, Mark Steven Taylor, Braden Te Ao, Olli Tenovuo, Alice Theadom, Aurore Thibaut, Matt Thomas, Dick Tibboel, Marjolijn Timmers, Christos Tolias, Tony Trapani, Cristina Maria Tudora, Andreas Unterberg, Peter Vajkoczy, Egils Valeinis, Shirley Vallance, Zoltán Vámos, Mathieu van der Jagt, Joukje van der Naalt, Gregory Van der Steen, Jeroen T.J.M. van Dijck, Inge A. van Erp, Thomas A. van Essen, Wim Van Hecke, Caroline van Heugten, Dominique Van Praag, Ernest van Veen, Roel van Wijk, Thijs Vande Vyvere, Alessia Vargiolu, Emmanuel Vega, Kimberley Velt, Jan Verheyden, Paul M. Vespa, Anne Vik, Rimantas Vilcinis, Victor Volovici, Nicole von Steinbüchel, Daphne Voormolen, Peter Vulekovic, Daniel Whitehouse, Eveline Wiegers, Guy Williams, Stefan Wolf, Zhihui Yang, Peter Ylén, Alexander Younsi, Frederick A. Zeiler, Agate Ziverte, Tommaso Zoerle, Opeolu Adeoye, Neeraj Badjatia, Jason Barber, Michael Bergin, Kim Boase, Yelena Bodien, Randall Chesnut, John Corrigan, Karen Crawford, Ramon Diaz-Arrastia, Sureyya Dikmen, Ann-Christine Duhaime, Richard Ellenbogen, Venkata Feeser, Adam R. Ferguson, Brandon Foreman, Etienne Gaudette, Joseph Giacino, Luis Gonzalez, Shankar Gopinath, Ramesh Grandhi, Rao Gullapalli, Claude Hemphill, Gillian Hotz, Russell Huie, Ruchira Jha, Dirk C. Keene, Ryan Kitagawa, Frederick Korley, Joel Kramer, Natalie Kreitzer, Harvey Levin, Chris Lindsell, Joan Machamer, Christopher Madden, Alastair Martin, Thomas McAllister, Michael McCrea, Randall Merchant, Pratik Mukherjee, Lindsay Nelson, Laura B. Ngwenya, Florence Noel, Amber Nolan, David Okonkwo, Eva Palacios, Daniel Perl, Ava Puccio, Miri Rabinowitz, Claudia Robertson, Richard Ben Rodgers, Eric Rosenthal, Angelle Sander, Danielle Sandsmark, Andrea Schneider, David Schnyer, Seth Seabury, Mark Sherer, Gabriella Sugar, Nancy Temkin, Arthur Toga, Abel Torres-Espin, Alex Valadka, Mary Vassar, Kevin Wang, Vincent Wang, John K. Yue, Esther Yuh, and Ross Zafonte

Subjects
Traumatic brain injury, Mental health, Post-traumatic stress disorder, Depression, Polygenic risk score, Medicine (General), R5-920
Abstract

Summary: Background: Post-traumatic stress disorder (PTSD) and depression are common after mild traumatic brain injury (mTBI), but their biological drivers are uncertain. We therefore explored whether polygenic risk scores (PRS) derived for PTSD and major depressive disorder (MDD) are associated with the development of cognate TBI-related phenotypes. Methods: Meta-analyses were conducted using data from two multicenter, prospective observational cohort studies of patients with mTBI: the CENTER-TBI study (ClinicalTrials.gov ID NCT02210221) in Europe (December 2014–December 2017) and the TRACK-TBI study in the US (March 2014–July 2018). In both cohorts, the most common causes of injury were road traffic accidents and falls. Primary outcomes, specifically probable PTSD and depression, were defined at 6 months post-injury using scores ≥33 on the PTSD Checklist-5 and ≥15 on the Patient Health Questionnaire-9, respectively. We calculated PTSD-PRS and MDD-PRS for patients aged ≥17 years who had a Glasgow Coma Scale score of 13–15 upon hospital arrival and assessed their association with PTSD and depression following TBI. We also evaluated the transferability of the findings in a cohort of African Americans. Findings: Overall, 11.8% (219/1869) and 6.7% (124/1869) patients were classified as having probable PTSD and depression, respectively. The PTSD-PRS was significantly associated with higher adjusted odds of PTSD in both cohorts, with a pooled odds ratio (OR) of 1.55 [95% confidence interval (CI) 1.30–1.84, p

Published
2024
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18. Rise and decline of the land planarian genus Geoplana (Platyhelminthes: Tricladida)

Author

Fernando Carbayo

Subjects
Brazil, Geoplaninae, Geoplanini, Neotropical, nomenclature, taxonomy, systematics, Zoology, QL1-991
Abstract

ABSTRACT A taxonomic history of Geoplana, one of the most popular names for land planarians, is presented. The number of species within the genus has varied significantly since its unnoticed establishment in 1847. The number rose to 339 species in the 1980s before declining to 61 species, including 48 species incertae sedis. Eight diagnoses of Geoplana have since been proposed, resulting in the inclusion or removal of species. Accordingly, the genus underwent changes in its geographic range, expanding to encompass several continents in 1899 but ultimately being reduced to its current presence in Southeast Brazil. The genus is considered monophyletic, though it still houses 48 species incertae sedis. To better understand Geoplana, it is necessary to reassess the taxonomic status of these 48 species and describe several undescribed species.

Published
2024
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19. Present and future of the study on Korean land planarians (Platyhelminthes: Tricladida: Geoplanidae)

Author

Ji-Hun Song and Fernando Carbayo

Subjects
ASAP, COI, molecular phylogeny, Republic of Korea, richness, Zoology, QL1-991
Abstract

ABSTRACT To date, two species of land planarians, Diversibipalium koreense (Frieb, 1923) and Microplana unilineata (Frieb, 1923), have been recorded for the Republic of Korea. Meanwhile, various morphotypes of land planarians were discovered during the terrestrial invertebrate sampling campaign conducted in 2020, predicting that many species may inhabit the country. Accordingly, we initiated an international collaborative research in 2021 to elucidate the biodiversity of land planarians in the country and to document each species. We grouped more than 70 specimens collected from 2020 to 2022 into 22 morphospecies based on their external appearance. Subsequently, we tested the morphospecies independently in a molecular phylogeny inferred from the mitochondrial COI gene. Maximum likelihood (ML) analysis suggests that the 22 morphospecies are assigned to each clade as follows: (1) Bipaliinae (15 spp.), (2) Microplaninae (6 spp.), and (3) Rhynchodeminae (1 sp.). The Assemble Species by Automatic Partitioning (ASAP) analysis predicted that there would be 14 species of Bipaliinae instead of 15, reducing the number of putative species to 21. As previous sampling campaigns have only covered a small portion of the Republic of Korea, the expected number of species should be much higher. We hope that this international collaboration will continue to help reveal the true richness of Korean land planarians.

Published
2024
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21. Do Geoplaninae (Platyhelminthes: Tricladida), a neotropical subfamily of land planarians, regenerate well?

Author

Vitor Milanese and Fernando Carbayo

Subjects
Adaptation, Geoplaninae, Imbira, Issoca, regeneration, terrestrial planarians, Zoology, QL1-991
Abstract

ABSTRACT Knowledge on the regeneration capacity of land planarians is very scarce and focused on a few species. Here we studied the regeneration capacity of 104 animals from 23 species (genera Cephaloflexa, Choeradoplana, Cratera, Geoplana, Imbira, Xerapoa, Luteostriata, Notogynaphallia, Obama, Pasipha, plus an unidentified species) of the neotropical Geoplaninae and also two species (genera Dolichoplana and Endeavouria) of Rhynchodeminae under laboratory conditions. When cut in two, 17 flatworm species regenerated head and/or tail within 5 to 27 days, apparently depending on how flattened their bodies were. Issoca rezendei (Schirch, 1929) was the geoplanin species that regenerated the fastest, while Obama species regenerated the slowest. Five species did not survive long enough to begin regeneration, while Imbira marcusi Carbayo et al., 2013, survived up to 15 days after sectioning but did not form the blastema, seemingly due to its sensitivity to laboratory conditions. The shape of the blastema varied according to the body shape of the species, as did the re-pigmentation of the newly formed tissue, which began in the early stages of regeneration in Obama species. Issoca rezendei showed some characteristics that make it a good candidate species as a model organism for further study of regeneration. In nature, geoplanins often show injuries, some of which may have been caused by predator-prey interactions. This variation in regeneration capacity raises questions such as if their regeneration capacity could also be maintained by direct selection.

Published
2024
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23. 20274. NODOPATÍA AUTOINMUNE ANTI-CONTACTIN-1: CARACTERÍSTICAS CLÍNICAS, BIOMARCADORES Y SEGUIMIENTO A LARGO PLAZO

Author

M. Caballero Ávila, L. Martín Aguilar, E. Pascual Goñi, M. Michael, M. Koel-Simmelink, R. Höftberger, J. Wanschitz, A. Alonso Jiménez, T. Armangué, A. Baars, Á. Carbayo, B. Castek, R. Collet Vidiella, J. de Winter, M. del Real, E. Delmont, L. Diamanti, P. Doneddu, F. Hiew, E. Gallardo, A. González, S. Grinzinger, A. Horga, S. Iglseder, B. Jacobs, A. Jauregui, J. Killestein, E. Lindeck Pozza, L. Martínez Martínez, E. Nobile-Orazio, N. Ortiz, H. Pérez Pérez, K. Poppert, P. Ripellino, J. Roche, F. Rodríguez de Rivera, K. Rostasy, D. Sparasci, C. Tejada Illa, C. Teunissen, E. Vegezzi, T. Xuclà Ferrarons, F. Zach, L. Wieske, F. Eftimov, C. Lleixà, and L. Querol

Subjects
Neurology. Diseases of the nervous system, RC346-429
Published
2024
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24. 20965. PROTEOMA SÉRICO EN EL SÍNDROME DE GUILLAINBARRÉ UTILIZANDO UNA PLATAFORMA BASADA EN APTÁMEROS

Author

R. Collet Vidiella, L. Martín Aguilar, C. Lleixà Rodríguez, M. Caballero Ávila, C. Tejada Illa, E. Pascual Goñi, P. Llarch Cegarra, L. Llucià Carol, A. Vesperinas Castro, A. Carbayo Viejo, E. Gallardo Vigo, L. Llansó Caldentey, M. Sedano Tous, C. Casasnovas, G. Gutiérrez Gutiérrez, J. Pardo Fernández, I. Fernández Cadenas, and L. Querol Gutiérrez

Subjects
Neurology. Diseases of the nervous system, RC346-429
Published
2024
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27. Deep learning-based lung segmentation and automatic regional template in chest X-ray images for pediatric tuberculosis

Author

Capellán-Martín, Daniel, Gómez-Valverde, Juan J., Sanchez-Jacob, Ramon, Bermejo-Peláez, David, García-Delgado, Lara, López-Varela, Elisa, and Ledesma-Carbayo, Maria J.

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Image and Video Processing, 68T07, I.4.0, I.4.6, I.4.9
Abstract

Tuberculosis (TB) is still considered a leading cause of death and a substantial threat to global child health. Both TB infection and disease are curable using antibiotics. However, most children who die of TB are never diagnosed or treated. In clinical practice, experienced physicians assess TB by examining chest X-rays (CXR). Pediatric CXR has specific challenges compared to adult CXR, which makes TB diagnosis in children more difficult. Computer-aided diagnosis systems supported by Artificial Intelligence have shown performance comparable to experienced radiologist TB readings, which could ease mass TB screening and reduce clinical burden. We propose a multi-view deep learning-based solution which, by following a proposed template, aims to automatically regionalize and extract lung and mediastinal regions of interest from pediatric CXR images where key TB findings may be present. Experimental results have shown accurate region extraction, which can be used for further analysis to confirm TB finding presence and severity assessment. Code publicly available at https://github.com/dani-capellan/pTB_LungRegionExtractor., Comment: This work has been accepted at the SPIE Medical Imaging 2023, Image Processing conference

Published
2023
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28. Efficacy and Safety of Ravulizumab in IgA Nephropathy: A Phase 2 Randomized Double-Blind Placebo-Controlled Trial

Author

Lafayette, Richard, Tumlin, James, Fenoglio, Roberta, Kaufeld, Jessica, Pérez Valdivia, Miguel Angel, Wu, Mai-Szu, Susan Huang, Shih-Han, Alamartine, Eric, Kim, Sung Gyun, Yee, Min, Kateifides, Andreas, Rice, Kara, Garlo, Katherine, Barratt, Jonathan, Ranganathan, Dwarakanathan, Wolley, Martin, Tan, Sven-Jean, Chow, Kevin, Tin Law, Mandy Man, Nichols, Kathleen, See, Emily Jan, Steinberg, Adam Glenn, Tiong, Mark, Susan Huang, Shih-Han, Jain, Arsh, Laurin, Louis-Phillipe, Lafrance, Jean-Phillipe, Lamarche, Caroline, Philibert, David, Agharazii, Mohsen, Mac-Way, Fabrice, Alamartine, Eric, Maillard, Nicolas, Mariat, Christophe, Masson, Ingrid, Boffa, Jean-Jacques, Cez, Alexandre, Esteve, Emmanuel, Marchal, Armance, Chauveau, Dominique, Belliere, Julie, Monrozeis, Pauline Bernadet, Colliou, Eloise, Faguer, Stanislas, Huart, Antoine, Ribes, David, Garrouste, Cyril, Heng, Anne-Elisabeth, Mayet, Valentin, Philipponnet, Carole, Moulin, Bruno, Parvez, Laura Braun, Vargas, Gabriela Gautier, Perrin, Peggy, Ohlmann, Sophie, Olagne, Jerome, Haller, Herman, Schmidt-Ott, Kai, Bahlmann-Kroll, Elisabeth, Kaufeld, Jessica, Reinhardt, Martin, Gaeckler, Anja, Dolff, Sebastian, Wilde, Benjamin, Schreiber, Adrian, Koch, Nadine, Seelow, Evelyn, Alberici, Federico, Affatato, Stefania, Mainardi, Allesandro, Mescia, Federica, Scolari, Francesco, Tedesco, Martina, Venturini, Margherita, Cirami, Calogero Lino, Antognoli, Giulia, Caroti, Leonardo, Gualtiero, Gabriele, Moscato, Marcos, Trivioli, Giorgio, La Manna, Gaetano, Baraldi, Olga, Campus, Anita, Stefanini, Carlo, Vischini, Gisella, Roccatello, Dario, Fenoglio, Roberta, Lalloni, Stefania, Sciascia, Savino, Nowicki, Michal, Holub, Tomasz, Kieszek, Błażej, Makówka, Agnieszka, Masajtis, Anna, Piechota, Piotr, Tylski, Maciej, Han, Seung Hyeok, Lee, Sang-Won, Lim, Beon Jin, Pyo, Jung Yoon, Kim, Sung Gyun, An, Jung Nam, Chung, Byung Ha, Kim, Jwa-Kyung, Lee, Hyung Seok, Seo, Young Il, Song, Young Rim, Lee, Hajeong, Park, Sehoon, Han, Seung Seok, Kim, Dong Ki, Kim, Yong Chul, Ryu, Hyunjin, Agraz, Irene, Bolufer, Monica, Hernandez, Josefina Cortés, Gabaldon, Maria Alejandra, Soler, Maria José, Arroyo, David, Carbayo, Javier, Diezhandino, Marian Goicoechea, Perez de Jose, Ana, Guzman, Ursula Verdalles, Moreno, Eduardo Verde, García, Olga Gracia, Albines Fiestas, Zoila Stany, Aladren Gonzalvo, Daniel Joaquin, Gonzalez, Andrea Bernal, Lou Arnal, Luis Miguel, Villarroya, Cristina Medrano, Lopez, Paula Mora, Moncasi, Eduardo Parra, Martin Conde, Maria Luisa, Gonzalez, Jorge, Jatem, Elias, Estor, Aida Moroba, Medrano, Alfons Segarra, Moreno, Antolina Rodriguez, Sanchez de la Nieta, Dolores, Garcia, Clara, Velo, Mercedes, Rocha Castilla, Jose Luis, Bermejo, Sheila, Mendoza, Manuel Lopez, Pérez Valdivia, Miguel Angel, Rojas, Remedios Toledo, Hidalgo, Pilar, Sanchez, Teresa Vazquez, Uriol Rivera, Miguel Giovanni, Fuentes, Mario Lado, Chiu, Yi-Wen, Chang, Jer-Ming, Chen, Hung-Chun, Hsiao, Mei-Ju, Hung, Chi-Chih, Hwang, Shang-Jih, Kuo, Mei Chuan, Kuo, Hung-Tien, Lim, Lee-Moay, Lee, Jia-Jung, Lai, Ping Chin, Chang, David Ray, Chang, Yun-Lun, Chen, I-Ru, Hsieh, Ming-Han, Huang, Chiu-Ching, Kuo, Huey-Liang, Wang, I-Kuan, Wu, Mai-Szu, Chen, Yu-Wei, Chiu, I-Jen, Hsu, Yung-Ho, Hung, Lie-Yee, Laio, Chia-Te, Lin, Gua-Hong, Lin, Yuh-Feng, Wu, Mei-Yi, Zheng, Cai-Mei, Lightstone, Elizabeth, Cairns, Tommy, Mcadoo, Stephen, Medjeral-Thomas, Nicholas, Pickering, Matthew, Robson, Michael, Sinha, Smeeta, Chukwu, Chukwuma, Storrar, Joshua, Ahmed, Sadiq, Cornea, Virgilius, Nadim, Amina, Sims, Tyler, Awad, Ahmed, Aldahan, Mohammed, Clark, Laurie, Lustig, Ryan, Niles, John, Jeyabalan, Anushya, Zonozi, Reza, Tumlin, James, and Paxton, William

Published
2024
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29. 20769. IDENTIFICACIÓN DE UNA MUTACIÓN PATOGÉNICA EN ARPP21 EN PACIENTES CON ESCLEROSIS LATERAL AMIOTRÓFICA

Author

Á. Carbayo Viejo, O. Dols Icardo, I. Jericó Pascual, O. Blasco Martínez, M. López Pérez, S. Bernal Noguera, B. Rodríguez Santiago, I. Cusco, J. Turon Sans, A. Vesperinas Castro, L. Llansó Caldentey, M. Caballero Álvarez, M. Cabezas Torres, I. Pagoda Lorz, L. Torné, I. Illán Gala, S. Rubio Guerra, E. Álvarez Sánchez, L. Muñoz Llahuna, N. Valle Tamayo, E. Gelpi, E. Cortés Vicente, and R. Rojas García

Subjects
Neurology. Diseases of the nervous system, RC346-429
Published
2024
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30. 20424. AUSENCIA DE MUTACIONES PATOGÉNICAS Y FUERTE ASOCIACIÓN CON HLA-DRB1*11:01 EN PACIENTES JÓVENES NO EXPUESTOS A ESTATINAS CON MIOPATÍA NECROTIZANTE ANTI-HMGCR

Author

L. Llansó Caldentey, A. Segarra Casas, C. Domínguez González, E. Malfatti, S. Kapetanovic, B. Rodríguez Santiago, O. de la Calle, R. Blanco, A. Dobrescu, A. Nascimento, A. Paipa, A. Hernández Laín, C. Jou, A. Mariscal, L. González Mera, A. Arteche, C. Lleixà, M. Caballero Ávila, Á. Carbayo, A. Vesperinas, L. Querol, E. Gallardo, and M. Olivé

Subjects
Neurology. Diseases of the nervous system, RC346-429
Published
2024
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34. Parsimonious immune-response endotypes and global outcome in patients with traumatic brain injuryResearch in context

Author

Romit J. Samanta, Anne-Cécile Chiollaz, Edward Needham, John K. Yue, Adel Helmy, Elisa R. Zanier, Kevin K.W. Wang, Firas Kobeissy, Jussi P. Posti, Charlotte Summers, Geoffrey T. Manley, Andrew IR. Maas, Olli Tenovuo, Jean-Charles Sanchez, David K. Menon, Neeraj Badjatia, Ramon Diaz-Arrastia, Ann-Christine Duhaime, V Ramana Feeser, Shankar Gopinath, Ramesh Grandhi, Ruchira J. Ha, Dirk Keene, Christopher Madden, Michael McCrea, Randall Merchant, Laura B. Ngwenya, Richard B. Rodgers, David Schnyer, Sabrina R. Taylor, Ross Zafonte, Cecilia Ackerlund, Krisztina Amrein, Nada Andelic, Lasse Andreassen, Audny Anke, Gérard Audibert, Philippe Azouvi, Maria Luisa Azzolini, Ronald Bartels, Ronny Beer, Bo-Michael Bellander, Habib Benali, Maurizio Berardino, Luigi Beretta, Erta Beqiri, Morten Blaabjerg, Stine Borgen Lund, Camilla Brorsson, Andras Buki, Manuel Cabeleira, Alessio Caccioppola, Emiliana Calappi, Maria Rosa Calvi, Peter Cameron, Guillermo Carbayo Lozano, Marco Carbonara, Ana M. Castaño-León, Simona Cavallo, Giorgio Chevallard, Arturo Chieregato, Giuseppe Citerio, Hans Clusmann, Mark Steven Coburn, Jonathan Coles, Jamie D. Cooper, Marta Correia, Endre Czeiter, Marek Czosnyka, Claire Dahyot-Fizelier, Paul Dark, Véronique De Keyser, Vincent Degos, Francesco Della Corte, Hugo den Boogert, Bart Depreitere, Đula Đilvesi, Abhishek Dixit, Jens Dreier, Guy-Loup Dulière, Ari Ercole, Erzsébet Ezer, Martin Fabricius, Kelly Foks, Shirin Frisvold, Alex Furmanov, Damien Galanaud, Dashiell Gantner, Alexandre Ghuysen, Lelde Giga, Jagoš Golubović, Pedro A. Gomez, Benjamin Gravesteijn, Francesca Grossi, Deepak Gupta, Iain Haitsma, Raimund Helbok, Eirik Helseth, Jilske Huijben, Peter J. Hutchinson, Stefan Jankowski, Faye Johnson, Mladen Karan, Angelos G. Kolias, Daniel Kondziella, Evgenios Kornaropoulos, Lars-Owe Koskinen, Noémi Kovács, Ana Kowark, Alfonso Lagares, Steven Laureys, Fiona Lecky, Didier Ledoux, Roger Lightfoot, Hester Lingsma, Andrew I.R. Maas, Alex Manara, Hugues Maréchal, Costanza Martino, Julia Mattern, Catherine McMahon, David Menon, Tomas Menovsky, Benoit Misset, Visakh Muraleedharan, Lynnette Murray, Ancuta Negru, David Nelson, Virginia Newcombe, József Nyirádi, Fabrizio Ortolano, Jean-François Payen, Vincent Perlbarg, Paolo Persona, Wilco Peul, Anna Piippo-Karjalainen, Horia Ples, Inigo Pomposo, Louis Puybasset, Andreea Rădoi, Arminas Ragauskas, Rahul Raj, Jonathan Rhodes, Sophie Richter, Saulius Rocka, Cecilie Roe, Olav Roise, Jeffrey Rosenfeld, Christina Rosenlund, Guy Rosenthal, Rolf Rossaint, Sandra Rossi, Juan Sahuquillo, Oliver Sakowitz, Renan Sanchez-Porras, Oddrun Sandrød, Kari Schirmer-Mikalsen, Rico Frederik Schou, Charlie Sewalt, Peter Smielewski, Abayomi Sorinola, Emmanuel Stamatakis, Ewout W. Steyerberg, Nino Stocchetti, Nina Sundström, Riikka Takala, Viktória Tamás, Tomas Tamosuitis, Matt Thomas, Dick Tibboel, Christos Tolias, Tony Trapani, Cristina Maria Tudora, Andreas Unterberg, Peter Vajkoczy, Egils Valeinis, Shirley Vallance, Zoltán Vámos, Gregory Van der Steen, Jeroen T.J.M. van Dijck, Thomas A. van Essen, Roel van Wijk, Alessia Vargiolu, Emmanuel Vega, Anne Vik, Rimantas Vilcinis, Victor Volovici, Peter Vulekovic, Eveline Wiegers, Guy Williams, Stefan Winzeck, Stefan Wolf, Alexander Younsi, Frederick A. Zeiler, Agate Ziverte, and Tommaso Zoerle

Subjects
Traumatic brain injury, Stratified medicine, Inflammation, Clustering, Medicine, Medicine (General), R5-920
Abstract

Summary: Background: The inflammatory response in patients with traumatic brain injury (TBI) offers opportunities for stratification and intervention. Previous unselected approaches to immunomodulation in patients with TBI have not improved patient outcomes. Methods: Serum and plasma samples from two prospective, multi-centre observational studies of patients with TBI were used to discover (Collaborative European NeuroTrauma Effectiveness Research [CENTER-TBI], Europe) and validate (Transforming Research and Clinical Knowledge in Traumatic Brain Injury [TRACK-TBI] Pilot, USA) individual variations in the immune response using a multiplex panel of 30 inflammatory mediators. Mediators that were associated with unfavourable outcomes (Glasgow outcome score-extended [GOS-E] ≤ 4) were used for hierarchical clustering to identify patients with similar signatures. Findings: Two clusters were identified in both the discovery and validation cohorts, termed early-inflammatory and pauci-inflammatory. The early-inflammatory phenotype had higher concentrations of interleukin-6 (IL-6), IL-15, and monocyte chemoattractant protein 1 (MCP1). Patients with the early-inflammatory phenotype were older and more likely to have an unfavourable GOS-E at 6 months. There were no differences in the baseline injury severity scores between patients in each phenotype. A combined IL-15 and MCP1 signature identified patients with the early-inflammatory phenotype in both cohorts. Inflammatory processes mediated outcomes in older patients with moderate-severe TBI. Interpretation: Our findings offer a precision medicine approach for future clinical trials of immunomodulation in patients with TBI, by using inflammatory signatures to stratify patients. Funding: CENTER-TBI study was supported by the European Union 7th Framework Programme. TRACK-TBI is supported by the National Institute of Neurological Disorders and Stroke.

Published
2024
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35. Association of early blood-based biomarkers and six-month functional outcomes in conventional severity categories of traumatic brain injury: capturing the continuous spectrum of injuryResearch in context

Author

Lindsay Wilson, Virginia F.J. Newcombe, Daniel P. Whitehouse, Stefania Mondello, Andrew I.R. Maas, David K. Menon, Cecilia Ackerlund, Krisztina Amrein, Nada Andelic, Lasse Andreassen, Audny Anke, Anna Antoni, Gérard Audibert, Philippe Azouvi, Maria Luisa Azzolini, Ronald Bartels, Pál Barzó, Romuald Beauvais, Ronny Beer, Bo-Michael Bellander, Antonio Belli, Habib Benali, Maurizio Berardino, Luigi Beretta, Morten Blaabjerg, Peter Bragge, Alexandra Brazinova, Vibeke Brinck, Joanne Brooker, Camilla Brorsson, Andras Buki, Monika Bullinger, Manuel Cabeleira, Alessio Caccioppola, Emiliana Calappi, Maria Rosa Calvi, Peter Cameron, Guillermo Carbayo Lozano, Marco Carbonara, Ana M. Castaño-León, Simona Cavallo, Giorgio Chevallard, Arturo Chieregato, Giuseppe Citerio, Hans Clusmann, Mark Steven Coburn, Jonathan Coles, Jamie D. Cooper, Marta Correia, Amra Čović, Nicola Curry, Endre Czeiter, Marek Czosnyka, Claire Dahyot-Fizelier, Paul Dark, Helen Dawes, Véronique De Keyser, Vincent Degos, Francesco Della Corte, Hugo den Boogert, Bart Depreitere, Đula Đilvesi, Abhishek Dixit, Emma Donoghue, Jens Dreier, Guy-Loup Dulière, Ari Ercole, Patrick Esser, Erzsébet Ezer, Martin Fabricius, Valery L. Feigin, Kelly Foks, Shirin Frisvold, Alex Furmanov, Pablo Gagliardo, Damien Galanaud, Dashiell Gantner, Guoyi Gao, Pradeep George, Alexandre Ghuysen, Lelde Giga, Ben Glocker, Jagoš Golubović, Pedro A. Gomez, Johannes Gratz, Benjamin Gravesteijn, Francesca Grossi, Russell L. Gruen, Deepak Gupta, Juanita A. Haagsma, Iain Haitsma, Raimund Helbok, Eirik Helseth, Lindsay Horton, Jilske Huijben, Peter J. Hutchinson, Bram Jacobs, Stefan Jankowski, Mike Jarrett, Ji-yao Jiang, Faye Johnson, Kelly Jones, Mladen Karan, Angelos G. Kolias, Erwin Kompanje, Daniel Kondziella, Evgenios Kornaropoulos, Lars-Owe Koskinen, Noémi Kovács, Ana Kowark, Alfonso Lagares, Linda Lanyon, Steven Laureys, Fiona Lecky, Didier Ledoux, Rolf Lefering, Valerie Legrand, Aurelie Lejeune, Leon Levi, Roger Lightfoot, Hester Lingsma, Marc Maegele, Marek Majdan, Alex Manara, Geoffrey Manley, Hugues Maréchal, Costanza Martino, Julia Mattern, Catherine McMahon, Béla Melegh, Tomas Menovsky, Ana Mikolic, Benoit Misset, Visakh Muraleedharan, Lynnette Murray, Nandesh Nair, Ancuta Negru, David Nelson, Daan Nieboer, József Nyirádi, Matej Oresic, Fabrizio Ortolano, Olubukola Otesile, Aarno Palotie, Paul M. Parizel, Jean-François Payen, Natascha Perera, Vincent Perlbarg, Paolo Persona, Wilco Peul, Anna Piippo-Karjalainen, Matti Pirinen, Dana Pisica, Horia Ples, Suzanne Polinder, Inigo Pomposo, Jussi P. Posti, Louis Puybasset, Andreea Rădoi, Arminas Ragauskas, Rahul Raj, Malinka Rambadagalla, Isabel Retel Helmrich, Jonathan Rhodes, Sylvia Richardson, Sophie Richter, Samuli Ripatti, Saulius Rocka, Cecilie Roe, Olav Roise, Jonathan Rosand, Jeffrey Rosenfeld, Christina Rosenlund, Guy Rosenthal, Rolf Rossaint, Sandra Rossi, Daniel Rueckert, Martin Rusnák, Juan Sahuquillo, Oliver Sakowitz, Renan Sanchez-Porras, Janos Sandor, Nadine Schäfer, Silke Schmidt, Herbert Schoechl, Guus Schoonman, Rico Frederik Schou, Elisabeth Schwendenwein, Ranjit D. Singh, Charlie Sewalt, Toril Skandsen, Peter Smielewski, Abayomi Sorinola, Emmanuel Stamatakis, Simon Stanworth, Robert Stevens, William Stewart, Ewout W. Steyerberg, Nino Stocchetti, Nina Sundström, Riikka Takala, Viktória Tamás, Tomas Tamosuitis, Mark Steven Taylor, Braden Te Ao, Olli Tenovuo, Alice Theadom, Matt Thomas, Aurore Thibaut, Dick Tibboel, Marjolijn Timmers, Christos Tolias, Tony Trapani, Cristina Maria Tudora, Andreas Unterberg, Peter Vajkoczy, Egils Valeinis, Shirley Vallance, Zoltán Vámos, Mathieu van der Jagt, Joukje van der Naalt, Gregory Van der Steen, Jeroen T.J.M. van Dijck, Inge A. van Erp, Thomas A. van Essen, Wim Van Hecke, Caroline van Heugten, Dominique Van Praag, Ernest van Veen, Roel P.J. van Wijk, Thijs Vande Vyvere, Alessia Vargiolu, Emmanuel Vega, Kimberley Velt, Jan Verheyden, Paul M. Vespa, Anne Vik, Rimantas Vilcinis, Victor Volovici, Nicole von Steinbüchel, Daphne Voormolen, Peter Vulekovic, Kevin K.W. Wang, Eveline Wiegers, Guy Williams, Stefan Winzeck, Stefan Wolf, Zhihui Yang, Peter Ylén, Alexander Younsi, Frederick A. Zeiler, Veronika Zelinkova, Agate Ziverte, and Tommaso Zoerle

Subjects
Traumatic brain injury, Blood biomarkers, GFAP, NFL, UCH-L1, Outcomes, Medicine, Medicine (General), R5-920
Abstract

Summary: Background: Traumatic brain injury is conventionally categorised as mild, moderate, or severe on the Glasgow Coma Scale (GCS). Recently developed biomarkers can provide more objective and nuanced measures of the extent of brain injury. Methods: Exposure–response relationships were investigated in 2479 patients aged ≥16 enrolled in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) prospective observational cohort study. Neurofilament protein-light (NFL), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), and glial fibrillary acidic protein (GFAP) were assayed from serum sampled in the first 24 h; concentrations were divided into quintiles within GCS severity groups. Relationships with the Glasgow Outcome Scale-Extended were examined using modified Poisson regression including age, sex, major extracranial injury, time to sample, and log biomarker concentration as covariates. Findings: Within severity groups there were associations between biomarkers and outcomes after adjustment for covariates: GCS 13–15 and negative CT imaging (relative risks [RRs] from 1.28 to 3.72), GCS 13–15 and positive CT (1.21–2.81), GCS 9–12 (1.16–2.02), GCS 3–8 (1.09–1.94). RRs were associated with clinically important differences in expectations of prognosis. In patients with GCS 3 (RRs 1.51–1.80) percentages of unfavourable outcome were 37–51% in the lowest quintiles of biomarker levels and reached 90–94% in the highest quintiles. Similarly, for GCS 15 (RRs 1.83–3.79), the percentages were 2–4% and 19–28% in the lowest and highest biomarker quintiles, respectively. Interpretation: Conventional TBI severity classification is inadequate and underestimates heterogeneity of brain injury and associated outcomes. The adoption of circulating biomarkers can add to clinical assessment of injury severity. Funding: European Union 7th Framework program (EC grant 602150), Hannelore Kohl Stiftung, One Mind, Integra LifeSciences, Neuro-Trauma Sciences, NIHR Rosetrees Trust.

Published
2024
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36. Catálogo Taxonômico da Fauna do Brasil: Setting the baseline knowledge on the animal diversity in Brazil

Author

Walter A. Boeger, Michel P. Valim, Hussam Zaher, José A. Rafael, Rafaela C. Forzza, Alexandre R. Percequillo, Cristiana S. Serejo, André R.S. Garraffoni, Adalberto J. Santos, Adam Slipinski, Adelita M. Linzmeier, Adolfo R. Calor, Adrian A. Garda, Adriano B. Kury, Agatha C.S. Fernandes, Aisur I. Agudo-Padrón, Alberto Akama, Alberto M. da Silva Neto, Alejandro L. Burbano, Aleksandra Menezes, Alessandre Pereira-Colavite, Alexander Anichtchenko, Alexander C. Lees, Alexandra M.R. Bezerra, Alexandre C. Domahovski, Alexandre D. Pimenta, Alexandre L.P. Aleixo, Alexandre P. Marceniuk, Alexandre S. de Paula, Alexandre Somavilla, Alexandre Specht, Alexssandro Camargo, Alfred F. Newton, Aline A.S. da Silva, Aline B. dos Santos, Aline D. Tassi, Allan C. Aragão, Allan P.M. Santos, Alvaro E. Migotto, Amanda C. Mendes, Amanda Cunha, Amazonas Chagas Júnior, Ana A.T. de Sousa, Ana C. Pavan, Ana C.S. Almeida, Ana L.B.G. Peronti, Ana L. Henriques-Oliveira, Ana L. Prudente, Ana L. Tourinho, Ana M.O. Pes, Ana P. Carmignotto, Ana P.G. da Silva Wengrat, Ana P.S. Dornellas, Anamaria Dal Molin, Anderson Puker, André C. Morandini, André da S. Ferreira, André L. Martins, André M. Esteves, André S. Fernandes, André S. Roza, Andreas Köhler, Andressa Paladini, Andrey J. de Andrade, Ângelo P. Pinto, Anna C. de A. Salles, Anne I. Gondim, Antonia C.Z. Amaral, Antonio A.A. Rondón, Antonio Brescovit, Antônio C. Lofego, Antonio C. Marques, Antonio Macedo, Artur Andriolo, Augusto L. Henriques, Augusto L. Ferreira Júnior, Aurino F. de Lima, Ávyla R. de A. Barros, Ayrton do R. Brito, Bárbara L.V. Romera, Beatriz M.C. de Vasconcelos, Benjamin W. Frable, Bernardo F. Santos, Bernardo R. Ferraz, Brunno B. Rosa, Brunno H.L. Sampaio, Bruno C. Bellini, Bruno Clarkson, Bruno G. de Oliveira, Caio C.D. Corrêa, Caleb C. Martins, Camila F. de Castro-Guedes, Camilla Souto, Carla de L. Bicho, Carlo M. Cunha, Carlos A. de M. Barboza, Carlos A.S. de Lucena, Carlos Barreto, Carlos D.C.M. de Santana, Carlos E.Q. Agne, Carlos G.C. Mielke, Carlos H.S. Caetano, Carlos H.W. Flechtmann, Carlos J.E. Lamas, Carlos Rocha, Carolina S. Mascarenhas, Cecilia B. Margaría, Cecilia Waichert, Celina Digiani, Célio F.B. Haddad, Celso O. Azevedo, Cesar J. Benetti, Charles M.D. dos Santos, Charles R. Bartlett, Cibele Bonvicino, Cibele S. Ribeiro-Costa, Cinthya S.G. Santos, Cíntia E.L. Justino, Clarissa Canedo, Claudia C. Bonecker, Cláudia P. Santos, Claudio J.B. de Carvalho, Clayton C. Gonçalves, Cleber Galvão, Cleide Costa, Cléo D.C. de Oliveira, Cristiano F. Schwertner, Cristiano L. Andrade, Cristiano M. Pereira, Cristiano Sampaio, Cristina de O. Dias, Daercio A. de A. Lucena, Daiara Manfio, Dalton de S. Amorim, Dalva L. de Queiroz, Daniara Colpani, Daniel Abbate, Daniel A. Aquino, Daniel Burckhardt, Daniel C. Cavallari, Daniel de C. Schelesky Prado, Daniel L. Praciano, Daniel S. Basílio, Daniela de C. Bená, Daniela G.P. de Toledo, Daniela M. Takiya, Daniell R.R. Fernandes, Danilo C. Ament, Danilo P. Cordeiro, Darliane E. Silva, Darren A. Pollock, David B. Muniz, David I. Gibson, David S. Nogueira, Dayse W.A. Marques, Débora Lucatelli, Deivys M.A. Garcia, Délio Baêta, Denise N.M. Ferreira, Diana Rueda-Ramírez, Diego A. Fachin, Diego de S. Souza, Diego F. Rodrigues, Diego G. de Pádua, Diego N. Barbosa, Diego R. Dolibaina, Diogo C. Amaral, Donald S. Chandler, Douglas H.B. Maccagnan, Edilson Caron, Edrielly Carvalho, Edson A. Adriano, Edson F. de Abreu Júnior, Edson H.L. Pereira, Eduarda F.G. Viegas, Eduardo Carneiro, Eduardo Colley, Eduardo Eizirik, Eduardo F. dos Santos, Eduardo M. Shimbori, Eduardo Suárez-Morales, Eliane P. de Arruda, Elisandra A. Chiquito, Élison F.B. Lima, Elizeu B. de Castro, Elton Orlandin, Elynton A. do Nascimento, Emanuel Razzolini, Emanuel R.R. Gama, Enilma M. de Araujo, Eric Y. Nishiyama, Erich L. Spiessberger, Érika C.L. dos Santos, Eugenia F. Contreras, Eunice A.B. Galati, Evaldo C. de Oliveira Junior, Fabiana Gallardo, Fabio A. Hernandes, Fábio A. Lansac-Tôha, Fabio B. Pitombo, Fabio Di Dario, Fábio L. dos Santos, Fabio Mauro, Fabio O. do Nascimento, Fabio Olmos, Fabio R. Amaral, Fabio Schunck, Fábio S. P. de Godoi, Fabrizio M. Machado, Fausto E. Barbo, Federico A. Agrain, Felipe B. Ribeiro, Felipe F.F. Moreira, Felipe F. Barbosa, Fenanda S. Silva, Fernanda F. Cavalcanti, Fernando C. Straube, Fernando Carbayo, Fernando Carvalho Filho, Fernando C.V. Zanella, Fernando de C. Jacinavicius, Fernando H.A. Farache, Fernando Leivas, Fernando M.S. Dias, Fernando Mantellato, Fernando Z. Vaz-de-Mello, Filipe M. Gudin, Flávio Albuquerque, Flavio B. Molina, Flávio D. Passos, Floyd W. Shockley, Francielly F. Pinheiro, Francisco de A.G. de Mello, Francisco E. de L. Nascimento, Francisco L. Franco, Francisco L. de Oliveira, Francisco T. de V. Melo, Freddy R.B. Quijano, Frederico F. Salles, Gabriel Biffi, Gabriel C. Queiroz, Gabriel L. Bizarro, Gabriela Hrycyna, Gabriela Leviski, Gareth S. Powell, Geane B. dos Santos, Geoffrey E. Morse, George Brown, George M.T. Mattox, Geraldo Zimbrão, Gervásio S. Carvalho, Gil F.G. Miranda, Gilberto J. de Moraes, Gilcélia M. Lourido, Gilmar P. Neves, Gilson R.P. Moreira, Giovanna G. Montingelli, Giovanni N. Maurício, Gláucia Marconato, Guilherme E.L. Lopez, Guilherme L. da Silva, Guilherme Muricy, Guilherme R.R. Brito, Guilherme S.T. Garbino, Gustavo E. Flores, Gustavo Graciolli, Gustavo S. Libardi, Heather C. Proctor, Helcio R. Gil-Santana, Henrique R. Varella, Hermes E. Escalona, Hermes J. Schmitz, Higor D.D. Rodrigues, Hilton de C. Galvão Filho, Hingrid Y.S. Quintino, Hudson A. Pinto, Hugo L. Rainho, Igor C. Miyahira, Igor de S. Gonçalves, Inês X. Martins, Irene A. Cardoso, Ismael B. de Oliveira, Ismael Franz, Itanna O. Fernandes, Ivan F. Golfetti, Ivanklin S. Campos-Filho, Ivo de S. Oliveira, Jacques H.C. Delabie, Jader de Oliveira, Jadila S. Prando, James L. Patton, Jamille de A. Bitencourt, Janaina M. Silva, Jandir C. Santos, Janine O. Arruda, Jefferson S. Valderrama, Jeronymo Dalapicolla, Jéssica P. Oliveira, Jiri Hájek, João P. Morselli, João P. Narita, João P.I. Martin, Jocélia Grazia, Joe McHugh, Jorge J. Cherem, José A.S. Farias Júnior, Jose A.M. Fernandes, José F. Pacheco, José L.O. Birindelli, José M. Rezende, Jose M. Avendaño, José M. Barbanti Duarte, José R. Inácio Ribeiro, José R.M. Mermudes, José R. Pujol-Luz, Josenilson R. dos Santos, Josenir T. Câmara, Joyce A. Teixeira, Joyce R. do Prado, Juan P. Botero, Julia C. Almeida, Julia Kohler, Julia P. Gonçalves, Julia S. Beneti, Julian P. Donahue, Juliana Alvim, Juliana C. Almeida, Juliana L. Segadilha, Juliana M. Wingert, Julianna F. Barbosa, Juliano Ferrer, Juliano F. dos Santos, Kamila M.D. Kuabara, Karine B. Nascimento, Karine Schoeninger, Karla M. Campião, Karla Soares, Kássia Zilch, Kim R. Barão, Larissa Teixeira, Laura D. do N.M. de Sousa, Leandro L. Dumas, Leandro M. Vieira, Leonardo H.G. Azevedo, Leonardo S. Carvalho, Leonardo S. de Souza, Leonardo S.G. Rocha, Leopoldo F.O. Bernardi, Letícia M. Vieira, Liana Johann, Lidianne Salvatierra, Livia de M. Oliveira, Lourdes M.A. El-moor Loureiro, Luana B. Barreto, Luana M. Barros, Lucas Lecci, Lucas M. de Camargos, Lucas R.C. Lima, Lucia M. Almeida, Luciana R. Martins, Luciane Marinoni, Luciano de A. Moura, Luciano Lima, Luciano N. Naka, Lucília S. Miranda, Lucy M. Salik, Luis E.A. Bezerra, Luis F. Silveira, Luiz A. Campos, Luiz A.S. de Castro, Luiz C. Pinho, Luiz F.L. Silveira, Luiz F.M. Iniesta, Luiz F.C. Tencatt, Luiz R.L. Simone, Luiz R. Malabarba, Luiza S. da Cruz, Lukas Sekerka, Lurdiana D. Barros, Luziany Q. Santos, Maciej Skoracki, Maira A. Correia, Manoel A. Uchoa, Manuella F.G. Andrade, Marcel G. Hermes, Marcel S. Miranda, Marcel S. de Araújo, Marcela L. Monné, Marcelo B. Labruna, Marcelo D. de Santis, Marcelo Duarte, Marcelo Knoff, Marcelo Nogueira, Marcelo R. de Britto, Marcelo R.S. de Melo, Marcelo R. de Carvalho, Marcelo T. Tavares, Marcelo V. Kitahara, Marcia C.N. Justo, Marcia J.C. Botelho, Márcia S. Couri, Márcio Borges-Martins, Márcio Felix, Marcio L. de Oliveira, Marco A. Bologna, Marco S. Gottschalk, Marcos D.S. Tavares, Marcos G. Lhano, Marcus Bevilaqua, Marcus T.T. Santos, Marcus V. Domingues, Maria A.M. Sallum, María C. Digiani, Maria C.A. Santarém, Maria C. do Nascimento, María de los A.M. Becerril, Maria E.A. dos Santos, Maria I. da S. dos Passos, Maria L. Felippe-Bauer, Mariana A. Cherman, Mariana Terossi, Marie L.C. Bartz, Marina F. de C. Barbosa, Marina V. Loeb, Mario Cohn-Haft, Mario Cupello, Marlúcia B. Martins, Martin L. Christofersen, Matheus Bento, Matheus dos S. Rocha, Maurício L. Martins, Melissa O. Segura, Melissa Q. Cardenas, Mércia E. Duarte, Michael A. Ivie, Michael M. Mincarone, Michela Borges, Miguel A. Monné, Mirna M. Casagrande, Monica A. Fernandez, Mônica Piovesan, Naércio A. Menezes, Natalia P. Benaim, Natália S. Reategui, Natan C. Pedro, Nathalia H. Pecly, Nelson Ferreira Júnior, Nelson J. da Silva Júnior, Nelson W. Perioto, Neusa Hamada, Nicolas Degallier, Ning L. Chao, Noeli J. Ferla, Olaf H.H. Mielke, Olivia Evangelista, Oscar A. Shibatta, Otto M.P. Oliveira, Pablo C.L. Albornoz, Pablo M. Dellapé, Pablo R. Gonçalves, Paloma H.F. Shimabukuro, Paschoal Grossi, Patrícia E. da S. Rodrigues, Patricia O.V. Lima, Paul Velazco, Paula B. dos Santos, Paula B. Araújo, Paula K.R. Silva, Paula R. Riccardi, Paulo C. de A. Garcia, Paulo G.H. Passos, Paulo H.C. Corgosinho, Paulo Lucinda, Paulo M.S. Costa, Paulo P. Alves, Paulo R. de O. Roth, Paulo R.S. Coelho, Paulo R.M. Duarte, Pedro F. de Carvalho, Pedro Gnaspini, Pedro G.B. Souza-Dias, Pedro M. Linardi, Pedro R. Bartholomay, Peterson R. Demite, Petr Bulirsch, Piter K. Boll, Rachel M.M. Pereira, Rafael A.P.F. Silva, Rafael B. de Moura, Rafael Boldrini, Rafaela A. da Silva, Rafaela L. Falaschi, Ralf T.S. Cordeiro, Ramon J.C.L. Mello, Randal A. Singer, Ranyse B. Querino, Raphael A. Heleodoro, Raphael de C. Castilho, Reginaldo Constantino, Reinaldo C. Guedes, Renan Carrenho, Renata S. Gomes, Renato Gregorin, Renato J.P. Machado, Renato S. Bérnils, Renato S. Capellari, Ricardo B. Silva, Ricardo Kawada, Ricardo M. Dias, Ricardo Siewert, Ricaro Brugnera, Richard A.B. Leschen, Robert Constantin, Robert Robbins, Roberta R. Pinto, Roberto E. dos Reis, Robson T. da C. Ramos, Rodney R. Cavichioli, Rodolfo C. de Barros, Rodrigo A. Caires, Rodrigo B. Salvador, Rodrigo C. Marques, Rodrigo C. Araújo, Rodrigo de O. Araujo, Rodrigo de V.P. Dios, Rodrigo Johnsson, Rodrigo M. Feitosa, Roger W. Hutchings, Rogéria I.R. Lara, Rogério V. Rossi, Roland Gerstmeier, Ronald Ochoa, Rosa S.G. Hutchings, Rosaly Ale-Rocha, Rosana M. da Rocha, Rosana Tidon, Rosangela Brito, Roseli Pellens, Sabrina R. dos Santos, Sandra D. dos Santos, Sandra V. Paiva, Sandro Santos, Sarah S. de Oliveira, Sávio C. Costa, Scott L. Gardner, Sebastián A. Muñoz Leal, Sergio Aloquio, Sergio L.C. Bonecker, Sergio L. de S. Bueno, Sérgio M. de Almeida, Sérgio N. Stampar, Sérgio R. Andena, Sergio R. Posso, Sheila P. Lima, Sian de S. Gadelha, Silvana C. Thiengo, Simone C. Cohen, Simone N. Brandão, Simone P. Rosa, Síria L.B. Ribeiro, Sócrates D. Letana, Sonia B. dos Santos, Sonia C.S. Andrade, Stephane Dávila, Stéphanie Vaz, Stewart B. Peck, Susete W. Christo, Suzan B.Z. Cunha, Suzete R. Gomes, Tácio Duarte, Taís Madeira-Ott, Taísa Marques, Talita Roell, Tarcilla C. de Lima, Tatiana A. Sepulveda, Tatiana F. Maria, Tatiana P. Ruschel, Thaiana Rodrigues, Thais A. Marinho, Thaís M. de Almeida, Thaís P. Miranda, Thales R.O. Freitas, Thalles P.L. Pereira, Thamara Zacca, Thaynara L. Pacheco, Thiago F. Martins, Thiago M. Alvarenga, Thiago R. de Carvalho, Thiago T.S. Polizei, Thomas C. McElrath, Thomas Henry, Tiago G. Pikart, Tiago J. Porto, Tiago K. Krolow, Tiago P. Carvalho, Tito M. da C. Lotufo, Ulisses Caramaschi, Ulisses dos S. Pinheiro, Ulyses F.J. Pardiñas, Valéria C. Maia, Valeria Tavares, Valmir A. Costa, Vanessa S. do Amaral, Vera C. Silva, Vera R. dos S. Wolff, Verônica Slobodian, Vinícius B. da Silva, Vinicius C. Espíndola, Vinicius da Costa-Silva, Vinicius de A. Bertaco, Vinícius Padula, Vinicius S. Ferreira, Vitor C.P. da Silva, Vítor de Q. Piacentini, Vivian E. Sandoval-Gómez, Vivian Trevine, Viviane R. Sousa, Vivianne B. de Sant’Anna, Wayne N. Mathis, Wesley de O. Souza, Wesley D. Colombo, Wioletta Tomaszewska, Wolmar B. Wosiacki, Ximena M.C. Ovando, and Yuri L.R. Leite

Subjects
Biodiversity, knowledge management, taxonomy, web services, zoology, Zoology, QL1-991
Abstract

ABSTRACT The limited temporal completeness and taxonomic accuracy of species lists, made available in a traditional manner in scientific publications, has always represented a problem. These lists are invariably limited to a few taxonomic groups and do not represent up-to-date knowledge of all species and classifications. In this context, the Brazilian megadiverse fauna is no exception, and the Catálogo Taxonômico da Fauna do Brasil (CTFB) (http://fauna.jbrj.gov.br/), made public in 2015, represents a database on biodiversity anchored on a list of valid and expertly recognized scientific names of animals in Brazil. The CTFB is updated in near real time by a team of more than 800 specialists. By January 1, 2024, the CTFB compiled 133,691 nominal species, with 125,138 that were considered valid. Most of the valid species were arthropods (82.3%, with more than 102,000 species) and chordates (7.69%, with over 11,000 species). These taxa were followed by a cluster composed of Mollusca (3,567 species), Platyhelminthes (2,292 species), Annelida (1,833 species), and Nematoda (1,447 species). All remaining groups had less than 1,000 species reported in Brazil, with Cnidaria (831 species), Porifera (628 species), Rotifera (606 species), and Bryozoa (520 species) representing those with more than 500 species. Analysis of the CTFB database can facilitate and direct efforts towards the discovery of new species in Brazil, but it is also fundamental in providing the best available list of valid nominal species to users, including those in science, health, conservation efforts, and any initiative involving animals. The importance of the CTFB is evidenced by the elevated number of citations in the scientific literature in diverse areas of biology, law, anthropology, education, forensic science, and veterinary science, among others.

Published
2024
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38. Chest X-Ray–Based Telemedicine Platform for Pediatric Tuberculosis Diagnosis in Low-Resource Settings: Development and Validation Study

Author

Juan J Gómez-Valverde, Ramón Sánchez-Jacob, José Luis Ribó, H Simon Schaaf, Lara García Delgado, Alicia Hernanz-Lobo, Daniel Capellán-Martín, Ángel Lancharro, Orvalho Augusto, Alberto L García-Basteiro, Begoña Santiago-García, Elisa López-Varela, and María J Ledesma-Carbayo

Subjects
Pediatrics, RJ1-570
Abstract

BackgroundTuberculosis (TB) remains a major cause of morbidity and death worldwide, with a significant impact on children, especially those under the age of 5 years. The complex diagnosis of pediatric TB, compounded by limited access to more accurate diagnostic tests, underscores the need for improved tools to enhance diagnosis and care in resource-limited settings. ObjectiveThis study aims to present a telemedicine web platform, BITScreen PTB (Biomedical Image Technologies Screen for Pediatric Tuberculosis), aimed at improving the evaluation of pulmonary TB in children based on digital chest x-ray (CXR) imaging and clinical information in resource-limited settings. MethodsThe platform was evaluated by 3 independent expert readers through a retrospective assessment of a data set with 218 imaging examinations of children under 3 years of age, selected from a previous study performed in Mozambique. The key aspects assessed were the usability through a standardized questionnaire, the time needed to complete the assessment through the platform, the performance of the readers to identify TB cases based on the CXR, the association between the TB features identified in the CXRs and the initial diagnostic classification, and the interreader agreement of the global assessment and the radiological findings. ResultsThe platform’s usability and user satisfaction were evaluated using a questionnaire, which received an average rating of 4.4 (SD 0.59) out of 5. The average examination completion time ranged from 35 to 110 seconds. In addition, the study on CXR showed low sensitivity (16.3%-28.2%) but high specificity (91.1%-98.2%) in the assessment of the consensus case definition of pediatric TB using the platform. The CXR finding having a stronger association with the initial diagnostic classification was air space opacification (χ21>20.38, P

Published
2024
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39. Tracheal agenesis: the importance of teamwork in an uncommon pathology, challenging diagnosis, and high mortality—a case report

Author

Belén Fernández Monteagudo, Salvador Piris Borregas, Lidia Niño Díaz, Tania Carbayo Jiménez, Rocío Morante Valverde, Jesús Vicente Redondo Sedano, and Maria Teresa Moral Pumarega

Subjects
tracheal agenesis, respiratory distress, tracheoesophageal fistula, single umbilical artery, polyhydramnios, Pediatrics, RJ1-570
Abstract

IntroductionWe present the case of a patient with an unexpected postnatal diagnosis of tracheal agenesis, a severe and rare pathology with fewer than 200 cases documented in the literature, typically diagnosed postmortem. In our instance, early diagnosis was achieved through collaborative efforts and teamwork among various professionals. We provide illustrative images and videos to assist colleagues in identifying this congenital anomaly.Case presentationThe patient is a term newborn with prenatal indicators of polyhydramnios and a single umbilical artery. Upon birth, the infant exhibited severe respiratory distress, and orotracheal intubation via direct laryngoscopy was unfeasible. Consequently, an urgent fibrobronchoscopy, conducted by pediatric surgeons, led to the diagnosis of tracheal agenesis with tracheoesophageal fistula and the placement of a directed endotracheal tube. This intervention facilitated temporary ventilation until parental consensus on management was achieved. Following a multidisciplinary consultation, the decision was made to proceed with extracorporeal membrane oxygenation. Unfortunately, the patient experienced a prolonged refractory cardiorespiratory arrest and died after 7 h of life in his mother’s arms.ConclusionTeamwork in neonatology is indispensable when addressing emergent pathologies. In our experience, multidisciplinary management, including anesthesiologists and pediatric surgeons, should be contemplated in complex scenarios.

Published
2024
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40. Validating the Spanish translation of the posttraumatic stress disorder checklist (PCL-5) in a sample of individuals with traumatic brain injury

Author

E. F. Haghish, Juan Sahuquillo, Andreea Radoi, Ingio Pomposo, and Guillermo Carbayo Lozano

Subjects
post-traumatic stress disorder, traumatic brain injury, PCL-5, Spanish, validation, Psychology, BF1-990
Abstract

IntroductionThere is controversy regarding the comorbidity of posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI). The present study translated the PTSD Checklist for DSM-5 (PCL-5) to Spanish and validated it in a sample of patients with TBI 6 months after the injury.MethodsThe study included 233 patients (162 males and 71 females) recruited from four Spanish hospitals within 24 h of traumatic brain injury. A total of 12.2% of the sample met the provisional PTSD diagnostic criteria, and the prevalence was equal between male and female participants.ResultsThe analysis confirmed the internal consistency of the translated instrument (α = 0.95). The concurrent validity of the instrument was confirmed based on high correlation coefficients of 0.7 and 0.74 with the General Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire (PHQ-9), respectively. Exploratory factor analysis also confirmed that the items on the PCL-5 can be differentiated from the GAD-7 and PHQ-9 items. Confirmatory factor analysis (CFA) was used to examine the structural validity of the Spanish translation of the PCL-5 with three different models. CFA partially confirmed the four-factor PTSD model, whereas both the six-factor anhedonia model and the seven-factor hybrid model showed adequate fit. However, the difference between the anhedonia and hybrid models was not statistically significant; moreover, both models showed signs of overfitting. Therefore, the utility of these models should be reexamined in future studies.ConclusionOverall, the results suggest that the Spanish translation of the PCL-5 is a reliable and valid instrument for screening PTSD symptoms among Spanish TBI patients. The Spanish translation of the PCL-5 is also presented in the manuscript.

Published
2024
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41. Fetal MRI by robust deep generative prior reconstruction and diffeomorphic registration: application to gestational age prediction

Author

Cordero-Grande, Lucilio, Ortuño-Fisac, Juan Enrique, Uus, Alena, Deprez, Maria, Santos, Andrés, Hajnal, Joseph V., and Ledesma-Carbayo, María Jesús

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Physics - Medical Physics, 92C50, 94A08, I.2.1, J.3
Abstract

Magnetic resonance imaging of whole fetal body and placenta is limited by different sources of motion affecting the womb. Usual scanning techniques employ single-shot multi-slice sequences where anatomical information in different slices may be subject to different deformations, contrast variations or artifacts. Volumetric reconstruction formulations have been proposed to correct for these factors, but they must accommodate a non-homogeneous and non-isotropic sampling, so regularization becomes necessary. Thus, in this paper we propose a deep generative prior for robust volumetric reconstructions integrated with a diffeomorphic volume to slice registration method. Experiments are performed to validate our contributions and compare with a state of the art method in a cohort of $72$ fetal datasets in the range of $20-36$ weeks gestational age. Results suggest improved image resolution and more accurate prediction of gestational age at scan when comparing to a state of the art reconstruction method. In addition, gestational age prediction results from our volumetric reconstructions compare favourably with existing brain-based approaches, with boosted accuracy when integrating information of organs other than the brain. Namely, a mean absolute error of $0.618$ weeks ($R^2=0.958$) is achieved when combining fetal brain and trunk information., Comment: 23 pages, 15 figures, 1 table

Published
2021

43. A genome-wide association study of outcome from traumatic brain injury

Author

Kals, Mart, Kunzmann, Kevin, Parodi, Livia, Radmanesh, Farid, Wilson, Lindsay, Izzy, Saef, Anderson, Christopher D, Puccio, Ava M, Okonkwo, David O, Temkin, Nancy, Steyerberg, Ewout W, Stein, Murray B, Manley, Geoff T, Maas, Andrew IR, Richardson, Sylvia, Diaz-Arrastia, Ramon, Palotie, Aarno, Ripatti, Samuli, Rosand, Jonathan, Menon, David K, Åkerlund, Cecilia, Amrein, Krisztina, Andelic, Nada, Andreassen, Lasse, Anke, Audny, Antoni, Anna, Audibert, Gérard, Azouvi, Philippe, Azzolini, Maria Luisa, Bartels, Ronald, Barzó, Pál, Beauvais, Romuald, Beer, Ronny, Bellander, Bo-Michael, Belli, Antonio, Benali, Habib, Berardino, Maurizio, Beretta, Luigi, Blaabjerg, Morten, Bragge, Peter, Brazinova, Alexandra, Brinck, Vibeke, Brooker, Joanne, Brorsson, Camilla, Buki, Andras, Bullinger, Monika, Cabeleira, Manuel, Caccioppola, Alessio, Calappi, Emiliana, Calvi, Maria Rosa, Cameron, Peter, Lozano, Guillermo Carbayo, Carbonara, Marco, Cavallo, Simona, Chevallard, Giorgio, Chieregato, Arturo, Citerio, Giuseppe, Clusmann, Hans, Coburn, Mark, Coles, Jonathan P, Cooper, Jamie D, Correia, Marta, Čović, Amra, Curry, Nicola, Czeiter, Endre, Czosnyka, Marek, DahyotFizelier, Claire, Dark, Paul, Dawes, Helen, De Keyser, Véronique, Degos, Vincent, Corte, Francesco Della, Boogert, Hugo den, Depreitere, Bart, Đilvesi, Đula, Dixit, Abhishek, Donoghue, Emma, Dreier, Jens, Dulière, GuyLoup, Ercole, Ari, Esser, Patrick, Ezer, Erzsébet, Fabricius, Martin, Feigin, Valery L, Foks, Kelly, Frisvold, Shirin, Furmanov, Alex, Gagliardo, Pablo, Galanaud, Damien, Gantner, Dashiell, Gao, Guoyi, George, Pradeep, Ghuysen, Alexandre, Giga, Lelde, Glocker, Ben, Golubovic, Jagoš, Gomez, Pedro A, Gratz, Johannes, Gravesteijn, Benjamin, and Grossi, Francesca

Subjects
Epidemiology, Health Sciences, Traumatic Head and Spine Injury, Genetics, Physical Injury - Accidents and Adverse Effects, Neurosciences, Brain Disorders, Human Genome, Traumatic Brain Injury (TBI), Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Brain Injuries, Traumatic, Genome-Wide Association Study, Humans, Mannose-Binding Lectin, Prospective Studies, Transcriptome, Traumatic brain injury, Genome-Wide association study, Outcome, Recovery, Consortia, Genetic Associations In Neurotrauma (GAIN) Consortium, Clinical Sciences, Public Health and Health Services, Clinical sciences
Abstract

BackgroundFactors such as age, pre-injury health, and injury severity, account for less than 35% of outcome variability in traumatic brain injury (TBI). While some residual outcome variability may be attributable to genetic factors, published candidate gene association studies have often been underpowered and subject to publication bias.MethodsWe performed the first genome- and transcriptome-wide association studies (GWAS, TWAS) of genetic effects on outcome in TBI. The study population consisted of 5268 patients from prospective European and US studies, who attended hospital within 24 h of TBI, and satisfied local protocols for computed tomography.FindingsThe estimated heritability of TBI outcome was 0·26. GWAS revealed no genetic variants with genome-wide significance (p < 5 × 10-8), but identified 83 variants in 13 independent loci which met a lower pre-specified sub-genomic statistical threshold (p < 10-5). Similarly, none of the genes tested in TWAS met tissue-wide significance. An exploratory analysis of 75 published candidate variants associated with 28 genes revealed one replicable variant (rs1800450 in the MBL2 gene) which retained significance after correction for multiple comparison (p = 5·24 × 10-4).InterpretationWhile multiple novel loci reached less stringent thresholds, none achieved genome-wide significance. The overall heritability estimate, however, is consistent with the hypothesis that common genetic variation substantially contributes to inter-individual variability in TBI outcome. The meta-analytic approach to the GWAS and the availability of summary data allows for a continuous extension with additional cohorts as data becomes available.FundingA full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Published
2022

44. Epidemiology and clinical features of Streptococcus pyogenes bloodstream infections in children in Madrid, Spain

Author

Cobo-Vázquez, Elvira, Aguilera-Alonso, David, Carbayo, Tania, Figueroa-Ospina, Lucía M, Sanz-Santaeufemia, Francisco, Baquero-Artigao, Fernando, Vázquez-Ordoñez, Carmen, Carrasco-Colom, Jaime, Blázquez-Gamero, Daniel, Jiménez-Montero, Beatriz, Grasa-Lozano, Carlos, Cilleruelo, María José, Álvarez, Ana, Comín-Cabrera, Cristina, Penin, María, Cercenado, Emilia, Del Valle, Rut, Roa, Miguel Ángel, Diego, Irene García-De, Calvo, Cristina, and Saavedra-Lozano, Jesús

Published
2023
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45. A new approach to prostate cancer screening

Author

Filella Xavier, González Álvaro, Augé Josep Maria, Barco Antonio, Carbonell Rosa, Gaspar María Jesús, Martínez-Peinado Antonio, Barrios Clara Pérez, Sánchez-Carbayo Marta, Santotoribio José Diego, and Trapé Jaume

Subjects
prostate cancer, psa, screening, Medical technology, R855-855.5
Abstract

Prostate cancer screening based on prostate-specific antigen (PSA) testing has been a matter of controversy. Although screening for prostate cancer was effective in reducing mortality, it resulted in overdiagnosis, which translated into unnecessary treatments and numerous adverse effects. As a result, recommendations from scientific societies became increasingly restrictive. In the recent years, new approaches to prostate cancer screening have been proposed. These new approaches are aimed at solving the controversy between widespread screening vs. no screening, and reconsidering PSA testing as a screening tool with a good benefit/risk balance. In this context, the European Association of Urology submitted a proposal to the European Commission for prostate cancer screening to be performed as a function of baseline PSA concentrations. The European Commission recently recommended the implementation of organized prostate cancer screening programs for men aged ≤70 years based on PSA values in combination with follow-up magnetic resonance imaging.

Published
2023
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46. Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy.

Author

Lin Lin, Elena Dacal, Nuria Díez, Claudia Carmona, Alexandra Martin Ramirez, Lourdes Barón Argos, David Bermejo-Peláez, Carla Caballero, Daniel Cuadrado, Oscar Darias-Plasencia, Jaime García-Villena, Alexander Bakardjiev, Maria Postigo, Ethan Recalde-Jaramillo, Maria Flores-Chavez, Andrés Santos, María Jesús Ledesma-Carbayo, José M Rubio, and Miguel Luengo-Oroz

Subjects
Arctic medicine. Tropical medicine, RC955-962, Public aspects of medicine, RA1-1270
Abstract

Filariasis, a neglected tropical disease caused by roundworms, is a significant public health concern in many tropical countries. Microscopic examination of blood samples can detect and differentiate parasite species, but it is time consuming and requires expert microscopists, a resource that is not always available. In this context, artificial intelligence (AI) can assist in the diagnosis of this disease by automatically detecting and differentiating microfilariae. In line with the target product profile for lymphatic filariasis as defined by the World Health Organization, we developed an edge AI system running on a smartphone whose camera is aligned with the ocular of an optical microscope that detects and differentiates filarias species in real time without the internet connection. Our object detection algorithm that uses the Single-Shot Detection (SSD) MobileNet V2 detection model was developed with 115 cases, 85 cases with 1903 fields of view and 3342 labels for model training, and 30 cases with 484 fields of view and 873 labels for model validation before clinical validation, is able to detect microfilariae at 10x magnification and distinguishes four species of them at 40x magnification: Loa loa, Mansonella perstans, Wuchereria bancrofti, and Brugia malayi. We validated our augmented microscopy system in the clinical environment by replicating the diagnostic workflow encompassed examinations at 10x and 40x with the assistance of the AI models analyzing 18 samples with the AI running on a middle range smartphone. It achieved an overall precision of 94.14%, recall of 91.90% and F1 score of 93.01% for the screening algorithm and 95.46%, 97.81% and 96.62% for the species differentiation algorithm respectively. This innovative solution has the potential to support filariasis diagnosis and monitoring, particularly in resource-limited settings where access to expert technicians and laboratory equipment is scarce.

Published
2024
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47. Computer Aided Detection for Pulmonary Embolism Challenge (CAD-PE)

Author

González, Germán, Jimenez-Carretero, Daniel, Rodríguez-López, Sara, Cano-Espinosa, Carlos, Cazorla, Miguel, Agarwal, Tanya, Agarwal, Vinit, Tajbakhsh, Nima, Gotway, Michael B., Liang, Jianming, Masoudi, Mojtaba, Eftekhari, Noushin, Saadatmand, Mahdi, Pourreza, Hamid-Reza, Fraga-Rivas, Patricia, Fraile, Eduardo, Rybicki, Frank J., Kassarjian, Ara, Estépar, Raúl San José, and Ledesma-Carbayo, Maria J.

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

Rationale: Computer aided detection (CAD) algorithms for Pulmonary Embolism (PE) algorithms have been shown to increase radiologists' sensitivity with a small increase in specificity. However, CAD for PE has not been adopted into clinical practice, likely because of the high number of false positives current CAD software produces. Objective: To generate a database of annotated computed tomography pulmonary angiographies, use it to compare the sensitivity and false positive rate of current algorithms and to develop new methods that improve such metrics. Methods: 91 Computed tomography pulmonary angiography scans were annotated by at least one radiologist by segmenting all pulmonary emboli visible on the study. 20 annotated CTPAs were open to the public in the form of a medical image analysis challenge. 20 more were kept for evaluation purposes. 51 were made available post-challenge. 8 submissions, 6 of them novel, were evaluated on the 20 evaluation CTPAs. Performance was measured as per embolus sensitivity vs. false positives per scan curve. Results: The best algorithms achieved a per-embolus sensitivity of 75% at 2 false positives per scan (fps) or of 70% at 1 fps, outperforming the state of the art. Deep learning approaches outperformed traditional machine learning ones, and their performance improved with the number of training cases. Significance: Through this work and challenge we have improved the state-of-the art of computer aided detection algorithms for pulmonary embolism. An open database and an evaluation benchmark for such algorithms have been generated, easing the development of further improvements. Implications on clinical practice will need further research., Comment: 8 pages, 3 figures

Published
2020

48. Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients

Author

Farina, Benito, Guerra, Ana Delia Ramos, Bermejo-Peláez, David, Miras, Carmelo Palacios, Peral, Andrés Alcazar, Madueño, Guillermo Gallardo, Jaime, Jesús Corral, Vilalta-Lacarra, Anna, Pérez, Jaime Rubio, Muñoz-Barrutia, Arrate, Peces-Barba, German R., Maceiras, Luis Seijo, Gil-Bazo, Ignacio, Gómez, Manuel Dómine, and Ledesma-Carbayo, María J.

Published
2023
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49. Pathological Computed Tomography Features Associated With Adverse Outcomes After Mild Traumatic Brain Injury

Author

Yuh, Esther L, Jain, Sonia, Sun, Xiaoying, Pisică, Dana, Harris, Mark H, Taylor, Sabrina R, Markowitz, Amy J, Mukherjee, Pratik, Verheyden, Jan, Giacino, Joseph T, Levin, Harvey S, McCrea, Michael, Stein, Murray B, Temkin, Nancy R, Diaz-Arrastia, Ramon, Robertson, Claudia S, Lingsma, Hester F, Okonkwo, David O, Maas, Andrew IR, Manley, Geoffrey T, Amrein, Krisztina, Andelic, Nada, Andreassen, Lasse, Anke, Audny, Antoni, Anna, Audibert, Gérard, Azouvi, Philippe, Azzolini, Maria Luisa, Bartels, Ronald, Barzó, Pál, Beauvais, Romuald, Beer, Ronny, Bellander, Bo-Michael, Belli, Antonio, Benal, Habib, Berardino, Maurizio, Beretta, Luigi, Blaabjerg, Morten, Bragge, Peter, Brazinova, Alexandra, Brinck, Vibeke, Brooker, Joanne, Brorsson, Camilla, Buki, Andras, Bullinger, Monika, Cabeleira, Manuel, Caccioppola, Alessio, Calappi, Emiliana, Calvi, Maria Rosa, Cameron, Peter, Carbayo Lozano, Guillermo, Carbonara, Marco, Castaño‑León, Ana M, Cavallo, Simona, Chevallard, Giorgio, Chieregato, Arturo, Citerio, Giuseppe, Clusmann, Hans, Coburn, Mark, Coles, Jonathan, Cooper, Jamie D, Correia, Marta, Čović, Amra, Curry, Nicola, Czeiter, Endre, Czosnyka, Marek, Dahyot‑Fizelier, Claire, Dark, Paul, Dawes, Helen, De Keyser, Véronique, Degos, Vincent, Della Corte, Francesco, den Boogert, Hugo, Depreitere, Bart, Đilvesi, Đula, Dixit, Abhishek, Donoghue, Emma, Dreier, Jens, Dulière, Guy‑Loup, Ercole, Ari, Esser, Patrick, Ezer, Erzsébet, Fabricius, Martin, Feigin, Valery L, Foks, Kelly, Frisvold, Shirin, Furmanov, Alex, Gagliardo, Pablo, Galanaud, Damien, Gantner, Dashiell, Gao, Guoyi, George, Pradeep, Ghuysen, Alexandre, Giga, Lelde, Glocker, Ben, Golubovic, Jagoš, Gomez, Pedro A, Gratz, Johannes, Gravesteijn, Benjamin, and Grossi, Francesca

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Traumatic Head and Spine Injury, Biomedical Imaging, Physical Injury - Accidents and Adverse Effects, Traumatic Brain Injury (TBI), Brain Disorders, Cerebrovascular, 4.2 Evaluation of markers and technologies, Injuries and accidents, Good Health and Well Being, Adult, Aged, Brain Concussion, Cohort Studies, Female, Humans, Intracranial Hemorrhages, Male, Middle Aged, Prognosis, Recovery of Function, Tomography, X-Ray Computed, TRACK-TBI Investigators for the CENTER-TBI Investigators
Abstract

ImportanceA head computed tomography (CT) with positive results for acute intracranial hemorrhage is the gold-standard diagnostic biomarker for acute traumatic brain injury (TBI). In moderate to severe TBI (Glasgow Coma Scale [GCS] scores 3-12), some CT features have been shown to be associated with outcomes. In mild TBI (mTBI; GCS scores 13-15), distribution and co-occurrence of pathological CT features and their prognostic importance are not well understood.ObjectiveTo identify pathological CT features associated with adverse outcomes after mTBI.Design, setting, and participantsThe longitudinal, observational Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study enrolled patients with TBI, including those 17 years and older with GCS scores of 13 to 15 who presented to emergency departments at 18 US level 1 trauma centers between February 26, 2014, and August 8, 2018, and underwent head CT imaging within 24 hours of TBI. Evaluations of CT imaging used TBI Common Data Elements. Glasgow Outcome Scale-Extended (GOSE) scores were assessed at 2 weeks and 3, 6, and 12 months postinjury. External validation of results was performed via the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) study. Data analyses were completed from February 2020 to February 2021.ExposuresAcute nonpenetrating head trauma.Main outcomes and measuresFrequency, co-occurrence, and clustering of CT features; incomplete recovery (GOSE scores

Published
2021

50. One-year employment outcome prediction after traumatic brain injury: A CENTER-TBI study

Author

Åkerlund, Cecilia, Amrein, Krisztina, Andelic, Nada, Andreassen, Lasse, Anke, Audny, Antoni, Anna, Audibert, Gérard, Azouvi, Philippe, Azzolini, Maria Luisa, Bartels, Ronald, Barzó, Pál, Beauvais, Romuald, Beer, Ronny, Bellander, Bo-Michael, Belli, Antonio, Benali, Habib, Berardino, Maurizio, Beretta, Luigi, Blaabjerg, Morten, Bragge, Peter, Brazinova, Alexandra, Brinck, Vibeke, Brooker, Joanne, Brorsson, Camilla, Buki, Andras, Bullinger, Monika, Cabeleira, Manuel, Caccioppola, Alessio, Calappi, Emiliana, Calvi, Maria Rosa, Cameron, Peter, Lozano, Guillermo Carbayo, Carbonara, Marco, Cavallo, Simona, Chevallard, Giorgio, Chieregato, Arturo, Citerio, Giuseppe, Clusmann, Hans, Coburn, Mark, Coles, Jonathan, Cooper, Jamie D., Correia, Marta, Čović, Amra, Curry, Nicola, Czeiter, Endre, Czosnyka, Marek, Dahyot-Fizelier, Claire, Dark, Paul, Dawes, Helen, De Keyser, Véronique, Degos, Vincent, Della Corte, Francesco, Boogert, Hugo den, Depreitere, Bart, Đilvesi, Đula, Dixit, Abhishek, Donoghue, Emma, Dreier, Jens, Dulière, Guy-Loup, Ercole, Ari, Esser, Patrick, Ezer, Erzsébet, Fabricius, Martin, Feigin, Valery L., Foks, Kelly, Frisvold, Shirin, Furmanov, Alex, Gagliardo, Pablo, Galanaud, Damien, Gantner, Dashiell, Gao, Guoyi, George, Pradeep, Ghuysen, Alexandre, Giga, Lelde, Glocker, Ben, Golubovic, Jagoš, Gomez, Pedro A., Gratz, Johannes, Gravesteijn, Benjamin, Grossi, Francesca, Gruen, Russell L., Gupta, Deepak, Haagsma, Juanita A., Haitsma, Iain, Helbok, Raimund, Helseth, Eirik, Horton, Lindsay, Huijben, Jilske, Hutchinson, Peter J., Jacobs, Bram, Jankowski, Stefan, Jarrett, Mike, Jiang, Ji-yao, Johnson, Faye, Jones, Kelly, Karan, Mladen, Kolias, Angelos G., Kompanje, Erwin, Kondziella, Daniel, Kornaropoulos, Evgenios, Koskinen, Lars-Owe, Kovács, Noémi, Kowark, Ana, Lagares, Alfonso, Lanyon, Linda, Laureys, Steven, Lecky, Fiona, Ledoux, Didier, Lefering, Rolf, Legrand, Valerie, Lejeune, Aurelie, Levi, Leon, Lightfoot, Roger, Lingsma, Hester, Maas, Andrew I.R., Castaño-León, Ana M., Maegele, Marc, Majdan, Marek, Manara, Alex, Manley, Geoffrey, Martino, Costanza, Maréchal, Hugues, Mattern, Julia, McMahon, Catherine, Melegh, Béla, Menon, David, Menovsky, Tomas, Mikolic, Ana, Misset, Benoit, Muraleedharan, Visakh, Murray, Lynnette, Negru, Ancuta, Nelson, David, Newcombe, Virginia, Nieboer, Daan, Nyirádi, József, Olubukola, Otesile, Oresic, Matej, Ortolano, Fabrizio, Palotie, Aarno, Parizel, Paul M., Payen, Jean-François, Perera, Natascha, Perlbarg, Vincent, Persona, Paolo, Peul, Wilco, Piippo-Karjalainen, Anna, Pirinen, Matti, Pisica, Dana, Ples, Horia, Polinder, Suzanne, Pomposo, Inigo, Posti, Jussi P., Puybasset, Louis, Radoi, Andreea, Ragauskas, Arminas, Raj, Rahul, Rambadagalla, Malinka, Helmrich, Isabel Retel, Rhodes, Jonathan, Richardson, Sylvia, Richter, Sophie, Ripatti, Samuli, Rocka, Saulius, Roe, Cecilie, Roise, Olav, Rosand, Jonathan, Rosenfeld, Jeffrey V., Rosenlund, Christina, Rosenthal, Guy, Rossaint, Rolf, Rossi, Sandra, Rueckert, Daniel, Rusnák, Martin, Sahuquillo, Juan, Sakowitz, Oliver, Sanchez-Porras, Renan, Sandor, Janos, Schäfer, Nadine, Schmidt, Silke, Schoechl, Herbert, Schoonman, Guus, Schou, Rico Frederik, Schwendenwein, Elisabeth, Sewalt, Charlie, Singh, Ranjit D., Skandsen, Toril, Smielewski, Peter, Sorinola, Abayomi, Stamatakis, Emmanuel, Stanworth, Simon, Stevens, Robert, Stewart, William, Steyerberg, Ewout W., Stocchetti, Nino, Sundström, Nina, Takala, Riikka, Tamás, Viktória, Tamosuitis, Tomas, Taylor, Mark Steven, Thibaut, Aurore, Ao, Braden Te, Tenovuo, Olli, Theadom, Alice, Thomas, Matt, Tibboel, Dick, Timmers, Marjolein, Tolias, Christos, Trapani, Tony, Tudora, Cristina Maria, Unterberg, Andreas, Vajkoczy, Peter, Vallance, Shirley, Valeinis, Egils, Vámos, Zoltán, van der Jagt, Mathieu, Van der Steen, Gregory, van der Naalt, Joukje, van Dijck, Jeroen T.J.M., van Erp, Inge A.M., van Essen, Thomas A., Van Hecke, Wim, van Heugten, Caroline, van Veen, Ernest, Vyvere, Thijs Vande, van Wijk, Roel P.J., Vargiolu, Alessia, Vega, Emmanuel, Velt, Kimberley, Verheyden, Jan, Vespa, Paul M., Vik, Anne, Vilcinis, Rimantas, Volovici, Victor, von Steinbüchel, Nicole, Voormolen, Daphne, Vulekovic, Petar, Wang, Kevin K.W., Whitehouse, Daniel, Wiegers, Eveline, Williams, Guy, Wilson, Lindsay, Winzeck, Stefan, Wolf, Stefan, Yang, Zhihui, Ylén, Peter, Younsi, Alexander, Zeiler, Frederick A., Zelinkova, Veronika, Ziverte, Agate, Zoerle, Tommaso, Van Deynse, Helena, Cools, Wilfried, De Deken, Viktor-Jan, Hubloue, Ives, Tisseghem, Ellen, and Putman, Koen

Published
2024
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