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Rashid A, Al-Obeida F, Hafez W, Benakatti G, Malik RA, Koutentis C, Sharief J, Brierley J, Quraishi N, Malik ZA, Anwary A, Alkhzaimi H, Zaki SA, Khilnani P, Kadwa R, Phatak R, Schumacher M, Shaikh G, Al-Dubai A, Hussain A. ADVANCING THE UNDERSTANDING OF CLINICAL SEPSIS USING GENE EXPRESSION-DRIVEN MACHINE LEARNING TO IMPROVE PATIENT OUTCOMES. Shock 2024; 61:4-18. [PMID: 37752080 DOI: 10.1097/shk.0000000000002227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/28/2023]
Abstract
ABSTRACT Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.
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Affiliation(s)
| | | | | | | | | | | | | | - Joe Brierley
- Great Ormond Street Children's Hospital, London, UK
| | - Nasir Quraishi
- Centre for Spinal Studies & Surgery, Queen's Medical Centre. The University of Nottingham. Nottingham, UK
| | - Zainab A Malik
- College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences. Dubai, U.A.E
| | - Arif Anwary
- School of Computing, Edinburgh Napier University. Edinburgh, UK
| | | | | | | | | | - Rajesh Phatak
- Pediatric Intensive Care, Burjeel Hospital, Najda, Abu Dhabi
| | | | - Guftar Shaikh
- Endocrinology, Royal Hospital for Children. Glasgow, UK
| | - Ahmed Al-Dubai
- School of Computing, Edinburgh Napier University. Edinburgh, UK
| | - Amir Hussain
- School of Computing, Edinburgh Napier University. Edinburgh, UK
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2
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Schweizer L. Drug Intelligence Science (DIS®): Pioneering a high-resolution translational platform to enhance the probability of success for drug discovery and development. Drug Discov Today 2023; 28:103795. [PMID: 37805064 DOI: 10.1016/j.drudis.2023.103795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/26/2023] [Accepted: 10/02/2023] [Indexed: 10/09/2023]
Abstract
Translational research has a crucial role in bridging the gap between basic biology discoveries and their clinical applications. Deep scientific understanding and advanced technology platforms are both crucial for translational research. Here, I describe a novel integrated Drug Intelligence Science (DIS®) translational platform that combines single cell technology with artificial intelligence (AI) and machine learning (ML) to gain insights into high-resolution cell biology, thus enabling the discovery of disease-relevant targets, high-quality drug candidates, and predictive biomarkers. The innovative DIS® approach has the potential to provide unprecedented mechanistic understanding of human diseases and enable in-depth pharmacological profiling of drug candidates to increase the probability of success (POS) in drug discovery and development.
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Affiliation(s)
- Liang Schweizer
- HiFiBiO Therapeutics, 237 Putnam Avenue, Cambridge, MA 02139, USA.
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Ayton SG, Treviño V. MuTATE-an R package for comprehensive multi-objective molecular modeling. Bioinformatics 2023; 39:btad507. [PMID: 37688581 PMCID: PMC10500092 DOI: 10.1093/bioinformatics/btad507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 07/14/2023] [Accepted: 09/07/2023] [Indexed: 09/11/2023] Open
Abstract
MOTIVATION Comprehensive multi-omics studies have driven advances in disease modeling for effective precision medicine but pose a challenge for existing machine-learning approaches, which have limited interpretability across clinical endpoints. Automated, comprehensive disease modeling requires a machine-learning approach that can simultaneously identify disease subgroups and their defining molecular biomarkers by explaining multiple clinical endpoints. Current tools are restricted to individual endpoints or limited variable types, necessitate advanced computation skills, and require resource-intensive manual expert interpretation. RESULTS We developed Multi-Target Automated Tree Engine (MuTATE) for automated and comprehensive molecular modeling, which enables user-friendly multi-objective decision tree construction and visualization of relationships between molecular biomarkers and patient subgroups characterized by multiple clinical endpoints. MuTATE incorporates multiple targets throughout model construction and allows for target weights, enabling construction of interpretable decision trees that provide insights into disease heterogeneity and molecular signatures. MuTATE eliminates the need for manual synthesis of multiple non-explainable models, making it highly efficient and accessible for bioinformaticians and clinicians. The flexibility and versatility of MuTATE make it applicable to a wide range of complex diseases, including cancer, where it can improve therapeutic decisions by providing comprehensive molecular insights for precision medicine. MuTATE has the potential to transform biomarker discovery and subtype identification, leading to more effective and personalized treatment strategies in precision medicine, and advancing our understanding of disease mechanisms at the molecular level. AVAILABILITY AND IMPLEMENTATION MuTATE is freely available at GitHub (https://github.com/SarahAyton/MuTATE) under the GPLv3 license.
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Affiliation(s)
- Sarah G Ayton
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Mexico
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Mexico
| | - Víctor Treviño
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Mexico
- Tecnologico de Monterrey, The Institute for Obesity Research, Monterrey, Mexico
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Sun J, Dong QX, Wang SW, Zheng YB, Liu XX, Lu TS, Yuan K, Shi J, Hu B, Lu L, Han Y. Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian J Psychiatr 2023; 87:103705. [PMID: 37506575 DOI: 10.1016/j.ajp.2023.103705] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023]
Abstract
Psychiatric disorders are now responsible for the largest proportion of the global burden of disease, and even more challenges have been seen during the COVID-19 pandemic. Artificial intelligence (AI) is commonly used to facilitate the early detection of disease, understand disease progression, and discover new treatments in the fields of both physical and mental health. The present review provides a broad overview of AI methodology and its applications in data acquisition and processing, feature extraction and characterization, psychiatric disorder classification, potential biomarker detection, real-time monitoring, and interventions in psychiatric disorders. We also comprehensively summarize AI applications with regard to the early warning, diagnosis, prognosis, and treatment of specific psychiatric disorders, including depression, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, addiction, sleep disorders, and Alzheimer's disease. The advantages and disadvantages of AI in psychiatry are clarified. We foresee a new wave of research opportunities to facilitate and improve AI technology and its long-term implications in psychiatry during and after the COVID-19 era.
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Affiliation(s)
- Jie Sun
- Pain Medicine Center, Peking University Third Hospital, Beijing 100191, China; Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Qun-Xi Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - San-Wang Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yong-Bo Zheng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Xiao-Xing Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Tang-Sheng Lu
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Kai Yuan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Jie Shi
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China.
| | - Ying Han
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China.
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Lambri N, Hernandez V, Sáez J, Pelizzoli M, Parabicoli S, Tomatis S, Loiacono D, Scorsetti M, Mancosu P. Multicentric evaluation of a machine learning model to streamline the radiotherapy patient specific quality assurance process. Phys Med 2023; 110:102593. [PMID: 37104920 DOI: 10.1016/j.ejmp.2023.102593] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/02/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023] Open
Abstract
PURPOSE Patient-specific quality assurance (PSQA) is performed to ensure that modulated treatment plans can be delivered as intended, but constitutes a substantial workload that could slow down the radiotherapy process and delay the start of clinical treatments. In this study, we investigated a machine learning (ML) tree-based ensemble model to predict the gamma passing rate (GPR) for volumetric modulated arc therapy (VMAT) plans. MATERIALS AND METHODS 5622 VMAT plans from multiple treatment sites were selected from a database of Institution 1 and the ML model trained using 19 metrics. PSQA analyses were performed automatically using criteria 3%/1 mm (global normalization, absolute dose, 10% threshold) and 95% action limit. Model's performance was evaluated on an out-of-sample test set of Institution 1 and on two independent sets of measurements collected at Institution 2 and Institution 3. Mean absolute error (MAE), as well as the model's sensitivity and specificity, were computed. RESULTS The model obtained a MAE of 2.33%, 2.54% and 3.91% for the three Institutions, with a specificity of 0.90, 0.90 and 0.68, and a sensitivity of 0.61, 0.25, and 0.55, respectively. Small positive median values of the residuals (i.e., the difference between measurements and predictions) were observed for each Institution (0.95%, 1.66%, and 3.42%). Thus, the model's predictions were, on average, close to the real values and provided a conservative estimation of the GPR. CONCLUSIONS ML models can be integrated into clinical practice to streamline the radiotherapy workflow, but they should be center-specific or thoroughly verified within centers before clinical use.
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Affiliation(s)
- Nicola Lambri
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy; Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy
| | - Victor Hernandez
- Department of Medical Physics, Hospital Universitari Sant Joan de Reus, IISPV, Tarragona, Spain
| | - Jordi Sáez
- Department of Radiation Oncology, Hospital Clínic de Barcelona, Barcelona, Spain
| | - Marco Pelizzoli
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy; Dipartimento di Fisica "Aldo Pontremoli", Università degli Studi di Milano, Milan, Italy
| | - Sara Parabicoli
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy; Dipartimento di Fisica "Aldo Pontremoli", Università degli Studi di Milano, Milan, Italy
| | - Stefano Tomatis
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy
| | - Daniele Loiacono
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
| | - Marta Scorsetti
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy; Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy
| | - Pietro Mancosu
- IRCCS Humanitas Research Hospital, Radiotherapy and Radiosurgery Department, via Manzoni 56, 20089 Rozzano, Milan, Italy.
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Sanchez K, Kamal K, Manjaly P, Ly S, Mostaghimi A. Clinical Application of Artificial Intelligence for Non-melanoma Skin Cancer. Curr Treat Options Oncol 2023; 24:373-379. [PMID: 36917395 PMCID: PMC10011774 DOI: 10.1007/s11864-023-01065-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/26/2023] [Indexed: 03/15/2023]
Abstract
OPINION STATEMENT The development and implementation of artificial intelligence is beginning to impact the care of dermatology patients. Although the clinical application of AI in dermatology to date has largely focused on melanoma, the prevalence of non-melanoma skin cancers, including basal cell and squamous cell cancers, is a critical application for this technology. The need for a timely diagnosis and treatment of skin cancers makes finding more time efficient diagnostic methods a top priority, and AI may help improve dermatologists' performance and facilitate care in the absence of dermatology expertise. Beyond diagnosis, for more severe cases, AI may help in predicting therapeutic response and replacing or reinforcing input from multidisciplinary teams. AI may also help in designing novel therapeutics. Despite this potential, enthusiasm in AI must be tempered by realistic expectations regarding performance. AI can only perform as well as the information that is used to train it, and development and implementation of new guidelines to improve transparency around training and performance of algorithms is key for promoting confidence in new systems. Special emphasis should be placed on the role of dermatologists in curating high-quality datasets that reflect a range of skin tones, diagnoses, and clinical scenarios. For ultimate success, dermatologists must not be wary of AI as a potential replacement for their expertise, but as a new tool to complement their diagnostic acumen and extend patient care.
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Affiliation(s)
- Katherine Sanchez
- Lake Erie College of Osteopathic Medicine, Erie, PA, USA.,Department of Dermatology, Brigham and Women's Hospital, 221 Longwood Ave, Boston, MA, 02115, USA
| | - Kanika Kamal
- Department of Dermatology, Brigham and Women's Hospital, 221 Longwood Ave, Boston, MA, 02115, USA.,Harvard Medical School, Boston, MA, 02115, USA
| | - Priya Manjaly
- Department of Dermatology, Brigham and Women's Hospital, 221 Longwood Ave, Boston, MA, 02115, USA.,Boston University School of Medicine, Boston, USA
| | - Sophia Ly
- Department of Dermatology, Brigham and Women's Hospital, 221 Longwood Ave, Boston, MA, 02115, USA.,College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, US, USA
| | - Arash Mostaghimi
- Department of Dermatology, Brigham and Women's Hospital, 221 Longwood Ave, Boston, MA, 02115, USA. .,Harvard Medical School, Boston, MA, 02115, USA.
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7
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Sobrino S, Magnani A, Semeraro M, Martignetti L, Cortal A, Denis A, Couzin C, Picard C, Bustamante J, Magrin E, Joseph L, Roudaut C, Gabrion A, Soheili T, Cordier C, Lortholary O, Lefrere F, Rieux-Laucat F, Casanova JL, Bodard S, Boddaert N, Thrasher AJ, Touzot F, Taque S, Suarez F, Marcais A, Guilloux A, Lagresle-Peyrou C, Galy A, Rausell A, Blanche S, Cavazzana M, Six E. Severe hematopoietic stem cell inflammation compromises chronic granulomatous disease gene therapy. Cell Rep Med 2023; 4:100919. [PMID: 36706754 PMCID: PMC9975109 DOI: 10.1016/j.xcrm.2023.100919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 10/20/2022] [Accepted: 01/06/2023] [Indexed: 01/27/2023]
Abstract
X-linked chronic granulomatous disease (CGD) is associated with defective phagocytosis, life-threatening infections, and inflammatory complications. We performed a clinical trial of lentivirus-based gene therapy in four patients (NCT02757911). Two patients show stable engraftment and clinical benefits, whereas the other two have progressively lost gene-corrected cells. Single-cell transcriptomic analysis reveals a significantly lower frequency of hematopoietic stem cells (HSCs) in CGD patients, especially in the two patients with defective engraftment. These two present a profound change in HSC status, a high interferon score, and elevated myeloid progenitor frequency. We use elastic-net logistic regression to identify a set of 51 interferon genes and transcription factors that predict the failure of HSC engraftment. In one patient, an aberrant HSC state with elevated CEBPβ expression drives HSC exhaustion, as demonstrated by low repopulation in a xenotransplantation model. Targeted treatments to protect HSCs, coupled to targeted gene expression screening, might improve clinical outcomes in CGD.
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Affiliation(s)
- Steicy Sobrino
- Human Lymphohematopoiesis Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Alessandra Magnani
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Michaela Semeraro
- Clinical Investigation Center CIC 1419, Necker-Enfants Malades Hospital, GH Paris Centre, Université Paris Cité, AP-HP, Paris, France
| | - Loredana Martignetti
- Clinical Bioinformatics Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Akira Cortal
- Clinical Bioinformatics Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Adeline Denis
- Human Lymphohematopoiesis Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Chloé Couzin
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Capucine Picard
- Study Center for Primary Immunodeficiencies, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, Paris, France; Lymphocyte Activation and Susceptibility to EBV Infection Laboratory, INSERM UMR 1163, Imagine Institute, Paris, France; Centre de Références des Déficits Immunitaires Héréditaires (CEREDIH), Necker-Enfants Malades Hospital, AP-HP, Paris, France
| | - Jacinta Bustamante
- Study Center for Primary Immunodeficiencies, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, Paris, France; Human Genetics of Infectious Diseases Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France; St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY, USA
| | - Elisa Magrin
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Laure Joseph
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France
| | - Cécile Roudaut
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Aurélie Gabrion
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Tayebeh Soheili
- Human Lymphohematopoiesis Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Corinne Cordier
- Plateforme de Cytométrie en Flux, Structure Fédérative de Recherche Necker, INSERM US24-CNRS UAR3633, Paris, France
| | - Olivier Lortholary
- Necker-Pasteur Center for Infectious Diseases and Tropical Medicine, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, Imagine Institute, Paris, France
| | - François Lefrere
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Department of Adult Hematology, Necker-Enfants Malades Hospital, AP-HP, Paris, France
| | - Frédéric Rieux-Laucat
- Immunogenetics of Pediatric Autoimmune Diseases Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
| | - Jean-Laurent Casanova
- Human Genetics of Infectious Diseases Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France; St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY, USA
| | - Sylvain Bodard
- Department of Adult Radiology, Necker Enfants-Malades Hospital, AP-HP, Université Paris Cité, Paris, France; Laboratoire d'Imagerie Biomédicale, LIB, Sorbonne Université, CNRS, INSERM, Paris, France
| | - Nathalie Boddaert
- Département de Radiologie Pédiatrique, INSERM UMR 1163 and UMR 1299, Imagine Institute, AP-HP, Necker-Enfants Malades Hospital, Paris, France
| | - Adrian J Thrasher
- UCL Great Ormond Street Institute of Child Health, London, UK; Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK
| | - Fabien Touzot
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Sophie Taque
- CHU de Rennes, Département de Pédiatrie, Rennes, France
| | - Felipe Suarez
- Necker-Pasteur Center for Infectious Diseases and Tropical Medicine, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, Imagine Institute, Paris, France; Imagine Institute, Université Paris Cité, Paris, France
| | - Ambroise Marcais
- Necker-Pasteur Center for Infectious Diseases and Tropical Medicine, Necker-Enfants Malades Hospital, AP-HP, Université Paris Cité, Imagine Institute, Paris, France
| | - Agathe Guilloux
- Mathematics and Modelization Laboratory, CNRS, Université Paris-Saclay, Université d'Evry, Evry, France
| | - Chantal Lagresle-Peyrou
- Human Lymphohematopoiesis Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France
| | - Anne Galy
- Genethon, Evry-Courcouronnes, France; Université Paris-Saclay, University Evry, Inserm, Genethon (UMR_S951), Evry-Courcouronnes, France
| | - Antonio Rausell
- Clinical Bioinformatics Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France; Service de Médecine Génomique des Maladies Rares, AP-HP, Necker-Enfants Malades Hospital, Paris, France
| | - Stephane Blanche
- Department of Pediatric Immunology, Hematology, and Rheumatology, Necker-Enfants Malades Hospital, AP-HP, Paris, France
| | - Marina Cavazzana
- Biotherapy Department, Necker-Enfants Malades Hospital, AP-HP, Paris, France; Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, AP-HP, INSERM, Paris, France; Imagine Institute, Université Paris Cité, Paris, France.
| | - Emmanuelle Six
- Human Lymphohematopoiesis Laboratory, Université Paris Cité, Imagine Institute, INSERM UMR 1163, Paris, France
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