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Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix®. Here, we dissect disease mechanisms and interactions with environmental triggers in a multi-ethnic sample of 6,073 cases and 84,856 controls. We fine-mapped GWAS signals within HLA (DQ0602, DQB1*03:01 and DPB1*04:02) and discovered seven novel associations (CD207, NAB1, IKZF4-ERBB3, CTSC, DENND1B, SIRPG, PRF1). Significant signals at TRA and DQB1*06:02 loci were found in 245 vaccination-related cases, who also shared polygenic risk. T cell receptor associations in NT1 modulated TRAJ*24, TRAJ*28 and TRBV*4-2 chain-usage. Partitioned heritability and immune cell enrichment analyses found genetic signals to be driven by dendritic and helper T cells. Lastly comorbidity analysis using data from FinnGen, suggests shared effects between NT1 and other autoimmune diseases. NT1 genetic variants shape autoimmunity and response to environmental triggers, including influenza A infection and immunization with Pandemrix®.

Background and purpose: Magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis is essential in MRI-only radiotherapy workflows, particularly through deep learning techniques known for their accuracy. However, current supervised methods are limited to specific center’s learnings and depend on registration precision. The aim of this study was to evaluate the accuracy of unsupervised and supervised approaches in the context of prostate MRI-to-CT generation for radiotherapy dose calculation. Methods: CT/MRI image pairs from 99 prostate cancer patients across three different centers were used. A comparison between supervised and unsupervised conditional Generative Adversarial Networks (cGAN) was conducted. Unsupervised training incorporates a style transfer method with. Content and Style Representation for Enhanced Perceptual synthesis (CREPs) loss. For dose evaluation, the photon prescription dose was 60 Gy delivered in volumetric modulated arc therapy (VMAT). Imaging endpoint for sCT evaluation was Mean Absolute Error (MAE). Dosimetric endpoints included absolute dose differences and gamma analysis between CT and sCT dose calculations. Results: The unsupervised paired network exhibited the highest accuracy for the body with a MAE at 33.6 HU, the highest MAE was 45.5 HU obtained with unsupervised unpaired learning. All architectures provided clinically acceptable results for dose calculation with gamma pass rates above 94 % (1 % 1 mm 10 %). Conclusions: This study shows that multicenter data can produce accurate sCTs via unsupervised learning, eliminating CT-MRI registration. The sCTs not only matched HU values but also enabled precise dose calculations, suggesting their potential for wider use in MRI-only radiotherapy workflows.

Abstract Narcolepsy type 1 (NT1) is caused by a loss of hypocretin/orexin transmission. Risk factors include pandemic 2009 H1N1 influenza A infection and immunization with Pandemrix®. Here, we dissect disease mechanisms and interactions with environmental triggers in a multi-ethnic sample of 6,073 cases and 84,856 controls. We fine-mapped GWAS signals within HLA (DQ0602, DQB1*03:01 and DPB1*04:02) and discovered seven novel associations (CD207, NAB1, IKZF4-ERBB3, CTSC, DENND1B, SIRPG, PRF1). Significant signals at TRA and DQB1*06:02 loci were found in 245 vaccination-related cases, who also shared polygenic risk. T cell receptor associations in NT1 modulated TRAJ*24, TRAJ*28 and TRBV*4-2 chain-usage. Partitioned heritability and immune cell enrichment analyses found genetic signals to be driven by dendritic and helper T cells. Lastly comorbidity analysis using data from FinnGen, suggests shared effects between NT1 and other autoimmune diseases. NT1 genetic variants shape autoimmunity and response to environmental triggers, including influenza A infection and immunization with Pandemrix®.

IntroductionFor radiotherapy based solely on magnetic resonance imaging (MRI), generating synthetic computed tomography scans (sCT) from MRI is essential for dose calculation. The use of deep learning (DL) methods to generate sCT from MRI has shown encouraging results if the MRI images used for training the deep learning network and the MRI images for sCT generation come from the same MRI device. The objective of this study was to create and evaluate a generic DL model capable of generating sCTs from various MRI devices for prostate radiotherapyMaterials and methodsIn total, 90 patients from three centers (30 CT-MR prostate pairs/center) underwent treatment using volumetric modulated arc therapy for prostate cancer (PCa) (60 Gy in 20 fractions). T2 MRI images were acquired in addition to computed tomography (CT) images for treatment planning. The DL model was a 2D supervised conditional generative adversarial network (Pix2Pix). Patient images underwent preprocessing steps, including nonrigid registration. Seven different supervised models were trained, incorporating patients from one, two, or three centers. Each model was trained on 24 CT-MR prostate pairs. A generic model was trained using patients from all three centers. To compare sCT and CT, the mean absolute error in Hounsfield units was calculated for the entire pelvis, prostate, bladder, rectum, and bones. For dose analysis, mean dose differences of D99% for CTV, V95% for PTV, Dmax for rectum and bladder, and 3D gamma analysis (local, 1%/1 mm) were calculated from CT and sCT. Furthermore, Wilcoxon tests were performed to compare the image and dose results obtained with the generic model to those with the other trained models.ResultsConsidering the image results for the entire pelvis, when the data used for the test comes from the same center as the data used for training, the results were not significantly different from the generic model. Absolute dose differences were less than 1 Gy for the CTV D99% for every trained model and center. The gamma analysis results showed nonsignificant differences between the generic and monocentric models.ConclusionThe accuracy of sCT, in terms of image and dose, is equivalent to whether MRI images are generated using the generic model or the monocentric model. The generic model, using only eight MRI-CT pairs per center, offers robust sCT generation, facilitating PCa MRI-only radiotherapy for routine clinical use.

BackgroundNarcolepsy Type I (NT1) is a rare, life-long sleep disorder arising as a consequence of the extensive destruction of orexin-producing hypothalamic neurons. The mechanisms involved in the destruction of orexin neurons are not yet elucidated but the association of narcolepsy with environmental triggers and genetic susceptibility (strong association with the HLA, TCRs and other immunologically-relevant loci) implicates an immuno-pathological process. Several studies in animal models and on human samples have suggested that T-cells are the main pathogenic culprits.MethodsRNA sequencing was performed on four CD4 and CD8 T-cell subsets (naive, effector, effector memory and central memory) sorted by flow cytometry from peripheral blood mononuclear cells (PBMCs) of NT1 patients and HLA-matched healthy donors as well as (age- and sex-) matched individuals suffering from other sleep disorders (OSD). The RNAseq analysis was conducted by comparing the transcriptome of NT1 patients to that of healthy donors and other sleep disorder patients (collectively referred to as the non-narcolepsy controls) in order to identify NT1-specific genes and pathways.ResultsWe determined NT1-specific differentially expressed genes, several of which are involved in tubulin arrangement found in CD4 (TBCB, CCT5, EML4, TPGS1, TPGS2) and CD8 (TTLL7) T cell subsets, which play a role in the immune synapse formation and TCR signaling. Furthermore, we identified genes (GZMB, LTB in CD4 T-cells and NLRP3, TRADD, IL6, CXCR1, FOXO3, FOXP3 in CD8 T-cells) and pathways involved in various aspects of inflammation and inflammatory response. More specifically, the inflammatory profile was identified in the “naive” subset of CD4 and CD8 T-cell.ConclusionWe identified NT1-specific differentially expressed genes, providing a cell-type and subset specific catalog describing their functions in T-cells as well as their potential involvement in NT1. Several genes and pathways identified are involved in the formation of the immune synapse and TCR activation as well as inflammation and the inflammatory response. An inflammatory transcriptomic profile was detected in both “naive” CD4 and CD8 T-cell subsets suggesting their possible involvement in the development or progression of the narcoleptic process.

Background and Purpose: Addressing the need for accurate dose calculation in MRI-only radiotherapy, the generation of synthetic Computed Tomography (sCT) from MRI has emerged. Deep learning (DL) techniques, have shown promising results in achieving high sCT accuracies. However, existing sCT synthesis methods are often center-specific, posing a challenge to their generalizability. To overcome this limitation, recent studies have proposed approaches, such as multicenter training . Material and methods: The purpose of this work was to propose a multicenter sCT synthesis by DL, using a 2D cycle-GAN on 128 prostate cancer patients, from four different centers. Four cases were compared: monocenter cases, monocenter training and test on another center, multicenter trainings and a test on a center not included in the training and multicenter trainings with an included center in the test. Trainings were performed using 20 patients. sCT accuracy evaluation was performed using Mean Absolute Error, Mean Error and Peak-Signal-to-Noise-Ratio. Dose accuracy was assessed with gamma index and Dose Volume Histogram comparison. Results: Qualitative, quantitative and dose results show that the accuracy of sCTs for monocenter trainings and multicenter trainings using a seen center in the test did not differ significantly. However, when the test involved an unseen center, the sCT quality was inferior. Conclusions: The aim of this work was to propose generalizable multicenter training for MR-to-CT synthesis. It was shown that only a few data from one center included in the training cohort allows sCT accuracy equivalent to a monocenter study.

IntroductionNarcolepsy type 1 (NT1) is a rare, chronic and disabling neurological disease causing excessive daytime sleepiness and cataplexy. NT1 is characterized pathologically by an almost complete loss of neurons producing the orexin neuropeptides in the lateral hypothalamus. Genetic and environmental factors strongly suggest the involvement of the immune system in the loss of orexin neurons. The cerebrospinal fluid (CSF), secreted locally and surrounding the central nervous system (CNS), represents an accessible window into CNS pathological processes.MethodsTo gain insight into the biological and molecular changes in NT1 patients, we performed a comparative proteomics analysis of the CSF from 21 recent-onset NT1 patients and from two control groups: group 1 with somatoform disorders, and group 2 patients with hypersomnia other than NT1, to control for any potential effect of sleep disturbances on CSF composition. To achieve an optimal proteomic coverage analysis, the twelve most abundant CSF proteins were depleted, and samples were analyzed by nano-flow liquid chromatography tandem mass spectrometry (nano-LC-MS/MS) using the latest generation of hybrid Orbitrap mass spectrometer.Results and discussionOur study allowed the identification and quantification of up to 1943 proteins, providing a remarkably deep analysis of the CSF proteome. Interestingly, gene set enrichment analysis indicated that the complement and coagulation systems were enriched and significantly activated in NT1 patients in both cohorts analyzed. Notably, the lectin and alternative complement pathway as well as the downstream lytic membrane attack complex were congruently increased in NT1. Our data suggest that the complement dysregulation in NT1 patients can contribute to immunopathology either by directly promoting tissue damage or as part of local inflammatory responses. We therefore reveal an altered composition of the CSF proteome in NT1 patients, which points to an ongoing inflammatory process contributed, at least in part, by the complement system.

Zhongxing Zhang,1 Yves Dauvilliers,2– 4 Giuseppe Plazzi,5,6 Geert Mayer,7 Gert Jan Lammers,8,9 Joan Santamaria,10 Markku Partinen,11 Sebastiaan Overeem,12,13 Rafael del Rio Villegas,14 Karel Sonka,15 Rosa Peraita-Adrados,16 Raphaël Heinzer,17 Aleksandra Wierzbicka,18 Birgit Högl,19 Mauro Manconi,20 Eva Feketeova,21 Antonio Martins da Silva,22 Jitka Bušková,23 Claudio LA Bassetti,24,25 Lucie Barateau,2– 4 Fabio Pizza,5,26 Elena Antelmi,5,6 Jari K Gool,8,9 Rolf Fronczek,8,9 Carles Gaig,10 Ramin Khatami1,24 1Center for Sleep Medicine, Sleep Research and Epileptology, Klinik Barmelweid AG, Barmelweid, Aargau, Switzerland; 2Sleep-Wake Disorders Unit, Department of Neurology, Gui-de-Chauliac Hospital, CHU Montpellier, Montpellier, France; 3National Reference Centre for Orphan Diseases, Narcolepsy, Idiopathic Hypersomnia, and Kleine-Levin Syndrome, Montpellier, France; 4Institute for Neurosciences of Montpellier INM, Univ Montpellier, INSERM, Montpellier, France; 5Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy; 6IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy; 7Neurology Department, Hephata Klinik, Schwalmstadt, Germany; 8Sleep Wake Center SEIN Heemstede, Stichting Epilepsie Instellingen Nederland, Heemstede, the Netherlands; 9Department of Neurology and Clinical Neurophysiology, Leiden University Medical Center, Leiden, the Netherlands; 10Neurology Service, Institut de Neurociències Hospital Clínic, University of Barcelona, Barcelona, Spain; 11Helsinki Sleep Clinic, Vitalmed Research Center, Helsinki, Finland; 12Sleep Medicine Center Kempenhaeghe, Heeze, the Netherlands; 13Eindhoven University of Technology, Eindhoven, the Netherlands; 14Neurophysiology and Sleep Disorders Unit, Hospital Vithas Nuestra Señora de América, Madrid, Spain; 15Neurology Department and Centre of Clinical Neurosciences, First Faculty of Medicine, Charles University and General University Hospital, Prague, Czech Republic; 16Sleep and Epilepsy Unit – Clinical Neurophysiology Service, University General Hospital Gregorio Marañón, Research Institute Gregorio Marañón, University Complutense of Madrid, Madrid, Spain; 17Center for Investigation and Research in Sleep, Lausanne University Hospital, Lausanne, Vaud, Switzerland; 18Department of Clinical Neurophysiology, Institute of Psychiatry and Neurology, Warsaw, Poland; 19Neurology Department, Sleep Disorders Clinic, Innsbruck Medical University, Innsbruck, Austria; 20Neurology Department, EOC, Ospedale Regionale di Lugano, Lugano, Ticino, Switzerland; 21Neurology Department, Medical Faculty of P. J. Safarik University, University Hospital of L. Pasteur Kosice, Kosice, Slovak Republic; 22Serviço de Neurofisiologia, Hospital Santo António/Centro Hospitalar Universitário do Porto and UMIB-Instituto Ciências Biomédicas Abel Salazar, Universidade do Porto, Porto, Portugal; 23Department of Sleep Medicine, National Institute of Mental Health, Klecany, Czech Republic; 24Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland; 25Department of Neurology, Sechenov First Moscow State University, Moscow, Russia; 26Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, ItalyCorrespondence: Yves Dauvilliers, National Reference Network for Narcolepsy, Sleep-Disorders Center, Department of Neurology, Hopital Gui de Chauliac, INSERM U1061, Montpellier, UM1, France, Email ydauvilliers@yahoo.fr Ramin Khatami, Center for Sleep Medicine, Sleep Research and Epileptology, Klinik Barmelweid AG, Barmelweid, CH-5017, Switzerland, Email ramin.khatami@barmelweid.chPurpose: Narcolepsy type-1 (NT1) is a rare chronic neurological sleep disorder with excessive daytime sleepiness (EDS) as usual first and cataplexy as pathognomonic symptom. Shortening the NT1 diagnostic delay is the key to reduce disease burden and related low quality of life. Here we investigated the changes of diagnostic delay over the diagnostic years (1990– 2018) and the factors associated with the delay in Europe.Patients and Methods: We analyzed 580 NT1 patients (male: 325, female: 255) from 12 European countries using the European Narcolepsy Network database. We combined machine learning and linear mixed-effect regression to identify factors associated with the delay.Results: The mean age at EDS onset and diagnosis of our patients was 20.9± 11.8 (mean ± standard deviation) and 30.5± 14.9 years old, respectively. Their mean and median diagnostic delay was 9.7± 11.5 and 5.3 (interquartile range: 1.7− 13.2 years) years, respectively. We did not find significant differences in the diagnostic delay over years in either the whole dataset or in individual countries, although the delay showed significant differences in various countries. The number of patients with short (≤ 2-year) and long (≥ 13-year) diagnostic delay equally increased over decades, suggesting that subgroups of NT1 patients with variable disease progression may co-exist. Younger age at cataplexy onset, longer interval between EDS and cataplexy onsets, lower cataplexy frequency, shorter duration of irresistible daytime sleep, lower daytime REM sleep propensity, and being female are associated with longer diagnostic delay.Conclusion: Our findings contrast the results of previous studies reporting shorter delay over time which is confounded by calendar year, because they characterized the changes in diagnostic delay over the symptom onset year. Our study indicates that new strategies such as increasing media attention/awareness and developing new biomarkers are needed to better detect EDS, cataplexy, and changes of nocturnal sleep in narcolepsy, in order to shorten the diagnostic interval.Keywords: cataplexy, diagnostic delay, misdiagnosis, symptom onset, machine learning

Summary: We aim to identify genetic markers associated with idiopathic hypersomnia, a disabling orphan central nervous system disorder of hypersomnolence that is still poorly understood. In our study, DNA was extracted from 79 unrelated patients diagnosed with idiopathic hypersomnia with long sleep time at the National Reference Center for Narcolepsy‐France according to very stringent diagnostic criteria. Whole exome sequencing on the first 30 patients with idiopathic hypersomnia (25 females and 5 males) allowed the single nucleotide variants to be compared with a control population of 574 healthy subjects from the French Exome project database. We focused on the identification of genetic variants among 182 genes related to the regulation of sleep and circadian rhythm. Candidate variants obtained by exome sequencing analysis were then validated in a second sample of 49 patients with idiopathic hypersomnia (37 females and 12 males). Our study characterised seven variants from six genes significantly associated with idiopathic hypersomnia compared with controls. A targeted sequencing analysis of these seven variants on 49 other patients with idiopathic hypersomnia confirmed the relative over‐representation of the A➔C variant of rs2859390, located in a potential splicing‐site of PER3 gene. Our findings support a genetic predisposition and identify pathways involved in the pathogeny of idiopathic hypersomnia. A variant of the PER3 gene may predispose to idiopathic hypersomnia with long sleep time. [ABSTRACT FROM AUTHOR]

The quality assurance of synthetic CT (sCT) is crucial for safe clinical transfer to an MRI-only radiotherapy planning workflow. The aim of this work is to propose a population-based process assessing local errors in the generation of sCTs and their impact on dose distribution. For the analysis to be anatomically meaningful, a customized interpatient registration method brought the population data to the same coordinate system. Then, the voxel-based process was applied on two sCT generation methods: a bulk-density method and a generative adversarial network. The CT and MRI pairs of 39 patients treated by radiotherapy for prostate cancer were used for sCT generation, and 26 of them with delineated structures were selected for analysis. Voxel-wise errors in sCT compared to CT were assessed for image intensities and dose calculation, and a population-based statistical test was applied to identify the regions where discrepancies were significant. The cumulative histograms of the mean absolute dose error per volume of tissue were computed to give a quantitative indication of the error for each generation method. Accurate interpatient registration was achieved, with mean Dice scores higher than 0.91 for all organs. The proposed method produces three-dimensional maps that precisely show the location of the major discrepancies for both sCT generation methods, highlighting the heterogeneity of image and dose errors for sCT generation methods from MRI across the pelvic anatomy. Hence, this method provides additional information that will assist with both sCT development and quality control for MRI-based planning radiotherapy.