Spanish cohort of VEXAS syndrome : clinical manifestations, outcome of treatments and novel evidences about UBA1 mosaicism

The vacuoles, E1-enzyme, X linked, autoinflammatory and somatic (VEXAS) syndrome is an adult-onset autoinflammatory disease (AID) due to postzygotic UBA1 variants. To investigate the presence of VEXAS syndrome among patients with adult-onset undiagnosed AID. Additional studies evaluated the mosaicism distribution and the circulating cytokines. Gene analyses were performed by both Sanger and amplicon-based deep sequencing. Patients' data were collected from their medical charts. Cytokines were quantified by Luminex. Genetic analyses of enrolled patients (n=42) identified 30 patients carrying UBA1 pathogenic variants, with frequencies compatible for postzygotic variants. All patients were male individuals who presented with a late-onset disease (mean 67.5 years; median 67.0 years) characterised by cutaneous lesions (90%), fever (66.7%), pulmonary manifestations (66.7%) and arthritis (53.3%). Macrocytic anaemia and increased erythrocyte sedimentation rate and ferritin were the most relevant analytical abnormalities. Glucocorticoids ameliorated the inflammatory manifestations, but most patients became glucocorticoid-dependent. Positive responses were obtained when targeting the haematopoietic component of the disease with either decitabine or allogeneic haematopoietic stem cell transplantation. Additional analyses detected the UBA1 variants in both haematopoietic and non-haematopoietic tissues. Finally, analysis of circulating cytokines did not identify inflammatory mediators of the disease. Thirty patients with adult-onset AID were definitively diagnosed with VEXAS syndrome through genetic analyses. Despite minor interindividual differences, their main characteristics were in concordance with previous reports. We detected for the first time the UBA1 mosaicism in non-haematopoietic tissue, which questions the previous concept of myeloid-restricted mosaicism and may have conceptual consequences for the disease mechanisms

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Clinical characteristics related to disease severity with respect to the rs4819554 genotype distribution.

<p>Clinical characteristics related to disease severity with respect to the rs4819554 genotype distribution.</p

Clinical characteristics related to disease severity with respect to the rs4819554 genotype distribution.

<p>Clinical characteristics related to disease severity with respect to the rs4819554 genotype distribution.</p