Prevalence, Severity, and Clinical Management of Brain Incidental Findings in Healthy Young Adults: MRi-Share Cross-Sectional Study

Background and Objectives: Young adults represent an increasingly large proportion of healthy volunteers in brain imaging research, but descriptions of incidental findings (IFs) in this age group are scarce. We aimed to assess the prevalence and severity of IFs on brain MRIs of healthy young research participants aged 18-35 years, and to describe the protocol implemented to handle them. Methods: The study population comprised 1,867 participants aged 22.1 ± 2.3 years (72% women) from MRi-Share, the cross-sectional brain MRI substudy of the i-Share student cohort. IFs were flagged during the MRI quality control. We estimated the proportion of participants with IFs [any, requiring medical referral, potentially serious (PSIFs) as defined in the UK biobank]: overall, by type and severity of the final diagnosis, as well as the number of IFs. Results: 78/1,867 participants had at least one IF [4.2%, 95% Confidence Interval (CI) 3.4-5.2%]. IFs requiring medical referral (n = 38) were observed in 36/1,867 participants (1.9%, 1.4-2.7%), and represented 47.5% of the 80 IFs initially flagged. Referred IFs were retrospectively classified as PSIFs in 25/1,867 participants (1.3%, 0.9-2.0%), accounting for 68.4% of anomalies referred (26/38). The most common final diagnosis was cysts or ventricular abnormalities in all participants (9/1,867; 0.5%, 0.2-0.9%) and in those with referred IFs (9/36; 25.0%, 13.6-41.3%), while it was multiple sclerosis or radiologically isolated syndrome in participants with PSIFs (5/19; 26.3%, 11.5-49.1%) who represented 0.1% (0.0-0.4%) and 0.2% (0.03-0.5%) of all participants, respectively. Final diagnoses were considered serious in 11/1,867 participants (0.6%, 0.3-1.1%). Among participants with referred IFs, 13.9% (5/36) required active intervention, while 50.0% (18/36) were put on clinical surveillance. Conclusions: In a large brain imaging study of young healthy adults participating in research we observed a non-negligible frequency of IFs. The etiological pattern differed from what has been described in older adults.Programme d'investissements - Idex Bordeaux - LAPHIAStopping cognitive decline and dementia by fighting covert cerebral small vessel diseaseInvestissement d'aveni

Autoantibodies against type I IFNs in patients with critical influenza pneumonia

In an international cohort of 279 patients with hypoxemic influenza pneumonia, we identified 13 patients (4.6%) with autoantibodies neutralizing IFN-alpha and/or -omega, which were previously reported to underlie 15% cases of life-threatening COVID-19 pneumonia and one third of severe adverse reactions to live-attenuated yellow fever vaccine. Autoantibodies neutralizing type I interferons (IFNs) can underlie critical COVID-19 pneumonia and yellow fever vaccine disease. We report here on 13 patients harboring autoantibodies neutralizing IFN-alpha 2 alone (five patients) or with IFN-omega (eight patients) from a cohort of 279 patients (4.7%) aged 6-73 yr with critical influenza pneumonia. Nine and four patients had antibodies neutralizing high and low concentrations, respectively, of IFN-alpha 2, and six and two patients had antibodies neutralizing high and low concentrations, respectively, of IFN-omega. The patients' autoantibodies increased influenza A virus replication in both A549 cells and reconstituted human airway epithelia. The prevalence of these antibodies was significantly higher than that in the general population for patients 70 yr of age (3.1 vs. 4.4%, P = 0.68). The risk of critical influenza was highest in patients with antibodies neutralizing high concentrations of both IFN-alpha 2 and IFN-omega (OR = 11.7, P = 1.3 x 10(-5)), especially those <70 yr old (OR = 139.9, P = 3.1 x 10(-10)). We also identified 10 patients in additional influenza patient cohorts. Autoantibodies neutralizing type I IFNs account for similar to 5% of cases of life-threatening influenza pneumonia in patients <70 yr old

Data Fusion : an evidential approach for waste sorting

Le tri automatique des déchets est un sujetcomplexe en raison de la diversité des objets et desmatériaux présents. Il nécessite un apport de donnéesvariées et hétérogènes. Cette thèse traite du problème defusion de données découlant d’un dispositif de troiscapteurs dont une caméra hyperspectrale dans ledomaine NIR. Nous avons étudié l’avantage d’utiliser lecadre des fonctions de croyance (BFT) tout au long de ladémarche de fusion en utilisant notamment la mesure deconflit comme un critère clé de notre approche. Dans unepremière partie, nous avons étudié l'intérêt de la BFTpour la classification multiclasse des donnéeshyperspectrales à partir d’Error Correcting OutputCodes (ECOC) qui consistent à séparer le problèmemulticlasse en un ensemble de sous-problèmes binairesplus simples à résoudre. Les questions de commentidéalement séparer le problème multiclasse (codage)ainsi que celle de la combinaison des réponses de cesproblèmes binaires (décodage) sont encore aujourd’huides questions ouvertes. Le cadre des fonctions decroyance permet de proposer une étape de décodage quimodélise chaque classifieur binaire comme une sourceindividuelle d'information grâce notamment à lamanipulation des hypothèses composées. Par ailleurs laBFT fournit des indices pour détecter les décisions peufiables ce qui permet une auto-évaluation de la méthoderéalisée sans vérité terrain. Dans une deuxième partietraitant de la fusion de données, nous proposons unedémarche ‘orientée-objet’ composée d’un module desegmentation et d’un module de classification afin defaire face aux problèmes d’échelle, de différences derésolutions et de recalage des capteurs. L’objectif estalors d’estimer une segmentation où les segmentscoïncident avec les objets individuels et sont labellisés entermes de matériau. Nous proposons une interactionentre les modules à base de validation mutuelle. Ainsi,d’une part la fiabilité de la labellisation est évaluée auniveau des segments, d’autre part l’information declassification interagit sur les segments initiaux pour serapprocher d’une segmentation au niveau « objet » : leconsensus (ou l’absence de consensus) parmi lesinformations de classification au sein d’un segment ouentre segments connexes permet de faire évoluer lesupport spatial vers le niveau objet.Automatic waste sorting is a complex matterbecause of the diversity of the objects and of the presentmaterials. It requires input from various andheterogeneous data. This PhD work deals with the datafusion problem derived from an acquisition devicecomposed of three sensors, including an hyperspectralsensor in the NIR field. We first studied the benefit ofusing the belief function theory framework (BFT)throughout the fusion approach, using in particularconflict measures to drive the process. We first studiedthe BFT in the multiclass classification problem createdby hyperspectral data. We used the Error CorrectingOutput Codes (ECOC) framework which consists inseparating the multiclass problem into several binaryones, simpler to solve. The questions of the idealdecomposition of the multiclass problem (coding) and ofthe answer combination coming from the binaryclassifiers (decoding) are still open-ended questions. Thebelief function framework allows us to propose adecoding step modelling each binary classifier as anindividual source of information, thanks to the possibilityof handling compound hypotheses. Besides, the BFTprovides indices to detect non reliable decisions whichallow for an auto-evaluation of the method performedwithout using any ground truth. In a second part dealingwith the data fusion,we propose an evidential version ofan object-based approach composed with a segmentationmodule and a classification module in order to tackle theproblems of the differences in scale, resolutions orregistrations of the sensors. The objective is then toestimate a relevant spatial support corresponding to theobjects while labelling them in terms of material. Weproposed an interactive approach with cooperationbetween the two modules in a cross-validation kind ofway. This way, the reliability of the labelling isevaluated at the segment level, while the classificationinformation acts on the initial segments in order toevolve towards an object level segmentation: consensusamong the classification information within a segment orbetween adjacent regions allow the spatial support toprogressively reach object leve

Multi-Layer Joint Segmentation Using MRF and Graph Cuts

International audienceThe problem of jointly segmenting objects, according to a set of labels (of cardinality L), from a set of images (of cardinality K) to produce K individual segmentations plus one joint segmentation, can be cast as a Markov Random Field model. Coupling terms in the considered energy function enforce the consistency between the individual segmentations and the joint segmentation. However, neither optimality on the minimizer (at least for particular cases), nor the sensitivity of the parameters, nor the robustness of this approach against standard ones have been clearly discussed before. This paper focuses on the case where L>1, K>1 and the segmentation problem is handled using graph cuts. Noticeably, some properties of the considered energy function are demonstrated, such as global optimality when L=2 and K>1, the link with majority voting and the link with naive Bayes segmentation. Experiments on synthetic and real images depict superior segmentation performance and better robustness against noisy observations

Iron Nanoparticle Growth in Organic Superstructures

A tunable synthesis of iron nanoparticles (NIPS) based on the decomposition of {Fe[N(SiMe(3))(2)](2)}(2) in the presence of organic superstructures composed of palmitic acid and hexadlecylamine is reported. Control of the size (from 1.5 to 27 nm) and shape (spheres, cubes, or stars) of the NIPS has been achieved. An environment-dependent growth model is proposed on the basis of results obtained for the NP morphology under various conditions and a complete Mossbauer study of the colloid composition at different reacting stages. It involves (i) an anisotropic growth process inside organic superstructures, leading to monocrystalline cubic NIPS, and (ii) isotropic growth outside these superstructures, yielding polycrystalline spherical NPs

New generation of magnetic and luminescent nanoparticles for in-vivo real-time imaging

International audienceA new generation of optimized contrast agents is emerging, based on metallic nanoparticles (NPs) and semiconductor nanocrystals (NCs) for respectively magnetic resonant imaging (MRI) and near-infra-red (NIR) fluorescent imaging techniques. Compared with established contrast agents such as iron oxide NPs or organic dyes, these NPs benefit from several advantages: their magnetic and optical properties can be tuned through size, shape and composition engineering, their efficiency can excess by several order of magnitude that one of contrast agents clinically used, their surface can be modified to incorporate specific targeting agents and antifolding polymers to increase the blood circulation time and the tumor recognition, they can possibly be integrated in complex architecture to yield multimodal imaging agents. In this review, we will report the materials of choice based on the understanding of the physics basics of NIR and MRI techniques and their corresponding syntheses as NPs. Surface engineering, water transfer, and specific targeting will be highlighted prior to their first use for in-vivo real-time imaging. Highly efficient NPs, safer in use and target specific are likely to be entering clinical applications in a near future

Nanoparticules de fer de taille contrôlée pour l'hyperthermie

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Evidential split-and-merge: Application to object-based image analysis

International audienceThis paper addresses the difficult problem of segmenting objects in a scene and simultaneously estimating their material class. Focusing on the case where, individually, no dataset can achieve such a task, multiple sensor datasets are considered, including some images for retrieving the spatial information. The proposed approach is based on mutual validation between class decision (using the most relevant dataset) and segmen-tation (derived from image data). The main originality relies in the ability to make these two modules (classification and segmentation) interactive. Specifically, our application focuses on object-level material labeling using classic RGB images, laser profilome-ter images and a NIR spectral sensor. Starting from a superpixel segmentation, the relevant data are introduced as constraints modifying the initial segmentation in a split-and-merge process, which interacts with the material labeling process. In this work, we use the belief function framework to model the information extracted from each kind of data and to transfer it from one processing module to another. In particular we show the relevance of evidential conflict measure to drive the split process and to control the merge one. Experiments have been performed on actual scenes with stacked objects and difficult cases of material such as transparent polymers. They allow us to assess the performance of the proposed approach both in terms of material labeling and object segmentation as well as to illustrate some borderline cases

Evidential multi-class classification from binary classifiers: application to waste sorting quality control from hyperspectral data

International audienceOur application deals with waste sorting using an automatic system involving a hyperspectral camera. This latter provides the data for classification of the different kinds of waste allowing the evaluation of mechanical pre-sorting and its refinement. Hyperspectral data are processed using Support Vector Machine (SVM) binary classifiers that we propose to combine in the belief function theory (BFT) framework to take into account not only the performance of each binary classifier, but also its imprecision related for instance to the number of samples during the learning step. Having underlined the interest of BFT framework to deal with sparse classifiers, we study the performance of different combinations of classifiers

Iron Nanoparticle Growth in Organic Superstructures

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