T4 apoptosis in the acute phase of SARS-CoV-2 infection predicts long COVID

BackgroundAs about 10% of patients with COVID-19 present sequelae, it is important to better understand the physiopathology of so-called long COVID.MethodTo this aim, we recruited 29 patients hospitalized for SARS-CoV-2 infection and, by Luminex®, quantified 19 soluble factors in their plasma and in the supernatant of their peripheral blood mononuclear cells, including inflammatory and anti-inflammatory cytokines and chemokines, Th1/Th2/Th17 cytokines, and endothelium activation markers. We also measured their T4, T8 and NK differentiation, activation, exhaustion and senescence, T cell apoptosis, and monocyte subpopulations by flow cytometry. We compared these markers between participants who developed long COVID or not one year later.ResultsNone of these markers was predictive for sequelae, except programmed T4 cell death. T4 lymphocytes from participants who later presented long COVID were more apoptotic in culture than those of sequelae-free participants at Month 12 (36.9 ± 14.7 vs. 24.2 ± 9.0%, p = 0.016).ConclusionsOur observation raises the hypothesis that T4 cell death during the acute phase of SARS-CoV-2 infection might pave the way for long COVID. Mechanistically, T4 lymphopenia might favor phenomena that could cause sequelae, including SARS-CoV-2 persistence, reactivation of other viruses, autoimmunity and immune dysregulation. In this scenario, inhibiting T cell apoptosis, for instance, by caspase inhibitors, could prevent long COVID

Isolating rare events in large-scale applications using a backward approach

While significant work in data mining has been dedicated to the detection of single outliers in the data, less research has approached the problem of isolating a group of outliers, i.e. rare events representing micro-clusters of less - or significantly less - than 1% of the whole dataset. This research issue is critical for example in medical applications. The problem is difficult to handle as it lies at the frontier between outlier detection and clustering and distinguishes by a clear challenge to avoid missing true positives. We address this challenge and propose a novel two-stage framework, based on a backward approach, to isolate abnormal groups of events in large datasets. The key of our backward approach is to first detect the core of the dense regions and then gradually augment them based on a density-driven condition. The framework outputs a small subset of the dataset that contains both outliers and rare events. Experiments are performed on both synthetic data and a medical application and compared against state-of-the-art outlier detection algorithms. The results show a very good performance of our approach and confirm the fact that dedicated algorithms are needed for the detection of rare events in large-scale applications

A Density-Based Backward Approach to Isolate Rare Events in Large-Scale Applications

International audienceWhile significant work in data mining has been dedicated to the detection of single outliers in the data, less research has approached the problem of isolating a group of outliers, i.e. rare events representing micro-clusters of less - or significantly less - than 1% of the whole dataset. This research issue is critical for example in medical applications. The problem is difficult to handle as it lies at the frontier between outlier detection and clustering and distinguishes by a clear challenge to avoid missing true positives. We address this challenge and propose a novel two-stage framework, based on a backward approach, to isolate abnormal groups of events in large datasets. The key of our backward approach is to first identify the core of the dense regions and then gradually augments them based on a density-driven condition. The framework outputs a small subset of the dataset containing both rare events and outliers. We tested our framework on a biomedical application to find micro-clusters of pathological cells. The comparison against two common clustering (DBSCAN) and outlier detection (LOF) algorithms show that our approach is a very efficient alternative to the detection of rare events - generally a recall of 100% and a higher precision, positively correlated wih the size of the rare event - while also providing a O(N) solution to the existing algorithms dominated by a O(N2) complexity

CT scan does not make a diagnosis of Covid-19: A cautionary case report

International audienceHere, we report the clinical case of a 12-year-old girl presenting with flu-like symptoms, cough, anosmia, ageusia, breathing difficulties, and patchy ground glass opacities on TDM chest scan who turned out to be Coronavirus 229E-infected. This case draws attention to the risk of false COVID-19 diagnosis when over-relying on CT scan imaging

希少事象を同定する方法

Related patent: EP2951722A2;EP2951722B1;JP2016511397A;US2015363551A1;WO2014118343A2;WO2014118343A3The invention relates to a method for identifying a subpopulation of specific cells among a large population of cells, comprising - a step of exposing the cells of said large population to n- reagents, - a step of detecting said n- reagents, - a step of grouping the cells by clusterisation into k different clusters, and - a step of eliminating cells that are not rare cells.More precisely the invention proposes a method for identifying a subpopulation of specific cells among a large population of cells that includes: a step of exposing the cells of the large population to n-reagents, a step of detecting the n-reagent, a step of grouping the cells by clusterization into k different clusters; and a step of eliminating cells that are not rare cells.L'invention concerne un procédé d'identification d'une sous-population de cellules spécifiques parmi une grande population de cellules, comprenant - une étape consistant à exposer les cellules de ladite grande population à n réactifs, - une étape consistant à détecter lesdits n réactifs, - une étape consistant à regrouper les cellules par agrégation en k groupes différents, et - une étape consistant à éliminer les cellules qui ne sont pas des cellules rares.本発明は、細胞大集団中の特異的細胞の亜集団を同定するための方法において、前記大集団の細胞をｎ種の試薬に曝露するステップと、前記ｎ種の試薬を検出するステップと、クラスタ化により細胞をｋ個の異なるクラスタにグループ化するステップと、希少細胞でない細胞を除去するステップと、を含む方法に関する

Process for identifying rare events

The invention relates to a method for identifying a subpopulation of specific cells among a large population of cells, comprising - a step of exposing the cells of said large population to n- reagents, - a step of detecting said n- reagents, - a step of grouping the cells by clusterisation into k different clusters, and - a step of eliminating cells that are not rare cells.More precisely the invention proposes a method for identifying a subpopulation of specific cells among a large population of cells that includes: a step of exposing the cells of the large population to n-reagents, a step of detecting the n-reagent, a step of grouping the cells by clusterization into k different clusters; and a step of eliminating cells that are not rare cells

Procédé d'identification d'événements rares

Également publié en tant que EP2951722 JP2016511397 US20150363551The invention relates to a method for identifying a subpopulation of specific cells among a large population of cells, comprising - a step of exposing the cells of said large population to n- reagents, - a step of detecting said n- reagents, - a step of grouping the cells by clusterisation into k different clusters, and - a step of eliminating cells that are not rare cells.L'invention concerne un procédé d'identification d'une sous-population de cellules spécifiques parmi une grande population de cellules, comprenant - une étape consistant à exposer les cellules de ladite grande population à n réactifs, - une étape consistant à détecter lesdits n réactifs, - une étape consistant à regrouper les cellules par agrégation en k groupes différents, et - une étape consistant à éliminer les cellules qui ne sont pas des cellules rares