Embolisation des malformations artérioveineuses cérébrales (place du Glubran 2® et de l'Onyx®)

PARIS-BIUP (751062107) / SudocSudocFranceF

Reduced exercise capacity in a mouse model of asthma

status: publishe

Evaluation of Impedance-Based Label-Free Technology as a Tool for Pharmacology and Toxicology Investigations

The use of label-free technologies based on electrical impedance is becoming more and more popular in drug discovery. Indeed, such a methodology allows the continuous monitoring of diverse cellular processes, including proliferation, migration, cytotoxicity and receptor-mediated signaling. The objective of the present study was to further assess the usefulness of the real-time cell analyzer (RTCA) and, in particular, the xCELLigence platform, in the context of early drug development for pharmacology and toxicology investigations. In the present manuscript, four cellular models were exposed to 50 compounds to compare the cell index generated by RTCA and cell viability measured with a traditional viability assay. The data revealed an acceptable correlation (ca. 80%) for both cell lines (i.e., HepG2 and HepaRG), but a lack of correlation (ca. 55%) for the primary human and rat hepatocytes. In addition, specific RTCA profiles (signatures) were generated when HepG2 and HepaRG cells were exposed to calcium modulators, antimitotics, DNA damaging and nuclear receptor agents, with a percentage of prediction close to 80% for both cellular models. In a subsequent experiment, HepG2 cells were exposed to 81 proprietary UCB compounds known to be genotoxic or not. Based on the DNA damaging signatures, the RTCA technology allowed the detection of ca. 50% of the genotoxic compounds (n = 29) and nearly 100% of the non-genotoxic compounds (n = 52). Overall, despite some limitations, the xCELLigence platform is a powerful and reliable tool that can be used in drug discovery for toxicity and pharmacology studies

A comparison of unsupervised curve classification methods for sport training data

International audienceAchieving peak performance at a specified time is the primary goal of athletes’ training programs. To optimize performance and reduce the risk of injury, a comprehensive list of training program parameters (e.g. intensity, volume, frequency, distribution, duration and type) requires careful management. This work focuses on clustering of time evolution curves of training measurements.Training data are recorded densely over time. However, duration of follow-up and duration of the seasons vary among subjects. Also, subject-specific variation can induce substantial error. Functional data analysis (FDA) and longitudinal data analysis (LDA) are the main approaches to analyze repeated measures data (in which multiple measurements are made on the same subject across time). Typically, FDA is applied when the data are dense, assumed to be observed in the continuum, and a function of time. LDA is usually applied when data are sparse, possibly with different number of measurements across individuals, and subject to error. We compared several FDA and LDA methods implemented through publicly available R code: k-means based on the standard Euclidian distance, a discrete Fréchet distance [2], and a functional distance [1]; Gaussian mixture model–based clustering for standard [4], longitudinal [5] and functional [3] data; and latent class mixed models [6]. We discuss advantages and limitations including computational and practical aspects.References[1] Febrero-Bande, M. and Oviedo de la Fuente, M. (2012). Statistical computing in functional data analysis: the R package fda.usc. Journal of Statistical Software, 51, 1–28.[2] Genolini, C. and Falissard, B. (2011). Kml : A package to cluster longitudinal data. Computer Methods and Programs in Biomedicine.[3] Jacques, J. and Preda, C. (2013). Funclust: A curves clustering method using functional random variables density approximation. Neurocomputing, 112, 164–171.[4] Lebret, R., Iovleff, S., Langrognet, F., Biernacki, C., Celeux, G., and Govaert, G. (2014). Rmixmod: The R package of the model–based unsupervised, supervised and semi–supervised classification mixmod library. Journal of Statistical Software.[5] McNicholas, P. D. and Murphy, T. B. (2010). Model–based clustering of longitudinal data. Canadian Journal of Statistics, 38, 153–168.[6] Proust-Lima, C., Philipps, V., and Liquet, B. (2015). Estimation of extended mixed models using latent classes and latent processes: the R package lcmm. Technical report, University of Bordeaux. arXiv:1503.00890v2

Clustering of temporal sport training curves: a comparison of FDA and LDA approaches

International audienceFunctional data analysis (FDA) and longitudinal data analysis (LDA) are the main approaches to analyze repeated measures data (in which multiple measurements are made on the same subject across time). Typically, FDA is applied when the data are dense, assumed to be observed in the continuum, and without noise. LDA is usually applied when data are sparse, possibly with different number of measurements across individuals, and subject to error.In elite sport, the parameters of the training program (intensity, volume, frequency, distribution and duration of high-intensity, recovery, and competition periods) should be manipulated systematically to optimize performance and reduce the risk of injury. Sport training data are recorded densely over time. However, measurements, such as duration of follow-up or duration of the season, vary among subjects. Subject-specific variations may introduce substantial measurement error.The statistical objectives of this study were the following:- First, to review the literature on the most commonly used methods for clustering of time evolution curves with a publicly available R code.- Second, to implement FDA and LDA methods presenting publicly available R code: k-means based on the standard Euclidian distance, a discrete Frèchet distance, and a distance of functions; Gaussian mixture model - based clustering for standard, longitudinal and functional data; and latent class mixed models.- Third, using data from a twenty - year longitudinal study of training practices of elite athletes, to perform a clustering analysis using relevant methods.- Fourth, to compare the results and interpret them. Comparison criteria were mainly based on computational and practical aspects.The practical goal of this project was to identify training profiles and to characterize them to provide relevant tools for supporting decision-making in monitoring athletes' training programs

Elite Swimmers’ Training Patterns in the 25 Weeks Prior to Their Season’s Best Performance: Insights Into Periodization From a 20-Years Cohort

International audienceBackground: This study investigated the periodization of elite swimmers' training over the 25 weeks preceding the major competition of the season

Role of up-front autologous stem cell transplantation in peripheral T-cell lymphoma for patients in response after induction: An analysis of patients from LYSA centers.

Background: Peripheral T-cell lymphoma (PTCL) remains a therapeutic challenge. Due to the rarity and the heterogeneity of PTCL, no consensus has been achieved regarding even the type of first-line treatment. The benefit of autologous stem-cell transplantation (ASCT) is, therefore, still intensely debated. Patients and methods: In the absence of randomized trials addressing the role of ASCT, we performed a large multicentric retrospective study and used both a multivariate proportional hazard model and a propensity score matching approach to correct for sample selection bias between patients allocated or not to ASCT in intention-to-treat (ITT). Results: Among 527 patients screened from 14 centers in France, Belgium and Portugal, a final cohort of 269 patients 65 years old with PTCL-not otherwise specified (NOS) (N¼78, 29%), angioimmunoblastic T-cell lymphoma (AITL) (N¼123, 46%) and anaplastic lymphoma kinase-positive anaplastic large cell lymphoma (ALK-ALCL) (N¼68, 25%) with partial (N¼52, 19%) or complete responses (N¼217, 81%) after induction was identified and information about treatment allocation was carefully collected before therapy initiation from medical records. One hundred and thirty-four patients were allocated to ASCT in ITT and 135 were not. Neither the Cox multivariate model (HR¼1.02; 95% CI: 0.69–1.50 for PFS and HR¼1.08; 95% CI: 0.68– 1.69 for OS) nor the propensity score analysis after stringent matching for potential confounding factors (logrank P¼0.90 and 0.66 for PFS and OS, respectively) found a survival advantage in favor of ASCT as a consolidation procedure for patients in response after induction. Subgroup analyses did not reveal any further difference for patients according to response status, stage disease or risk category. Conclusions: The present data do not support the use of ASCT for up-front consolidation for all patients with PTCL-NOS, AITL, or ALK-ALCL with partial or complete response after induction