Cellular and Behavioral Effects of Cranial Irradiation of the Subventricular Zone in Adult Mice

Background: In mammals, new neurons are added to the olfactory bulb (OB) throughout life. Most of these new neurons, granule and periglomerular cells originate from the subventricular zone (SVZ) lining the lateral ventricles and migrate via the rostral migratory stream toward the OB. Thousands of new neurons appear each day, but the function of this ongoing neurogenesis remains unclear. Methodology/Principal Findings: In this study, we irradiated adult mice to impair constitutive OB neurogenesis, and explored the functional impacts of this irradiation on the sense of smell. We found that focal irradiation of the SVZ greatly decreased the rate of production of new OB neurons, leaving other brain areas intact. This effect persisted for up to seven months after exposure to 15 Gray. Despite this robust impairment, the thresholds for detecting pure odorant molecules and short-term olfactory memory were not affected by irradiation. Similarly, the ability to distinguish between odorant molecules and the odorant-guided social behavior of irradiated mice were not affected by the decrease in the number of new neurons. Only long-term olfactory memory was found to be sensitive to SVZ irradiation. Conclusion/Significance: These findings suggest that the continuous production of adult-generated neurons is involved i

Analysis of shared common genetic risk between amyotrophic lateral sclerosis and epilepsy

Because hyper-excitability has been shown to be a shared pathophysiological mechanism, we used the latest and largest genome-wide studies in amyotrophic lateral sclerosis (n = 36,052) and epilepsy (n = 38,349) to determine genetic overlap between these conditions. First, we showed no significant genetic correlation, also when binned on minor allele frequency. Second, we confirmed the absence of polygenic overlap using genomic risk score analysis. Finally, we did not identify pleiotropic variants in meta-analyses of the 2 diseases. Our findings indicate that amyotrophic lateral sclerosis and epilepsy do not share common genetic risk, showing that hyper-excitability in both disorders has distinct origins

Increasing secondary diagnosis encoding quality using data mining techniques

International audienceIn order to measure the medical activity, hospitals are required to manually encode information concerning an inpatient episode using International Classification of Disease (ICD-10). This task is time consuming and requires substantial training for the staff. We propose to help by speeding up and facilitating the tedious task of coding patient information, specially while coding some secondary diagnoses that are not well described in the medical resources such as discharge letter and medical records. Our approach leverages data mining techniques in order to explore medical databases of previously encoded secondary diagnoses and use the stored structured information (age, gender, diagnoses count, medical procedures...) to build a decision tree that assigns the proper secondary diagnosis code into the corresponding inpatient episode or indicates the impatient episodes that contains implausible secondary diagnoses. The results suggest that better performance could be achieved by using low level of diagnoses granularity along with adding some filters to balance the repartition of the negative and positive examples in the training set. The obtained results show that there is big variation in the evaluation scores of the studied diagnoses, the highest score is 75% using F1 measurement and the lowest 25% using F1 measurement which indicates further enhancements are needed to achieve better performance regardless of the encoded diagnosis. However, the average accuracy of all the studied secondary diagnoses is around 80% which indicates better negative predictions therefore it could be useful in the prevention or the detection of wrong coding assignments of secondary diagnoses in the inpatient stay

Predicting the encoding of secondary diagnoses. An experience based on decision trees

International audienceIn order to measure the medical activity, hospitals are required to manually encode diagnoses concerning an inpatient episode using the International Classification of Disease (ICD-10). This task is time consuming and requires substantial training for the staff. In this paper, we are proposing an approach able to speed up and facilitate the tedious manual task of coding patient information, especially while coding some secondary diagnoses that are not well described in the medical resources such as discharge letters and medical records. Our approach leverages data mining techniques, and specifically decision trees, in order to explore medical databases that encode such diagnoses knowledge. It uses the stored structured information (age, gender, diagnoses count, medical procedures, etc.) to build a decision tree which assigns the appropriate secondary diagnosis code into the corresponding inpatient episode. We have evaluated our approach on the PMSI database using fine and coarse levels of diagnoses granularity. Three types of experimentations have been performed using different techniques to balance datasets. The results show a significant variation in the evaluation scores between the different techniques for the same studied diagnoses. We highlight the efficiency of the random sampling techniques regardless of the type of diagnoses and the type of measure (F1-measure, recall and precision)

Une approche pour la sélection de variables stables : application à l'encodage des diagnostics secondaires

International audienceIn this paper, we focus on applying feature selection in the context of secondary diagnoses prediction starting from medico-economic data sources. The results of the prediction is used as guidelines for encoding secondary diagnoses which is a sensitive task in the hospitals requiring a lot of attention in order to be achieved properly. We propose a practical approach to select stable and relevant features from imbalanced datasets. The stability of features is obtained through the convergence of several FS methods to a fair number of features without being impacted by the sampled dataset. The quality of featured shall be deducted from the quality prediction of machine learning algorithms on the selected features. We evaluate the proposed approach on the PMSI database of the CHIC-CM hospital. Our results are quite interesting and opening discussions for these specific health care data supports.Dans cet article, nous proposons une approche pour sélectionner des variables stables dans le contexte de prédiction des diagnostiques secondaires en partant d’une base de données médico-économique, en l’occurrence le PMSI. Les résultats de prédiction se présentent sous forme de guides pour l’activité d’encodage des diagnostiques secondaires dans les départements d’information médicale (DIM). L’approche que nous proposons dans ce papier consiste à exploiter les paradigmes ensemblistes sur les sources de données réduites et équilibrées pour déduire un ensemble stable et fiable de variables utiles à la prédiction. Cet ensemble est construit de façon très indépendante par rapport à l’échantillon de données utilisé pour l’apprentissage du modèle. La qualité des variables est déduite en fonction de la qualité de prédiction des algorithmes de ML. L’évaluation de notre approche sur les données de PMSI montre le réel intérêt de cette proposition et ouvre le débat sur l’application de ces méthodes à ces sources de données très rarement exploitées par la communauté scientifique

Predicting Patient’s Waiting Times in Emergency Department: A Retrospective Study in the CHIC Hospital Since 2019

International audiencePredicting patient waiting times in public emergency department rooms (EDs) has relied on inaccurate rolling average or median estimators. This inefficiency negatively affects EDs resources and staff management and causes patient dissatisfaction and adverse outcomes. This paper proposes a data science-oriented method to analyze real retrospective data. Using different error metrics, we applied various Machine Learning (ML) and Deep learning (DL) techniques to predict patient waiting times, including RF, Lasso, Huber regressor, SVR, and DNN. We examined data on 88,166 patients’ arrivals at the ED of the Intercommunal Hospital Center of Castres-Mazamet (CHIC). The results show that the DNN algorithm has the best predictive capability among other models. By precise and real-time prediction of patient waiting times, EDs can optimize their activities and improve the quality of services offered to patients

The European Society of Cardiology Cardiac Resynchronization Therapy Survey II A comparison of cardiac resynchronization therapy implantation practice in Europe and France

International audienceBackground - The first European Cardiac Resynchronization Therapy (CRT) Survey, conducted in 2008-2009, showed considerable variations in guideline adherence and implantation practice. A second prospective survey (CRT Survey II) was then performed to describe contemporary clinical practice regarding CRT among 42 European countries. Aim - To compare the characteristics of French CRT recipients with the overall European population of CRT Survey II. Methods - Demographic and procedural data from French centres recruiting all consecutive patients undergoing either de novo CRT implantation or an upgrade to a CRT system were collected and compared with data from the European population. Results - A total of 11,088 patients were enrolled in CRT Survey II, 754 of whom were recruited in France. French patients were older (44.7% aged≥75 years vs 31.1% in the European group), had less severe heart failure symptoms, a higher baseline left ventricular ejection fraction and fewer co-morbidities. Additionally, French patients had a shorter intrinsic QRS duration (19.1% had a QRS<130ms vs 12.3% in the European cohort). Successful implantation rates were similar, but procedural and fluoroscopy times were shorter in France. French patients were more likely to receive a CRT pacemaker than European patients overall. Of note, antibiotic prophylaxis was reported to be administered less frequently in France, and a higher rate of early device-related infection was observed. Importantly, French patients were less likely to receive optimal drugs for treating heart failure at hospital discharge. Conclusion - This study highlights contemporary clinical practice in France, and describes substantial differences in patient selection, implantation procedure and outcomes compared with the other European countries participating in CRT Survey II

Development of genomic resources to study the structure, function and evolution of the oak genome

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Development of genomic resources to study the structure, function and evolution of the oak genome

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