Hydroxyzine Use and Mortality in Patients Hospitalized for COVID-19: A Multicenter Observational Study.

(1) Background: Based on its antiviral activity, anti-inflammatory properties, and functional inhibition effects on the acid sphingomyelinase/ceramide system (FIASMA), we sought to examine the potential usefulness of the H1 antihistamine hydroxyzine in patients hospitalized for COVID-19. (2) Methods: In a multicenter observational study, we included 15,103 adults hospitalized for COVID-19, of which 164 (1.1%) received hydroxyzine within the first 48 h of hospitalization, administered orally at a median daily dose of 25.

External validation of prognostic scores for COVID-19: a multicenter cohort study of patients hospitalized in Greater Paris University Hospitals.

Purpose: The Coronavirus disease 2019 (COVID-19) has led to an unparalleled influx of patients. Prognostic scores could help optimizing healthcare delivery, but most of them have not been comprehensively validated. We aim to externally validate existing prognostic scores for COVID-19.

Dexamethasone use and mortality in hospitalized patients with coronavirus disease 2019: A multicentre retrospective observational study.

Aims: To examine the association between dexamethasone use and mortality among patients hospitalized for COVID-19.

Methods: We examined the association between dexamethasone use and mortality at AP-HP Greater Paris University hospitals. Study baseline was defined as the date of hospital admission.

Observational study of haloperidol in hospitalized patients with COVID-19.

Background: Haloperidol, a widely used antipsychotic, has been suggested as potentially useful for patients with COVID-19 on the grounds of its in-vitro antiviral effects against SARS-CoV-2, possibly through sigma-1 receptor antagonist effect.

Methods: We examined the associations of haloperidol use with intubation or death and time to discharge home among adult patients hospitalized for COVID-19 at Assistance Publique-Hôpitaux de Paris (AP-HP) Greater Paris University hospitals. Study baseline was defined as the date of hospital admission.

Observational Study of Chlorpromazine in Hospitalized Patients with COVID-19.

Introduction: Chlorpromazine has been suggested as being potentially useful in patients with coronavirus disease 2019 (COVID-19) on the grounds of its potential antiviral and anti-inflammatory effects.

Objective: The aim of this study was to examine the association between chlorpromazine use and mortality among adult patients hospitalized for COVID-19.

Methods: We conducted an observational, multicenter, retrospective study at Assistance Publique-Hôpitaux de Paris (AP-HP) Greater Paris University hospitals.

Association between antidepressant use and reduced risk of intubation or death in hospitalized patients with COVID-19: results from an observational study.

A prior meta-analysis showed that antidepressant use in major depressive disorder was associated with reduced plasma levels of several pro-inflammatory mediators, which have been associated with severe COVID-19. Recent studies also suggest that several antidepressants may inhibit acid sphingomyelinase activity, which may prevent the infection of epithelial cells with SARS-CoV-2, and that the SSRI fluoxetine may exert in-vitro antiviral effects on SARS-CoV-2. We examined the potential usefulness of antidepressant use in patients hospitalized for COVID-19 in an observational multicenter retrospective cohort study conducted at AP-HP Greater Paris University hospitals.

International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium.

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.

Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers.

Electrophysiological methods, that is M/EEG, provide unique views into brain health. Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods. Information can be redundant, useful common representations of multimodal data may not be obvious and multimodal data collection can be medically contraindicated, which reduces applicability.

Observation and Interview-based Diurnal Sleepiness Inventory for measurement of sleepiness in patients referred for narcolepsy or idiopathic hypersomnia.

Study Objectives: First, to determine whether the 3-item Observation and Interview-based Diurnal Sleepiness Inventory (ODSI) measures the degree of excessive daytime sleepiness in patients with suspected narcolepsy or idiopathic hypersomnia (IH). Second, to assess the correlation between the ODSI and the Epworth Sleepiness Scale (ESS) as well as objective polysomnographic measurements. Third, to test the accuracy of the ODSI to detect narcolepsy or IH (narcolepsy/IH) compared with the ESS.

Is loudness part of a sound recognition process?

Influence of loudness on sound recognition was investigated in an explicit memory experiment based on a conscious recollection-test phase-of previously encoded information-study phase. Three encoding conditions were compared: semantic (sounds were sorted in three different categories), sensory (sounds were rated in loudness), and control (participants were solely asked to listen to the sounds). Results revealed a significant study-to-test change effect: loudness change between the study and the test phases affects recognition.

Prediction of three articulatory categories in vocal sound imitations using models for auditory receptive fields.

Vocal sound imitations provide a new challenge for understanding the coupling between articulatory mechanisms and the resulting audio. In this study, the classification of three articulatory categories, , , and , have been modeled from audio recordings. Two data sets were assembled, consisting of different vocal imitations by four professional imitators and four non-professional speakers in two different experiments.

Computer-aided detection for prostate cancer detection based on multi-parametric magnetic resonance imaging.

Prostate cancer (CaP) is the second most diagnosed cancer in men all over the world. In the last decades, new imaging techniques based on magnetic resonance imaging (MRI) have been developed improving diagnosis. In practice, diagnosis is affected by multiple factors such as observer variability and visibility and complexity of the lesions.

Rising tones and rustling noises: Metaphors in gestural depictions of sounds.

Communicating an auditory experience with words is a difficult task and, in consequence, people often rely on imitative non-verbal vocalizations and gestures. This work explored the combination of such vocalizations and gestures to communicate auditory sensations and representations elicited by non-vocal everyday sounds. Whereas our previous studies have analyzed vocal imitations, the present research focused on gestural depictions of sounds.

Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images.

Background: Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well as the retina layers.

Methods: The dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc.

Video-based depression detection using local Curvelet binary patterns in pairwise orthogonal planes.

Depression is an increasingly prevalent mood disorder. This is the reason why the field of computer-based depression assessment has been gaining the attention of the research community during the past couple of years. The present work proposes two algorithms for depression detection, one Frame-based and the second Video-based, both employing Curvelet transform and Local Binary Patterns.

Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections.

This paper deals with the automated detection of Diabetic Macular Edema (DME) on Optical Coherence Tomography (OCT) volumes. Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan. Features such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are extracted and combined to create a set of different feature vectors which are fed to a linear-Support Vector Machines (SVM) Classifier.

An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images.

This paper proposes a method for automatic classification of spectral domain OCT data for the identification of patients with retinal diseases such as Diabetic Macular Edema (DME). We address this issue as an anomaly detection problem and propose a method that not only allows the classification of the OCT volume, but also allows the identification of the individual diseased B-scans inside the volume. Our approach is based on modeling the appearance of normal OCT images with a Gaussian Mixture Model (GMM) and detecting abnormal OCT images as outliers.

Who's that Knocking at My Door? Neural Bases of Sound Source Identification.

When hearing knocking on a door, a listener typically identifies both the action (forceful and repeated impacts) and the object (a thick wooden board) causing the sound. The current work studied the neural bases of sound source identification by switching listeners' attention toward these different aspects of a set of simple sounds during functional magnetic resonance imaging scanning: participants either discriminated the action or the material that caused the sounds, or they simply discriminated meaningless scrambled versions of them. Overall, discriminating action and material elicited neural activity in a left-lateralized frontoparietal network found in other studies of sound identification, wherein the inferior frontal sulcus and the ventral premotor cortex were under the control of selective attention and sensitive to task demand.

Vocal Imitations of Non-Vocal Sounds.

Imitative behaviors are widespread in humans, in particular whenever two persons communicate and interact. Several tokens of spoken languages (onomatopoeias, ideophones, and phonesthemes) also display different degrees of iconicity between the sound of a word and what it refers to. Thus, it probably comes at no surprise that human speakers use a lot of imitative vocalizations and gestures when they communicate about sounds, as sounds are notably difficult to describe.

Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection.

This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with DME versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step.

The Egocentric Nature of Action-Sound Associations.

Actions that produce sounds infuse our daily lives. Some of these sounds are a natural consequence of physical interactions (such as a clang resulting from dropping a pan), but others are artificially designed (such as a beep resulting from a keypress). Although the relationship between actions and sounds has previously been examined, the frame of reference of these associations is still unknown, despite it being a fundamental property of a psychological representation.

Vocal imitations of basic auditory features.

Describing complex sounds with words is a difficult task. In fact, previous studies have shown that vocal imitations of sounds are more effective than verbal descriptions [Lemaitre and Rocchesso (2014). J.

Priming Gestures with Sounds.

We report a series of experiments about a little-studied type of compatibility effect between a stimulus and a response: the priming of manual gestures via sounds associated with these gestures. The goal was to investigate the plasticity of the gesture-sound associations mediating this type of priming. Five experiments used a primed choice-reaction task.

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: a review.

Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions.