Cooleystilling1433
From the onset of schizophrenia, verbal memory (VM) deficits and negative symptoms are strongly associated, and both additively predict functional outcomes. Emotion recognition (ER) and theory of mind (ToM; the ability to infer others' mental states), two components of social cognition, are also particularly affected in schizophrenia. Explanatory models of negative symptoms have integrated these cognitive impairments as potential precursors and previous studies revealed relationships between ER and/or ToM and VM, as well as with negative symptoms, but the organization of these associations remains unclear. We aimed to determine whether impairments in VM and social cognition sequentially pave the way for negative symptoms in schizophrenia. To this end, we used mediation analyses. One hundred and forty participants with a diagnosis of schizophrenia or schizoaffective disorder were recruited. First, correlational analyses were conducted between our variables of interest. The mediating effect of social cognition between VM and negative symptoms was then examined using the PROCESS macro. FF-10101 Variables of interest were significantly correlated (r = |0.166| to |0.391|), except for ER and negative symptoms. Only the serial multiple mediation model with 2 mediators (ER followed by ToM) revealed a significant indirect effect of VM on negative symptoms (β = - 0.160, 95% CI = -.370 to -.004). This relationship was selective for expressive negative symptoms (e.g., blunted affect and alogia). This study illustrates the richness of the relationship between cognitive deficits and negative symptoms and provides additional information for the involvement of social cognition in negative symptoms' etiology.Major depressive disorder (MDD) is characterized by dysregulation of stress systems and by abnormalities in cerebral energy metabolism. Stress induction has been shown to impact neurometabolism in healthy individuals. Contrarily, neurometabolic changes in response to stress are insufficiently investigated in MDD patients. Metabolic stress was induced in MDD patients (MDD, N = 24) and in healthy individuals (CTRL, N = 22) by application of an established fasting protocol in which calorie intake was omitted for 72 h. Both study groups were comparable regarding age, gender distribution, and body mass index (BMI). Fasting-induced effects on brain high-energy phosphate levels and membrane phospholipid metabolism were assessed using phosphorus-31 magnetic resonance spectroscopy (31P-MRS). Two-way repeated measures ANOVAs did not reveal significant interaction effects (group x fasting) or group differences in adenosine triphosphate (ATP), phosphocreatine (PCr), inorganic phosphate (Pi), phosphomonoesters (PME), phosphodiesters (PDE), or pH levels between MDD and CTRL. Fasting, independent of group, significantly increased ATP and decreased Pi levels and an overall increase in PME/PDE ratio as marker for membrane turnover was observed. Overall these results indicate reactive changes in cerebral energetics and in membrane phospholipid metabolism in response to fasting. The observed effects did not significantly differ between CTRL and MDD, indicating that neurometabolic adaptation to metabolic stress is preserved in MDD patients.
Clinical practice forces the necessity to conduct a clinical trial concerning the group of outpatients with chronically advanced heart failure in III or IV NYHA functional class, frequently requiring hospitalizations due to HF exacerbation, and often left without any additional therapeutic option. The current trial aims to determine the efficacy and safety of repeated levosimendan infusions in the group of severe outpatients with reduced ejection fraction (HFrEF).
LEIA-HF (LEvosimendan In Ambulatory Heart Failure Patients) is a multicentre, randomized, double-blind, placebo-controlled, phase 4 clinical trial to determine whether the repetitive use of levosimendan reduces the incidence of adverse cardiovascular events in ambulatory patients with chronic, advanced HFrEF. A total of 350 patients will be randomized in a 11 ratio to receive either levosimendan or placebo, which will be administered as continuous 24h infusions, every 4 weeks for 48 weeks (12 infusions in total - phase I), and followed by double-blind 6 visits, every 4 weeks (phase II of the trial including the option of restarting levosimendan or placebo, based on the fulfillment of additional criteria). The primary endpoint for efficacy assessment will be death from any cause or unplanned hospitalization for HF assessed together, whichever occurs first, in a 12-month follow-up period.
A well-designed study with a consistent protocol, including the drug side effects, comprehensive clinical assessment, appropriate definition of endpoints, and monitoring therapy, may provide a complete overview of the effectiveness and safety profile of the repetitive levosimendan administration in ambulatory severe HFrEF patients.
A well-designed study with a consistent protocol, including the drug side effects, comprehensive clinical assessment, appropriate definition of endpoints, and monitoring therapy, may provide a complete overview of the effectiveness and safety profile of the repetitive levosimendan administration in ambulatory severe HFrEF patients.Computed tomography (CT) scans and magnetic resonance imaging (MRI) of spines are state-of-the-art for the evaluation of spinal cord lesions. This paper analyses micro-CT scans of rat spinal cords with the aim of generating lesion progression through the aggregation of anomaly-based scores. Since reliable labelling in spinal cords is only reasonable for the healthy class in the form of untreated spines, semi-supervised deviation-based anomaly detection algorithms are identified as powerful approaches. The main contribution of this paper is a large evaluation of different autoencoders and variational autoencoders for aggregated lesion quantification and a resulting spinal cord lesion quantification method that generates highly correlating quantifications. The conducted experiments showed that several models were able to generate 3D lesion quantifications of the data. These quantifications correlated with the weakly labelled true data with one model, reaching an average correlation of 0.83. We also introduced an area-based model, which correlated with a mean of 0.84. The possibility of the complementary use of the autoencoder-based method and the area feature were also discussed. Additionally to improving medical diagnostics, we anticipate features built on these quantifications to be useful for further applications like clustering into different lesions.To date, much attention has been paid to phytochemicals because of their diverse pharmacological effects on a variety of diseases such as cancer. In this regard, computer-aided drug design, as a cost- and time-effective approach, is primarily applied to investigate the drug candidates before their further costly in vitro and in vivo experimental evaluations. Accordingly, different signaling pathways and proteins can be targeted using such strategies. As a key protein for the initiation of eukaryotic DNA replication, mini-chromosome maintenance complex component 7 (MCM7) overexpression is related to the initiation and progression of aggressive malignancies. The current study was conducted to identify new potential natural compounds from the yellow sweet clover, Melilotus officinalis (Linn.) Pall, by examining the potential of 40 isolated phytochemicals against MCM7 protein. A structure-based pharmacophore model to the protein active site cavity was generated and followed by virtual screening and molecular docking. Overall, four compounds were selected for further evaluation based on their binding affinities. Our analyses revealed that two novel compounds, namely rosmarinic acid (PubChem CID5281792) and melilotigenin (PubChem CID14059499) might be druggable and offer safe usage in human. link2 The stability of these two protein-ligand complex structures was confirmed through molecular dynamics simulation. The findings of this study reveal the potential of these two phytochemicals to serve as anticancer agents, while further pharmacological experiments are required to confirm their effectiveness against human cancers.COVID-19 heavily affects breathing and voice and causes symptoms that make patients' voices distinctive, creating recognizable audio signatures. link3 Initial studies have already suggested the potential of using voice as a screening solution. In this article we present a dataset of voice, cough and breathing audio recordings collected from individuals infected by SARS-CoV-2 virus, as well as non-infected subjects via large scale crowdsourced campaign. We describe preliminary results for detection of COVID-19 from cough patterns using standard acoustic features sets, wavelet scattering features and deep audio embeddings extracted from low-level feature representations (VGGish and OpenL3). Our models achieve accuracy of 88.52%, sensitivity of 88.75% and specificity of 90.87%, confirming the applicability of audio signatures to identify COVID-19 symptoms. We furthermore provide an in-depth analysis of the most informative acoustic features and try to elucidate the mechanisms that alter the acoustic characteristics of coughs of people with COVID-19.Alzheimer's disease (AD) is a severe neurodegenerative disorder that usually starts slowly and progressively worsens. Predicting the progression of Alzheimer's disease with longitudinal analysis on the time series data has recently received increasing attention. However, training an accurate progression model for brain network faces two major challenges missing features, and the small sample size during the follow-up study. According to our analysis on the AD progression task, we thoroughly analyze the correlation among the multiple predictive tasks of AD progression at multiple time points. Thus, we propose a multi-task learning framework that can adaptively impute missing values and predict future progression over time from a subject's historical measurements. Progression is measured in terms of MRI volumetric measurements, trajectories of a cognitive score and clinical status. To this end, we propose a new perspective of predicting the AD progression with a multi-task learning paradigm. In our multi-task learning paradigm, we hypothesize that the inherent correlations exist among (i). the prediction tasks of clinical diagnosis, cognition and ventricular volume at each time point; (ii). the tasks of imputation and prediction; and (iii). the prediction tasks at multiple future time points. According to our findings of the task correlation, we develop an end-to-end deep multi-task learning method to jointly improve the performance of assigning missing value and prediction. We design a balanced multi-task dynamic weight optimization. With in-depth analysis and empirical evidence on Alzheimer's Disease Neuroimaging Initiative (ADNI), we show the benefits and flexibility of the proposed multi-task learning model, especially for the prediction at the M60 time point. The proposed approach achieves 5.6%, 5.7%, 4.0% and 11.8% improvement with respect to mAUC, BCA and MAE (ADAS-Cog13 and Ventricles), respectively.
