Sex Disparities in Risk of Mortality Among Children With ESRD

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Effect of lacosamide on ethanol induced conditioned place preference and withdrawal associated behavior in mice: Possible contribution of hippocampal CRMP-2

BackgroundExcessive consumption of ethanol is known to activate the mTORC1 pathway and to enhance the Collapsin Response Mediator Protein-2 (CRMP-2) levels in the limbic region of brain. The latter helps in forming microtubule assembly that is linked to drug taking or addiction-like behavior in rodents. Therefore, in this study, we investigated the effect of lacosamide, an antiepileptic drug and a known CRMP-2 inhibitor, which binds to CRMP-2 and inhibits the formation of microtubule assembly, on ethanol-induced conditioned place preference (CPP) in mice.MethodsThe behavior of mice following ethanol addiction and withdrawal was assessed by performing different behavioral paradigms. Mice underwent ethanol-induced CPP training with alternate dose of ethanol (2 g/kg, po) and saline (10 ml/kg, po). The effect of lacosamide on the expression of ethanol-induced CPP and on ethanol withdrawal associated anxiety and depression-like behavior was evaluated. The effect of drug on locomotor activity was also assessed and hippocampal CRMP-2 levels were measured.ResultsEthanol-induced CPP was associated with enhanced CRMP-2 levels in the hippocampus. Lacosamide significantly reduced the expression of ethanol-induced CPP and alleviated the levels of hippocampal CRMP-2 but aggravated withdrawal-associated anxiety and depression in mice.ConclusionThe present study demonstrated the beneficial effect of lacosamide in attenuation of expression of ethanol induced conditioned place preference via reduction of hippocampal CRMP-2 level. These findings suggest that lacosamide may be investigated further for ethanol addiction but not for managing withdrawal.     

Toxicity of ZnO nanoparticles (NPs) to THP-1 macrophages: interactions with saturated or unsaturated free fatty acids

In a biological microenvironment, free fatty acids (FFA) as ubiquitous biological molecules might interact with nanoparticles (NPs) and consequently change the toxicological responses. However, whether the chemical structures of FFA could influence their interactions with NPs remain unknown. This study investigated the interactions between ZnO NPs and saturated or unsaturated FFA (complexed to BSA), namely stearic acid (SA, C18:0), oleic acid (OA, C18:1), and α-linolenic acid (ALA, C18:3). It was shown that BSA, SA, OA, and ALA increased the atomic force microscope (AFM) heights as well the polydispersity index (PDI) of ZnO NPs. BSA modestly protected THP-1 macrophages from ZnO NP exposure, whereas OA and ALA led to relatively less cyto-protective effects of BSA. Moreover, only co-exposure to ZnO NPs and SA significantly promoted the release of interleukin-8. BSA, SA, OA, and ALA equally changed intracellular ROS and Zn ions associated with ZnO exposure, but co-exposure to ZnO NPs and OA/ALA particularly activated the expression of endoplasmic reticulum stress-apoptosis genes. In combination, these results showed that FFA could influence the colloidal aspects and toxicological signaling pathway of ZnO NPs, which is dependent on the number of unsaturated bonds of FFA.     

Building and Exploitation of Learning Curves to Train Radiographer Students in X-Ray CT Image Postprocessing

IntroductionThis study aims to construct learning curves related to the realization of standardized postprocessing by radiographer students and to discuss their exploitation and interest.Materials and MethodsThis study was carried out in 21 French students in their 3rd year of training. Two postprocessing protocols in CT (#1 traumatic shoulder; #2 petrous bone) were repeated 15 times by each student. Each achievement was timed to obtain overall learning curves. The realization accuracy was also assessed for each student at each repetition.ResultsThe learning rates for the two protocols are 63% and 56%, respectively. The number of repetitions to reach the reference time for each protocol is 11 and 12, respectively. In both protocols, the standard deviations are significantly reduced and stabilized during repetitions. The mean accuracy progresses more quickly in protocol #1.DiscussionThe measured learning rates reflect a rapid learning process for each protocol. The analysis of the standard deviations shows that students have reached a homogeneous level. The average times and accuracies measured during the last repetitions show that the group has reached a high level of performance. Building learning curves helps students measure their progress and motivates them.ConclusionObtaining learning curves allows trainers/supervisors to qualify the learning difficulty of a task while motivating students/radiographers. The use of learning curves is inline with the competency-based training paradigm.     

A Multi-Scale DNN Algorithm for Nonlinear Elliptic Equations with Multiple Scales

Algorithms based on deep neural networks (DNNs) have attracted increasing attention from the scientific computing community. DNN based algorithms are easy to implement, natural for nonlinear problems, and have shown great potential to overcome the curse of dimensionality. In this work, we utilize the multi-scale DNN-based algorithm (MscaleDNN) proposed by Liu, Cai and Xu (2020) to solve multi-scale elliptic problems with possible nonlinearity, for example, the p-Laplacian problem. We improve the MscaleDNN algorithm by a smooth and localized activation function. Several numerical examples of multi-scale elliptic problems with separable or non-separable scales in low-dimensional and high-dimensional Euclidean spaces are used to demonstrate the effectiveness and accuracy of the MscaleDNN numerical scheme.