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In addition, overexpression of human AR produced no benefit to disease onset and progression in SOD1G93A mice. In conclusion, the disease course of SOD1G93A mice is independent of AR expression levels, implicating other mechanisms involved in mediating the sex differences in ALS. Our findings using Nestin-Cre mice, which show an inherent metabolic phenotype, led us to hypothesise that targeting hypermetabolism associated with ALS may be a more potent modulator of disease, than AR in this mouse model.This paper presents the results of experimental investigations of the plasma surface modification of a poly(methyl methacrylate) (PMMA) polymer and PMMA composites with a [6,6]-phenyl-C61-butyric acid methyl ester fullerene derivative (PC61BM). An atmospheric pressure microwave (2.45 GHz) argon plasma sheet was used. The experimental parameters were an argon (Ar) flow rate (up to 20 NL/min), microwave power (up to 530 W), number of plasma scans (up to 3) and, the kind of treated material. In order to assess the plasma effect, the possible changes in the wettability, roughness, chemical composition, and mechanical properties of the plasma-treated samples' surfaces were evaluated by water contact angle goniometry (WCA), atomic force microscopy (AFM), attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and X-ray photoelectron spectroscopy (XPS). The best result concerning the water contact angle reduction was from 83° to 29.7° for the PMMA material. The ageing studies of the PMMA plasma-modified surface showed long term (100 h) improved wettability. As a result of plasma treating, changes in the samples surface roughness parameters were observed, however their dependence on the number of plasma scans is irregular. The ATR-FTIR spectra of the PMMA plasma-treated surfaces showed only slight changes in comparison with the spectra of an untreated sample. The more significant differences were demonstrated by XPS measurements indicating the surface chemical composition changes after plasma treatment and revealing the oxygen to carbon ratio increase from 0.1 to 0.4.A major number of studies have demonstrated Beta-tricalcium phosphate (β-TCP) biocompatibility, bioactivity, and osteoconductivity characteristics in bone regeneration. The aim of this research was to enhance β-TCP's biocompatibility, and evaluate its physicochemical properties by argon glow discharge plasma (GDP) plasma surface treatment without modifying its surface. Treated β-TCP was analyzed by scanning electron microscopy (SEM), energy-dispersive spectrometry, X-ray photoelectron spectroscopy (XPS), X-ray diffraction analysis, and Fourier transform infrared spectroscopy characterization. To evaluate treated β-TCP biocompatibility and osteoblastic differentiation, water-soluble tetrazolium salts-1 (WST-1), immunofluorescence, alkaline phosphatase (ALP) assay, and quantitative real-time polymerase chain reaction (QPCR) were done using human mesenchymal stem cells (hMSCs). Catechin hydrate research buy The results indicated a slight enhancement of the β-TCP by GDP sputtering, which resulted in a higher Ca/P ratio (2.05) than the control. Furthermore, when compared with control β-TCP, we observed an improvement of WST-1 on all days (p  less then  0.05) as well as of ALP activity (day 7, p  less then  0.05), with up-regulation of ALP, osteocalcin, and Osteoprotegerin osteogenic genes in cells cultured with the treated β-TCP. XPS and SEM results indicated that treated β-TCP's surface was not modified. In vivo, micro-computed tomography and histomorphometric analysis indicated that the β-TCP test managed to regenerate more new bone than the untreated β-TCP and control defects at 8 weeks (p  less then  0.05). Argon GDP treatment is a viable method for removing macro and micro particles of  less then  7 μm in size from β-TCP bigger particles surfaces and therefore improving its biocompatibility with slight surface roughness modification, enhancing hMSCs proliferation, osteoblastic differentiation, and stimulating more new bone formation.This cross-sectional study aimed to build a deep learning model for detecting neovascular age-related macular degeneration (AMD) and to distinguish retinal angiomatous proliferation (RAP) from polypoidal choroidal vasculopathy (PCV) using a convolutional neural network (CNN). Patients from a single tertiary center were enrolled from January 2014 to January 2020. Spectral-domain optical coherence tomography (SD-OCT) images of patients with RAP or PCV and a control group were analyzed with a deep CNN. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) were used to evaluate the model's ability to distinguish RAP from PCV. The performances of the new model, the VGG-16, Resnet-50, Inception, and eight ophthalmologists were compared. A total of 3951 SD-OCT images from 314 participants (229 AMD, 85 normal controls) were analyzed. In distinguishing the PCV and RAP cases, the proposed model showed an accuracy, sensitivity, and specificity of 89.1%, 89.4%, and 88.8%, respectively, with an AUROC of 95.3% (95% CI 0.727-0.852). The proposed model showed better diagnostic performance than VGG-16, Resnet-50, and Inception-V3 and comparable performance with the eight ophthalmologists. The novel model performed well when distinguishing between PCV and RAP. Thus, automated deep learning systems may support ophthalmologists in distinguishing RAP from PCV.A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments.