Songgormsen0646

Three panels of lipid biomarkers specific to AECOPD, AECOPD subtypes 1 and 2 vs SCOPD yielded areas under the receiver operating characteristic curve of 0.788, 0.921 and 0.920, respectively, with sensitivity of 77.5%, 80.7% and 91.3%, respectively, and specificity of 75.8%, 97.0% and 87.9%, respectively. IM156 mouse The result indicated differences in lipid metabolism may underlie AECOPD and its 2 subtypes and can serve as biomarkers for early diagnosis, and high-coverage lipidomics proved to be an accurate approach to profile the lipid metabolism in biological samples.Brain networks constructed with regions of interest (ROIs) from the structural magnetic resonance imaging (sMRI) image are widely investigated for detecting Alzheimer's disease (AD). However, the ROI is generally represented by spatial domain-based features, so attentions are hardly paid to constructing a brain network with the frequency domain-based feature. In order to accurately characterize the ROI in the frequency domain and then construct an individual network, in this study, a novel method, which can describe the ROI properly by directional subbands and capture correlations between those ROIs, is proposed to construct a shearlet subband energy feature-based individual network (SSBIN) for AD detection. Specifically, the SSBIN is constructed with 90 ROIs which are segmented from the pre-processed sMRI image based on the automated anatomical labeling atlas, the 90 ROIs are represented by directional subband-based energy feature vectors (SVs) formed by jointing energy features extracted from their directional subbands, and the weight values of the SSBIN are computed by Pearson's correlation coefficient (PCC). Subsequently, two network features are extracted from the SSBIN the node feature vector (NV) is computed by averaging the 90 SVs; the low dimensional edge feature vector (LV) is obtained by kernel principal component analysis (KPCA). Following that the concatenation of NV and LV is used as a SSBIN-based feature for the sMRI image. Finally, we use support vector machine (SVM) with the radial basis function kernel as classifier to categorize 680 subjects selected from the AD Neuroimaging Initiative (ADNI) database. Experimental results validate that the ROI can be properly characterized by the NV, and correlations between ROIs captured by the LV play an important role in AD detection. Besides, a series of comparisons with four current state-of-the-art approaches demonstrate the higher AD detecting performance of the SSBIN method.An image reconstruction method that can simultaneously provide high image quality and frame rate is necessary for diagnosis on cardiovascular imaging but is challenging for plane-wave ultrasound imaging. To overcome this challenge, an end-to-end ultrasound image reconstruction method is proposed for reconstructing a high-resolution B-mode image from radio frequency (RF) data. A modified U-Net architecture that adopts EfficientNet-B5 and U-Net as the encoder and decoder parts, respectively, is proposed as a deep learning beamformer. The training data comprise pairs of pre-beamformed RF data generated from random scatterers with random amplitudes and corresponding high-resolution target data generated from coherent plane-wave compounding (CPWC). To evaluate the performance of the proposed beamforming model, simulation and experimental data are used for various beamformers, such as delay-and-sum (DAS), CPWC, and other deep learning beamformers, including U-Net and EfficientNet-B0. Compared with single plane-wave imaging with DAS, the proposed beamforming model reduces the lateral full width at half maximum by 35% for simulation and 29.6% for experimental data and improves the contrast-to-noise ratio and peak signal-to-noise ratio, respectively, by 6.3 and 9.97 dB for simulation, 2.38 and 3.01 dB for experimental data, and 3.18 and 1.03 dB for in vivo data. Furthermore, the computational complexity of the proposed beamforming model is four times less than that of the U-Net beamformer. The study results demonstrate that the proposed ultrasound image reconstruction method employing a deep learning beamformer, trained by the RF data from scatterers, can reconstruct a high-resolution image with a high frame rate for single plane-wave ultrasound imaging.Electrokinetic phenomena, especially electroosmosis in ion-selective environments, play a key role in many systems, from ion-selective nanopores to cellular processes. In this paper, the impact of ionic size on the electroosmotic flow through an ion-selective soft slit nanochannel is analytically studied. Meanwhile, the modified Poisson-Boltzmann and the modified Navier-Stokes equations were used for modeling the electrostatics and the electrohydrodynamics of the problem, respectively, and the derived equations were solved by linearizing method. The results reveal the importance of considering the effect of ionic size in the calculation, as the steric effects, especially at high charge densities of polyelectrolytes (PELs), dramatically alter both the ions arrangement and the electric potential; and amplify the electroosmotic flow. Considering Debye-Huckel parameters of 4 and 10 for the electrolyte layer and the PEL, respectively, we demonstrate that the dimensionless electroosmotic velocity in a soft nanochannel having a dimensionless soft layer thickness of 0.2, from 3.2 by ignoring the steric effect, can reach the value of 6 by considering the steric effect of ν=0.3.Resveratrol is a well-studied dietary polyphenol with diverse health-promoting bioactivities. However, the aqueous insolubility and chemical instability of resveratrol hamper its practical application. This study set out to address these limitations by constructing zein-fucoidan composite nanoparticles as a delivery system of resveratrol. The optimized resveratrol-loaded zein-fucoidan particles (RE-ZFP) were obtained at zein-to-fucoidan ratio of 21 (w/w) and zein-to-resveratrol ratio of 101 (w/w), and RE-ZFP showed evenly distributed and smoothly spherical microstructures, mean particle size of 121 nm, ζ-potential of - 41 mV, encapsulation efficiency for resveratrol of 95.4%. Electrostatic, steric, hydrophobic, and hydrogen-bonding interactions were major forces required to form RE-ZFP. In addition, RE-ZFP exhibited greater photostability and colloidal stability (including pH, ionic, and storage stabilities) than resveratrol-loaded zein particles (RE-ZP). Particularly, RE-ZFP showed fairly good pH stability. Moreover, zein-fucoidan-based delivery system exhibited a controlled release of resveratrol under in vitro digestion. Finally, zein-fucoidan nanocarriers presented extremely low cytotoxicity to HIEC-6 cells. All the findings demonstrate that the zein-fucoidan nanoparticles developed in the current work will be a prospective strategy for loading resveratrol and other hydrophobic bioactive ingredients and thus extending their application in nutraceuticals or pharmaceuticals.Peptide derivatives and, most specifically, their self-assembled supramolecular structures are being considered in the design of novel biofunctional materials. Although the self-assembly of triphenylalanine homopeptides has been found to be more versatile than that of homopeptides containing an even number of residues (i.e. diphenylalanine and tetraphenylalanine), only uncapped triphenylalanine (FFF) and a highly aromatic analog blocked at both the N- and C-termini with fluorenyl-containing groups (Fmoc-FFF-OFm), have been deeply studied before. In this work, we have examined the self-assembly of a triphenylalanine derivative bearing 9-fluorenylmethyloxycarbonyl and benzyl ester end-capping groups at the N- and C-termini, respectively (Fmoc-FFF-OBzl). The antiparallel arrangement clearly dominates in β-sheets formed by Fmoc-FFF-OBzl, whereas the parallel and antiparallel dispositions are almost isoenergetic in Fmoc-FFF-OFm β-sheets and the parallel one is slightly favored for FFF. The effects of both the peptide concentration and the medium on the self-assembly process have been examined considering Fmoc-FFF-OBzl solutions in a wide variety of solventco-solvent mixtures. In addition, Fmoc-FFF-OBzl supramolecular structures have been compared to those obtained for FFF and Fmoc-FFF-OFm under identical experimental conditions. The strength of π-π stacking interactions involving the end-capping groups plays a crucial role in the nucleation and growth of supramolecular structures, which determines the resulting morphology. Finally, the influence of a non-invasive external stimulus, ultrasounds, on the nucleation and growth of supramolecular structures has been examined. Overall, FFF-based peptides provide a wide range of supramolecular structures that can be of interest in the biotechnological field.The present study was intended to prepare and optimize agomelatine-loaded nanostructured lipid carriers (AGM-NLCs) for augmented in vivo antidepressant potential. AGM-NLCs were optimized on several parameters including cumulative hydrophilic-lipophilic balance of surfactants, proportions of solid and liquid lipids, total amounts of drug and surfactants. AGM-NLCs were assessed for their physicochemical properties, in vitro AGM release and in vivo antidepressant effects in mice model. The optimized AGM-NLCs demonstrated spherical morphology with average particle size of 99.8 ± 2.6 nm, PDI of 0.142 ± 0.017, zeta potential of - 23.2 ± 1.2 mV and entrapment efficiency of 97.1 ± 2.1%. Thermal and crystallinity studies depict amorphous nature of AGM after its incorporation into NLCs. AGM-NLCs exhibit a sustained drug release profile after initial 2 h. Mice treated with AGM-NLCs exhibited reduced immobility time in behavioral analysis. Furthermore, cresyl violet staining demonstrated an improved neuronal morphology and survival in AGM-NLCs group. The concentrations and the expression of inflammatory markers (TNF-α and COX-2) in mice brain were significantly reduced by AGM-NLCs. Taken together, therapeutic effectiveness of AGM was markedly augmented in AGM-NLCs and thereby they could be promising nanocarriers for the effective delivery of antidepressants to brain.Women with alcohol use disorder (AUD) often present to treatment with heightened negative emotionality, including negative affect, anxiety, stress, and depression. Negative emotionality might impact women's alcohol abstinence self-efficacy (AASE), or confidence in their ability to remain sober, which is an important predictor of treatment outcomes. It is also plausible that other variables, such as alcohol craving, influence AASE. The present work examined the indirect effect of negative emotionality on AASE via alcohol craving as a mediator cross-sectionally among a sample of women enrolled in AUD treatment reporting co-occurring depressive symptoms (N = 73). Participants completed baseline measures of negative emotionality (e.g. anxiety and depression symptoms, stress, negative affect), alcohol craving, and AASE. All indices of negative emotionality were positively correlated with each other and alcohol craving (r's ranging from 0.244 to 0.671) and all but depression were inversely associated with AASE (r's ranging from -0.341 to -0.234; p less then .05). In separate simple mediation models, we found that alcohol craving mediated the association of each of the four measures of negative emotionality with AASE. Further longitudinal and experimental work is necessary to determine if teaching skills to cope with alcohol craving in the context of co-occurring negative emotionality might lead to better therapeutic outcomes.