Smithfinch2064

The extract markedly reduced brain AChE, MDA, and nitrite contents in mice injected with scopolamine (p less then 0.05). It also attenuated scopolamine-induced deregulated GSH contents and antioxidant enzymes in mouse brain. Conclusions The results of this study suggest that regular consumption of TINUT might offer beneficial effects in memory-related disorders.Objectives Alzheimer's disease (AD) is a chronic and progressive neurodegenerative disease in which one of the most prominent pathological features is accumulation of amyloid (Aβ) plaques. This occurs due to the process of aggregation from monomeric to polymeric forms of Aβ peptide and thus represents one of the attractive targets to treat AD. Methods After initial evaluation of a set of molecules containing N-acetylpyrazoline moiety flanked by aromatic rings on both sides as Aβ aggregation inhibitors, the most potent molecules were further investigated for mechanistic insights. These were carried out by employing techniques such as circular dichroism (CD) spectroscopy, transmission electron microscopy (TEM), in vitro PAMPA-BBB (Blood-Brain Barrier) assay and cytotoxicity evaluation. Results Two molecules among the exploratory set displayed Aβ aggregation inhibition comparable to standard curcumin. Among the follow-up molecules, several molecules displayed more inhibition than curcumin. These molecules displayed good inhibitory activity even at lower concentrations. CD and TEM confirmed the mechanism of Aβ aggregation. These molecules were found to alleviate Aβ induced cytotoxicity. BBB penetration studies highlighted the potential of these molecules to reach central nervous system (CNS). Conclusions Thus, several promising Aβ-aggregation inhibitors were obtained as a result of this study.

Reference materials are important in the standardization of autoantibody testing and only a few are freely available for many known autoantibodies. Our goal was to develop three reference materials for antibodies to PML bodies/multiple nuclear dots (MND), antibodies to GW bodies (GWB), and antibodies to the nuclear mitotic apparatus (NuMA).

Reference materials for identifying autoantibodies to MND (MND-REF), GWB (GWB-REF), and NuMA (NuMA-REF) were obtained from three donors and validated independently by seven laboratories. The sera were characterized using indirect immunofluorescence assay (IFA) on HEp-2cell substrates including two-color immunofluorescence using antigen-specific markers, western blot (WB), immunoprecipitation (IP), line immunoassay (LIA), addressable laser bead immunoassay (ALBIA), enzyme-linked immunosorbent assay (ELISA), and immunoprecipitation-mass spectrometry (IP-MS).

MND-REF stained 6-20 discrete nuclear dots that colocalized with PML bodies. Antibodies to Sp100 and PML were detected by LIA and antibodies to Sp100 were also detected by ELISA. GWB-REF stained discrete cytoplasmic dots in interphase cells, which were confirmed to be GWB using two-color immunofluorescence. Anti-Ge-1 antibodies were identified in GWB-REF by ALBIA, IP, and IP-MS. All reference materials produced patterns at dilutions of 1160 or greater. NuMA-REF produced fine speckled nuclear staining in interphase cells and staining of spindle fibers and spindle poles. The presence of antibodies to NuMA was verified by IP, WB, ALBIA, and IP-MS.

MND-REF, GWB-REF, and NuMA-REF are suitable reference materials for the corresponding antinuclear antibodies staining patterns and will be accessible to qualified laboratories.

MND-REF, GWB-REF, and NuMA-REF are suitable reference materials for the corresponding antinuclear antibodies staining patterns and will be accessible to qualified laboratories.

Congenital disorders of N-glycosylation (CDG) are a large group of rare metabolic disorders caused by defects in the most common post-translational modification of proteins. CDGs are often difficult to diagnose as they are manifested with non-specific symptoms and signs. Analysis of serum transferrin (TRF) isoforms, as the classical procedure used to identify a CDG patient, enables to predict pathological steps in the N-linked glycosylation process.

We devised a new strategy based on liquid chromatography-mass spectrometry (LC-MS) for the analysis of TRF isoforms by combining a simple and fast sample preparation with a specific chromatographic cleanup/separation step followed by mass-spectrometric measurement. Single TRF isoform masses were obtained through reconstruction of multiply charged electrospray data collected by quadrupole-MS technology. Hereby, we report the first analyzed serum samples obtained from 20 CDG patients and 100 controls.

The ratio of desialylated isoforms to total TRF was calculated for patients and controls. CDG-Type I patients showed higher amounts of bi-sialo isoform (range 6.7-29.6%) compared to controls (<5.5%, mean percentage 3.9%). CDG-Type II pattern showed an increased peak of tri-sialo isoforms. The mean percentage of tri-sialo-TRF was 9.3% (range 2.9-12.9%) in controls, which was lower than that obtained from two patients with COG5-CDG and MAN1B1-CDG (18.5 and 24.5%). Intraday and between-day imprecisions were less than 9 and 16%, respectively, for bi-sialo- and less than 3 and 6% for tri-sialo-TRF.

This LC-MS-based approach provides a simple, sensitive and fast analytical tool for characterizing CDG disorders in a routine clinical biochemistry while improving diagnostic accuracy and speeding clinical decision-making.

This LC-MS-based approach provides a simple, sensitive and fast analytical tool for characterizing CDG disorders in a routine clinical biochemistry while improving diagnostic accuracy and speeding clinical decision-making.The severe stage of Diabetic Retinopathy (DR) is characterized by the growth of new blood vessels which is called Neovascularization (NV). The abnormally grown blood vessels on the disc are breakable in nature thus the patient is at high risk of sudden blindness. Therefore, the significance of early and accurate detection of Neovascularization on Disc (NVD) should not be neglected. This paper presents an automatic detection of the optic disc using a Controlled Differential Evolution (CDE) algorithm. Further, the Region of Interest (ROI) is created automatically by extending the extreme boundaries of the optic disc by 100 pixels to ensure the presence of NV around the optic disc also. From the ROI so created, blood vessels are segmented using multi-scale Gabor filtering and subsequently, both the morphological and textural features are extracted. Simultaneously, statistical features are directly extracted from the earlier created ROI. Finally, the fundus image is classified by a Support Vector Machine (SVM) using the extracted features from all three feature sets. From each individual image, 16 features are extracted and the feature dimension is reduced to 13 using a sequential backward feature (SBF) selection algorithm. The optimal features are obtained from a total of 205 fundus images, which consists of 99 NVD positive and 106 NVD negative images. This paper attains an average accuracy of 98.75%, the specificity of 100%, the sensitivity of 97.8%, and area under the curve (AUC) as 100% when tested over image selected randomly.The quality of the medical image plays a major role in decision making by the radiologists. There exists a visual differentiation between the normal scene color images and medical images. Due to the low illumination and unavailability of the color parameter, medical images require more attention by radiologists for decision making. In this paper a new approach is proposed that enhances the quality of the Magnetic Resonance (MR) images. Proposed approach uses the spectral information present in form of Amplitude and Frequency within the MR image slices for an enhancement. The extracted enhanced spectral information gives better visualization as compared with original signal image generated from MR scanner. The quantitative analysis of the proposed approach suggests that the new method is far better than the traditional state-of-art image enhancement methods.[This corrects the article DOI 10.2196/19170.].This article concerns the robust consensus problem of continuous-time linear multiagent systems (MASs) with uncertainty and discrete-time measurement information, where the output measurement information is in the data-sampled form. Distributed output-feedback protocol with or without controller interaction is proposed for each agent. Specifically, the output-feedback protocol runs in continuous time with an output error correction term mixed with the discrete-time measurement information. The concrete algorithm is given for the construction of the feedback matrices. Then, by using the delay-input approach, sufficient conditions are provided for the robust consensus of this kind of MASs interacting over networks described by the directed graphs. Finally, numerical simulations are given to illustrate the theoretical results.This article focuses on the solution to the coordinated formation problem of heterogeneous vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) in the presence of parametric uncertainties. In particular, their inertial parameters are distinct and unavailable. For the sake of the accomplishment of the coordinated formation objective of multiple underactuated VTOL UAVs through local information exchange, an adaptive distributed control algorithm is developed under a cascaded structure. Specifically, by introducing an immersion and invariance (I&I) adaption strategy for the exponential mass estimation, a distributed command force is first synthesized in the position loop. Next, an applied torque with adaption is synthesized for the attitude tracking to a command attitude. This command attitude, as well as the applied thrust, is extracted from the synthesized command force without singularity. It is shown in terms of the Lyapunov theory that driven by the proposed adaptive distributed control algorithm, the concerned coordinated formation control of multiple VTOL UAVs is achieved asymptotically. Finally, an illustrative example is simulated to validate the effectiveness of the proposed control algorithm.Data-driven evolutionary algorithms (DDEAs) aim to utilize data and surrogates to drive optimization, which is useful and efficient when the objective function of the optimization problem is expensive or difficult to access. However, the performance of DDEAs relies on their surrogate quality and often deteriorates if the amount of available data decreases. To solve these problems, this article proposes a new DDEA framework with perturbation-based ensemble surrogates (DDEA-PES), which contain two efficient mechanisms. The first is a diverse surrogate generation method that can generate diverse surrogates through performing data perturbations on the available data. The second is a selective ensemble method that selects some of the prebuilt surrogates to form a final ensemble surrogate model. By combining these two mechanisms, the proposed DDEA-PES framework has three advantages, including larger data quantity, better data utilization, and higher surrogate accuracy. To validate the effectiveness of the proposed framework, this article provides both theoretical and experimental analyses.