Glomerular capillary C3 deposition as a risk factor for unfavorable renal outcome in pediatric primary focal segmental glomerular sclerosis

IntroductionSome patients with primary focal segmental sclerosis (FSGS) demonstrate complement 3 (C3) deposition in glomerular capillary loops (Cap-C3) and/or mesangial area (Mes-C3). The clinicopathological and prognostic significance of C3 deposition remains incompletely investigated, especially in the pediatric cohort.MethodsWe retrospectively analyzed 264 children of biopsy-proven primary FSGS between January 2003 and December 2020. The correlation between Cap-C3 and renal outcome was evaluated by the Kaplan-Meier method and Cox multivariate regression analysis. Renal end-point event was defined as the development of end-stage renal disease, death for renal disease, or an estimated glomerular filtration rate reduction by at least 50% from baseline.ResultsAmong the 264 patients, 30 (11.4%) had Cap-C3. Kaplan-Meier analysis showed that patients with Cap-C3 had significantly lower renal survival rates than patients without Cap-C3 (60.17% vs. 84.71% at 5 years, 39.49% vs. 65.55% at 10 years, P < 0.01). Cox multivariate regression analysis showed that Cap-C3 was an independent risk factor for poor renal outcome (HR 3.53, 95% CI 1.22–10.19, P = 0.02).ConclusionGlomerular capillary C3 deposition was an independent risk factor for unfavorable renal outcome in children with primary FSGS

Analysis of Alzheimer’s Disease Based on the Random Neural Network Cluster in fMRI

As Alzheimer’s disease (AD) is featured with degeneration and irreversibility, the diagnosis of AD at early stage is important. In recent years, some researchers have tried to apply neural network (NN) to classify AD patients from healthy controls (HC) based on functional MRI (fMRI) data. But most study focus on a single NN and the classification accuracy was not high. Therefore, this paper used the random neural network cluster which was composed of multiple NNs to improve classification performance. Sixty one subjects (25 AD and 36 HC) were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. This method not only could be used in the classification, but also could be used for feature selection. Firstly, we chose Elman NN from five types of NNs as the optimal base classifier of random neural network cluster based on the results of feature selection, and the accuracies of the random Elman neural network cluster could reach to 92.31% which was the highest and stable. Then we used the random Elman neural network cluster to select significant features and these features could be used to find out the abnormal regions. Finally, we found out 23 abnormal regions such as the precentral gyrus, the frontal gyrus and supplementary motor area. These results fully show that the random neural network cluster is worthwhile and meaningful for the diagnosis of AD

The Diagnosis of Autism Spectrum Disorder Based on the Random Neural Network Cluster

As the autism spectrum disorder (ASD) is highly heritable, pervasive and prevalent, the clinical diagnosis of ASD is vital. In the existing literature, a single neural network (NN) is generally used to classify ASD patients from typical controls (TC) based on functional MRI data and the accuracy is not very high. Thus, the new method named as the random NN cluster, which consists of multiple NNs was proposed to classify ASD patients and TC in this article. Fifty ASD patients and 42 TC were selected from autism brain imaging data exchange (ABIDE) database. First, five different NNs were applied to build five types of random NN clusters. Second, the accuracies of the five types of random NN clusters were compared to select the highest one. The random Elman NN cluster had the highest accuracy, thus Elman NN was selected as the best base classifier. Then, we used the significant features between ASD patients and TC to find out abnormal brain regions which include the supplementary motor area, the median cingulate and paracingulate gyri, the fusiform gyrus (FG) and the insula (INS). The proposed method provides a new perspective to improve classification performance and it is meaningful for the diagnosis of ASD

An Efficient Large-Scale Synthesis of a Naphthylacetic Acid CRTH2 Receptor Antagonist

An efficient and practical synthesis of a naphthylacetic acid CRTH2 receptor antagonist is reported. Michael addition of ethyl <i>t</i>-butyl malonate to an allenoate afforded a triester, which was selectively hydrolyzed and decarboxylated to give a benzylidenepentanedioic acid monoester. Treatment of this compound with potassium acetate and acetic anhydride produced the naphthylacetate core. The triflate of the key building block was coupled with a zinc reagent of the side chain under improved Negishi coupling conditions to afford the target product. The process was successfully scaled up to produce over 2 kg of the API

An ultra-thin high-efficiency plasmonic metalens with symmetric split ring transmitarray metasurfaces

Metasurface lenses (or metalenses) have aroused great attentions and efforts in the community of metamaterials or metasurfaces due to its ultrathin device dimension and superior focusing performances. High-efficiency transmissive metalenses with an ultra-thin device thickness are an important aspect especially in the low frequency by using plasmonic transmitarray antennas. In this paper, an ultra-thin plasmonic metalens with only 0.1λ (λ is working wavelength, the aperture size is 7λ) device thickness is designed by changing the radius of the proposed symmetric complementary split ring resonator antenna metasurfaces. Thanks to its high transmittance and large phase shift of the plasmonic meta-atoms, the designed metalens achieves both high transmissive efficiency of 80% and high focusing efficiency of 50% on the focal plane of F = 4.6λ in the simulations. The designed ultra-thin plasmonic metalens has a moderate large numerical aperture of 0.67 (NA = 0.67). In order to verify its high working efficiency of the proposed plasmonic metalens, a sample is also fabricated and a much higher focusing efficiency of 65% is realized in the measurements. The influence of the open angles of the symmetric split ring transmitarray metasurface on the focusing performances such as working efficiency and NA of the designed metalens is also studied and analyzed finally, which can add new degree of freedoms to optimize its focusing performance. The presented studies can facilitate the development of high-efficiency metalenses in the low frequency and have significant potential applications in high-resolution microwave imaging, high-gain metalens antennas and others