Protein fold recognition is the key to study protein structure and function. As a representative pattern recognition task, there are two main categories of approaches to improve the protein fold recognition performance: 1) extracting more discriminative descriptors, and 2) designing more effective distance metrics. The existing protein fold recognition approaches focus on the first category to finding a robust and discriminative descriptor to represent each protein sequence as a compact feature vector, where different protein sequence is expected to be separated as much as possible in the fold space. These methods have brought huge improvements to the task of protein fold recognition. However, so far, little attention has been paid to the second category. In this paper, we focus not only on the first category, but also on the second point that how to measure the similarity between two proteins more effectively. First, we employ deep convolutional neural network techniques to extract the discriminative fold-specific features from the potential protein residue-residue relationship, we name it SSAfold. On the other hand, due to different feature representation usually subject to varying distributions, the measurement of similarity needs to vary according to different feature distributions. Before, almost all protein fold recognition methods perform the same metrics strategy on all the protein feature ignoring the differences in feature distribution. This paper presents a new protein fold recognition by employing siamese network, we named it PFRSN. The objective of PFRSN is to learns a set of hierarchical nonlinear transformations to project protein pairs into the same fold feature subspace to ensure the distance between positive protein pairs is reduced and that of negative protein pairs is enlarged as much as possible. The experimental results show that the results of SSAfold and PFRSN are highly competitive.

Background Chronic kidney disease (CKD) disease affects gut flora by causing dysbiosis and lead to systemic inflammatory conditions. Here, we provide intestinal flora changes of CKD patients undertook different hemodialysis therapy.Methods Patients were recruited during 2017-2019 and divided into healthy control group (CT), CKD non-dialysis group (CKD), hemodialysis group (HD) and peritoneal dialysis group (PD). Intestinal flora genome 16S rDNA sequencing and further bio-informatic analysis were performed.Results Decreased diversity and altered communities of intestinal flora in PD patients, in which microbial diversity was positive correlated with the albumin level were observed. A total of 20 intestinal flora phyla were detected in 166 fecal samples, divided into 3 dominant intestinal types including Bacteroides-dominant gut type, Firmicutes-dominant type and Proteobacteria-dominant gut type. Further analyses found 198 genera, the abundance of 86 genera were significantly different. Butyrate-producing taxa as Faecalibacterium in genera level and Bifidobacteriaceae and Prevotellaceae in family level were dominant genus in CT, CKD, and HD groups, while urease containing-, indole- and p-cresol-forming taxa as Escherichia in genera and Enterobacteriaceae , Enterococcaceae in family level was dominated genus in PD group. Number of differential expressed genes in KEGG enrichment pathways were significantly different in PD group in carbohydrate metabolism, amino acid metabolism, energy metabolism, translation, and membrane transport.Conclusion Our results suggest peritoneal dialysis therapy could result in reduced diversity and altered microbial communities, with reduced probiotic butyrate-producing taxa and increased urease containing-, indole- and p-cresol-forming taxa. The disordered intestinal flora can seriously affect the nutrition level in CKD patients with PD therapy.

BackgroundIn previous studies, faster heart rates in patients with atrial fibrillation combined with heart failure have been associated with poor long-term patient prognosis. However, the classical pharmacological regimen of beta-blockers has not reduced mortality in patients with atrial fibrillation combined with heart failure. Therefore, in patients with atrial fibrillation combined with heart failure with an ejection fraction >40%, we further screened patients with a diagnosis of atrial fibrillation cardiomyopathy and compared the combination of diltiazem with standard anti-heart failure drug therapy.Objective:To observe the effect of diltiazem hydrochloride on cardiac function and prognosis in patients with Atrial Fibrillation–Mediated Cardiomyopathy.Methods: A total of 186 patients diagnosed with atrial fibrillation–mediated cardiomyopathy who were admitted to the First Affiliated Hospital of Zhengzhou University from August 2018 to June 2020 were randomly divided into two groups: 93 cases in the experimental group and 93 cases in the control group, both groups were given standardized pharmacological treatment for heart failure (diuretics, digoxin, β-blockers, perindopril), and the experimental group was given diltiazem 30 mg on the basis of standardized treatment, 3 times a day. The patients were followed up for 30 days to observe the target heart rate <110 beats/min, left ventricular ejection fraction, proBNP, the rate of decrease in activity tolerance during the treatment period, and readmission rate within 30 days.Results:After the addition of diltiazem, the attainment rate of target heart rate was significantly higher in the experimental group than in the control group (p<0.05) . The improvement of left ventricular ejection fraction and proBNP was more significant in the experimental group than in the control group (p<0.05). The incidence of decreased activity tolerance during the follow-up period was higher in the experimental group than in the control group, but the difference was not statistically significant (p>0.05). The readmission rate for heart failure within 30 days was significantly lower in the experimental group than in the control group (p < 0.05).Conclusion：Diltiazem hydrochloride is effective in improving cardiac function and prognosis in patients with atrial fibrillation–mediated cardiomyopathy, and is a safe and effective method.