rs671 a/A Genotype is Associated with an Increased Risk of Early Onset Coronary Artery Stenosis.

Background: The role of aldehyde dehydrogenase 2 (ALDH2) in cardiovascular diseases has been gradually studied. However, it is unclear whether polymorphism is associated with the risk of early onset (onset age ≤55 years old in men and ≤65 years old in women) coronary artery stenosis (CAS). The association between single nucleotide polymorphism (SNP) rs671 and risk in patients with early onset CAS was investigated in this study.

MF-MNER: Multi-models Fusion for MNER in Chinese Clinical Electronic Medical Records.

To address the problem of poor entity recognition performance caused by the lack of Chinese annotation in clinical electronic medical records, this paper proposes a multi-medical entity recognition method F-MNER using a fusion technique combining BART, Bi-LSTM, and CRF. First, after cleaning, encoding, and segmenting the electronic medical records, the obtained semantic representations are dynamically fused using a bidirectional autoregressive transformer (BART) model. Then, sequential information is captured using a bidirectional long short-term memory (Bi-LSTM) network.

Proteomic analysis of plasma proteins from patients with cardiac rupture after acute myocardial infarction using TMT-based quantitative proteomics approach.

Background: Cardiac rupture (CR) is a rare but catastrophic mechanical complication of acute myocardial infarction (AMI) that seriously threatens human health. However, the reliable biomarkers for clinical diagnosis and the underlying signaling pathways insights of CR has yet to be elucidated.

Methods: In the present study, a quantitative approach with tandem mass tag (TMT) labeling and liquid chromatography-tandem mass spectrometry was used to characterize the differential protein expression profiles of patients with CR.

MAHyNet: Parallel Hybrid Network for RNA-Protein Binding Sites Prediction Based on Multi-Head Attention and Expectation Pooling.

RNA-binding proteins (RBPs) can regulate biological functions by interacting with specific RNAs, and play an important role in many life activities. Therefore, the rapid identification of RNA-protein binding sites is crucial for functional annotation and site-directed mutagenesis. In this work, a new parallel network that integrates the multi-head attention mechanism and the expectation pooling is proposed, named MAHyNet.

A granularity-level information fusion strategy on hypergraph transformer for predicting synergistic effects of anticancer drugs.

Combination therapy has exhibited substantial potential compared to monotherapy. However, due to the explosive growth in the number of cancer drugs, the screening of synergistic drug combinations has become both expensive and time-consuming. Synergistic drug combinations refer to the concurrent use of two or more drugs to enhance treatment efficacy.

SMGCN: Multiple Similarity and Multiple Kernel Fusion Based Graph Convolutional Neural Network for Drug-Target Interactions Prediction.

Accurately identifying potential drug-target interactions (DTIs) is a critical step in accelerating drug discovery. Despite many studies that have been conducted over the past decades, detecting DTIs remains a highly challenging and complicated process. Therefore, we propose a novel method called SMGCN, which combines multiple similarity and multiple kernel fusion based on Graph Convolutional Network (GCN) to predict DTIs.

Predicting DNA-binding protein and coronavirus protein flexibility using protein dihedral angle and sequence feature.

The flexibility of protein structure is related to various biological processes, such as molecular recognition, allosteric regulation, catalytic activity, and protein stability. At the molecular level, protein dynamics and flexibility are important factors to understand protein function. DNA-binding proteins and Coronavirus proteins are of great concern and relatively unique proteins.

GCHN-DTI: Predicting drug-target interactions by graph convolution on heterogeneous networks.

Determining the interaction of drug and target plays a key role in the process of drug development and discovery. The calculation methods can predict new interactions and speed up the process of drug development. In recent studies, the network-based approaches have been proposed to predict drug-target interactions.

PseAraUbi: predicting arabidopsis ubiquitination sites by incorporating the physico-chemical and structural features.

We makes three kinds of important features from Arabidopsis thaliana: protein secondary structure based on the Chou-Fasman parameter, amino acids hydrophobicity and polarity information, and analyze their properties. Ubiquitination modification is an important post-translational modification of proteins, which participates in the regulation of many important life activities in cells. At present, ubiquitination proteomics research is mostly concentrated in animals and yeasts, while relatively few studies have been carried out in plants.

Prediction of DNA-Binding Protein-Drug-Binding Sites Using Residue Interaction Networks and Sequence Feature.

Identification of protein-ligand binding sites plays a critical role in drug discovery. However, there is still a lack of targeted drug prediction for DNA-binding proteins. This study aims at the binding sites of DNA-binding proteins and drugs, by mining the residue interaction network features, which can describe the local and global structure of amino acids, combined with sequence feature.

Drug repositioning of COVID-19 based on mixed graph network and ion channel.

Research on the relationship between drugs and targets is the key to precision medicine. Ion channel is a kind of important drug targets. Aiming at the urgent needs of corona virus disease 2019 (COVID-19) treatment and drug development, this paper designed a mixed graph network model to predict the affinity between ion channel targets of COVID-19 and drugs.

Combined Triple Transcatheter Aortic Procedure in a Patient With Aortic Stenosis, Coarctation, and Aneurysm.

We present the case of a 71-year-old man admitted because of chest tightness, palpitations, and progressive shortness of breath. The diagnosis of severe aortic stenosis, coarctation, and aneurysm was established, as well as severely depressed left ventricular ejection fraction. Three consecutive transcatheter procedures were successfully performed in a single session.

PPDTS: Predicting potential drug-target interactions based on network similarity.

Identification of drug-target interactions (DTIs) has great practical importance in the drug discovery process for known diseases. However, only a small proportion of DTIs in these databases has been verified experimentally, and the computational methods for predicting the interactions remain challenging. As a result, some effective computational models have become increasingly popular for predicting DTIs.

Global potential distribution prediction of Xanthium italicum based on Maxent model.

Alien invasive plants pose a threat to global biodiversity and the cost of control continues to rise. Early detection and prediction of potential risk areas are essential to minimize ecological and socio-economic costs. In this study, the Maxent model was used to predict current and future climatic conditions to estimate the potential global distribution of the invasive plant Xanthium italicum.

Prediction of Protein Solubility Based on Sequence Feature Fusion and DDcCNN.

Background: Prediction of protein solubility is an indispensable prerequisite for pharmaceutical research and production. The general and specific objective of this work is to design a new model for predicting protein solubility by using protein sequence feature fusion and deep dual-channel convolutional neural networks (DDcCNN) to improve the performance of existing prediction models.

Methods: The redundancy of raw protein is reduced by CD-HIT.

Transcatheter closure for the treatment of pseudoventricular aneurysm after acute myocardial infarction: a case report.

Left ventricular free wall rupture (LVFWR) is a rare but lethal complication of acute myocardial infarction (AMI). Urgent surgery is essential but associated with high postoperative mortality. Even worse, LVFWR patients may experience sudden death without a chance for surgery.

MicroRNA-147 targets BDNF to inhibit cell proliferation, migration and invasion in non-small cell lung cancer.

Lung cancer is one of the most common cancers that threaten human life and health. Recently, microRNAs (miRNAs) have been shown to play a unique role in many malignancies. Although the dysregulation of miR-147 has been detected in non-small cell lung cancer (NSCLC), the biological function of miR-147 is still unknown in NSCLC.

Retrograde transcatheter closure of ventricular septal perforation after acute myocardial infarction: a case report.

The use of the Lunderquist exchange guide wire via the retrograde approach of the right femoral vein-inferior vena cava-right atrium-right ventricle-ventricular septal perforation-left ventricle-descending aorta can maintain guide wire tension and significantly reduce the operative time. The patient was admitted due to chest pain for 3 hours. The diagnosis was acute anterior septal myocardial infarction with ventricular septal perforation.

Remarks on Computational Method for Identifying Acid and Alkaline Enzymes.

The catalytic efficiency of the enzyme is thousands of times higher than that of ordinary catalysts. Thus, they are widely used in industrial and medical fields. However, enzymes with protein structure can be destroyed and inactivated in high temperature, over acid or over alkali environment.

Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction.

Deep learning is an effective method to capture drug-target binding affinity, but low accuracy is still an obstacle to be overcome. Thus, we propose a novel predictor for drug-target binding affinity based on dipeptide frequency of word frequency encoding and a hybrid graph convolutional network. Word frequency characteristics of natural language are used to improve the frequency characteristics of peptides to express target proteins.

Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features.

Bacteriophage is a type of virus that could infect the host bacteria. They have been applied in the treatment of pathogenic bacterial infection. Phage enzymes and hydrolases play the most important role in the destruction of bacterial cells.

Retracted Article: Long noncoding RNA ANRIL knockdown increases sensitivity of non-small cell lung cancer to cisplatin by regulating the miR-656-3p/SOX4 axis.

Long noncoding RNAs (lncRNAs) are implicated in the development of chemoresistance in many cancers. However, the effect and mechanism of lncRNA antisense noncoding RNA in the INK4 locus (ANRIL) on cisplatin (CDDP) resistance in non-small cell lung cancer (NSCLC) remain unclear. The levels of ANRIL, microRNA (miR)-656-3p and sex-determining region Y-related high-mobility group box 4 (SOX4) in NSCLC tissues and cells were detected by quantitative real-time polymerase chain reaction or western blotting.

Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model.

The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine).

Online Fault Diagnosis for Biochemical Process Based on FCM and SVM.

Fault diagnosis is becoming an important issue in biochemical process, and a novel online fault detection and diagnosis approach is designed by combining fuzzy c-means (FCM) and support vector machine (SVM). The samples are preprocessed via FCM algorithm to enhance the ability of classification firstly. Then, those samples are input to the SVM classifier to realize the biochemical process fault diagnosis.