Kinematic Analysis of Plasticization and Transportation System of Tri-Screw Dynamic Extruder.

With the growing demand for high-performance polymer composites, conventional single- and twin-screw extruders often fall short of meeting industrial requirements for effective mixing and compounding. This research investigates the kinematic behavior of the plasticization and transport mechanisms in tri-screw extruders when subjected to a vibrational force field. The study specifically examines how applying vibrational force technology can improve the efficiency of polymer mixing.

Corrigendum: Sini san regulates intestinal flora and short-chain fatty acids to ameliorate hepatocyte apoptosis and relieve CCl-induced liver fibrosis in mice.

[This corrects the article DOI: 10.3389/fphar.2024.

EDDINet: Enhancing drug-drug interaction prediction via information flow and consensus constrained multi-graph contrastive learning.

Predicting drug-drug interactions (DDIs) is crucial for understanding and preventing adverse drug reactions (ADRs). However, most existing methods inadequately explore the interactive information between drugs in a self-supervised manner, limiting our comprehension of drug-drug associations. This paper introduces EDDINet: Enhancing Drug-Drug Interaction Prediction via Information Flow and Consensus-Constrained Multi-Graph Contrastive Learning for precise DDI prediction.

webTWAS 2.0: update platform for identifying complex disease susceptibility genes through transcriptome-wide association study.

Transcriptome-wide association study (TWAS) has successfully identified numerous complex disease susceptibility genes in the post-genome-wide association study (GWAS) era. Over the past 3 years, the focus of TWAS algorithms has shifted from merely identifying associations to understanding how single nucleotide polymorphisms (SNPs) regulate gene expression, with a growing emphasis on incorporating fine-mapping techniques. Additionally, the rapid increase in GWAS summary statistics, driven largely by the UK Biobank and other consortia, has made it essential to update our webTWAS resource.

Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization.

Therapeutic peptides are therapeutic agents synthesized from natural amino acids, which can be used as carriers for precisely transporting drugs and can activate the immune system for preventing and treating various diseases. However, screening therapeutic peptides using biochemical assays is expensive, time-consuming, and limited by experimental conditions and biological samples, and there may be ethical considerations in the clinical stage. In contrast, screening therapeutic peptides using machine learning and computational methods is efficient, automated, and can accurately predict potential therapeutic peptides.

DrugDAGT: a dual-attention graph transformer with contrastive learning improves drug-drug interaction prediction.

Background: Drug-drug interactions (DDIs) can result in unexpected pharmacological outcomes, including adverse drug events, which are crucial for drug discovery. Graph neural networks have substantially advanced our ability to model molecular representations; however, the precise identification of key local structures and the capture of long-distance structural correlations for better DDI prediction and interpretation remain significant challenges.

Results: Here, we present DrugDAGT, a dual-attention graph transformer framework with contrastive learning for predicting multiple DDI types.

Sini san regulates intestinal flora and short-chain fatty acids to ameliorate hepatocyte apoptosis and relieve CCl-induced liver fibrosis in mice.

Introduction: Si-Ni-San (SNS), a traditional Chinese medicine, is effective in treating liver fibrosis with an unclear mechanism. Although disturbance of intestinal flora and the subsequent secretion of short-chain fatty acids (SCFAs) is suggested to be involved in the progression of liver fibrosis, whether SNS produces the anti-fibrosis effect through the regulation of intestinal flora and SCFAs remains unclear.

Methods: In the current study, carbon tetrachloride (CCl)-treated mice were dosed with SNS to examine the anti-fibrotic effects and the involved mechanism.

A review of deep learning methods for ligand based drug virtual screening.

Drug discovery is costly and time consuming, and modern drug discovery endeavors are progressively reliant on computational methodologies, aiming to mitigate temporal and financial expenditures associated with the process. In particular, the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic. Recently, the performance of deep learning methods in drug virtual screening has been particularly prominent.

scRNMF: An imputation method for single-cell RNA-seq data by robust and non-negative matrix factorization.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool in genomics research, enabling the analysis of gene expression at the individual cell level. However, scRNA-seq data often suffer from a high rate of dropouts, where certain genes fail to be detected in specific cells due to technical limitations. This missing data can introduce biases and hinder downstream analysis.

Deep learning based method for predicting DNA N6-methyladenosine sites.

DNA N6 methyladenine (6mA) plays an important role in many biological processes, and accurately identifying its sites helps one to understand its biological effects more comprehensively. Previous traditional experimental methods are very labor-intensive and traditional machine learning methods also seem to be somewhat insufficient as the database of 6mA methylation groups becomes progressively larger, so we propose a deep learning-based method called multi-scale convolutional model based on global response normalization (CG6mA) to solve the prediction problem of 6mA site. This method is tested with other methods on three different kinds of benchmark datasets, and the results show that our model can get more excellent prediction results.

Infection‑associated bile acid disturbance contributes to macrophage activation in patients with cirrhosis.

Cirrhosis impairs macrophage function and disrupts bile acid homeostasis. Although bile acids affect macrophage function in patients with sepsis, whether and how the bile acid profile is changed by infection in patients with cirrhosis to modulate macrophage function remains unclear. The present study aimed to investigate the changes in the bile acid profile of patients with cirrhosis and infection and their effects on macrophage function.

Sequence homology score-based deep fuzzy network for identifying therapeutic peptides.

The detection of therapeutic peptides is a topic of immense interest in the biomedical field. Conventional biochemical experiment-based detection techniques are tedious and time-consuming. Computational biology has become a useful tool for improving the detection efficiency of therapeutic peptides.

[Detection and analysis technologies for single biological nanoparticles].

In recent years, nanoscale detection has played an increasingly important role in the research on viruses, exosomes, small bacteria, and organelles. The small size and complex biological natures of these particles, with the smallest known virus particle measuring only 17 nm in diameter and exosomes ranging from 30 nm to 150 nm in size, pose challenges to the classical large-scale (typically micron-scale) characterization methods, which has become a major obstacle in the research. The emergence of nanoscale detection and analysis technologies has filled the gap of optical microscopy, a conventional technique in this field.

PseU-KeMRF: A Novel Method for Identifying RNA Pseudouridine Sites.

Pseudouridine is a type of abundant RNA modification that is seen in many different animals and is crucial for a variety of biological functions. Accurately identifying pseudouridine sites within the RNA sequence is vital for the subsequent study of various biological mechanisms of pseudouridine. However, the use of traditional experimental methods faces certain challenges.

Fuzzy kernel evidence Random Forest for identifying pseudouridine sites.

Pseudouridine is an RNA modification that is widely distributed in both prokaryotes and eukaryotes, and plays a critical role in numerous biological activities. Despite its importance, the precise identification of pseudouridine sites through experimental approaches poses significant challenges, requiring substantial time and resources.Therefore, there is a growing need for computational techniques that can reliably and quickly identify pseudouridine sites from vast amounts of RNA sequencing data.

MFD-GDrug: multimodal feature fusion-based deep learning for GPCR-drug interaction prediction.

The accurate identification of drug-protein interactions (DPIs) is crucial in drug development, especially concerning G protein-coupled receptors (GPCRs), which are vital targets in drug discovery. However, experimental validation of GPCR-drug pairings is costly, prompting the need for accurate predictive methods. To address this, we propose MFD-GDrug, a multimodal deep learning model.

Postoperative delirium prediction after cardiac surgery using machine learning models.

Objective: Postoperative delirium (POD) is a common postoperative complication in elderly patients, especially those undergoing cardiac surgery, which seriously affects the short- and long-term prognosis of patients. Early identification of risk factors for the development of POD can help improve the perioperative management of surgical patients. In the present study, five machine learning models were developed to predict patients at high risk of delirium after cardiac surgery and their performance was compared.

AttentionMGT-DTA: A multi-modal drug-target affinity prediction using graph transformer and attention mechanism.

The accurate prediction of drug-target affinity (DTA) is a crucial step in drug discovery and design. Traditional experiments are very expensive and time-consuming. Recently, deep learning methods have achieved notable performance improvements in DTA prediction.

MLapRVFL: Protein sequence prediction based on Multi-Laplacian Regularized Random Vector Functional Link.

Protein sequence classification is a crucial research field in bioinformatics, playing a vital role in facilitating functional annotation, structure prediction, and gaining a deeper understanding of protein function and interactions. With the rapid development of high-throughput sequencing technologies, a vast amount of unknown protein sequence data is being generated and accumulated, leading to an increasing demand for protein classification and annotation. Existing machine learning methods still have limitations in protein sequence classification, such as low accuracy and precision of classification models, rendering them less valuable in practical applications.

RAVAR: a curated repository for rare variant-trait associations.

Rare variants contribute significantly to the genetic causes of complex traits, as they can have much larger effects than common variants and account for much of the missing heritability in genome-wide association studies. The emergence of UK Biobank scale datasets and accurate gene-level rare variant-trait association testing methods have dramatically increased the number of rare variant associations that have been detected. However, no systematic collection of these associations has been carried out to date, especially at the gene level.

Identification of drug-side effect association via correntropy-loss based matrix factorization with neural tangent kernel.

Adverse drug reactions include side effects, allergic reactions, and secondary infections. Severe adverse reactions can cause cancer, deformity, or mutation. The monitoring of drug side effects is an important support for post marketing safety supervision of drugs, and an important basis for revising drug instructions.

Effect of childbearing-age women's family status on the health status of three generations: evidence from China.

It is widely recognized that inequalities in social status cause inequalities in health. Women in a family often directly influence three generations-women themselves, their children and their parents -yet the effect of women's family status on their own health status and that of the two generations before and after is not clear. Taking data from the China Family Panel Studies, this study used an ordered response model to investigate the effect of childbearing-age women's family status on the health status of three generations.

Identification of DNA-binding protein based multiple kernel model.

DNA-binding proteins (DBPs) play a critical role in the development of drugs for treating genetic diseases and in DNA biology research. It is essential for predicting DNA-binding proteins more accurately and efficiently. In this paper, a Laplacian Local Kernel Alignment-based Restricted Kernel Machine (LapLKA-RKM) is proposed to predict DBPs.