Unexplored viral diversity in Siberian cranes and wild geese: metagenomic insights from a global wintering haven.

Migratory birds are critical global carriers and vectors of various viruses, potentially driving the mutation and transmission of novel pathogens, especially zoonotic viruses. Despite advancements in sequencing technologies, the viral diversity in wintering Siberian cranes () and wild geese ( spp.) remains poorly understood.

MalHAPGNN: An Enhanced Call Graph-Based Malware Detection Framework Using Hierarchical Attention Pooling Graph Neural Network.

While deep learning techniques have been extensively employed in malware detection, there is a notable challenge in effectively embedding malware features. Current neural network methods primarily capture superficial characteristics, lacking in-depth semantic exploration of functions and failing to preserve structural information at the file level. Motivated by the aforementioned challenges, this paper introduces MalHAPGNN, a novel framework for malware detection that leverages a hierarchical attention pooling graph neural network based on enhanced call graphs.

From acute lung injury to cerebral ischemia: a unified concept involving intercellular communication through extracellular vesicle-associated miRNAs released by macrophages/microglia.

Ischemic stroke and acute lung injury are prevalent life-threatening conditions marked by intricate molecular mechanisms and elevated mortality rates. Despite evident pathophysiological distinctions, a notable similarity exists in the gene responses to tissue injury observed in both pathologies. This similarity extends to both protein-encoding RNAs and non-coding RNAs.

CGPDTA: An Explainable Transfer Learning-Based Predictor With Molecule Substructure Graph for Drug-Target Binding Affinity.

Identifying interactions between drugs and targets is crucial for drug discovery and development. Nevertheless, the determination of drug-target binding affinities (DTAs) through traditional experimental methods is a time-consuming process. Conventional approaches to predicting drug-target interactions (DTIs) frequently prove inadequate due to an insufficient representation of drugs and targets, resulting in ineffective feature capture and questionable interpretability of results.

Three-Dimensional Printing of Yttrium Oxide Transparent Ceramics via Direct Ink Writing.

The utilization of 3D printing technology for the fabrication of intricate transparent ceramics overcomes the limitations associated with conventional molding processes, thereby presenting a highly promising solution. In this study, we employed direct ink writing (DIW) to prepare yttrium oxide transparent ceramics using a ceramic slurry with excellent moldability, solid content of 45 vol%, and shear-thinning behavior. A successfully printed transparent yttrium oxide ring measuring 30 mm in diameter, 10 mm in inner diameter, and 0.

Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks.

In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of computational models to predict drug clearance. However, concerns persist regarding the reliability of data collected from public sources, and a majority of current in silico quantitative structure-property relationship models tend to neglect the influence of molecular chirality.

Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein-Ligand Interactions.

Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors.

In silico construction of a focused fragment library facilitating exploration of chemical space.

Fragment-based drug design (FBDD) has emerged as a captivating subject in the realm of computer-aided drug design, enabling the generation of novel molecules through the rearrangement of ring systems within known compounds. The construction of focused fragment library plays a pivotal role in FBDD, necessitating the compilation of all potential bioactive ring systems capable of interacting with a specific target. In our study, we propose a workflow for the development of a focused fragment library and combinatorial compound library.

Epigenetic target identification strategy based on multi-feature learning.

The identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are used in cancer clinical treatment and the availability of chemogenomic data related to epigenetics increases. In this study, we introduce a novel epigenetic target identification strategy (ETI-Strategy) that integrates a multi-task graph convolutional neural network prior model and a protein-ligand interaction classification discriminating model using large-scale bioactivity data for a panel of 55 epigenetic targets. Our approach utilizes machine learning techniques to achieve an AUC value of 0.

RPBP: Deep Retrosynthesis Reaction Prediction Based on Byproducts.

Retrosynthesis prediction is crucial in organic synthesis and drug discovery, aiding chemists in designing efficient synthetic routes for target molecules. Data-driven deep retrosynthesis prediction has gained importance due to new algorithms and enhanced computing power. Although existing models show certain predictive power on the USPTO-50K benchmark data set, no one considers the effects of byproducts during the prediction process, which may be due to the lack of byproduct information in the benchmark data set.

The Application of Probe-Based Confocal Laser Endomicroscopy as a Diagnostic Tool in Choledochoscopic Gallbladder-Preserving Surgery.

Background And Aims: Choledochoscopic gallbladder-preserving surgery (CGPS) has the advantage of treating benign gallbladder diseases on the premise of gallbladder preservation. However, it has no reliable preoperative diagnosis if the gallbladder is benign. Probe-based confocal laser endomicroscopy (pCLE) can obtain real-time and clear endoscopic images at the cell level in vivo.

The role of lncRNA ANRIL in the progression of hepatocellular carcinoma.

Objectives: The aim of the current study was to identify the long noncoding RNAs (lncRNAs) ANRIL function and molecular pathways underlying hepatocellular carcinoma progression.

Methods: ANRIL knockdown with specific siRNA, and transfected into HepG2 cells according to the protocol of Lipofectamine 2000. Cell proliferation, apoptosis, migration and metastasis were assessed with MTT assay, flow cytometry and wound healing assay, respectively.

Confocal laser endomicroscopy distinguishing benign and malignant gallbladder polyps during choledochoscopic gallbladder-preserving polypectomy: A case report.

Background: In the last decade, confocal laser endomicroscopy (CLE) has emerged as a new endoscopic imaging modality for real-time histological examination at the microscopic level. CLE has been shown to be useful for distinguishing benign and malignant lesions and has been widely used in many digestive diseases. In our study, we used CLE for the first time to examine the morphology of cholesterol polyps as well as the different parts of normal gallbladder mucosa.

An Image Encryption Algorithm Based on Random Hamiltonian Path.

In graph theory, Hamiltonian path refers to the path that visits each vertex exactly once. In this paper, we designed a method to generate random Hamiltonian path within digital images, which is equivalent to permutation in image encryption. By these means, building a Hamiltonian path across bit planes can shuffle the distribution of the pixel's bits.

Potential of C1QTNF1-AS1 regulation in human hepatocellular carcinoma.

The aim of our study is to explore the regulation of C1QTNF1-AS1 on its target miR-221-3p/SOCS3 in human hepatocellular carcinoma (HCC). To explore the underlying molecular regulation of non-coding RNA for HCC, differentially expressed patterns of lncRNAs and genes were examined by RNA-seq. GO and KEGG pathway analysis were done based on the function of mRNAs that mediated by differentially expressed lncRNAs.

Is concomitant splenectomy necessary in radical gastric cancer surgery? A systematic review and meta-analysis.

Purpose: This study is a systematic review and meta-analysis compare the short- and long-term outcomes of splenectomy (SP) versus splenic preservation (NSP) in radical gastric cancer surgery.

Methods: A comprehensive search of PubMed, Embase, Cochrane Library and Web of Knowledge was performed. Evaluation of short- and long-term outcomes was collected and analyzed by a fixed or random effects model, according to the heterogeneity using RevMan 5.