A heterogeneous information network learning model with neighborhood-level structural representation for predicting lncRNA-miRNA interactions.

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are closely related to the treatment of human diseases. Traditional biological experiments often require time-consuming and labor-intensive in their search for mechanisms of disease. Computational methods are regarded as an effective way to predict unknown lncRNA-miRNA interactions (LMIs).

Differential Diagnosis of Early-Stage Atypical Primary Central Nervous System Lymphoma and Low-Grade Glioma Using Magnetic Resonance Imaging-Based Radiomics.

Background: Different from typical primary central nervous system lymphoma (PCNSL), early-stage atypical PCNSL usually presents as patchy signal abnormalities without evident mass effect or significant contrast enhancement and is prone to confusion with low-grade glioma (LGG). This study aims to develop a magnetic resonance imaging (MRI)-based radiomics model to differentiate early-stage atypical PCNSL from LGG.

Methods: Two cohorts consisting of early-stage atypical PCNSL patients, as well as LGG patients with similar radiological manifestations, were retrospectively recruited from West China Hospital of Sichuan University (PCNSL = 75; LGG = 138) and Chengdu Shangjin Nanfu Hospital (PCNSL = 35; LGG = 72) to serve as the training set and external validation set, respectively.

Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning.

Graph representation learning has been leveraged to identify cancer genes from biological networks. However, its applicability is limited by insufficient interpretability and generalizability under integrative network analysis. Here we report the development of an interpretable and generalizable transformer-based model that accurately predicts cancer genes by leveraging graph representation learning and the integration of multi-omics data with the topologies of homogeneous and heterogeneous networks of biological interactions.

Capturing short-range and long-range dependencies of nucleotides for identifying RNA N6-methyladenosine modification sites.

N-methyladenosine (mA) plays a crucial role in enriching RNA functional and genetic information, and the identification of mA modification sites is therefore an important task to promote the understanding of RNA epigenetics. In the identification process, current studies are mainly concentrated on capturing the short-range dependencies between adjacent nucleotides in RNA sequences, while ignoring the impact of long-range dependencies between non-adjacent nucleotides for learning high-quality representation of RNA sequences. In this work, we propose an end-to-end prediction model, called mASLD, to improve the identification accuracy of mA modification sites by capturing the short-range and long-range dependencies of nucleotides.

Activity Cliff-Informed Contrastive Learning for Molecular Property Prediction.

Modeling molecular activity and quantitative structure-activity relationships of chemical compounds is critical in drug design. Graph neural networks, which utilize molecular structures as frames, have shown success in assessing the biological activity of chemical compounds, guiding the selection and optimization of candidates for further development. However, current models often overlook activity cliffs (ACs)-cases where structurally similar molecules exhibit different bioactivities-due to latent spaces primarily optimized for structural features.

Knowledge Graph Neural Network With Spatial-Aware Capsule for Drug-Drug Interaction Prediction.

Uncovering novel drug-drug interactions (DDIs) plays a pivotal role in advancing drug development and improving clinical treatment. The outstanding effectiveness of graph neural networks (GNNs) has garnered significant interest in the field of DDI prediction. Consequently, there has been a notable surge in the development of network-based computational approaches for predicting DDIs.

A new strategy for overcoming drug resistance in liver cancer: Epigenetic regulation.

Drug resistance in hepatocellular carcinoma has posed significant obstacles to effective treatment. Recent evidence indicates that, in addition to traditional gene mutations, epigenetic recoding plays a crucial role in HCC drug resistance. Unlike irreversible gene mutations, epigenetic changes are reversible, offering a promising avenue for preventing and overcoming drug resistance in liver cancer.

Efficacy and safety of low-dose cyclophosphamide combined with lenvatinib, pembrolizumab and TACE for unresectable hepatocellular carcinoma: A single-center, prospective, single-arm clinical trial.

Objective: Unresectable hepatocellular carcinoma (uHCC) continues to pose effective treatment options. The objective of this study was to assess the efficacy and safety of combining low-dose cyclophosphamide with lenvatinib, pembrolizumab and transarterial chemoembolization (TACE) for the treatment of uHCC.

Methods: From February 2022 to November 2023, a total of 40 patients diagnosed with uHCC were enrolled in this small-dose, single-center, single-arm, prospective study.

Motif-Aware miRNA-Disease Association Prediction via Hierarchical Attention Network.

As post-transcriptional regulators of gene expression, micro-ribonucleic acids (miRNAs) are regarded as potential biomarkers for a variety of diseases. Hence, the prediction of miRNA-disease associations (MDAs) is of great significance for an in-depth understanding of disease pathogenesis and progression. Existing prediction models are mainly concentrated on incorporating different sources of biological information to perform the MDA prediction task while failing to consider the fully potential utility of MDA network information at the motif-level.

Unraveling the efficacy network: A network meta-analysis of adjuvant external beam radiation therapy methods after hepatectomy.

Background: Primary liver cancer is a malignant tumor with a high recurrence rate that significantly affects patient prognosis. Postoperative adjuvant external radiation therapy (RT) has been shown to effectively prevent recurrence after liver cancer resection. However, there are multiple RT techniques available, and the differential effects of these techniques in preventing postoperative liver cancer recurrence require further investigation.

Discovering Consensus Regions for Interpretable Identification of RNA N6-Methyladenosine Modification Sites via Graph Contrastive Clustering.

As a pivotal post-transcriptional modification of RNA, N6-methyladenosine (m6A) has a substantial influence on gene expression modulation and cellular fate determination. Although a variety of computational models have been developed to accurately identify potential m6A modification sites, few of them are capable of interpreting the identification process with insights gained from consensus knowledge. To overcome this problem, we propose a deep learning model, namely M6A-DCR, by discovering consensus regions for interpretable identification of m6A modification sites.

Screening and Stability Evaluation of Freeze-Dried Protective Agents for a Live Recombinant Pseudorabies Virus Vaccine.

Infection of pigs with the pseudorabies virus (PRV) causes significant economic losses in the pig industry. Immunization with live vaccines is a crucial aspect in the prevention of pseudorabies in swine. The TK/gE/gI/11k/28k deleted pseudorabies vaccine is a promising alternative for the eradication of epidemic pseudorabies mutant strains.

MRI characteristics of H3 G34-mutant diffuse hemispheric gliomas and possible differentiation from IDH-wild-type glioblastomas in adolescents and young adults.

Objective: H3 G34-mutant diffuse hemispheric gliomas (G34m-DHGs) are rare and constitute a new infiltrating brain tumor entity whose characteristics require elucidation, and their difference from isocitrate dehydrogenase-wild-type glioblastomas (IDH-WT-GBMs) needs to be clarified. In this study, the authors report the demographic, clinical, and neuroradiological features of G34m-DHG and investigate the capability of quantitative MRI features in differentiating them.

Methods: Twenty-three patients with G34m-DHG and 30 patients with IDH-WT-GBM were included in this retrospective study.

Biocaiv: an integrative webserver for motif-based clustering analysis and interactive visualization of biological networks.

Background: As an important task in bioinformatics, clustering analysis plays a critical role in understanding the functional mechanisms of many complex biological systems, which can be modeled as biological networks. The purpose of clustering analysis in biological networks is to identify functional modules of interest, but there is a lack of online clustering tools that visualize biological networks and provide in-depth biological analysis for discovered clusters.

Results: Here we present BioCAIV, a novel webserver dedicated to maximize its accessibility and applicability on the clustering analysis of biological networks.

CCK1R2R ameliorates myocardial damage caused by unpredictable stress via altering fatty acid metabolism.

The heart is the main organ of the circulatory system and requires fatty acids to maintain its activity. Stress is a contributor to aggravating cardiovascular diseases and even death, and exacerbates the abnormal lipid metabolism. The cardiac metabolism may be disturbed by stress.

Lnc-ZEB2-19 Inhibits the Progression and Lenvatinib Resistance of Hepatocellular Carcinoma by Attenuating the NF-κB Signaling Pathway through the TRA2A/RSPH14 Axis.

Long non-coding RNAs have been reported to play a crucial role in tumor progression in hepatocellular carcinoma (HCC). Lnc-ZEB2-19 has been validated to be deficiently expressed in HCC. However, the capabilities and underlying mechanisms of lnc-ZEB2-19 remain uncertain.

Development and External Validation of an MRI-based Radiomics Nomogram to Distinguish Circumscribed Astrocytic Gliomas and Diffuse Gliomas: A Multicenter Study.

Rationale And Objectives: The 5th edition of the World Health Organization classification of tumors of the Central Nervous System (WHO CNS) has introduced the term "diffuse" and its counterpart "circumscribed" to the category of gliomas. This study aimed to develop and validate models for distinguishing circumscribed astrocytic gliomas (CAGs) from diffuse gliomas (DGs).

Materials And Methods: We retrospectively analyzed magnetic resonance imaging (MRI) data from patients with CAGs and DGs across three institutions.