Publications by authors named "Xiaorui Su"

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).

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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.

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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.

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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.

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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.

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  • - The study investigates how cholecystokinin (CCK) receptors, specifically CCK1 receptors, play a role in methamphetamine (METH)-induced addiction by affecting the nucleus accumbens core (NAcC) and its connections with other brain areas.
  • - Using a mouse model, researchers created a condition that mimics METH addiction and explored the effects of genetically knocking out CCK receptor subtypes to understand their specific roles in the METH addiction process.
  • - Results showed that disruption of CCK1R in NAcC hindered the development of METH-induced conditioned place preference and altered neuronal excitability, indicating that CCK1R is essential for the synaptic changes in the
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  • * The most common infections reported were onychomycosis, tinea cruris, and tinea corporis, with variations in infection types related to factors like gender, age, and season.
  • * Antifungal treatments like terbinafine were highly effective against dermatophytes, while resistance to fluconazole and voriconazole was noted in some Candida strains, highlighting the need for careful antifungal use and ongoing monitoring of resistance patterns. *
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  • Abnormal climate changes are causing extreme weather, which increases the risk of hypothermia in outdoor activities, leading to serious health issues like coma or death.
  • The study investigates the mechanisms of brain injury from hypothermia by analyzing gene expression changes in nerve cells and identifying specific genes related to ferroptosis (a form of cell death caused by iron accumulation).
  • Experiments show that severe hypothermia affects the metabolism of brain cells, promoting ferroptosis through multiple pathways, but using an iron death inhibitor called Ferrostatin-1 can reduce these harmful effects.
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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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  • This study investigates the neurometabolic differences in three common types of malformations of cortical development (MCDs) - gray matter heterotopia (GMH), focal cortical dysplasia (FCD), and polymicrogyria (PMG) - by using proton magnetic resonance spectroscopy (H-MRS).
  • A total of 29 patients with MCDs and epilepsy were compared against 25 healthy controls, with focus on metabolite concentrations like N-acetyl aspartate (NAA), myoinositol (Ins), and choline (Cho) in their brain lesions.
  • Results show that MCD lesions displayed lower NAA and higher Ins levels compared to healthy controls, with FCD showing the most pronounced
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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.

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  • The text discusses the importance of discovering new uses for existing drugs, emphasizing the need for better connection patterns in biological information networks to enhance accuracy in drug discovery.
  • It introduces a computational model called SFRLDDA, which utilizes a heterogeneous information network that includes various associations between drugs, diseases, and proteins to predict drug-disease associations.
  • SFRLDDA employs representation learning strategies and a Random Forest classifier, showing superior performance compared to other models and highlighting the benefits of integrating semantic graphs and function similarity for predicting drug-disease associations.
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  • This study compares amino acid metabolism in myocardial infarction (MI) and strangulation death (STR) using mouse models and various analysis techniques.
  • Researchers established controlled groups and employed liquid chromatography-mass spectrometry (LC-MS) for metabolomics profiling, and utilized multiple statistical methods to identify metabolic differences.
  • Key findings indicated that PPM1K expression was lower in the MI group, while p-mTOR and p-S6K1 levels were higher, highlighting distinct metabolic pathways activated in MI versus STR.
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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.

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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.

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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.

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