Novel Strain-Based Triple Inactivated Vaccine Confers Rapid Neutralizing Immunity to Feline Multisystemic Pathogens With Two-Dose Regimen.

The rapid global increase in companion animal populations and the rising risks of zoonotic diseases necessitate urgent advancements in veterinary vaccines. In China, over 100 million domestic cats are vulnerable to three deadly pathogens: feline calicivirus (FCV), feline herpesvirus type 1 (FHV-1), and feline panleukopenia virus (FPV). Existing trivalent vaccines face challenges, such as antigenic mismatches, supply chain inefficiencies, and delayed regional adaptability, highlighting the need for localized solutions.

Integrated transcriptomics and metabolomics confirms the oxidative stress mechanism of hypothermia-induced neuronal necroptosis.

Abnormal climate change seriously endangers the safety of outdoor work and life, often causing hypothermia-induced coma or death. As the underlying mechanism has not been fully elucidated, a targeted treatment for hypothermia-triggered neuronal injury and forensic pathology indicators of fatal hypothermia are lacking. Herein, we aimed to explore hypothermia-induced changes in gene expression and metabolite profiles of cerebral cortical tissues to elucidate the mechanism of hypothermia-promoted necroptosis of cerebral cortical neurons.

The mechanism of parvalbumin interneurons regulating glutamatergic neurons involvement in stress induced anxiety in the basolateral amygdala of male mice.

Modern life's fast-paced and the unexpected conditions contribute to escalating stress levels, often leading to anxiety disorders and posing significant challenges to physical and mental health. In judicial practice, the parties often suffer from anxiety disorder under the great stress. However, the precise mechanisms underlying stress-induced anxiety disorders remain incompletely understood.

Interpretability-guided RNA N-methyladenosine modification site prediction with invertible neural networks.

As one of the most common and abundant post-transcriptional modifications, N-methyladenosine (mA) has been extensively studied for its essential regulatory role in gene expression and cell functions. The location of mA RNA modification sites, however, remains a challenging problem, because of the inability to characterize mA modified sites at a multi-scale level in their native RNA context. Here, we introduce an interpretability-guided invertible neural network (mA-IIN), a deep learning model to accurately identify mA RNA modification sites by integrating both primary and secondary structure information under an invertible coupling framework.

Machine learning-assisted multi-dimensional transcriptomic analysis of cytoskeleton-related molecules and their relationship with prognosis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, with a poor prognosis due to its aggressive nature and limited treatment options. Cytoskeletal dynamics play a critical role in tumor progression, but the prognostic and therapeutic potential of cytoskeleton-related genes in HCC remains underexplored. In this study, transcriptomic data from the TCGA-LIHC dataset were used to identify differentially expressed cytoskeleton-related genes associated with overall survival (OS).

Exosome-derived hsa_circ_0007132 promotes lenvatinib resistance by inhibiting the ubiquitin-mediated degradation of NONO.

Hepatocellular carcinoma (HCC) is a highly heterogeneous solid tumor, with its incidence showing a troubling upward trend over the past decade. Lenvatinib is one of the first-line medications for treating advanced HCC, however, the development of resistance significantly undermines its potential to improve patient prognosis. In recent years, exosomal circRNAs have been implicated in the resistance mechanisms of various cancers, yet their role in mediating lenvatinib resistance (LR) remains largely unexplored.

Exosome-derived lnc-FAM72D-3 promotes lenvatinib resistance by remodeling hepatocellular carcinoma cytoskeleton via MBNL1/FAK axis.

Lenvatinib resistance (LR) profoundly exacerbates the prognosis of patients afflicted with advanced hepatocellular carcinoma (HCC). As pivotal mediators of intercellular communication, exosomes have been implicated in the development of LR. Nonetheless, the precise contributions of exosome-derived long non-coding RNAs (lncRNAs) to this phenomenon remain inadequately elucidated.

Histopathological grades as prognostic factors of H3 K27-altered diffuse midline glioma and their prediction using multiparametric MR imaging.

Background: H3K27-altered diffuse midline gliomas (H3K27-altered DMGs) are classified as WHO grade 4 tumors despite their histopathological characteristics. However, histopathological grades are known to have an important effect on prognosis. This study aims to investigate the prognostic impact of histopathological grades on H3K27-altered DMGs and to predict the grades using multiparametric MRI.

Post-treatment adverse events ranking in targeted immunotherapy for hepatocellular carcinoma: A network meta-analysis based on risk probability assessment.

Background: Despite the rapid evolution of targeted and immunotherapies for hepatocellular carcinoma (HCC), a systematic comparison of their adverse event profiles remains limited. This review addresses this critical gap by synthesizing data from 13 randomized controlled trials (RCTs) to prioritize treatment regimens on the basis of safety, thereby guiding clinical decision-making in an era of expanding therapeutic options.

Methods: Clinical studies focusing on targeted and immunotherapies in HCC patients were chosen from databases such as PubMed, Embase, Web of Science and the Cochrane Library, which spans from 2008 to 2023.

Integrating Transformer and Graph Attention Network for circRNA-miRNA Interaction Prediction.

CircRNA-miRNA interaction (CMI) plays a crucial role in the gene regulatory network of the cell. Numerous experiments have shown that abnormalities in CMI can impact molecular functions and physiological processes, leading to the occurrence of specific diseases. Current computational models for predicting CMI typically focus on local molecular entity relationships, thereby neglecting inherent molecular attributes and global structural information.

Comparative analysis of hepatectomy for HCC with PVTT: Insights from a 30-year single-center experience: Hepatectomy for HCC with PVTT.

Background And Aim: Portal vein tumor thrombosis (PVTT) is frequent in hepatocellular carcinoma (HCC). Although hepatectomy is the primary treatment for HCC, no consensus exists on its role in PVTT between Eastern and Western clinicians. This study aims to assess the efficacy of hepatectomy in HCC patients with PVTT by analyzing perioperative outcomes and prognosis.

Multimodal AI predicts clinical outcomes of drug combinations from preclinical data.

Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations. Current models rely on structural or target-based features to identify high-efficacy, low-toxicity drug combinations. However, these approaches fail to incorporate the multimodal data necessary for accurate, clinically-relevant predictions.

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.

ClinVec: Unified Embeddings of Clinical Codes Enable Knowledge-Grounded AI in Medicine.

Integrating structured clinical knowledge into artificial intelligence (AI) models remains a major challenge. Medical codes primarily reflect administrative workflows rather than clinical reasoning, limiting AI models' ability to capture true clinical relationships and undermining their generalizability. To address this, we introduce , a clinical knowledge graph that integrates eight EHR-based vocabularies, and , a set of 153,166 clinical code embeddings derived from using a graph transformer neural network.

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.