Uncovering the mystery of the atrioventricular node dual-pathway electrophysiology.

With the discovery and validation of a novel index of dual-pathway electrophysiology, known as the His electrogram alternans, major advances have been made in understanding the electrical propagation through the atrioventricular (AV) node. At slow rates, AV conduction starts at the interatrial septal boundary of the node (superior nodal domain) and spreads toward the tricuspid annulus side (inferior nodal domain) in a direction perpendicular to the AV axis and across fiber orientation. Such activation results in an early, superior input into the superior His bundle.

Failing hearts are more vulnerable to dobutamine and caffeine-induced ventricular arrhythmias: Ameliorated with dantrolene treatment.

Background: Compared with normal hearts, failing hearts are more vulnerable to develop atrial fibrillation under sympathetic stimulation. It has been shown that dobutamine and caffeine challenge can induce spontaneous ventricular tachyarrhythmias in normal hearts.

Objective: This study was designed to investigate whether failing hearts have increased vulnerability to dobutamine- and caffeine-induced ventricular arrhythmias, and whether dantrolene treatment provides therapeutic benefits in this condition.

Preliminary exploration of metagenomic sequencing for pathogenic identification in infectious uveitis.

Purpose: To evaluate the advantages and clinical utility of metagenomic sequencing (MGS) in diagnosing infectious uveitis pathogens.

Methods: A retrospective study was conducted on 20 infectious uveitis patients (20 eyes) who received treatments at the Affiliated Eye Hospital of Shandong University of Traditional Chinese Medicine from May 2020 to February 2021. Anterior aqueous humor of the patients was collected and analyzed using MGS.

BES-Designer: A Web Tool to Design Guide RNAs for Base Editing to Simplify Library.

CRISPR/Cas base editors offer precise conversion of single nucleotides without inducing double-strand breaks. This technology finds extensive applications in gene therapy, gene function analysis, and other domains. However, a crucial challenge lies in selecting the appropriate guide RNAs (gRNAs) for base editing.

Interpretable Dynamic Directed Graph Convolutional Network for Multi-Relational Prediction of Missense Mutation and Drug Response.

Tumor heterogeneity presents a significant challenge in predicting drug responses, especially as missense mutations within the same gene can lead to varied outcomes such as drug resistance, enhanced sensitivity, or therapeutic ineffectiveness. These complex relationships highlight the need for advanced analytical approaches in oncology. Due to their powerful ability to handle heterogeneous data, graph convolutional networks (GCNs) represent a promising approach for predicting drug responses.

A jump in the atrioventricular conduction curve is not caused by a switch from fast pathway to slow pathway conduction.

A jump in the atrioventricular (AV) conduction curve is the current clinical criterion of dual-pathway electrophysiology. However, the assumption that a jump indicates a switch from fast pathway (FP) to slow pathway (SP) conduction remains unconfirmed. This study was carried out to investigate whether a jump indeed indicates a transition from FP to SP conduction, and if not, what the potential cause is.

Gastrointestinal Dysmotility Predisposes to Colitis through Regulation of Gut Microbial Composition and Linoleic Acid Metabolism.

Disrupted gastrointestinal (GI) motility is highly prevalent in patients with inflammatory bowel disease (IBD), but its potential causative role remains unknown. Herein, the role and the mechanism of impaired GI motility in colitis pathogenesis are investigated. Increased colonic mucosal inflammation is found in patients with chronic constipation (CC).

Fusobacterium nucleatum promotes tumor progression in KRAS p.G12D-mutant colorectal cancer by binding to DHX15.

Fusobacterium nucleatum (F. nucleatum) promotes intestinal tumor growth and its relative abundance varies greatly among patients with CRC, suggesting the presence of unknown, individual-specific effectors in F. nucleatum-dependent carcinogenesis.

Crop-GPA: an integrated platform of crop gene-phenotype associations.

With the increasing availability of large-scale biology data in crop plants, there is an urgent demand for a versatile platform that fully mines and utilizes the data for modern molecular breeding. We present Crop-GPA ( https://crop-gpa.aielab.

ECDEP: identifying essential proteins based on evolutionary community discovery and subcellular localization.

Background: In cellular activities, essential proteins play a vital role and are instrumental in comprehending fundamental biological necessities and identifying pathogenic genes. Current deep learning approaches for predicting essential proteins underutilize the potential of gene expression data and are inadequate for the exploration of dynamic networks with limited evaluation across diverse species.

Results: We introduce ECDEP, an essential protein identification model based on evolutionary community discovery.

Targeting integrin α5 in fibroblasts potentiates colorectal cancer response to PD-L1 blockade by affecting extracellular-matrix deposition.

Background: One reason patients with cancer cannot benefit from immunotherapy is the lack of immune cell infiltration in tumor tissues. Cancer-associated fibroblasts (CAFs) are emerging as central players in immune regulation that shapes tumor microenvironment (TME). Earlier we reported that integrin α5 was enriched in CAFs in colorectal cancer (CRC), however, its role in TME and cancer immunotherapy remains unclear.

Named entity recognition of rice genes and phenotypes based on BiGRU neural networks.

Named Entity Recognition (NER) is a fundamental but crucial task in natural language processing (NLP) and big data analysis, with wide application range. NER for rice genes and phenotypes is a technique to identify genes and phenotypes from a large amount of text. NER for rice genes and phenotypes can facilitate the acquisition of information in the field of crops and provide references for our research on higher quality crops.

The RNA m6A-Binding Protein YTHDC1 Is Downregulated and Associated With M2 Macrophage Infiltration in Muscle-Invasive Bladder Cancer.

Background: Dysregulation of RNA N6-methyladenosine (m6A) modification is indispensable in tumorigenesis. However, in muscle-invasive bladder cancer (MIBC), the key regulators and mechanisms involved in this process remain largely unknown. This study aimed to screen the key m6A regulators and explore its possible role in MIBC.

MFBP-UNet: A Network for Pear Leaf Disease Segmentation in Natural Agricultural Environments.

The accurate prevention and control of pear tree diseases, especially the precise segmentation of leaf diseases, poses a serious challenge to fruit farmers globally. Given the possibility of disease areas being minute with ambiguous boundaries, accurate segmentation becomes difficult. In this study, we propose a pear leaf disease segmentation model named MFBP-UNet.

Diagnosis and application of rice diseases based on deep learning.

Background: Rice disease can significantly reduce yields, so monitoring and identifying the diseases during the growing season is crucial. Some current studies are based on images with simple backgrounds, while realistic scene settings are full of background noise, making this task challenging. Traditional artificial prevention and control methods not only have heavy workload, low efficiency, but are also haphazard, unable to achieve real-time monitoring, which seriously limits the development of modern agriculture.

LRP12 is an endogenous transmembrane inactivator of α4 integrins.

Dynamic regulation of integrin activation and inactivation is critical for precisely controlled cell adhesion and migration in physiological and pathological processes. The molecular basis for integrin activation has been intensively studied; however, the understanding of integrin inactivation is still limited. Here, we identify LRP12 as an endogenous transmembrane inhibitor for α4 integrin activation.

TRSRD: a database for research on risky substances in tea using natural language processing and knowledge graph-based techniques.

During the production and processing of tea, harmful substances are often introduced. However, they have never been systematically integrated, and it is impossible to understand the harmful substances that may be introduced during tea production and their related relationships when searching for papers. To address these issues, a database on tea risk substances and their research relationships was constructed.

PredinID: Predicting Pathogenic Inframe Indels in Human Through Graph Convolution Neural Network With Graph Sampling Technique.

Inframe insertion/deletion (indel) variants may alter protein sequence and function, which are closely related to an extensive variety of diseases. Although recent researches have paid attention to the associations between inframe indels and diseases, modeling indels in silico and interpreting their pathogenicity remain challenging, mainly due to the lack of experimental information and computational methodologies. In this article, we propose a novel computational method named PredinID (Predictor for inframe InDels) via graph convolutional network (GCN).

Early mean absolute lymphocyte count in acute necrotizing pancreatitis is associated with infected pancreatic necrosis.

Objectives: Peripheral absolute lymphocyte count (ALC) has the potential to predict infected pancreatic necrosis (IPN), but requires verification. This study aimed to assess whether early mean absolute lymphocyte count is associated with the development of IPN in ANP patients using pooled data from a multicenter, randomized controlled trial and a retrospective study.

Methods: The study subjects are from the TRACE trial and a single-center cohort study.

Mast cells inhibit colorectal cancer development by inducing ER stress through secreting Cystatin C.

Mast cells (MCs) are abundantly distributed in the human intestinal mucosa and submucosa. However, their roles and mechanisms in the development of colorectal cancer (CRC) are still unclear. In the present research, we found that the infiltration density of MCs in CRC tissues was positively correlated with improved patients' prognoses.

A deep learning framework for identifying essential proteins based on multiple biological information.

Background: Essential Proteins are demonstrated to exert vital functions on cellular processes and are indispensable for the survival and reproduction of the organism. Traditional centrality methods perform poorly on complex protein-protein interaction (PPI) networks. Machine learning approaches based on high-throughput data lack the exploitation of the temporal and spatial dimensions of biological information.

β-Adrenergic Receptor Desensitization/Down-Regulation in Heart Failure: A Friend or Foe?

Cardiac sympathetic activation, mediated by β-adrenergic receptors (β-ARs), normally increases cardiac contraction and relaxation. Accomplishing this task requires a physiological, concerted Ca signaling, being able to increase Ca release from sarcoplasmic reticulum (SR) in systole and speed up Ca re-uptake in diastole. In heart failure (HF) myocardial β-ARs undergo desensitization/down-regulation due to sustained sympathetic adrenergic activation.