Colorectal mucosal exposure area assessment based on artificial intelligence: a multi-center, prospective, observational study.

Background: This study proposed a new quality control indicator, cumulative colorectal mucosal exposure area (CCMEA) for colonoscopy to assess mucosal exposure, constructed CCMEA system based on deep learning. and validated the indicator in a multi-center prospective observational study.

Methods: The CCMEA system worked based on ResNet50 and UNet++.

A fully annotated pathology slide dataset for early gastric cancer and precancerous lesions.

Gastric cancer, a significant global health concern, exhibits high morbidity and mortality, especially in advanced stages. Timely diagnosis and intervention are crucial for improving patient outcomes, with Endoscopic Submucosal Dissection (ESD) playing a pivotal role in precise, minimally invasive early-stage treatments. Despite its importance, challenges include significant interobserver variability among pathologists and the intensive labor required for detailed pathological analysis of ESD specimens impede optimal outcomes.

Evidential deep learning-based drug-target interaction prediction.

Drug-target interaction (DTI) prediction is a crucial component of drug discovery. Recent deep learning methods show great potential in this field but also encounter substantial challenges. These include generating reliable confidence estimates for predictions, enhancing robustness when handling novel, unseen DTIs, and mitigating the tendency toward overconfident and incorrect predictions.

Identify drug-drug interactions via deep learning: A real world study.

Identifying drug-drug interactions (DDIs) is essential to prevent adverse effects from polypharmacy. Although deep learning has advanced DDI identification, the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits. Here, we developed a Multi-Dimensional Feature Fusion model named MDFF, which integrates one-dimensional simplified molecular input line entry system sequence features, two-dimensional molecular graph features, and three-dimensional geometric features to enhance drug representations for predicting DDIs.

Development and clinical validation of deep learning-based immunohistochemistry prediction models for subtyping and staging of gastrointestinal cancers.

Background: Immunohistochemistry (IHC) is a critical tool for tumor diagnosis and treatment, but it is time and tissue consuming, and highly dependent on skilled laboratory technicians. Recently, deep learning-based IHC biomarker prediction models have been widely developed, but few investigations have explored their clinical application effectiveness.

Methods: In this study, we aimed to create an automatic pipeline for the construction of deep learning models to generate AI-IHC (Artificial Intelligence) output using H&E whole slide images (WSIs) and compared the pathology reports by pathologists on AI-IHC versus conventional IHC.

ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment.

Multi-species acute toxicity assessment forms the basis for chemical classification, labelling and risk management. Existing deep learning methods struggle with diverse experimental conditions, imbalanced data, and scarce target data, hindering their ability to reveal endpoint associations and accurately predict data-scarce endpoints. Here we propose a machine learning paradigm, Adjoint Correlation Learning, for multi-condition acute toxicity assessment (ToxACoL) to address these challenges.

Clinical decision tree for optimizing endoscopic assessment of signet ring cell carcinoma in hereditary diffuse gastric cancer surveillance.

Prophylactic total gastrectomy is the definitive treatment for hereditary diffuse gastric cancer syndrome (HDGC). Endoscopic surveillance informs the requirement for and optimal timing of surgery. However, endoscopic recognition of early signet ring cell carcinoma (SRCC) remains challenging.

Screening and surveillance of hereditary upper GI cancers.

Upper GI cancer has poor prognosis. Of all upper GI malignancies, 1-3 % have a well-defined germline genetic cause. Hereditary upper GI cancers include squamous cell carcinoma as part of Tylosis, signet ring cell carcinoma of the stomach occurring in the context of hereditary diffuse gastric cancer syndrome, gastric adenocarcinoma affecting individuals with familial adenomatous polyposis syndrome (FAP), Gastric adenocarcinoma and proximal polyposis of the stomach (GAPPS), Lynch, Juvenile Polyposis and Peutz-Jeghers Syndromes, and duodenal adenocarcinoma, for which individuals with FAP and Lynch syndromes are at increased risk.

Pan-cancer analysis shows that TRIP13 as a potential prognostic and immunotherapeutic biomarker for multiple cancer types including LIHC and LUAD.

Growing evidence indicates that thyroid hormone receptor interactor 13 (TRIP13) plays an oncogenic role in various malignancies. Pan-cancer analysis was performed to elucidate the prognostic value and oncogenic role of TRIP13, and detect TRIP13 expression levels in diverse cancer types. We found that TRIP13 was overexpressed in multiple tumors.

Machine Learning-Enabled Drug-Induced Toxicity Prediction.

Unexpected toxicity has become a significant obstacle to drug candidate development, accounting for 30% of drug discovery failures. Traditional toxicity assessment through animal testing is costly and time-consuming. Big data and artificial intelligence (AI), especially machine learning (ML), are robustly contributing to innovation and progress in toxicology research.

The Effect of Computer-Aided Device on Adenoma Detection Rate in Different Implement Scenarios: A Real-World Study.

Background: Several recent studies have found that the efficacy of computer-aided polyp detection (CADe) on the adenoma detection rate (ADR) diminished in real-world settings. The role of unmeasured factors in AI-human interaction, such as monitor approaches, remains unknown. This study aimed to validate the effectiveness of CADe in the real world and assess the impact of monitor approaches.

Gastric neoplasm detection of computer-aided detection-assisted esophagogastroduodenoscopy changes with implement scenarios: a real-world study.

Background And Aim: The implementation of computer-aided detection (CAD) devices in esophagogastroduodenoscopy (EGD) could autonomously identify gastric precancerous lesions and neoplasms and reduce the miss rate of gastric neoplasms in prospective trials. However, there is still insufficient evidence of their use in real-life clinical practice.

Methods: A real-world, two-center study was conducted at Wenzhou Central Hospital (WCH) and Renmin Hospital of Wuhan University (RHWU).

Development and Validation of an AI-Driven System for Automatic Literature Analysis and Molecular Regulatory Network Construction.

Decoding gene regulatory networks is essential for understanding the mechanisms underlying many complex diseases. GENET is developed, an automated system designed to extract and visualize extensive molecular relationships from published biomedical literature. Using natural language processing, entities and relations are identified from a randomly selected set of 1788 scientific articles, and visualized in a filterable knowledge graph.

The prognostic value of bioinformatics analysis of ECM receptor signaling pathways and LAMB1 identification as a promising prognostic biomarker of lung adenocarcinoma.

The extracellular matrix (ECM) is a complex and dynamic network of cross-linked proteins and a fundamental building block in multicellular organisms. Our study investigates the impact of genes related to the ECM receptor interaction pathway on immune-targeted therapy and lung adenocarcinoma (LUAD) prognosis. This study obtained LUAD chip data (GSE68465, GSE31210, and GSE116959) from NCBI GEO.

Multistep validation of a post-ERCP pancreatitis prediction system integrating multimodal data: a multicenter study.

Background And Aims: The impact of various categories of information on the prediction of post-ERCP pancreatitis (PEP) remains uncertain. We comprehensively investigated the risk factors associated with PEP by constructing and validating a model incorporating multimodal data through multiple steps.

Methods: Cases (n = 1916) of ERCP were retrospectively collected from multiple centers for model construction.

A commensal protozoan attenuates Clostridioides difficile pathogenesis in mice via arginine-ornithine metabolism and host intestinal immune response.

Antibiotic-induced dysbiosis is a major risk factor for Clostridioides difficile infection (CDI), and fecal microbiota transplantation (FMT) is recommended for treating CDI. However, the underlying mechanisms remain unclear. Here, we show that Tritrichomonas musculis (T.

The effect of incorporating domain knowledge with deep learning in identifying benign and malignant gastric whitish lesions: A retrospective study.

Background And Aim: Early whitish gastric neoplasms can be easily misdiagnosed; differential diagnosis of gastric whitish lesions remains a challenge. We aim to build a deep learning (DL) model to diagnose whitish gastric neoplasms and explore the effect of adding domain knowledge in model construction.

Methods: We collected 4558 images from two institutions to train and test models.

Conditional Knockout of IL-1R1 in Endothelial Cells Attenuates Seizures and Neurodegeneration via Inhibiting Neuroinflammation Mediated by Nrf2/HO-1/NLRP3 Signaling in Status Epilepticus Model.

Studies on the bench and at bedside have demonstrated that the process of epileptogenesis is involved in neuroinflammatory responses. As the receptor of proinflammatory cytokine IL-1β, IL-1β type 1 receptor (IL-1R1) is reported to express abundantly in the endothelial cells in epileptic brains, which is deemed to be implicated in the epileptogenic process. However, whether and how endothelial IL-1R1 modulates neuroinflammatory responses in the pathological process of epileptic seizures and/or status epilepticus (SE) remains obscure.

An interpretable artificial intelligence framework for designing synthetic lethality-based anti-cancer combination therapies.

Introduction: Synthetic lethality (SL) provides an opportunity to leverage different genetic interactions when designing synergistic combination therapies. To further explore SL-based combination therapies for cancer treatment, it is important to identify and mechanistically characterize more SL interactions. Artificial intelligence (AI) methods have recently been proposed for SL prediction, but the results of these models are often not interpretable such that deriving the underlying mechanism can be challenging.

Comprehensive characterization of MUC16 mutations in lung adenocarcinoma for immunotherapies and prognosis: An observational study.

Lung adenocarcinoma (LUAD) is a non-small-cell lung cancer and is the leading cause of cancer-related deaths worldwide. Immunotherapy is a promising candidate for LUAD, and tumor mutation burden (TMB) could be a new biomarker to monitor the response of cancer patients to immunotherapy. It is known that the mucin 16 (MUC16) mutation is the most common and affects the progression and prognosis of several cancers.

A real-time deep learning-based system for colorectal polyp size estimation by white-light endoscopy: development and multicenter prospective validation.

Background: The choice of polypectomy device and surveillance intervals for colorectal polyps are primarily decided by polyp size. We developed a deep learning-based system (ENDOANGEL-CPS) to estimate colorectal polyp size in real time.

Methods: ENDOANGEL-CPS calculates polyp size by estimating the distance from the endoscope lens to the polyp using the parameters of the lens.

Deep Learning-Based Model for Automatic Assessment of Anterior Angle Chamber in Ultrasound Biomicroscopy.

Objective: The goal of the work described here was to develop and assess a deep learning-based model that could automatically segment anterior chamber angle (ACA) tissues; classify iris curvature (I-Curv), iris root insertion (IRI), and angle closure (AC); automatically locate scleral spur; and measure ACA parameters in ultrasound biomicroscopy (UBM) images.

Methods: A total of 11,006 UBM images were obtained from 1538 patients with primary angle-closure glaucoma who were admitted to the Eye Center of Renmin Hospital of Wuhan University (Wuhan, China) to develop an imaging database. The UNet++ network was used to segment ACA tissues automatically.

Development and Evaluation of a Surveillance System for Follow-Up After Colorectal Polypectomy.

Importance: The adherence of physicians and patients to published colorectal postpolypectomy surveillance guidelines varies greatly, and patient follow-up is critical but time consuming.

Objectives: To evaluate the accuracy of an automatic surveillance (AS) system in identifying patients after polypectomy, assigning surveillance intervals for different risks of patients, and proactively following up with patients on time.

Design, Setting, And Participants: In this diagnostic/prognostic study, endoscopic and pathological reports of 47 544 patients undergoing colonoscopy at 3 hospitals between January 1, 2017, and June 30, 2022, were collected to develop an AS system based on natural language processing.