Gastroenterology

Automatic delineation of the clinical target volume and organs at risk by deep learning for rectal cancer postoperative radiotherapy.

BACKGROUND AND PURPOSE: Manual delineation of clinical target volumes (CTVs) and organs at risk (OARs) is time-consuming, and automatic contouring tools lack clinical validation. We aimed to construct and validate the use of convolutional neural networks (CNNs) to set better contouring standards for rectal cancer radiotherapy.

Image Based Liver Toxicity Prediction.

The drugs we use to cure our diseases can cause damage to the liver as it is the primary organ responsible for metabolism of environmental chemicals and drugs. To identify and eliminate potentially problematic drug candidates in the early stages of drug discovery, in silico techniques provide quick and practical solutions for toxicity determination. Deep learning has emerged as one of the solution...

DeepCPI: A Deep Learning-based Framework for Large-scale in silico Drug Screening.

Accurate identification of compound-protein interactions (CPIs) in silico may deepen our understanding of the underlying mechanisms of drug action and...

Machine learning for syndromic surveillance using veterinary necropsy reports.

The use of natural language data for animal population surveillance represents a valuable opportunity to gather information about potential disease ou...

Computational analysis of morphological and molecular features in gastric cancer tissues.

Biological morphologies of cells and tissues represent their physiological and pathological conditions. The importance of quantitative assessment of m...

Real-time colorectal cancer diagnosis using PR-OCT with deep learning.

Prior reports have shown optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering an alternativ...

Liver lesion localisation and classification with convolutional neural networks: a comparison between conventional and spectral computed tomography.

PURPOSE: To evaluate the benefit of the additional available information present in spectral CT datasets, as compared to conventional CT datasets, whe...

Predicted Prognosis of Patients with Pancreatic Cancer by Machine Learning.

PURPOSE: Pancreatic cancer remains a disease of high mortality despite advanced diagnostic techniques. Mucins (MUC) play crucial roles in carcinogenes...

Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection.

Automated detection and classification of gastric infections (i.e., ulcer, polyp, esophagitis, and bleeding) through wireless capsule endoscopy (WCE) ...

Robotic fluidic coupling and interrogation of multiple vascularized organ chips.

Organ chips can recapitulate organ-level (patho)physiology, yet pharmacokinetic and pharmacodynamic analyses require multi-organ systems linked by vas...

Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals.

Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remai...

Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics.

Given that the biological processes governing the oncogenesis of pancreatic cancers could present useful therapeutic targets, there is a pressing need...

Application of artificial intelligence using convolutional neural networks in determining the invasion depth of esophageal squamous cell carcinoma.

OBJECTIVES: In Japan, endoscopic resection (ER) is often used to treat esophageal squamous cell carcinoma (ESCC) when invasion depths are diagnosed as...

Deep pancreas segmentation with uncertain regions of shadowed sets.

Pancreas segmentation is a challenging task in medical image analysis especially for the patients with pancreatic cancer. First, the images often have...

Deep principal dimension encoding for the classification of early neoplasia in Barrett's Esophagus with volumetric laser endomicroscopy.

Barrett cancer is a treatable disease when detected at an early stage. However, current screening protocols are often not effective at finding the dis...

Hepatotoxicity Modeling Using Counter-Propagation Artificial Neural Networks: Handling an Imbalanced Classification Problem.

Drug-induced liver injury is a major concern in the drug development process. Expensive and time-consuming and studies do not reflect the complexity...

Stability of Pentobarbital Hydrogel for Rectal Administration in Pediatric Procedural Sedation.

Pentobarbital is a sedative agent to limit children motion during computed tomography or magnetic resonance imaging (MRI) and ensures the successful ...

Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study.

BACKGROUND: Colonoscopy with computer-aided detection (CADe) has been shown in non-blinded trials to improve detection of colon polyps and adenomas by...

Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis.

OBJECTIVES: To propose a transfer learning (TL) radiomics model that efficiently combines the information from gray scale and elastogram ultrasound im...

Xuetonglactones A-F: Highly Oxidized Lanostane and Cycloartane Triterpenoids From Roxb. Craib.

Xuetonglactones A-F (-), six unreported highly oxidized lanostane- and cycloartane-type triterpenoids along with 22 known scaffolds (-) were isolated ...