Peptic Ulcer Disease

SLMF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization.

Synthetic lethality (SL) is a promising concept for novel discovery of anti-cancer drug targets. However, wet-lab experiments for detecting SLs are faced with various challenges, such as high cost, low consistency across platforms, or cell lines. Therefore, computational prediction methods are needed to address these issues. This paper proposes a novel SL prediction method, named SL MF, which empl...

Quantification of apixaban in human plasma using ultra performance liquid chromatography coupled with tandem mass spectrometry.

Apixaban, an inhibitor of direct factor Xa, is used for the treatment of venous thromboembolic events or prevention of stroke. Unlike many other anticoagulant agents, it does not need periodic monitoring. However, monitoring is still required to determine the risk of bleeding due to overdose or surgery. Usually, apixaban concentrations are indirectly quantified using an anti-factor Xa assay. Howev...

An artificial neural network model for prediction of hypoxemia during sedation for gastrointestinal endoscopy.

OBJECTIVE: This study was designed to assess clinical predictors of hypoxemia and develop an artificial neural network (ANN) model for prediction of h...

Low-light image enhancement of high-speed endoscopic videos using a convolutional neural network.

Laryngeal endoscopy is one of the primary diagnostic tools for laryngeal disorders. The main techniques are videostroboscopy and lately high-speed vid...

Application of Convolutional Neural Networks for Automated Ulcer Detection in Wireless Capsule Endoscopy Images.

Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex...

Applications of artificial intelligence to endoscopy practice: The view from Japan Digestive Disease Week 2018.

Addressing priority challenges in the detection and assessment of colorectal polyps from capsule endoscopy and colonoscopy in colorectal cancer screening using machine learning.

BACKGROUND: Colorectal capsule endoscopy (CCE) is a potentially valuable patient-friendly technique for colorectal cancer screening in large populatio...

Exploring semi-supervised variational autoencoders for biomedical relation extraction.

The biomedical literature provides a rich source of knowledge such as protein-protein interactions (PPIs), drug-drug interactions (DDIs) and chemical-...

Artificial intelligence and colonoscopy: Current status and future perspectives.

BACKGROUND AND AIM: Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endos...

PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions.

The prediction of peptide-protein or protein-protein interactions (PPI) is a challenging task, especially if amino acid sequences are the only informa...

Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective.

With recent breakthroughs in artificial intelligence, computer-aided diagnosis (CAD) for upper gastrointestinal endoscopy is gaining increasing attent...

Machine learning model to predict recurrent ulcer bleeding in patients with history of idiopathic gastroduodenal ulcer bleeding.

BACKGROUND: Patients with a history of Helicobacter pylori-negative idiopathic bleeding ulcers have an increased risk of recurring ulcer complications...

Proximal detection of guide wire perforation using feature extraction from bispectral audio signal analysis combined with machine learning.

Artery perforation during a vascular catheterization procedure is a potentially life threatening event. It is of particular importance for the surgeon...

Spotting malignancies from gastric endoscopic images using deep learning.

BACKGROUND: Gastric cancer is a common kind of malignancies, with yearly occurrences exceeding one million worldwide in 2017. Typically, ulcerous and ...

Early esophageal adenocarcinoma detection using deep learning methods.

PURPOSE: This study aims to adapt and evaluate the performance of different state-of-the-art deep learning object detection methods to automatically i...

Artificial intelligence for the real-time classification of intrapapillary capillary loop patterns in the endoscopic diagnosis of early oesophageal squamous cell carcinoma: A proof-of-concept study.

BACKGROUND: Intrapapillary capillary loops (IPCLs) represent an endoscopically visible feature of early squamous cell neoplasia (ESCN) which correlate...

An Enhancement of Computer Aided Approach for Colon Cancer Detection in WCE Images Using ROI Based Color Histogram and SVM2.

The colon cancer is formed by uncontrollable growth of abnormal cells in large intestine or colon that can affect both men and women and it is third c...

Synthesis of Ag/AgCl modified anhydrous basic bismuth nitrate from BiOCl and the antibacterial activity.

Novel nanocomposite of Ag/AgCl and a single phase of anhydrous basic bismuth nitrate (ABBN)-BiO(OH)(NO) with efficient antibacterial activity was prep...

Task activations produce spurious but systematic inflation of task functional connectivity estimates.

Most neuroscientific studies have focused on task-evoked activations (activity amplitudes at specific brain locations), providing limited insight into...

Sensitive electrochemical detection of gp120 based on the combination of NBD-556 and gp120.

As is known, the employment of molecular imprinting polymer (MIP) as specific sensing materials in sensors, namely MIP-based sensors. In this contribu...