A Comparison between Chicago Classification Versions 3.0 and 4.0 and Their Impact on Manometric Diagnoses in Esophageal High-Resolution Manometry Cases.

High-resolution manometry (HRM) facilitates the detailed evaluation of esophageal motility. In December 2020, Chicago classification (CC) version 4.0 introduced modifications to improve consistency and accuracy.

Prescription system to calculate precise doses of Chinese herbal medicine to avoid toxic effects.

Background And Objectives: Traditional Chinese Medicine (TCM) is a therapeutic system which has been practiced for thousands of years. Although for much of its history the decoction of medicinal herbs was the most common method of consuming the herbal treatments, TCM prescriptions are now primarily prepared using concentrated Chinese herbal extracts (CCHE) in powder or granular form. However, determining the precise dose of each single Chinese herbal constituent within a prescription creates a challenge in clinical practice due to the potential risk of toxicity.

Chinese Intelligence Prescription System improves prescription accuracy while decreasing labor and drug costs.

Background And Aim: The traditional method of taking Chinese Medicine involves creating a decoction by cooking medicinal Chinese herbs. However, this method has become less popular, being replaced by the more convenient method of consuming concentrated Chinese herbal extracts, which creates challenges related to the complexity of stacking multiple formulas.

Methods: We developed the Chinese Intelligence Prescription System (CIPS) to simplify the prescription process.

Use of U-Net Convolutional Neural Networks for Automated Segmentation of Fecal Material for Objective Evaluation of Bowel Preparation Quality in Colonoscopy.

Background: Adequate bowel cleansing is important for colonoscopy performance evaluation. Current bowel cleansing evaluation scales are subjective, with a wide variation in consistency among physicians and low reported rates of accuracy. We aim to use machine learning to develop a fully automatic segmentation method for the objective evaluation of the adequacy of colon preparation.

A novel machine learning-based algorithm to identify and classify lesions and anatomical landmarks in colonoscopy images.

Objectives: Computer-aided diagnosis (CAD)-based artificial intelligence (AI) has been shown to be highly accurate for detecting and characterizing colon polyps. However, the application of AI to identify normal colon landmarks and differentiate multiple colon diseases has not yet been established. We aimed to develop a convolutional neural network (CNN)-based algorithm (GUTAID) to recognize different colon lesions and anatomical landmarks.

Electron transport in a GaPSb film.

We have performed transport measurements on a gallium phosphide antimonide (GaPSb) film grown on GaAs. At low temperatures (T), transport is governed by three-dimensional Mott variable range hopping (VRH) due to strong localization. Therefore, electron-electron interactions are not significant in GaPSb.