Hepatitis

Detecting Pre-Analytically Delayed Blood Samples for Laboratory Diagnostics Using Raman Spectroscopy.

In this proof-of-principle study, we systematically studied the potential of Raman spectroscopy for detecting pre-analytical delays in blood serum samples. Spectra from 330 samples from a liver cirrhosis cohort were acquired over the course of eight days, stored one day at room temperature, and stored subsequently at 4 °C. The spectra were then used to train Convolutional Neural Networks (CNN) to ...

Bifunctional robots inducing targeted protein degradation.

The gaining importance of Targeted Protein Degradation (TPD) and PROTACs (PROteolysis-TArgeting Chimeras) have drawn the scientific community's attention. PROTACs are considered bifunctional robots owing to their avidity for the protein of interest (POI) and E3-ligase, which induce the ubiquitination of POI. These molecules are based on event-driven pharmacology and are applicable in different con...

A Deep-Learning-Computed Cancer Score for the Identification of Human Hepatocellular Carcinoma Area Based on a Six-Colour Multiplex Immunofluorescence Panel.

Liver cancer is one of the most frequently diagnosed and fatal cancers worldwide, with hepatocellular carcinoma (HCC) being the most common primary li...

Aggregated micropatch-based deep learning neural network for ultrasonic diagnosis of cirrhosis.

Despite the advancements in the diagnosis of early-stage cirrhosis, the accuracy in the diagnosis using ultrasound is still challenging owing to the p...

Leveraging deep learning to improve vaccine design.

Deep learning has led to incredible breakthroughs in areas of research, from self-driving vehicles to solutions, to formal mathematical proofs. In the...

Deep learning models for hepatitis E incidence prediction leveraging meteorological factors.

BACKGROUND: Infectious diseases are a major threat to public health, causing serious medical consumption and casualties. Accurate prediction of infect...

Basal ganglia network dynamics and function: Role of direct, indirect and hyper-direct pathways in action selection.

Basal ganglia (BG) are a widely recognized neural basis for action selection, but its decision-making mechanism is still a difficult problem for resea...

Deep learning based direct segmentation assisted by deformable image registration for cone-beam CT based auto-segmentation for adaptive radiotherapy.

Cone-beam CT (CBCT)-based online adaptive radiotherapy calls for accurate auto-segmentation to reduce the time cost for physicians. However, deep lear...

A deep learning model with data integration of ultrasound contrast-enhanced micro-flow cines, B-mode images, and clinical parameters for diagnosing significant liver fibrosis in patients with chronic hepatitis B.

OBJECTIVE: To develop and investigate a deep learning model with data integration of ultrasound contrast-enhanced micro-flow (CEMF) cines, B-mode imag...

Identification of Active Pulmonary Tuberculosis Among Patients With Positive Interferon-Gamma Release Assay Results: Value of a Deep Learning-based Computer-aided Detection System in Different Scenarios of Implementation.

PURPOSE: To evaluate the accuracy of a deep learning-based computer-aided detection (CAD) system in identifying active pulmonary tuberculosis on chest...

Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening.

Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, . It is possible to ...

Direct machine learning reconstruction of respiratory variation waveforms from resting state fMRI data in a pediatric population.

In many functional magnetic resonance imaging (fMRI) studies, respiratory signals are unavailable or do not have acceptable quality due to issues with...

Towards optimal deep fusion of imaging and clinical data via a model-based description of fusion quality.

BACKGROUND: Due to intrinsic differences in data formatting, data structure, and underlying semantic information, the integration of imaging data with...

Research progress on the structure and biological diversities of 2-phenylindole derivatives in recent 20 years.

The privileged structure binds to multiple receptors with high affinity, which is helpful to the development of new bioactive compounds. Indole is cla...

An AI Approach for Identifying Patients With Cirrhosis.

GOAL: The goal of this study was to evaluate an artificial intelligence approach, namely deep learning, on clinical text in electronic health records ...

Feedback-AVPGAN: Feedback-guided generative adversarial network for generating antiviral peptides.

In this study, we propose , a system that aims to computationally generate novel antiviral peptides (AVPs). This system relies on the key premise of t...

Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks.

Retrieving the pupil phase of a beam path is a central problem for optical systems across scales, from telescopes, where the phase information allows ...

[User-oriented needs assessment of the potential use of assistive robots in direct nursing care: A mixed methods study].

User-oriented needs assessment of the potential use of assistive robots in direct nursing care: A mixed methods study So far, hardly any robots have...

Artificial intelligence model with deep learning in nonalcoholic fatty liver disease diagnosis: genetic based artificial neural networks.

Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease in the world. The NAFLD spectrum includes simple st...

Integrative transcriptome analysis of SARS-CoV-2 human-infected cells combined with deep learning algorithms identifies two potential cellular targets for the treatment of coronavirus disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) quickly spread worldwide, leading coronavirus disease 2019 (COVID-19) to hit pandemic lev...