Infectious Disease

The predictive skill of convolutional neural networks models for disease forecasting.

In this paper we investigate the utility of one-dimensional convolutional neural network (CNN) models in epidemiological forecasting. Deep learning models, in particular variants of recurrent neural networks (RNNs) have been studied for ILI (Influenza-Like Illness) forecasting, and have achieved a higher forecasting skill compared to conventional models such as ARIMA. In this study, we adapt two n...

Development of novel-nanobody-based lateral-flow immunochromatographic strip test for rapid detection of recombinant human interferon α2b.

Recombinant human interferon α2b (rhIFNα2b) is widely used as an antiviral therapy agent for the treatment of hepatitis B and hepatitis C. The current identification test for rhIFNα2b is complex. In this study, an anti-rhIFNα2b nanobody was discovered and used for the development of a rapid lateral flow strip for the identification of rhIFNα2b. RhIFNα2b was used to immunize an alpaca, which establ...

Fuzzy rank-based fusion of CNN models using Gompertz function for screening COVID-19 CT-scans.

COVID-19 has crippled the world's healthcare systems, setting back the economy and taking the lives of several people. Although potential vaccines are...

Discovery of quinolone derivatives as antimycobacterial agents.

Tuberculosis (TB), an infectious disease caused by (), is an important public health issue. Current first-line drugs administered to TB patients have...

Bayesian convolutional neural network estimation for pediatric pneumonia detection and diagnosis.

BACKGROUND AND OBJECTIVES: Pneumonia is a disease that affects the lungs, making breathing difficult. Nowadays, pneumonia is the disease that kills th...

DeepHBV: a deep learning model to predict hepatitis B virus (HBV) integration sites.

BACKGROUND: The hepatitis B virus (HBV) is one of the main causes of viral hepatitis and liver cancer. HBV integration is one of the key steps in the ...

Deep learning applied to automatic disease detection using chest X-rays.

Deep learning (DL) has shown rapid advancement and considerable promise when applied to the automatic detection of diseases using CXRs. This is import...

4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection.

Due to the high availability of large-scale annotated image datasets, knowledge transfer from pretrained models showed outstanding performance in medi...

Optimizing COVID-19 vaccine distribution across the United States using deterministic and stochastic recurrent neural networks.

Optimizing COVID-19 vaccine distribution can help plan around the limited production and distribution of vaccination, particularly in early stages. On...

Evaluating the Efficacy of Povidone-Iodine Solution Infection Prophylaxis in Immediate Tissue Expander-Based Breast Reconstruction: A Controlled Retrospective Analysis.

There is currently no consensus among plastic surgeons regarding the optimal infection prophylaxis for immediate tissue expander placement following ...

COVID-19 lesion detection and segmentation-A deep learning method.

PURPOSE: In this paper, we utilized deep learning methods to screen the positive COVID-19 cases in chest CT. Our primary goal is to supply rapid and p...

Letter to the Editor: Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results.

Exploring Feasibility of Multivariate Deep Learning Models in Predicting COVID-19 Epidemic.

Mathematical models are powerful tools to study COVID-19. However, one fundamental challenge in current modeling approaches is the lack of accurate a...

Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial.

BACKGROUND: We have developed the computer-aided detection (CADe) system using an original deep learning algorithm based on a convolutional neural net...

Timesias: A machine learning pipeline for predicting outcomes from time-series clinical records.

The prediction of outcomes is a critical part of the clinical surveillance for hospitalized patients. Here, we present Timesias, a machine learning pi...

Visual comprehension and orientation into the COVID-19 CIDO ontology.

The current intensive research on potential remedies and vaccinations for COVID-19 would greatly benefit from an ontology of standardized COVID terms....

Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia.

PURPOSE: Comparison of deep learning algorithm, radiomics and subjective assessment of chest CT for predicting outcome (death or recovery) and intensi...

Using Artificial Intelligence for High-Volume Identification of Silicosis and Tuberculosis: A Bio-Ethics Approach.

Although Artificial Intelligence (AI) is being increasingly applied, considerable distrust about introducing "disruptive" technologies persists. Intri...

AI-based language models powering drug discovery and development.

The discovery and development of new medicines is expensive, time-consuming, and often inefficient, with many failures along the way. Powered by artif...

Hyperspectral imaging and deep learning for quantification of Clostridium sporogenes spores in food products using 1D- convolutional neural networks and random forest model.

Clostridium sporogenes spores are used as surrogates for Clostridium botulinum, to verify thermal exposure and lethality in sterilization regimes by f...