Infectious Disease

Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19.

OBJECTIVE: To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance.

Outcomes and Spectrum of Major Neurovascular Events Among COVID-19 Patients: A 3-Center Experience.

BACKGROUND: Preliminary data suggest that Coronavirus Disease-2019 (COVID-19) is associated with hypercoagulability and neurovascular events, but data on outcomes is limited.

Application of artificial neural networks to prediction of new substances with antimicrobial activity against Escherichia coli.

AIMS: This article presents models of artificial neural networks (ANN) employed to predict the biological activity of chemical compounds based of thei...

A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth.

Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genot...

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections.

This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen milli...

Identifying scenarios of benefit or harm from kidney transplantation during the COVID-19 pandemic: A stochastic simulation and machine learning study.

Clinical decision-making in kidney transplant (KT) during the coronavirus disease 2019 (COVID-19) pandemic is understandably a conundrum: both candida...

α-Satellite: An AI-Driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States.

The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infec...

Early triage of critically ill COVID-19 patients using deep learning.

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to ide...

The combination of artificial intelligence and systems biology for intelligent vaccine design.

INTRODUCTION: A new body of evidence depicts the applications of artificial intelligence and systems biology in vaccine design and development. The co...

Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays...

Accuracy of deep learning for automated detection of pneumonia using chest X-Ray images: A systematic review and meta-analysis.

BACKGROUND: Recently, deep learning (DL) algorithms have received widespread popularity in various medical diagnostics. This study aimed to evaluate t...

Preparation and evaluation of food-grade nanoemulsion of tarragon ( L.) essential oil: antioxidant and antibacterial properties.

This study aimed at to formulate a food-grade nanoemulsion of tarragon essential oil (NEO) and investigate its antioxidant and antimicrobial propertie...

Machine Learning Platform to Discover Novel Growth Inhibitors of Neisseria gonorrhoeae.

PURPOSE: To advance fundamental biological and translational research with the bacterium Neisseria gonorrhoeae through the prediction of novel small m...

Towards explainable deep neural networks (xDNN).

In this paper, we propose an elegant solution that is directly addressing the bottlenecks of the traditional deep learning approaches and offers an ex...

Evaluation of the VETSCAN IMAGYST: an in-clinic canine and feline fecal parasite detection system integrated with a deep learning algorithm.

BACKGROUND: Fecal examination is an important component of routine companion animal wellness exams. Sensitivity and specificity of fecal examinations,...

Probing the characteristics and biofunctional effects of disease-affected cells and drug response via machine learning applications.

Drug-induced transformations in disease characteristics at the cellular and molecular level offers the opportunity to predict and evaluate the efficac...

Aptamer based proteomic pilot study reveals a urine signature indicative of pediatric urinary tract infections.

OBJECTIVE: Current urinary tract infection (UTI) diagnostic strategies that rely on leukocyte esterase have limited accuracy. We performed an aptamer-...

Improving disaggregation models of malaria incidence by ensembling non-linear models of prevalence.

Maps of disease burden are a core tool needed for the control and elimination of malaria. Reliable routine surveillance data of malaria incidence, typ...

Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning.

Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large dat...

Metatranscriptomics reveals the gene functions and metabolic properties of the major microbial community during Chinese Sichuan Paocai fermentation.

Chinese Sichuan Paocai (CSP) is one of the world's best-known fermented vegetables with a large presence in the Chinese market. The dynamic microbial ...