Infectious Disease

Quantification of COVID-19 Opacities on Chest CT - Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients.

OBJECTIVES: To evaluate the potential of a fully automatic artificial intelligence (AI)-driven computed tomography (CT) software prototype to quantify severity of COVID-19 infection on chest CT in relationship with clinical and laboratory data.

Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases.

In the context of the recently emerging COVID-19 pandemic, we developed a deep learning model that can be used to predict the inhibitory activity of 3CLpro in severe acute respiratory syndrome coronavirus (SARS-CoV) for unknown compounds during the virtual screening process. This paper proposes a novel deep learning-based method to implement virtual screening with convolutional neural network (CNN...

Individualized prediction of COVID-19 adverse outcomes with MLHO.

The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led t...

Machine Learning Model for Computational Tracking and Forecasting the COVID-19 Dynamic Propagation.

A computational model with intelligent machine learning for analysis of epidemiological data, is proposed. The innovations of adopted methodology cons...

Epidemiological Surveillance of the Impact of the COVID-19 Pandemic on Stroke Care Using Artificial Intelligence.

BACKGROUND AND PURPOSE: The degree to which the coronavirus disease 2019 (COVID-19) pandemic has affected systems of care, in particular, those for ti...

Potential neutralizing antibodies discovered for novel corona virus using machine learning.

The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent ou...

Clinical presentation of COVID-19 - a model derived by a machine learning algorithm.

COVID-19 pandemic has flooded all triage stations, making it difficult to carefully select those most likely infected. Data on total patients tested, ...

Clinical Features of Emergency Department Patients from Early COVID-19 Pandemic that Predict SARS-CoV-2 Infection: Machine-learning Approach.

INTRODUCTION: Within a few months coronavirus disease 2019 (COVID-19) evolved into a pandemic causing millions of cases worldwide, but it remains chal...

A novel nano-lanthanum complex: synthesis, characterization and application as a macrofuran chemosensor in pharmaceutical, biological and environmental samples.

Macrofuran is widely used as an antibiotic for the treatment of urinary tract infections. Nevertheless, it is prohibited due to toxicity and environme...

Exploring the spatial reasoning ability of neural models in human IQ tests.

Although neural models have performed impressively well on various tasks such as image recognition and question answering, their reasoning ability has...

Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models.

Antibiotic resistance genes (ARGs) have been reported to threaten the public health of beachgoers worldwide. Although ARG monitoring and beach guideli...

COVID-19 Recognition Using Ensemble-CNNs in Two New Chest X-ray Databases.

The recognition of COVID-19 infection from X-ray images is an emerging field in the learning and computer vision community. Despite the great efforts ...

The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks.

: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this stud...

Robot-Based Assessment of HIV-Related Motor and Cognitive Impairment for Neurorehabilitation.

There is a pressing need for strategies to slow or treat the progression of functional decline in people living with HIV. This paper explores a novel ...

Cohort profile: St. Michael's Hospital Tuberculosis Database (SMH-TB), a retrospective cohort of electronic health record data and variables extracted using natural language processing.

BACKGROUND: Tuberculosis (TB) is a major cause of death worldwide. TB research draws heavily on clinical cohorts which can be generated using electron...

Food waste reduction and economic savings in times of crisis: The potential of machine learning methods to plan guest attendance in Swedish public catering during the Covid-19 pandemic.

Food waste is a significant problem within public catering establishments in any normal situation. During spring 2020 the Covid-19 pandemic placed the...

Generation of Dual functional Nanobody-Nanoluciferase Fusion and its potential in Bioluminescence Enzyme Immunoassay for trace Glypican-3 in Serum.

Glypican-3 (GPC3) is a serological biomarker for the diagnosis of Hepatocellular carcinoma (HCC), but it is a challenging task to develop a bioassay f...

Preferences for Artificial Intelligence Clinicians Before and During the COVID-19 Pandemic: Discrete Choice Experiment and Propensity Score Matching Study.

BACKGROUND: Artificial intelligence (AI) methods can potentially be used to relieve the pressure that the COVID-19 pandemic has exerted on public heal...

Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection.

Clusters of viral pneumonia occurrences over a short period may be a harbinger of an outbreak or pandemic. Rapid and accurate detection of viral pneum...

Prediction of Sepsis in COVID-19 Using Laboratory Indicators.

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has become a global public health concern. Many inpatients with COVID-19 have shown cl...