Infectious Disease

Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images.

Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the greatest human challenge of the 21st century. Robustness to noise and improvement of generalization are the major challenges in designing these networks. In this paper, we introduce a strategy for data augmentation using the determination of the type and value of noise density to improve the robust...

Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical-Genetic Interactions.

A common strategy for identifying molecules likely to possess a desired biological activity is to search large databases of compounds for high structural similarity to a query molecule that demonstrates this activity, under the assumption that structural similarity is predictive of similar biological activity. However, efforts to systematically benchmark the diverse array of available molecular fi...

A robot goes to rehab: a novel gamified system for long-term stroke rehabilitation using a socially assistive robot-methodology and usability testing.

BACKGROUND: Socially assistive robots (SARs) have been proposed as a tool to help individuals who have had a stroke to perform their exercise during t...

Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients.

COVID-19 has caused many deaths worldwide. The automation of the diagnosis of this virus is highly desired. Convolutional neural networks (CNNs) have ...

Promising antimicrobial and antibiofilm activities of reduced graphene oxide-metal oxide (RGO-NiO, RGO-AgO, and RGO-ZnO) nanocomposites.

Microbial infections are considered one of the most dangerous infections in humans due to their resistance to most antimicrobial agents. In this study...

Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images.

Coronavirus disease, which comes up in China at the end of 2019 and showed different symptoms in people infected, affected millions of people. Compute...

Country transition index based on hierarchical clustering to predict next COVID-19 waves.

COVID-19 has widely spread around the world, impacting the health systems of several countries in addition to the collateral damage that societies wil...

COVID-19 Automatic Diagnosis With Radiographic Imaging: Explainable Attention Transfer Deep Neural Networks.

Researchers seek help from deep learning methods to alleviate the enormous burden of reading radiological images by clinicians during the COVID-19 pan...

A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) has caused considerable morbidity and mortality, especially in patients with underlying health conditio...

Attention-RefNet: Interactive Attention Refinement Network for Infected Area Segmentation of COVID-19.

COVID-19 pneumonia is a disease that causes an existential health crisis in many people by directly affecting and damaging lung cells. The segmentatio...

Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is very contagious. Cases appear faster than the available Polymerase Chain Reaction test kits in many...

Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays.

INTRODUCTION: The objective of this study was to assess seven configurations of six convolutional deep neural network architectures for classification...

Understanding terror states of online users in the context of COVID-19: An application of Terror Management Theory.

The COVID-19 pandemic has provided psych challenges for many in society. One such challenge is the anxiety that is created in many people faced with t...

MSDS-UNet: A multi-scale deeply supervised 3D U-Net for automatic segmentation of lung tumor in CT.

Lung cancer is one of the most common and deadly malignant cancers. Accurate lung tumor segmentation from CT is therefore very important for correct d...

Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets.

COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures...

Comparative Outcomes of Double-J and Cutaneous Pyeloureteral Stents in Pediatric Robot-Assisted Laparoscopic Pyeloplasty.

Comparative outcome studies investigating internal Double-J (DJ) and externalized stents have primarily been performed for open and laparoscopic pyel...

Emerging Technologies for In Vitro Inhalation Toxicology.

Respiratory toxicology remains a major research area in the 21st century since current scenario of airborne viral infection transmission and pollutant...

Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks.

Malaria is a serious disease responsible for thousands of deaths each year. Many efforts have been made to aid in the diagnosis of malaria using machi...

Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling.

The significant health and economic effects of COVID-19 emphasize the requirement for reliable forecasting models to avoid the sudden collapse of heal...

Use of neurally adjusted ventilatory assist (NAVA) in a patient with severe SARS-CoV-2 pneumonia: A case report.

INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia may necessitate intubation and prolonged mechanical ventilation. ...