COVID-19

Machine Learning to Quantify Humoral Selection in Human Lupus Tubulointerstitial Inflammation.

In human lupus nephritis, tubulointerstitial inflammation (TII) is associated with expansion of B cells expressing anti-vimentin antibodies (AVAs). The mechanism by which AVAs are selected is unclear. Herein, we demonstrate that AVA somatic hypermutation (SHM) and selection increase affinity for vimentin. Indeed, germline reversion of several antibodies demonstrated that higher affinity AVAs can ...

COVID-AL: The diagnosis of COVID-19 with deep active learning.

The efficient diagnosis of COVID-19 plays a key role in preventing the spread of this disease. The computer-aided diagnosis with deep learning methods can perform automatic detection of COVID-19 using CT scans. However, large scale annotation of CT scans is impossible because of limited time and heavy burden on the healthcare system. To meet the challenge, we propose a weakly-supervised deep activ...

Hypergraph learning for identification of COVID-19 with CT imaging.

The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used a...

Simultaneously Detecting Monoamine Oxidase A and B in Disease Cell/Tissue Samples Using Paper-Based Devices.

As enzymes in the outer membrane of the mitochondrion, monoamine oxidases (MAOs) can catalyze the oxidative deamination of monoamines in the human bod...

An artificial intelligence process of immunoassay for multiple biomarkers based on logic gates.

We present a universal platform to synchronously analyze the possible existing state of two protein biomarkers. This platform is based on the integrat...

DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large U.S. Clinical Data Set.

Background There are characteristic findings of coronavirus disease 2019 (COVID-19) on chest images. An artificial intelligence (AI) algorithm to dete...

Development challenges of high concentration monoclonal antibody formulations.

High concentration monoclonal antibody drug products represent a special segment of biopharmaceuticals. In contrast to other monoclonal antibody produ...

Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients.

Due to the rapid spread of COVID-19 and its induced death worldwide, it is imperative to develop a reliable tool for the early detection of this disea...

An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images.

A newly emerged coronavirus (COVID-19) seriously threatens human life and health worldwide. In coping and fighting against COVID-19, the most critical...

DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era.

Coronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for onli...

FaNet: fast assessment network for the novel coronavirus (COVID-19) pneumonia based on 3D CT imaging and clinical symptoms.

The novel coronavirus (COVID-19) pneumonia has become a serious health challenge in countries worldwide. Many radiological findings have shown that X-...

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images.

COVID-19 pandemic is increasing in an exponential rate, with restricted accessibility of rapid test kits. So, the design and implementation of COVID-1...

Automatic detection of COVID-19 from chest radiographs using deep learning.

INTRODUCTION: The breakdown of a deadly infectious disease caused by a newly discovered coronavirus (named SARS n-CoV2) back in December 2019 has show...

Predicting antibody affinity changes upon mutations by combining multiple predictors.

Antibodies are proteins working in our immune system with high affinity and specificity for target antigens, making them excellent tools for both biot...

SVFX: a machine learning framework to quantify the pathogenicity of structural variants.

There is a lack of approaches for identifying pathogenic genomic structural variants (SVs) although they play a crucial role in many diseases. We pres...

Deep neural network to detect COVID-19: one architecture for both CT Scans and Chest X-rays.

Since December 2019, the novel COVID-19's spread rate is exponential, and AI-driven tools are used to prevent further spreading [1]. They can help pre...

Reliability of robotic transcranial magnetic stimulation motor mapping.

Robotic transcranial magnetic stimulation (TMS) is a noninvasive and safe tool that produces cortical motor maps using neuronavigational and neuroanat...

A gradient boosting machine learning approach in modeling the impact of temperature and humidity on the transmission rate of COVID-19 in India.

Meteorological parameters were crucial and effective factors in past infectious diseases, like influenza and severe acute respiratory syndrome (SARS),...

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks.

In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelli...

Lightning search algorithm: a comprehensive survey.

The lightning search algorithm (LSA) is a novel meta-heuristic optimization method, which is proposed in 2015 to solve constraint optimization problem...