COVID-19

Challenges and compromises: Predicting unbound antibody structures with deep learning.

Therapeutic antibodies are manufactured, stored and administered in the free state; this makes understanding the unbound form key to designing and improving development pipelines. Prediction of unbound antibodies is challenging, specifically modelling of the CDRH3 loop, where inaccuracies are potentially worse due to a bias in structural data towards antibody-antigen complexes. This class imbalanc...

A commercial AI tool untrained for COVID-19 demonstrates slight improvement in the interpretation of COVID-19 pneumonia x-rays, especially among inexperienced readers.

INTRODUCTION: Our objective is to evaluate how useful an artificial intelligence (AI) tool is to chest radiograph readers with various levels of expertise for the diagnosis of COVID-19 pneumonia when the tool has been trained on a non-COVID-19 pneumonia pathology.

Delta-Radiomics Using Machine Learning Classifiers With Auxiliary Data Sets to Predict Disease Progression During Magnetic Resonance-Guided Radiotherapy in Adrenal Metastases.

PURPOSE: Adaptive radiotherapy accounts for interfractional anatomic changes. We hypothesize that changes in the gross tumor volumes identified during...

MSMDL-DDI: Multi-Layer Soft Mask Dual-View Learning for Drug-Drug Interactions.

Drug-drug interactions (DDIs) occur when multiple medications are co-administered, potentially leading to adverse effects and compromising patient saf...

SAF-IS: A spatial annotation free framework for instance segmentation of surgical tools.

Instance segmentation of surgical instruments is a long-standing research problem, crucial for the development of many applications for computer-assis...

Interpretable COVID-19 chest X-ray detection based on handcrafted feature analysis and sequential neural network.

Deep learning methods have significantly improved medical image analysis, particularly in detecting COVID-19 chest X-rays. Nonetheless, these methodol...

Development of a machine learning tool to predict deep inspiration breath hold requirement for locoregional right-sided breast radiation therapy patients.

. This study presents machine learning (ML) models that predict if deep inspiration breath hold (DIBH) is needed based on lung dose in right-sided bre...

Machine learning based prediction models for the prognosis of COVID-19 patients with DKA.

Patients with Diabetic ketoacidosis (DKA) have increased critical illness and mortality during coronavirus diseases 2019 (COVID-19). The aim of our st...

A scalable tool for analyzing genomic variants of humans using knowledge graphs and graph machine learning.

Advances in high-throughput genome sequencing have enabled large-scale genome sequencing in clinical practice and research studies. By analyzing genom...

Serum decorin and biglycan levels as predictive biomarkers for lung fibrosis severity and mortality risk in COVID-19 patients.

INTRODUCTION: Individuals experiencing severe symptoms of COVID-19 are at the greatest risk of developing post-COVID lung fibrosis, which significantl...

Accelerated enzyme engineering by machine-learning guided cell-free expression.

Enzyme engineering is limited by the challenge of rapidly generating and using large datasets of sequence-function relationships for predictive design...

Deep learning algorithms for predicting pathological complete response in MRI of rectal cancer patients undergoing neoadjuvant chemoradiotherapy: a systematic review.

PURPOSE: This systematic review examines the utility of deep learning algorithms in predicting pathological complete response (pCR) in rectal cancer p...

Automated post-run analysis of arrayed quantitative PCR amplification curves using machine learning.

BACKGROUND: The TaqMan Array Card (TAC) is an arrayed, high-throughput qPCR platform that can simultaneously detect multiple targets in a single react...

An effective COVID-19 classification in X-ray images using a new deep learning framework.

BackgroundThe global concern regarding the diagnosis of lung-related diseases has intensified due to the rapid transmission of coronavirus disease 201...

Deep Learning-Derived Quantitative Scores for Chronic Rhinosinusitis Assessment: Correlation With Quality of Life Outcomes.

BackgroundComputed tomography (CT) plays a crucial role in assessing chronic rhinosinusitis, but lacks objective quantifiable indicators.ObjectiveThis...

Bacterial Wastewater-Based Epidemiology Using Surface-Enhanced Raman Spectroscopy and Machine Learning.

Although wastewater-based epidemiology has been used extensively for the surveillance of viral diseases, it has not been used to a similar extent for ...

Clinical evaluation of a multiplex droplet digital PCR for diagnosing suspected bloodstream infections: a prospective study.

BACKGROUND: Though droplet digital PCR (ddPCR) has emerged as a promising tool for early pathogen detection in bloodstream infections (BSIs), more stu...

"Three-in-one" Analysis of Proteinuria for Disease Diagnosis through Multifunctional Nanoparticles and Machine Learning.

Urinalysis is one of the predominant tools for clinical testing owing to the abundant composition, sufficient volume, and non-invasive acquisition of ...

Molecular detection of in captive black-faced black spider monkeys (mammalia; primate).

The present study aimed to assess spp. infection in 17 captive black-faced black spider monkeys (), living at the Zoological Garden of Curitiba, Para...

Deep learning-based assessment of missense variants in the gene presented with bilateral congenital cataract.

OBJECTIVE: We compared the protein structure and pathogenicity of clinically relevant variants of the gene with AlphaFold2 (AF2), Alpha Missense (AM)...