Rheumatology

Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19.

In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initia...

Clinical Case Report: Dissociation of Clinical Course of Coexisting Autoimmune Hepatitis and Graves Disease.

OBJECTIVE: Concurrent autoimmune disorders, including autoimmune hepatitis (AIH), with Graves disease have been reported. Glucocorticoids can simultaneously lower thyroid hormone levels and treat AIH. Recurrence of hyperthyroidism is associated with recurrence of hepatitis. We present a case of coexisting AIH and Graves thyrotoxicosis, which improved with prednisone, but the thyrotoxicosis recurre...

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs.

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Res...

Next-Generation Bioelectric Medicine: Harnessing the Therapeutic Potential of Neural Implants.

Bioelectric medicine leverages natural signaling pathways in the nervous system to counteract organ dysfunction. This novel approach has potential to ...

End-to-End Deep Learning Model for Predicting Treatment Requirements in Neovascular AMD From Longitudinal Retinal OCT Imaging.

Neovascular age-related macular degeneration (nAMD) is nowadays successfully treated with anti-VEGF substances, but inter-individual treatment require...

Identifying Mouse Autoimmune Uveitis from Fundus Photographs Using Deep Learning.

PURPOSE: To develop a deep learning model for objective evaluation of experimental autoimmune uveitis (EAU), the animal model of posterior uveitis tha...

Identifying longevity associated genes by integrating gene expression and curated annotations.

Aging is a complex process with poorly understood genetic mechanisms. Recent studies have sought to classify genes as pro-longevity or anti-longevity ...

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). T...

Deep learning segmentation of Primary Sjögren's syndrome affected salivary glands from ultrasonography images.

Salivary gland ultrasonography (SGUS) has proven to be a promising tool for diagnosing various diseases manifesting with abnormalities in salivary gla...

Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis.

COVID-19 caused by the SARS-CoV-2 is a current global challenge and urgent discovery of potential drugs to combat this pandemic is a need of the hour....

Broad Learning Enhanced H-MRS for Early Diagnosis of Neuropsychiatric Systemic Lupus Erythematosus.

In this paper, we explore the potential of using the multivoxel proton magnetic resonance spectroscopy (H-MRS) to diagnose neuropsychiatric systemic l...

Machine learning to predict early TNF inhibitor users in patients with ankylosing spondylitis.

We aim to generate an artificial neural network (ANN) model to predict early TNF inhibitor users in patients with ankylosing spondylitis. The baseline...

Can we use artificial intelligence for systematic literature review in rheumatology?

Knee Acoustic Emissions as a Digital Biomarker of Disease Status in Juvenile Idiopathic Arthritis.

In this paper, we quantify the joint acoustic emissions (JAEs) from the knees of children with juvenile idiopathic arthritis (JIA) and support their u...

Development of LC-MS/MS determination method and backpropagation artificial neural networks pharmacokinetic model of febuxostat in healthy subjects.

WHAT IS KNOWN AND OBJECTIVE: Febuxostat is a well-known drug for treating hyperuricemia and gout. The published methods for determination of febuxosta...

-Related Infections and their Significance: A 9-Year Retrospective Study.

INTRODUCTION: is a Gram-positive, catalase- and oxidase-negative, microaerophilic, nonmotile bacteria species rarely associated with human infections...

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.

Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, cur...

TNFPred: identifying tumor necrosis factors using hybrid features based on word embeddings.

BACKGROUND: Cytokines are a class of small proteins that act as chemical messengers and play a significant role in essential cellular processes includ...

Ensemble transfer learning for the prediction of anti-cancer drug response.

Transfer learning, which transfers patterns learned on a source dataset to a related target dataset for constructing prediction models, has been shown...

Effect of Obesity on Clinical Outcomes of Patients Treated With Cefepime.

As the prevalence of obesity climbs, dosing of antimicrobials, particularly cephalosporins, is becoming a greater challenge for clinicians. Data are ...