Rheumatology

Deep learning aided quantitative analysis of anti-tuberculosis fixed-dose combinatorial formulation by terahertz spectroscopy.

Anti-tuberculosis fixed-dose combinatorial formulation (FDCs) is an effective drug for the treatment of tuberculosis. As a compound medicine, its efficacy is based on the comprehensive action of multiple main ingredients. If the content of an active ingredient is insufficient, not only will it reduce the curative effect, but it also causes patients to develop drug resistance and leads to the evolu...

Alterations of Serum Leptin Levels in Patients with Autoimmune Thyroid Disorders.

Thyroid dysfunction is accompanied with significant metabolic alterations that affect body weight, appetite, and energy expenditure, as well as lipid and carbohydrate metabolism. Leptin, an adipokine produced by adipocytes, regulates food intake and energy storage. Thyroid hormones and leptin share some physiological effects. Changes in leptin have been shown in patients with dysfunction of the t...

Deep Learning-Based Approach to Automatically Assess Coronary Distensibility Following Kawasaki Disease.

Kawasaki disease is an acute vasculitis affecting children, which can lead to coronary artery (CA) aneurysms. Optical coherence tomography (OCT) has i...

Application of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.

In view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to s...

Deep learning model enables the discovery of a novel immunotherapeutic agent regulating the kynurenine pathway.

Kynurenine (Kyn) is a key inducer of an immunosuppressive tumor microenvironment (TME). Although indoleamine 2,3-dioxygenase (IDO)-selective inhibitor...

Imaging flow cytometry data analysis using convolutional neural network for quantitative investigation of phagocytosis.

Macrophages play an important role in the adaptive immune system. Their ability to neutralize cellular targets through Fc receptor-mediated phagocytos...

Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging.

In current clinical practice, tumor response assessment is usually based on tumor size change on serial computerized tomography (CT) scan images. Howe...

Can we predict anti-seizure medication response in focal epilepsy using machine learning?

OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning approach based on clinical factors and diffusion tensor imaging (...

A baseline model including quantitative anti-HBc to predict response of peginterferon in HBeAg-positive chronic hepatitis B patients.

BACKGROUND: Few models to predict antiviral response of peginterferon were used in hepatitis B e antigen (HBeAg)-positive chronic hepatitis B patients...

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis.

Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing significant disability in patients worldwide. Manual diagnosis,...

Proteome-Informed Machine Learning Studies of Cocaine Addiction.

No anti-cocaine addiction drugs have been approved by the Food and Drug Administration despite decades of effort. The main challenge is the intricate ...

[Type 1 diabetes mellitus and Graves Basedow's disease, a case of Autoimmune Polyglandular Syndrome].

INTRODUCTION: Type 1 diabetes mellitus (T1DM) is one of the most frequent autoimmune diseases in childhood. Its diagnosis requires the search for othe...

Antioxidant and anti-proliferative activity of free, conjugates and bound phenolic compounds from tomato and industrial tomato by-product.

The aim of this study was to evaluate the antioxidant and anti-proliferative activity of different fractions of phenolic compounds from tomato and tom...

Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review.

Aging refers to progressive physiological changes in a cell, an organ, or the whole body of an individual, over time. Aging-related diseases are highl...

Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning.

The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Pe...

Development of S4A-BSA-Au NPs for enhanced anti-tumor therapy of canine breast cancer.

S4A ((1,2,3)-1,2-propanediol acetal-zeylenone) is one of the derivatives of zeylenone and exhibits superior cytotoxicity against the canine breast can...

Editorial for "Deep Learning-Enabled Identification of Autoimmune Encephalitis on 3D Multi-Sequence MRI".

Public Perception and Reception of Robotic Applications in Public Health Emergencies Based on a Questionnaire Survey Conducted during COVID-19.

Various intelligent technologies have been applied during COVID-19, which has become a worldwide public health emergency and brought significant chall...

Lipoprotein-associated phospholipase A2 and carotid intima-media thickness in primary Sjögren syndrome.

OBJECTIVES: This study aims to evaluate serum lipoprotein-associated phospholipase A2 (Lp-PLA2) level and carotid intima-media thickness in primary Sj...

Job characteristics of a Malaysian bank's anti-money laundering system and its employees' job satisfaction.

Banks and financial institutions are vulnerable to money laundering (ML) as a result of crime proceeds infiltrating banks in the form of significant ...