Rheumatology

Redefining a new frontier in alkaptonuria therapy with AI-driven drug candidate design via innovation.

A rare metabolic condition called alkaptonuria (AKU) is caused by a decrease in homogentisate 1,2 dioxygenase (HGO) activity due to a mutation in homogentisate dioxygenase (HGD) gene. Homogentisic acid is a byproduct of the catabolism of tyrosine and phenylalanine that darkens the urine and accumulates in connective tissues which causes an agonizing arthritis. Employing the use of deep learning ar...

Molecular docking aided machine learning for the identification of potential VEGFR inhibitors against renal cell carcinoma.

Renal cell carcinoma is a highly vascular tumor associated with vascular endothelial growth factor (VEGF) expression. The Vascular Endothelial Growth Factor -2 (VEGF-2) and its receptor was identified as a potential anti-cancer target, and it plays a crucial role in physiology as well as pathology. Inhibition of angiogenesis via blocking the signaling pathway is considered an attractive target. In...

Multimodal Machine Learning-Based Marker Enables Early Detection and Prognosis Prediction for Hyperuricemia.

Hyperuricemia (HUA) has emerged as the second most prevalent metabolic disorder characterized by prolonged and asymptomatic period, triggering gout an...

Research on control strategy of pneumatic soft bionic robot based on improved CPG.

To achieve the accuracy and anti-interference of the motion control of the soft robot more effectively, the motion control strategy of the pneumatic s...

Investigation of ferroptosis-associated molecular subtypes and immunological characteristics in lupus nephritis based on artificial neural network learning.

BACKGROUND: Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus (SLE) with poor treatment outcomes. The role and underlying ...

Evaluation of Serum Visfatin as a Biomarker of Lupus Nephritis in Egyptian Patients with Systemic Lupus Erythematosus.

One of the most significant consequences of systemic lupus erythematosus (SLE) is lupus nephritis (LN). Visfatin, an adipokine that is significantly e...

Glycosylphosphatidylinositol anchor biosynthesis pathway-based biomarker identification with machine learning for prognosis and T cell exhaustion status prediction in breast cancer.

As the primary component of anti-tumor immunity, T cells are prone to exhaustion and dysfunction in the tumor microenvironment (TME). A thorough under...

A high hydrophobic moment arginine-rich peptide screened by a machine learning algorithm enhanced ADC antitumor activity.

Cell-penetrating peptides (CPPs) with better biomolecule delivery properties will expand their clinical applications. Using the MLCPP2.0 machine algor...

Artificial Intelligence-Powered Molecular Docking and Steered Molecular Dynamics for Accurate scFv Selection of Anti-CD30 Chimeric Antigen Receptors.

Chimeric antigen receptor (CAR) T cells represent a revolutionary immunotherapy that allows specific tumor recognition by a unique single-chain fragme...

Predicting renal damage in children with IgA vasculitis by machine learning.

BACKGROUND: Children with IgA Vasculitis (IgAV) may develop renal complications, which can impact their long-term prognosis. This study aimed to build...

Improved prediction of anti-angiogenic peptides based on machine learning models and comprehensive features from peptide sequences.

Angiogenesis is a key process for the proliferation and metastatic spread of cancer cells. Anti-angiogenic peptides (AAPs), with the capability of inh...

Artificial intelligence in autoimmune bullous dermatoses.

Dermatologists treating patients with autoimmune bullous dermatoses (AIBDs), as well as the patients themselves, encounter challenges at every stage o...

The state of artificial intelligence for systemic dermatoses: Background and applications for psoriasis, systemic sclerosis, and much more.

Artificial intelligence (AI) has been steadily integrated into dermatology, with AI platforms already attempting to identify skin cancers and diagnose...

From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors.

Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis,...

A deep-learning-based model for assessment of autoimmune hepatitis from histology: AI(H).

Histological assessment of autoimmune hepatitis (AIH) is challenging. As one of the possible results of these challenges, nonclassical features such a...

Machine learning and artificial intelligence within pediatric autoimmune diseases: applications, challenges, future perspective.

INTRODUCTION: Autoimmune disorders affect 4.5% to 9.4% of children, significantly reducing their quality of life. The diagnosis and prognosis of autoi...

Machine learning models predicts risk of proliferative lupus nephritis.

OBJECTIVE: This study aims to develop and validate machine learning models to predict proliferative lupus nephritis (PLN) occurrence, offering a relia...

Non-invasive detection of systemic lupus erythematosus using SERS serum detection technology and deep learning algorithms.

Systemic lupus erythematosus (SLE) is an autoimmune disease with multiple symptoms, and its rapid screening is the research focus of surface-enhanced ...

Alterations in dynamic regional homogeneity within default mode network in patients with thyroid-associated ophthalmopathy.

Thyroid-associated ophthalmopathy (TAO) is a significant autoimmune eye disease known for causing exophthalmos and substantial optic nerve damage. Pri...

A computational clinical decision-supporting system to suggest effective anti-epileptic drugs for pediatric epilepsy patients based on deep learning models using patient's medical history.

BACKGROUND: Epilepsy, a chronic brain disorder characterized by abnormal brain activity that causes seizures and other symptoms, is typically treated ...