Rheumatology

Predictive Model of Objective Response to Nivolumab Monotherapy for Advanced Renal Cell Carcinoma by Machine Learning Using Genetic and Clinical Data: The SNiP-RCC Study.

PURPOSE: Anti-PD-1 antibodies are widely used for cancer treatment, including in advanced renal cell carcinoma (RCC). However, the therapeutic response varies among patients. This study aimed to predict tumor response to nivolumab anti-PD-1 antibody treatment for advanced RCC by integrating genetic and clinical data using machine learning (ML).

Role of artificial intelligence in advancing immunology.

Artificial intelligence (AI) has revolutionized various biomedical fields, particularly immunology, by enhancing vaccine development, immunotherapies, and allergy treatments. AI helps identify potential vaccine candidates and predict how the body reacts to different antigens based on a vast number of genomic sequences and protein structures. AI can help cancer patients by analyzing their data and ...

Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study.

BACKGROUND: Retinopathy of prematurity (ROP) is the leading preventable cause of childhood blindness. A timely intravitreal injection of antivascular ...

Artificial intelligence systems for endoscopic diagnosis of autoimmune atrophic gastritis: opportunities and limits.

Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades.

OBJECTIVE: Autoimmune diseases have long been recognized for their intricate nature and elusive mechanisms, presenting significant challenges in both ...

Unsupervised machine learning analysis of optical coherence tomography radiomics features for predicting treatment outcomes in diabetic macular edema.

This study aimed to identify distinct clusters of diabetic macular edema (DME) patients with differential anti-vascular endothelial growth factor (VEG...

Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in intracranial aneurysma.

BACKGROUND: Increasing evidence indicates a connection between intracranial aneurysm (intracranial aneurysm, IA) and autoimmune diseases. However, the...

Applying AI to Canada's Financial Intelligence System: Promises and Perils in Combatting Money Laundering and Terrorism Financing.

Money laundering (ML) and terrorist financing (TF) are pernicious global challenges. Estimates suggest that ML represents 2 to 4 percent of global GDP...

Efficient and anti-interference plastic classification method suitable for one-shot learning based on laser induced breakdown spectroscopy.

Efficient recycling of plastics is critical for environmental sustainability. In this work, an efficient and anti-interference method for plastic clas...

Comparison of artificial intelligence applications and commercial system performances using selected ANA IIF images.

Accurate and accessible classification of anti-nuclear antibodies (ANA) through indirect immunofluorescence (IIF) imaging is crucial for diagnosing au...

Assessing the Usability of ChatGPT Responses Compared to Other Online Information in Hand Surgery.

BACKGROUND: ChatGPT is a natural language processing tool with potential to increase accessibility of health information. This study aimed to: (1) ass...

Multifaceted profiling of virus-specific CD8 T cells reveals distinct immune signatures against cytomegalovirus infection states during pregnancy.

Anti-cytomegalovirus (CMV) serological testing, including the IgG avidity index (AI), is used to assess CMV infection phases during pregnancy. However...

Engineering TCR-controlled fuzzy logic into CAR T cells enhances therapeutic specificity.

Chimeric antigen receptor (CAR) T cell immunotherapy represents a breakthrough in the treatment of hematological malignancies, but poor specificity ha...

Micro- and Nano-Bots for Infection Control.

Medical micro- and nano-bots (MMBs and MNBs) have attracted a lot of attention owing to their precise motion for accessing difficult-to-reach areas in...

Traditional Chinese medicine as a promising choice for future control of PEDV.

Porcine epidemic diarrhea virus (PEDV) is the major agent of the recent outbreaks of diarrhea in piglets, which has caused huge economic losses to the...

Optical Synaptic Devices with Multiple Encryption Features Based on SERS-Revealed Charge-Transfer Mechanism.

2D optical synaptic devices with atomic-scale thickness show potential for building highly integrated tunable artificial visual neural networks. Howev...

Unique and shared transcriptomic signatures underlying localized scleroderma pathogenesis identified using interpretable machine learning.

Using transcriptomic profiling at single-cell resolution, we investigated cell-intrinsic and cell-extrinsic signatures associated with pathogenesis an...

Application of machine learning in assessing disease activity in SLE.

OBJECTIVE: SLE is a chronic autoimmune disease with immune complex deposition in various organs, causing inflammation. The Systemic Lupus Erythematosu...

MIST: An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis.

Joint analysis of transcriptomic and T cell receptor (TCR) features at single-cell resolution provides a powerful approach for in-depth T cell immune ...

Comprehensive quality evaluation of crude material of Ligusticum chuanxiong Hort. through high performance liquid chromatography coupled with DenseNet-121 assisted hyperspectral imaging and anti-thrombotic zebrafish bioassay.

An innovative, integrated strategy was developed for rapid and comprehensive quality assessment of Ligusticum chuanxiong Hort., the key raw material f...