Lupus

Harnessing AI and social media to understand real-world patient experiences in systemic lupus erythematosus

Objective: To apply large language models (LLMs) to Reddit posts referencing systemic lupus erythematosus (SLE) to identify patient-expressed unmet medical needs, symptom experiences, and healthcare challenges, demonstrating how AI-enabled social media listening complements traditional patient experience research. Methods: We extracted 4,633 posts from ten SLE-related or health-focused Reddit comm...

GPAS: an online AI system for rapid and accurate pathogen identification and LLM-based interpretation

Accurate identification of unknown pathogens is critical for medicine and public health, yet current metagenomic workflows remain heavily dependent on specialized bioinformatics expertise and manual interpretation, creating substantial bottlenecks in time-sensitive diagnostic settings. The key challenges lie in achieving precise species identification amidst high background noise and translating c...

SLECA: a single-cell atlas of systemic lupus erythematosus enabling rare cell discovery using graph transformer

Systemic lupus erythematosus (SLE) is a highly heterogeneous autoimmune disease with complex immune and molecular dysregulation. While rare immune cel...

Universal Anti-forensics Attack against Image Forgery Detection via Multi-modal Guidance

The rapid advancement of AI-Generated Content (AIGC) technologies poses significant challenges for authenticity assessment. However, existing evaluati...

Pan-disease blood protein profiles of rheumatic autoimmune diseases

Systemic autoimmune rheumatic diseases (SARDs) are a heterogeneous group of autoimmune conditions characterized by immune system dysregulation leading...

Late-Stage Generalization Collapse in Grokking: Detecting anti-grokking with Weightwatcher

\emph{Memorization} in neural networks lacks a precise operational definition and is often inferred from the grokking regime, where training accuracy ...

SemBind: Binding Diffusion Watermarks to Semantics Against Black-Box Forgery Attacks

Latent-based watermarks, integrated into the generation process of latent diffusion models (LDMs), simplify detection and attribution of generated ima...

C-RLM: Schema-Enforced Recursive Synthesis for Auditable, Long-Context Clinical Documentation

Clinical decision-making for multi-morbid patients requires synthesizing evidence from lengthy, fragmented records-a task that exposes the limitations...

An anatomical hotspot for striatal dopamine-acetylcholine interactions during reward and movement

Dopamine (DA) and acetylcholine (ACh) are key neuromodulators that regulate striatal circuits underlying movement and reinforcement learning. Evidence...

High-dimensional spatial proteomics and novel machine learning pipeline identifies disease specific renal damage states

Lupus nephritis (LuN) and renal allograft rejection (RAR) manifest inflammation and fibrosis that ultimately lead to kidney failure. To quantitatively...

3D reconstruction of spatial transcriptomics with spatial pattern enhanced graph convolutional neural network

Spatially resolved transcriptomics (SRT) is a promising new technology that enables simultaneous analysis of gene expression and spatial information f...

Unveiling the role of oxidative stress in ANCA-associated glomerulonephritis through integrated machine learning and bioinformatics analyses.

Anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systemic autoimmune disease often leading to rapidly progressive glomerul...

Supervised machine learning and molecular docking modeling to identify potential Anti-Parkinson's agents.

Parkinson's disease is a neurodegenerative condition that affects the brain's neurons, and causes malfunction of nerve cells and their death. A neurot...

Anti-Symmetric Molecular Graph Learning Approach With Residual Adaptive Network Based Fuzzy Inference System for Lethal Dose Forecasting Problem.

In recent times, graph neural networks (GNNs) have become essential tools in molecular graph learning, due to its ability to model intricate structura...

Multi-Modal Face Anti-Spoofing via Cross-Modal Feature Transitions

Multi-modal face anti-spoofing (FAS) aims to detect genuine human presence by extracting discriminative liveness cues from multiple modalities, such...

Classification of autoimmune diseases from Peripheral blood TCR repertoires by multimodal multi-instance learning

T cell receptor (TCR) repertoires encode critical immunological signatures for autoimmune diseases, yet their clinical application remains limited b...

Cells Keep Diverse Company in Diseased Tissues.

Emerging spatial profiling technologies have revolutionized our understanding of how tissue architecture shapes disease progression, yet the contribut...

Traditional Chinese Medicine for Anti-Arrhythmias: Mechanisms via Potassium Channels.

Cardiac arrhythmia is a common life-threatening cardiovascular disorder. Potassium channels play a crucial role in cardiac electrophysiology, and thei...

Advancing T-cell immunotherapy for cellular senescence and disease: Mechanisms, challenges, and clinical prospects.

Cellular senescence is a complex biological process with a dual role in tissue homeostasis and aging-related pathologies. Accumulation of senescent ce...

Specific heat anomalies and local symmetry breaking in (anti-)fluorite materials: A machine learning molecular dynamics study.

Understanding the high-temperature properties of materials with (anti-)fluorite structures is crucial for their application in nuclear reactors. In th...