Lupus

AIP-TranLAC: A Transformer-Based Method Integrating LSTM and Attention Mechanism for Predicting Anti-inflammatory Peptides.

Anti-inflammatory peptides (AIPs) have emerged as potential therapeutic candidates for managing various inflammatory disorders, but their computational identification remains challenging. We propose AIP-TranLAC, a novel deep learning framework that integrates Transformer-based embedding, bidirectional long short-term memory (Bi-LSTM), multi-head attention, and convolutional neural network (CNN) to...

Deep learning-based quantitative assessment of renal chronicity indices in lupus nephritis.

Renal chronicity indices (CI) have been identified as strong predictors of long-term outcomes in lupus nephritis (LN) patients. However, assessment by pathologists is hindered by challenges such as substantial time requirements, high interobserver variation, and susceptibility to fatigue. This study aims to develop an effective deep learning (DL) pipeline that automates the assessment of CI and pr...

Systemic Lupus Erythematosus prediction using Epistatic-Quantile Fusion Transformer network with integrated multi-omics and clinical data.

Systemic Lupus Erythematosus (SLE) is a complex autoimmune disorder with heterogeneous symptoms and overlapping clinical presentations, making early p...

Emerging innovations in ophthalmic drug delivery for diabetic retinopathy: a translational perspective.

Diabetic retinopathy (DR) is a progressive microvascular complication of diabetes and a leading cause of vision impairment worldwide. Despite advancem...

Organic dual-channel transistors for reconfigurable signal modulation and anti-counterfeiting.

The development of integrated circuits and artificial intelligence demands electronic devices with versatile functions. While unconventional transisto...

Electrophysiological-based automatic subgroups diagnosis of patients with chronic dysimmune polyneuropathies.

BACKGROUND: Chronic Dysimmune Polyneuropathies (CDP) encompass a group of conditions characterized by autoimmune etiology targeting myelin and/or axon...

Unlocking potent anti-tuberculosis natural products through structure-activity relationship analysis.

Tuberculosis (TB) remains a world health problem due to the high number of affected individuals, high mortality rates, prolonged treatment durations, ...

D-optimal candexch algorithm-enhanced machine learning UV-spectrophotometry for five-analyte determination in novel anti-glaucoma formulations and ocular fluids: four-color sustainability framework with NQS assessment and UN-SDG integration.

The novel anti-glaucoma ophthalmic preparation containing latanoprost, netarsudil, and benzalkonium chloride has posed a significant challenge due to ...

Prioritizing perturbation-responsive gene patterns using interpretable deep learning.

Spatially resolved transcriptomics enables mapping of multiplexed gene expression within tissue contexts. While existing methods prioritize spatially ...

Driving Multifunctional Nanomedicine Design for Non-Inflammatory Tumor Therapy with Integrated Machine Learning and Density Functional Theory.

Due to the promotive role of inflammation in tumor progression, designing multifunctional nanomedicines that synergistically combine anti-tumor and an...

A genotype-to-drug diffusion model for generation of tailored anti-cancer small molecules.

Despite advances in precision oncology, developing effective cancer therapeutics remains a significant challenge due to tumor heterogeneity and the li...

The effects of robot anthropomorphic characteristics on employees' anti-robot sabotage.

Robot anthropomorphic characteristics have not only been widely manifested in production practices, but their positive effects on human-robot interact...

Predicting PLGA nanoparticle size and zeta potential in synthesis for application of drug delivery via machine learning analysis.

This study employed multiple machine learning (ML) methods to model and predict key attributes of PLGA nanoparticles, specifically particle size and z...

Closed circuit artificial ıntelligence model named morgaf for childhood onset systemic lupus erythematosus diagnosis.

Systemic Lupus Erythematosus (SLE) is a chronic, autoimmune disease characterized by multiple organ involvement and autoantibodies, and its diagnosis ...

MRI radiomics model for predicting tumor immune microenvironment types and efficacy of anti-PD-1/PD-L1 therapy in hepatocellular carcinoma.

BACKGROUND: To improve the prediction of immune checkpoint inhibitors (ICIs) efficacy in hepatocellular carcinoma (HCC), this study categorized the tu...

Ensemble machine learning algorithm for anti-VEGF treatment efficacy prediction in diabetic macular edema.

BACKGROUND: Diabetic macular edema (DME) is a leading cause of vision loss in diabetes, with variable responses to anti-vascular endothelial growth fa...

A deep learning model for predicting systemic lupus erythematosus-associated epitopes.

BACKGROUND: The accurate prediction of epitopes associated with Systemic Lupus Erythematosus (SLE) plays a vital role in advancing our understanding o...

An MOF-Enhanced Anti-Fouling Immunoprobe Platform for Efficient Direct Screening of Pancreatic Cancer.

Monitoring biomarkers offers insights for early disease (e.g., cancer, chronic diseases) screening, treatment guidance and response evaluation. To tac...

AI-augmented differential diagnosis of granulomatous rosacea and lupus miliaris disseminatus faciei: A 23-year retrospective pilot study.

Granulomatous rosacea (GR) and lupus miliaris disseminatus faciei (LMDF) exhibit overlapping clinical features, making their differentiation challengi...

Safety of nonselective nonsteroidal anti-inflammatory drugs in cardiac surgery: a historical cohort study.

PURPOSE: Pain management after cardiac surgery is imperative, as inadequate analgesia can increase the risk of myocardial ischemia, thromboembolism, a...