Rheumatology

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 transistors have broadened application possibilities in recent decades, further innovation remains essential. Here an organic dual-channel transistor (DCT) is proposed. This device features a bottom channel formed through vertical phase separation between an ...

Assessment of T2-weighted MRI-derived synthetic CT for the detection of suspected lumbar facet arthritis: a comparative analysis with conventional CT.

PURPOSE: We evaluated sCT generated from T2-weighted imaging (T2WI) using deep learning techniques to detect structural lesions in lumbar facet arthritis, with conventional CT as the reference standard.

Artificial intelligence-driven discovery of YH395A: A novel TGFβR1 inhibitor with potent anti-tumor activity against triple-negative breast cancer.

Characterized by high malignancy and limited treatment efficacy, triple-negative breast cancer (TNBC) remains a clinically challenging subtype within ...

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, ...

A robust machine learning approach to predicting remission and stratifying risk in rheumatoid arthritis patients treated with bDMARDs.

Rheumatoid arthritis (RA) is a chronic autoimmune disease affecting millions worldwide, leading to inflammation, joint damage, and reduced quality of ...

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 ...

Predicting carotid atherosclerosis in latent autoimmune diabetes in adult patients using machine learning models: a retrospective study.

BACKGROUND: Latent autoimmune diabetes in adults (LADA) is a slowly progressing form of diabetes with autoimmune origins. Patients with LADA are at an...

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 ...

Advanced machine learning applications in fibromyalgia to assess the relationship between 3D spinal alignment with clinical outcomes.

This study leveraged machine learning (ML) models to explore the relationship between three-dimensional (3D) spinal alignment parameters and clinical ...

Assessment of drug induced hyperuricemia and gout risk using the FDA adverse event reporting system.

Hyperuricemia, the key pathological basis of gout, is increasingly prevalent worldwide. While lifestyle factors contribute, various medications also p...

Exploring the Acceptance and Opportunities of Using a Specific Generative AI Chatbot to Assist Parents in Managing Pediatric Rheumatological Chronic Health Conditions: Mixed Methods Study.

BACKGROUND: Health care chatbots can be used to support patients and their families with everyday decision-making. While there is some research on int...

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 ...

Machine learning unveils hypoxia-immune gene hub for clinical stratification of thyroid-associated ophthalmopathy.

Thyroid-associated ophthalmopathy (TAO) is an autoimmune disorder affecting the orbit, potentially resulting in blindness. This study focused on the r...

Deep learning for occupation recognition and knowledge discovery in rheumatology clinical notes.

Occupational data is a crucial social determinant of health, influencing diagnostic accuracy, treatment strategies, and policy-making in healthcare. H...

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...