Lupus

Interpretable dynamic quantitative vascular morphometry features using SHAP for anti-angiogenic therapy response prediction.

Anti-angiogenic therapy benefits vary, with response rates of 40 to 70%, highlighting the need for early biomarkers to identify responders. We developed an automated machine learning framework that uses delta quantitative vascular morphometry features from standard contrast-enhanced CT to evaluate treatment response. This workflow combines automated tumor and vessel segmentation with feature extra...

Development of a risk prediction model for rheumatoid arthritis-associated interstitial lung disease based on interpretable machine learning.

OBJECTIVE: This study constructed a predictive model for interstitial lung disease (ILD) in patients with rheumatoid arthritis (RA) and explored the value of interpretable machine learning. METHODS: The medical records of 400 hospitalized RA patients treated in the Department of Rheumatology and Immunology, the First Affiliated Hospital of Anhui University of Chinese Medicine, from July 2021 to Ja...

Harnessing AI and social media to understand real-world patient experiences in SLE.

OBJECTIVE: To apply large language models (LLMs) to Reddit posts referencing SLE to identify patient-expressed unmet medical needs, symptom experience...

Interpretable machine learning for cardiovascular disease risk prediction in cancer survivors: development and internal validation.

BACKGROUND: Cardiovascular disease (CVD) is a major concern among cancer survivors. However, the intersection of cancer and CVD has only recently gain...

Decoding dry eye disease based on bioinformatics and in vitro experimental: the role of immune responses and natural product intervention.

BACKGROUND: Dry eye disease (DED), a prevalent ocular condition, has seen rising incidence rates. Aberrant inflammation and immune dysregulation are k...

Therapeutic potential of pachymic acid in glioma: a comprehensive approach combining experimental validation, network pharmacology, and machine learning.

Pachymic acid (PA) is a natural active component of Poria cocos(Schw.)Wol. Although PA exhibits antitumor activity in multiple cancers, its effects an...

Development and internal validation of an interpretable machine learning model using routine laboratory data for systemic sclerosis-associated pulmonary arterial hypertension.

This study aimed to develop and validate an interpretable machine learning (ML) model using routine laboratory data to support clinical decision-makin...

Calibration-aware genomic prediction of paratuberculosis serostatus in indigenous Turkish goats.

Paratuberculosis, caused by Mycobacterium avium subsp. paratuberculosis (MAP), is a chronic, incurable enteritis of ruminants in which late-onset clin...

A patient-derived benchmark for evaluating large language models in connective tissue diseases: blinded multi-stakeholder assessment and guideline comparison.

To co-develop disease-specific patient questions for connective tissue diseases (CTDs), compare patient/rheumatologist ratings of answers from languag...

fAI-BRO: a multimodal AI decision-support system to address diagnostic delay in fibromyalgia syndrome.

OBJECTIVE: To evaluate the diagnostic accuracy and patient acceptability of fAI-BRO (Fibromyalgia AI-Based Rheumatology Observer), a multimodal artifi...

Pan-disease blood protein profiles of rheumatic autoimmune diseases.

BACKGROUND: Systemic autoimmune rheumatic diseases (SARDs) are a heterogeneous group of autoimmune conditions characterized by immune system dysregula...

Disparities in severe infection risk associated with systemic lupus erythematosus: evidence from a causal forest analysis.

OBJECTIVES: Severe infections are a primary cause of morbidity and premature mortality in patients with Systemic Lupus Erythematosus (SLE). Although S...

Yin-Huang-Qing-Fei-Capsule derived diosgenin and its optimized nanoconjugate target STAT3 against pulmonary fibrosis.

BACKGROUND: Pulmonary fibrosis (PF) is a progressive, fatal interstitial lung disease with limited curative therapeutic options. Yin-Huang-Qing-Fei (Y...

Integrated machine learning framework identifies EPSTI1 as a key diagnostic biomarker for Sjögren's disease: multi-cohort transcriptomic validation and single-cell characterization.

OBJECTIVE: This study aimed to develop a robust transcriptomic diagnostic signature for Sjögren's disease (SjD; formerly Sjögren's syndrome) and eluci...

Screening of diagnostic biomarkers for taurine metabolism-related genes in lupus nephritis and analysis of immune infiltration.

BACKGROUND: Lupus nephritis (LN) represents a serious renal manifestation of systemic lupus erythematosus and is driven by intricate interactions amon...

ASTHMA AT THE CROSSROADS: FROM ANTI-TNF-α SETBACKS TO NEXT-GENERATION BIOLOGICS TARGETING TYPE 1 AND TYPE 2 INFLAMMATION.

Severe asthma remains a major unmet clinical challenge due to its marked immunological heterogeneity and the limited efficacy of current therapies in ...

Custom GPT models for complex rheumatology systematic reviews: A two-part evaluation of data extraction and prognosis appraisal.

BACKGROUND: Systematic reviews are essential for evidence-based practice but remain resource-intensive, particularly during full-text data extraction ...

Astragalus polysaccharide-based thermoresponsive hydrogel loaded with β-elemene nanoemulsion in combination with anti-PD-L1 therapy for triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is classified as an immunologically cold tumor, which markedly weakens the therapeutic efficacy of immune checkpo...

Network-based machine learning to identify biomarkers for systemic lupus erythematosus.

BACKGROUND: Systemic lupus erythematosus (SLE) is a complex autoimmune disease, making accurate diagnosis and effective treatment challenging. Despite...

Intraloop neoangiogenesis in an AI-classified scleroderma pattern: recognizing a morphological clue to suspected dermatomyositis.

OBJECTIVES: To describe a diagnostic near miss in AI-assisted nailfold capillaroscopy, illustrating how a correct AI-based classification as an early ...