Lupus

Machine Learning Analysis of Routine EEG Accurately Predicts Anti-Seizure Medication Response

Despite the availability of more than 20 anti-seizure medications (ASMs), approximately half of patients with newly diagnosed epilepsy fail their first drug trial. Unfortunately, clinicians lack objective tools or consensus guidelines to match individual patients with the most effective therapy, frequently leading to years of uncontrolled seizures. Here, we developed machine learning models to uti...

Metformin use is associated with lower mortality from bacterial sepsis and improved immunocompetence in Thai diabetes patients with acute melioidosis

Diabetes mellitus (DM) is a major risk factor for acquiring infections. Metformin, the first-line treatment for type 2 DM, is associated with beneficial outcome from various infectious diseases. The neglected tropical disease melioidosis has up to 50% in-hospital mortality rate and the highest risk association of DM with any infectious disease (12-fold increased risk). A better understanding of th...

A precision health approach to medication management in neurodevelopmental conditions: a model development and validation study using four international cohorts

Psychotropic medications are commonly used for children with neurodevelopmental conditions, but their effectiveness varies, making treatment selection...

Clinical characteristics, prognosis, and predictive modeling in class IV ± V lupus nephritis.

OBJECTIVE: The objective of this study is to compare the clinical features and survival outcomes of class IV ± V lupus nephritis (LN) patients, identi...

Inflammatory activity levels on patients with anti-TNF therapy: most important factors and a decision tree model based on REGISPONSER and RESPONDIA registries.

BACKGROUND: The effectiveness of anti-tumour necrosis factor (TNF) therapy in spondyloarthritis is traditionally associated with factors such as age, ...

Identification of hub immune-related genes and construction of predictive models for systemic lupus erythematosus by bioinformatics combined with machine learning.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that involves multiple systems. SLE is characterized by the production of autoantib...

Developing a machine learning algorithm to predict psychotropic drugs-induced weight gain and the effectiveness of anti-obesity drugs in patients with severe mental illness: Protocol for a prospective cohort study.

Obesity is a global public health concern, often co-occurring in patients with severe mental illnesses. The impact of psychotropic drugs-induced weigh...

EACVP: An ESM-2 LM Framework Combined CNN and CBAM Attention to Predict Anti-coronavirus Peptides.

BACKGROUND: The novel coronavirus pneumonia (COVID-19) outbreak in late 2019 killed millions worldwide. Coronaviruses cause diseases such as severe ac...

iACVP-MR: Accurate Identification of Anti-coronavirus Peptide based on Multiple Features Information and Recurrent Neural Network.

BACKGROUND: Over the years, viruses have caused human illness and threatened human health. Therefore, it is pressing to develop anti-coronavirus infec...

Dissecting the Predictors of Cyber-Aggression Through an Explainable Machine Learning Model.

The general aggression model (GAM) suggests that cyber-aggression stems from individual characteristics and situational contexts. Previous studies hav...

Optimal dosing of anti-cancer treatment under drug-induced plasticity

While cancer has traditionally been considered a genetic disease, mounting evidence indicates an important role for non-genetic (epigenetic) mechani...

Three-in-One: Robust Enhanced Universal Transferable Anti-Facial Retrieval in Online Social Networks

Deep hash-based retrieval techniques are widely used in facial retrieval systems to improve the efficiency of facial matching. However, it also carr...

DASK: Distribution Rehearsing via Adaptive Style Kernel Learning for Exemplar-Free Lifelong Person Re-Identification

Lifelong person re-identification (LReID) is an important but challenging task that suffers from catastrophic forgetting due to significant domain g...

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection

The security of AI-generated content (AIGC) detection is crucial for ensuring multimedia content credibility. To enhance detector security, research...

Anti-Reference: Universal and Immediate Defense Against Reference-Based Generation

Diffusion models have revolutionized generative modeling with their exceptional ability to produce high-fidelity images. However, misuse of such pot...

Multi-Omics Integration With Machine Learning Identified Early Diabetic Retinopathy, Diabetic Macula Edema and Anti-VEGF Treatment Response.

PURPOSE: Identify optimal metabolic features and pathways across diabetic retinopathy (DR) stages, develop risk models to differentiate diabetic macul...

MIMIC: Multimodal Islamophobic Meme Identification and Classification

Anti-Muslim hate speech has emerged within memes, characterized by context-dependent and rhetorical messages using text and images that seemingly mi...

An evolving machine-learning-based algorithm to early predict response to anti-CGRP monoclonal antibodies in patients with migraine.

BACKGROUND: The present study aimed to determine whether machine-learning (ML)-based models can predict 3-, 6, and 12-month responses to the monoclona...

Deciphering the Role of SLFN12: A Novel Biomarker for Predicting Immunotherapy Outcomes in Glioma Patients Through Artificial Intelligence.

Gliomas are the most prevalent form of primary brain tumours. Recently, targeting the PD-1 pathway with immunotherapies has shown promise as a novel g...

Machine learning-enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations.

Drug resistance in Mycobacterium tuberculosis (Mtb) is a significant challenge in the control and treatment of tuberculosis, making efforts to combat ...