Lupus

Micro- and Nano-Bots for Infection Control.

Medical micro- and nano-bots (MMBs and MNBs) have attracted a lot of attention owing to their precise motion for accessing difficult-to-reach areas in the body. These emerging tools offer the promise of non-invasive diagnostics and therapeutics for a wide range of ailments. Here, it is highlighted how MMBs and MNBs can revolutionize infection management. The latest applications of MMBs and MNBs ar...

Traditional Chinese medicine as a promising choice for future control of PEDV.

Porcine epidemic diarrhea virus (PEDV) is the major agent of the recent outbreaks of diarrhea in piglets, which has caused huge economic losses to the global swine industry. Since traditional vaccine strategies cannot provide complete protection for piglets, the development of safe, effective, and economical antiviral drugs is urgently needed. For many years, traditional Chinese medicines (TCMs) h...

Optical Synaptic Devices with Multiple Encryption Features Based on SERS-Revealed Charge-Transfer Mechanism.

2D optical synaptic devices with atomic-scale thickness show potential for building highly integrated tunable artificial visual neural networks. Howev...

Application of machine learning in assessing disease activity in SLE.

OBJECTIVE: SLE is a chronic autoimmune disease with immune complex deposition in various organs, causing inflammation. The Systemic Lupus Erythematosu...

Comprehensive quality evaluation of crude material of Ligusticum chuanxiong Hort. through high performance liquid chromatography coupled with DenseNet-121 assisted hyperspectral imaging and anti-thrombotic zebrafish bioassay.

An innovative, integrated strategy was developed for rapid and comprehensive quality assessment of Ligusticum chuanxiong Hort., the key raw material f...

Can artificial ıntelligence detect the anti-aging effect of rhinoplasty?

BACKGROUND: The quest for eternal youth has been a common theme in many cultures for centuries. While we have yet to discover a way to preserve youth ...

Role of eccentricity based topological descriptors to predict anti-HIV drugs attributes with supervised machine learning algorithms.

Chemical graphs are mathematical representations of molecular structures, where atoms are represented as vertices, while chemical bonds are depicted a...

Identification of glucocorticoid-related genes in systemic lupus erythematosus using bioinformatics analysis and machine learning.

BACKGROUND: Systemic lupus erythematosus (SLE) is a complex autoimmune disease that has significant impacts on patients' quality of life and poses a s...

Artificial intelligence in anti-obesity drug discovery: unlocking next-generation therapeutics.

Obesity, a multifactorial disease linked to severe health risks, requires innovative treatments beyond lifestyle changes and current medications. Exis...

Histopathology based AI model predicts anti-angiogenic therapy response in renal cancer clinical trial.

Anti-angiogenic (AA) therapy is a cornerstone of metastatic clear cell renal cell carcinoma (ccRCC) treatment, but not everyone responds, and predicti...

Application of Machine Learning (ML) approach in discovery of novel drug targets against Leishmania: A computational based approach.

Molecules with potent anti-leishmanial activity play a crucial role in identifying treatments for leishmaniasis and aiding in the design of novel drug...

CPHNet: a novel pipeline for anti-HAPE drug screening via deep learning-based Cell Painting scoring.

BACKGROUND: High altitude pulmonary edema (HAPE) poses a significant medical challenge to individuals ascending rapidly to high altitudes. Hypoxia-ind...

Screening and validating genes associated with cuproptosis in systemic lupus erythematosus by expression profiling combined with machine learning.

Cell death has long been a focal point in life sciences research, and recently, scientists have discovered a novel form of cell death induced by coppe...

TARSL: Triple-Attention Cross-Network Representation Learning to Predict Synthetic Lethality for Anti-Cancer Drug Discovery.

Cancer is a multifaceted disease that results from co-mutations of multi biological molecules. A promising strategy for cancer therapy involves in exp...

Retinal Vascularization Rate Predicts Retinopathy of Prematurity and Remains Unaffected by Low-Dose Bevacizumab Treatment.

PURPOSE: To assess the rate of retinal vascularization derived from ultra-widefield (UWF) imaging-based retinopathy of prematurity (ROP) screening as ...

Effects of psychological torture and cybertorture with emerging digital technologies under anti-torture legal obligations in China: A mixed methods research in risks and remedies.

International norms and domestic law-making and decision-making often underestimate the effects of psychological torture and the challenges posed by d...

Machine Learning-Based Identification of Novel Exosome-Derived Metabolic Biomarkers for the Diagnosis of Systemic Lupus Erythematosus and Differentiation of Renal Involvement.

OBJECTIVE: This study aims to investigate the exosome-derived metabolomics profiles in systemic lupus erythematosus (SLE), identify differential metab...

Evaluating large language models as a supplementary patient information resource on antimalarial use in systemic lupus erythematosus.

ObjectiveTo assess the accuracy, completeness, and reproducibility of Large Language Models (LLMs) (Copilot, GPT-3.5, and GPT-4) on antimalarial use i...

DeepTree-AAPred: Binary tree-based deep learning model for anti-angiogenic peptides prediction.

Anti-angiogenic peptides (AAPs) show important potential in tumor therapy by limiting the growth and metastasis of tumor cells. Accurate prediction of...

Machine Learning-Assisted Chemical Tongues Based on Dual-channel Inclusion Complexes for Rapid Identification of Nonsteroidal Anti-inflammatory Drugs in Food.

The improper application of nonsteroidal anti-inflammatory drugs (NSAIDs) presents significant health hazards via vector food contamination. A critica...