Rheumatology

Development of GBRT Model as a Novel and Robust Mathematical Model to Predict and Optimize the Solubility of Decitabine as an Anti-Cancer Drug.

The efficient production of solid-dosage oral formulations using eco-friendly supercritical solvents is known as a breakthrough technology towards developing cost-effective therapeutic drugs. Drug solubility is a significant parameter which must be measured before designing the process. Decitabine belongs to the antimetabolite class of chemotherapy agents applied for the treatment of patients with...

Recent advances in the use of machine learning and artificial intelligence to improve diagnosis, predict flares, and enrich clinical trials in lupus.

PURPOSE OF REVIEW: Machine learning is a computational tool that is increasingly used for the analysis of medical data and has provided the promise of more personalized care.

Anti-Colorectal Cancer Effects of (Bull.: Fr.) P. Karst. Spore Powder through Regulation of Gut Microbiota-Mediated JAK/STAT Signaling.

(Bull.: Fr.) P. Karst. spore powder (IHS) contains polyphenols and triterpenoids with pharmacological effects. Here, we analyzed its composition, and...

The Artificial Intelligence System for the Generation of Sports Education Guidance Model and Physical Fitness Evaluation Under Deep Learning.

In recent years, China's achievements in artificial intelligence (AI) have attracted the attention of the world, and AI technology has penetrated into...

Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method.

Machine learning methods trained on cancer cell line panels are intensively studied for the prediction of optimal anti-cancer therapies. While classif...

Revisiting Light Field Rendering With Deep Anti-Aliasing Neural Network.

The light field (LF) reconstruction is mainly confronted with two challenges, large disparity and the non-Lambertian effect. Typical approaches either...

Prediction of Anti-proliferation Effect of [1,2,3]Triazolo[4,5-d]pyrimidine Derivatives by Random Forest and Mix-Kernel Function SVM with PSO.

In order to predict the anti-gastric cancer effect of [1,2,3]triazolo[4,5-d]pyrimidine derivatives (1,2,3-TPD), quantitative structure-activity relati...

Deep Learning-Based Image Analysis for the Quantification of Tumor-Induced Angiogenesis in the 3D In Vivo Tumor Model-Establishment and Addition to Laser Speckle Contrast Imaging (LSCI).

(1) Background: angiogenesis plays an important role in the growth and metastasis of tumors. We established the CAM assay application, an image analys...

Deep learning for de-convolution of Smad2 versus Smad3 binding sites.

BACKGROUND: The transforming growth factor beta-1 (TGF β-1) cytokine exerts both pro-tumor and anti-tumor effects in carcinogenesis. An increasing bod...

Exploration of machine learning methods to predict systemic lupus erythematosus hospitalizations.

OBJECTIVES: Systemic lupus erythematosus (SLE) is a heterogeneous disease characterized by disease flares which can require hospitalization. Our objec...

Detection of mitotic HEp-2 cell images: role of feature representation and classification framework under class skew.

We propose and analyze a framework to detect and identify the mitotic type staining patterns among different non-mitotic (interphase) patterns on HEp-...

Aglow: A Fluorescence Assay and Machine Learning Model to Identify Inhibitors of Intracellular Infection.

is a genus of Gram-negative bacteria that has for centuries caused large-scale morbidity and mortality. In recent years, the resurgence of rickettsia...

Adaptive Anti-Disturbance Control for Systems With Saturating Input via Dynamic Neural Network Disturbance Modeling.

This article discusses the issue of disturbance rejection and anti-windup control for a class of complex systems with both saturating actuators and di...

Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses.

BACKGROUND : There are several types of pancreatic mass, so it is important to distinguish between them before treatment. Artificial intelligence (AI)...

A Combined Sensing System for Intrusion Detection Using Anti-Jamming Random Code Signals.

In order to prevent illegal intrusion, theft, and destruction, important places require stable and reliable human intrusion detection technology to ma...

Clinical Variables, Deep Learning and Radiomics Features Help Predict the Prognosis of Adult Anti-N-methyl-D-aspartate Receptor Encephalitis Early: A Two-Center Study in Southwest China.

OBJECTIVE: To develop a fusion model combining clinical variables, deep learning (DL), and radiomics features to predict the functional outcomes early...

Anti-CRISPR prediction using deep learning reveals an inhibitor of Cas13b nucleases.

As part of the ongoing bacterial-phage arms race, CRISPR-Cas systems in bacteria clear invading phages whereas anti-CRISPR proteins (Acrs) in phages i...

Towards autonomous analysis of chemical exchange saturation transfer experiments using deep neural networks.

Macromolecules often exchange between functional states on timescales that can be accessed with NMR spectroscopy and many NMR tools have been develope...

Integration of Transcriptomics and Metabolomics Reveals the Antitumor Mechanism Underlying Tadalafil in Colorectal Cancer.

The potential role of tadalafil, a PDE5 inhibitor, in anticancer activity and prolonged survival has been proposed. However, the systematic effects of...

Bumpless Transfer H∞ Anti-Disturbance Control of Switching Markovian LPV Systems Under the Hybrid Switching.

This article focuses on the bumpless transfer H anti-disturbance control problem for switching Markovian LPV systems under a hybrid switching law. A p...