Automatic detection and visualization of temporomandibular joint effusion with deep neural network.

This study investigated the usefulness of deep learning-based automatic detection of temporomandibular joint (TMJ) effusion using magnetic resonance imaging (MRI) in patients with temporomandibular disorder and whether the diagnostic accuracy of the model improved when patients' clinical information was provided in addition to MRI images. The sagittal MR images of 2948 TMJs were collected from 1017 women and 457 men (mean age 37.19 ± 18.

Glycogen synthase kinase 3 activity enhances liver inflammation in MASH.

Background & Aims: Metabolic dysfunction-associated steatohepatitis (MASH) is characterized by excessive circulating toxic lipids, hepatic steatosis, and liver inflammation. Monocyte adhesion to liver sinusoidal endothelial cells (LSECs) and transendothelial migration (TEM) are crucial in the inflammatory process. Under lipotoxic stress, LSECs develop a proinflammatory phenotype known as endotheliopathy.

CLDN18: Clinical, Pathological, and Genetic Signatures with Drug Screening in Gastric Adenocarcinoma.

Introduction: The CLDN18 gene, encoding claudin 18.1 and claudin 18.2, is a key component of tight junction strands in epithelial cells that form a paracellular barrier that is critical in Stomach Adenocarcinoma (STAD).

Prediction of xerostomia in elderly based on clinical characteristics and salivary flow rate with machine learning.

Xerostomia may be accompanied by changes in salivary flow rate and the incidence increases in elderly. We aimed to use machine learning algorithms, to identify significant predictors for the presence of xerostomia. This study is the first to predict xerostomia with salivary flow rate in elderly based on artificial intelligence.

Low CDKN1B Expression Associated with Reduced CD8+ T Lymphocytes Predicts Poor Outcome in Breast Cancer in a Machine Learning Analysis.

The cyclin-dependent kinase inhibitor 1B (CDKN1B) gene, which encodes the p27Kip1 protein, is important in regulating the cell cycle process and cell proliferation. Its role in breast cancer prognosis is controversial. We evaluated the significance and predictive role of CDKN1B expression in breast cancer prognosis.

JAK2 Loss Arising From Tumor-Spread-Through-Air-Spaces (STAS) Promotes Tumor Progression by Suppressing CD8+ T Cells in Lung Adenocarcinoma: A Machine Learning Approach.

Background: Tumor spread through air spaces (STAS) is a recently discovered risk factor for lung adenocarcinoma (LUAD). The aim of this study was to investigate specific genetic alterations and anticancer immune responses related to STAS. By using a machine learning algorithm and drug screening in lung cancer cell lines, we analyzed the effect of Janus kinase 2 (JAK2) on the survival of patients with LUAD and possible drug candidates.

DeepFold: enhancing protein structure prediction through optimized loss functions, improved template features, and re-optimized energy function.

Motivation: Predicting protein structures with high accuracy is a critical challenge for the broad community of life sciences and industry. Despite progress made by deep neural networks like AlphaFold2, there is a need for further improvements in the quality of detailed structures, such as side-chains, along with protein backbone structures.

Results: Building upon the successes of AlphaFold2, the modifications we made include changing the losses of side-chain torsion angles and frame aligned point error, adding loss functions for side chain confidence and secondary structure prediction, and replacing template feature generation with a new alignment method based on conditional random fields.

Pre-test probability for coronary artery disease in patients with chest pain based on machine learning techniques.

Background: A correct and prompt diagnosis of coronary artery disease (CAD) is a crucial component of disease management to reduce the risk of death and improve the quality of life in patients with CAD. Currently, the American College of Cardiology (ACC)/American Heart Association (AHA) and the European Society of Cardiology (ESC) guidelines recommend selecting an appropriate pre-diagnosis test for an individual patient according to the CAD probability. The purpose of this study was to develop a practical pre-test probability (PTP) for obstructive CAD in patients with chest pain using machine learning (ML); also, the performance of ML-PTP for CAD is compared to the final result of coronary angiography (CAG).

Sex Difference in Coronary Artery Spasm Tested by Intracoronary Acetylcholine Provocation Test in Patients with Nonobstructive Coronary Artery Disease.

Introduction: Cardiovascular diseases manifest differently in men and women. The purpose of this study is to compare the sex difference in the characteristics of coronary artery spasm (CAS) in patients with nonobstructive cardiovascular disease (NOCVD) and the clinical outcomes in accordance with sex in CAS patients.

Methods: The study analysed 5,491 patients with NOCVD who underwent an acetylcholine provocation test from November 2004 to May 2014 for evaluation of chest pain.

Identification of genes from ten oncogenic pathways associated with mortality and disease progression in glioblastoma.

Glioblastoma multiforme (GBM) is the most malignant brain tumor with an extremely poor prognosis. The Cancer Genome Atlas (TCGA) database has been used to confirm the roles played by 10 canonical oncogenic signaling pathways in various cancers. The purpose of this study was to evaluate the expression of genes in these 10 canonical oncogenic signaling pathways, which are significantly related to mortality and disease progression in GBM patients.

Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms.

The aim of this study is to investigate the relationship of 18 radiomorphometric parameters of panoramic radiographs based on age, and to estimate the age group of people with permanent dentition in a non-invasive, comprehensive, and accurate manner using five machine learning algorithms. For the study population (209 men and 262 women; mean age, 32.12 ± 18.

Advantages of deep learning with convolutional neural network in detecting disc displacement of the temporomandibular joint in magnetic resonance imaging.

This study investigated the usefulness of deep learning-based automatic detection of anterior disc displacement (ADD) from magnetic resonance imaging (MRI) of patients with temporomandibular joint disorder (TMD). Sagittal MRI images of 2520 TMJs were collected from 861 men and 399 women (average age 37.33 ± 18.

Machine Learning Techniques Differentiate Alcohol-Associated Hepatitis From Acute Cholangitis in Patients With Systemic Inflammation and Elevated Liver Enzymes.

Objective: To develop machine learning algorithms (MLAs) that can differentiate patients with acute cholangitis (AC) and alcohol-associated hepatitis (AH) using simple laboratory variables.

Methods: A study was conducted of 459 adult patients admitted to Mayo Clinic, Rochester, with AH (n=265) or AC (n=194) from January 1, 2010, to December 31, 2019. Ten laboratory variables (white blood cell count, hemoglobin, mean corpuscular volume, platelet count, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, total bilirubin, direct bilirubin, albumin) were collected as input variables.

High DKK3 expression related to immunosuppression was associated with poor prognosis in glioblastoma: machine learning approach.

Background: Glioblastoma multiforme (GBM) is an aggressive malignant primary brain tumor. Wnt/β-catenin is known to be related to GBM stemness. Cancer stem cells induce immunosuppressive and treatment resistance in GBM.

Cancer-associated fibroblasts are associated with poor prognosis in solid type of lung adenocarcinoma in a machine learning analysis.

Cancer-associated fibroblasts (CAFs) participate in critical processes in the tumor microenvironment, such as extracellular matrix remodeling, reciprocal signaling interactions with cancer cells and crosstalk with infiltrating inflammatory cells. However, the relationships between CAFs and survival are not well known in lung cancer. The aim of this study was to reveal the correlations of CAFs with survival rates, genetic alterations and immune activities.

Real-World Three-Year Clinical Outcomes of Biolimus-Eluting Stents versus Other Contemporary Drug-Eluting Stents in Patients with Acute Myocardial Infarction Patients: Data from the Korea Acute Myocardial Infarction Registry (KAMIR).

Introduction: Biolimus-eluting stents (BES) are known to be superior to bare-metal stents. This study aims to evaluate the safety and efficacy of BES compared to other drug-eluting stents (DES) based on big data from the Korea Acute Myocardial Infarction Registry (KAMIR).

Methods: The study analyzed a total of 9,759 acute myocardial infarction (AMI) patients who underwent percutaneous coronary intervention (PCI) with DES.

Age-group determination of living individuals using first molar images based on artificial intelligence.

Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs.

Ranked k-Spectrum Kernel for Comparative and Evolutionary Comparison of Exons, Introns, and CpG Islands.

Motivation: Existing k-mer based string kernel methods have been successfully used for sequence comparison. However, existing kernel methods have limitations for comparative and evolutionary comparisons of genomes due to the sensitiveness to over-represented k-mers and variable sequence lengths.

Results: In this study, we propose a novel ranked k-spectrum string (RKSS) kernel.

A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization.

The aim of this study is to predict acute coronary syndrome (ACS) requiring revascularization in those patients presenting early-stage angina-like symptom using machine learning algorithms. We obtained data from 2344 ACS patients, who required revascularization and from 3538 non-ACS patients. We analyzed 20 features that are relevant to ACS using standard algorithms, support vector machines and linear discriminant analysis.