Menopause

Risk factors of osteoporosis in soldiers of the Armed Forces: A cross-sectional study from Western India.

BACKGROUND: Osteoporosis may result from risk factors such as smoking, alcohol, low body mass index, less physical exercise, and dietary calcium deficiency. The risk of osteoporosis fractures can be reduced with lifestyle changes, which include diet, exercise, and preventing falls. The present study is an effort to measure the burden of risk factors of osteoporosis in adult male soldiers in the Ar...

The Emerging Role of Long Non-Coding RNAs and MicroRNAs in Neurodegenerative Diseases: A Perspective of Machine Learning.

Neurodegenerative diseases (NDs) are characterized by progressive neuronal dysfunction and death of brain cells population. As the early manifestations of NDs are similar, their symptoms are difficult to distinguish, making the timely detection and discrimination of each neurodegenerative disorder a priority. Several investigations have revealed the importance of microRNAs and long non-coding RNAs...

EHR-Oriented Knowledge Graph System: Toward Efficient Utilization of Non-Used Information Buried in Routine Clinical Practice.

Non-used clinical information has negative implications on healthcare quality. Clinicians pay priority attention to clinical information relevant to t...

Facial recognition accuracy in photographs of Thai neonates with Down syndrome among physicians and the Face2Gene application.

Down syndrome (DS) is typically recognizable in those who present with multiple dysmorphism, especially in regard to facial phenotypes. However, as th...

Development of convolutional neural networks for recognition of tenogenic differentiation based on cellular morphology.

BACKGROUND AND OBJECTIVE: The use of automated systems for image recognition is highly preferred for regenerative medicine applications to evaluate st...

Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data.

Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve...

Combination Therapy of Chloramphenicol and Daptomycin for the Treatment of Infective Endocarditis Secondary to Multidrug Resistant .

A 38-years-old female with an aortic valve replacement presented to an outside hospital (OSH) with fevers and malaise. Blood cultures revealed VRE whi...

Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning.

Over the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abun...

Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy.

Osteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment are to improve bone mineral density (BMD) and prev...

A machine learning based exploration of COVID-19 mortality risk.

Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning....

Clinical decision support algorithm based on machine learning to assess the clinical response to anti-programmed death-1 therapy in patients with non-small-cell lung cancer.

OBJECTIVE: Anti-programmed death (PD)-1 therapy confers sustainable clinical benefits for patients with non-small-cell lung cancer (NSCLC), but only s...

Rapid and sensitive mycoplasma detection system using image-based deep learning.

A major concern in the clinical application of cell therapy is the manufacturing cost of cell products, which mainly depends on quality control. The m...

Machine learning differentiates enzymatic and non-enzymatic metals in proteins.

Metalloenzymes are 40% of all enzymes and can perform all seven classes of enzyme reactions. Because of the physicochemical similarities between the a...

Prediction model for the risk of osteoporosis incorporating factors of disease history and living habits in physical examination of population in Chongqing, Southwest China: based on artificial neural network.

BACKGROUND: Osteoporosis is a gradually recognized health problem with risks related to disease history and living habits. This study aims to establis...

A deep learning system for automated, multi-modality 2D segmentation of vertebral bodies and intervertebral discs.

PURPOSE: Fractures in vertebral bodies are among the most common complications of osteoporosis and other bone diseases. However, studies that aim to p...

Are social robots ready yet to be used in care and therapy of autism spectrum disorder: A systematic review of randomized controlled trials.

Autism is a neurodevelopmental disorder that affects the everyday life of people who have this lifelong condition. Robots hold great promise for uplif...

A Cascade Graph Convolutional Network for Predicting Protein-Ligand Binding Affinity.

Accurate prediction of binding affinity between protein and ligand is a very important step in the field of drug discovery. Although there are many me...

Non-Technical Skill Assessment and Mental Load Evaluation in Robot-Assisted Minimally Invasive Surgery.

Sensor technologies and data collection practices are changing and improving quality metrics across various domains. Surgical skill assessment in Rob...

Calibrated uncertainty estimation for interpretable proton computed tomography image correction using Bayesian deep learning.

Integrated-type proton computed tomography (pCT) measures proton stopping power ratio (SPR) images for proton therapy treatment planning, but its imag...

Machine learning analysis of gene expression profile reveals a novel diagnostic signature for osteoporosis.

BACKGROUND: Osteoporosis (OP) is increasingly prevalent with the aging of the world population. It is urgent to identify efficient diagnostic signatur...