Menopause

Artificial neural network to estimate micro-architectural properties of cortical bone using ultrasonic attenuation: A 2-D numerical study.

The goal of this study is to estimate micro-architectural parameters of cortical porosity such as pore diameter (φ), pore density (ρ) and porosity (ν) of cortical bone from ultrasound frequency dependent attenuation using an artificial neural network (ANN). First, heterogeneous structures with controlled pore diameters and pore densities (mono-disperse) were generated, to mimic simplified structur...

Identification of Vertebral Fractures by Convolutional Neural Networks to Predict Nonvertebral and Hip Fractures: A Registry-based Cohort Study of Dual X-ray Absorptiometry.

Background Detection of vertebral fractures (VFs) aids in management of osteoporosis and targeting of fracture prevention therapies. Purpose To determine whether convolutional neural networks (CNNs) can be trained to identify VFs at VF assessment (VFA) performed with dual-energy x-ray absorptiometry and if VFs identified by CNNs confer a similar prognosis compared with the expert reader reference ...

econvRBP: Improved ensemble convolutional neural networks for RNA binding protein prediction directly from sequence.

RNA binding proteins (RBPs) determine RNA process from synthesis to decay, which play a key role in RNA transport, translation and degradation. Theref...

Locomotion Mode Recognition With Robotic Transtibial Prosthesis in Inter-Session and Inter-Day Applications.

Locomotion mode recognition across multiple sessions and days is an indispensable step towards the practical use of the robotic transtibial prosthesis...

A deep convolutional neural network for the estimation of gas chromatographic retention indices.

A deep convolutional neural network was used for the estimation of gas chromatographic retention indices on non-polar (polydimethylsiloxane and polydi...

Deep learning driven QSAR model for environmental toxicology: Effects of endocrine disrupting chemicals on human health.

Over 80,000 endocrine-disrupting chemicals (EDCs) are considered emerging contaminants (ECs), which are of great concern due to their effects on human...

Screening and Assessment of Bone Health in Indian Women Using an Indigenous ELISA of Human Osteocalcin a Bone Turnover Marker.

Osteoporosis a major public health problem of the elderly, is associated with substantial morbidity and socio economic burden. The aim of the study wa...

Analysis of the Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach.

Breast cancer is a leading cancer type and one of the major health issues faced by women around the world. Some of its major risk factors include body...

Detecting mitotic cells in HEp-2 images as anomalies via one class classifier.

We propose a novel framework for classification of mitotic v/s non-mitotic cells in a Computer Aided Diagnosis (CAD) system for Anti-Nuclear Antibodie...

An influent responsive control strategy with machine learning: Q-learning based optimization method for a biological phosphorus removal system.

Biological phosphorus removal (BPR) is an economical and sustainable processes for the removal of phosphorus (P) from wastewater, achieved by recircul...

Artificial neural network optimizes self-examination of osteoporosis risk in women.

OBJECTIVE: This study aimed to investigate the application of an artificial neural network (ANN) in optimizing the Osteoporosis Self-Assessment Tool f...

Intelligent ICU for Autonomous Patient Monitoring Using Pervasive Sensing and Deep Learning.

Currently, many critical care indices are not captured automatically at a granular level, rather are repetitively assessed by overburdened nurses. In ...

Ventricular geometry-regularized QRSd predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography.

Up to one-third of patients selected by current guidelines do not respond to cardiac resynchronization therapy (CRT), the aim of this study was to fin...

Simulating Dual-Energy X-Ray Absorptiometry in CT Using Deep-Learning Segmentation Cascade.

PURPOSE: Osteoporosis is an underdiagnosed condition despite effective screening modalities. Dual-energy x-ray absorptiometry (DEXA) screening, althou...

Natural language processing of radiology reports for identification of skeletal site-specific fractures.

BACKGROUND: Osteoporosis has become an important public health issue. Most of the population, particularly elderly people, are at some degree of risk ...

Swarm-Fuzzy Rule-Based Targeted Nano Delivery Using Bioinspired Nanomachines.

Cooperative navigation and swarm decision making take center stage in a broad range of distributed applications with high-environmental and measuremen...

Computational modeling of neuromuscular response to swing-phase robotic knee extension assistance in cerebral palsy.

Predicting subject-specific responses to exoskeleton assistance may aid in maximizing functional gait outcomes, such as achieving full knee-extension ...

Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data.

It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical dia...

Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms.

BACKGROUND: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent downstream signaling activities are crucial for r...

Network abnormalities among non-manifesting Parkinson disease related LRRK2 mutation carriers.

Non-manifesting carriers (NMC) of the G2019S mutation in the LRRK2 gene represent an "at risk" group for future development of Parkinson's disease (PD...