HRT

Triple-Negative Breast Cancer: A Review of Conventional and Advanced Therapeutic Strategies.

Triple-negative breast cancer (TNBC) cells are deficient in estrogen, progesterone and ERBB2 receptor expression, presenting a particularly challenging therapeutic target due to their highly invasive nature and relatively low response to therapeutics. There is an absence of specific treatment strategies for this tumor subgroup, and hence TNBC is managed with conventional therapeutics, often leadin...

Collective effects of long-range DNA methylations predict gene expressions and estimate phenotypes in cancer.

DNA methylation of various genomic regions has been found to be associated with gene expression in diverse biological contexts. However, most genome-wide studies have focused on the effect of (1) methylation in cis, not in trans and (2) a single CpG, not the collective effects of multiple CpGs, on gene expression. In this study, we developed a statistical machine learning model, geneEXPLORE (gene ...

Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening.

OBJECTIVE: Osteoporosis is a prevalent and treatable condition, but it remains underdiagnosed. In this study, a deep learning-based system was develop...

Decoding rejuvenating effects of mechanical loading on skeletal aging using in vivo μCT imaging and deep learning.

Throughout the process of aging, dynamic changes of bone material, micro- and macro-architecture result in a loss of strength and therefore in an incr...

Radiomics for classification of bone mineral loss: A machine learning study.

PURPOSE: The purpose of this study was to develop predictive models to classify osteoporosis, osteopenia and normal patients using radiomics and machi...

DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance.

The progesterone receptor (PR) is important therapeutic target for many malignancies and endocrine disorders due to its role in controlling ovulation ...

Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records.

Machine learning has become ubiquitous and a key technology on mining electronic health records (EHRs) for facilitating clinical research and practice...

Secretion of equine chorionic gonadotropin and its association with supplementary corpus luteum formation and progesterone concentration in Hokkaido native pony recipient mares.

The objectives of this study were to determine the plasma profile of equine chorionic gonadotropin (eCG) and its association with the formation of sup...

Diagnostic and Gradation Model of Osteoporosis Based on Improved Deep U-Net Network.

The measurement of bone mineral density for osteoporosis has always been the focus of researchers because it plays an important role in bone disease d...

The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population.

OBJECTIVE: Osteoporosis is hard to detect before it manifests symptoms and complications. In this study, we evaluated machine learning models for iden...

The Identification of Postovulation Infertility with the Measurement of Early Luteal Phase (Peak Day +3) Progesterone Production.

UNLABELLED: This study reports on 632 cycles from 105 women who were using the CREIGHTON MODEL Fertility™ System to avoid pregnancy and had either a s...

The Use of Transdermal Estrogen in Castrate-resistant, Steroid-refractory Prostate Cancer.

BACKGROUND: Androgen-deprivation therapy is the mainstay of treatment for metastatic prostate cancer. Corticosteroids and estrogens are also useful ag...

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)...

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 determ...

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...

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...

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...