Osteoporosis

Digital twin for sex-specific identification of class III antiarrhythmic drugs based on in vitro measurements, computer models, and machine learning tools.

Atrial fibrillation (AF) significantly affects morbidity and mortality rates. Class III antiarrhythmic drugs (AADs) play a crucial role in managing AF but often exhibit gender-specific complications. Our study aims to identify gender-specific Class III AADs by integrating in vitro measurements, in silico models, and machine learning (ML). By simulating drug effects on a diverse cardiomyocyte model...

Discovery of novel cathepsin K inhibitors for osteoporosis treatment using a deep learning-based strategy.

BACKGROUND: Cathepsin K (CTSK), a cysteine protease of the papain family, exhibits high expression in activated osteoclasts, making it a key therapeutic target for osteoporosis. However, there are currently no CTSK inhibitors available for clinical use.

Sitting time and risk of cancer incidence and cancer mortality in postmenopausal women: the Women's Health Accelerometry Collaboration.

PURPOSE: Few studies have explored whether accelerometer-measured sedentary behavior increases cancer risk. We examined the associations of accelerome...

Machine learning molecular dynamics reveals atomistic mechanisms of ion beam damage in calcium fluoride.

Calcium fluoride (CaF) crystals are regarded as ideal materials for optical components due to their excellent ultraviolet (UV) transmittance and optic...

CalTrig: A GUI-based Machine Learning Approach for Decoding Neuronal Calcium Transients in Freely Moving Rodents.

Advances in in vivo Ca imaging using miniature microscopes have enabled researchers to study single-neuron activity in freely-moving animals. Tools su...

Dynamic mode decomposition for analysis and prediction of metabolic oscillations from time-lapse imaging of cellular autofluorescence.

Oscillations are a common phenomenon in cell biology. They are based on non-linear coupling of biochemical reactions and can show rich dynamic behavio...

Photosynthetic performance index (PIabs) and malondialdehyde (MDA) content determine rice biomass under combined salt stress and prohexadione-calcium treatment.

BACKGROUND: Plant growth regulators (PGRs) have proven effective in alleviating salt stress damage in crops, yet the predictive hierarchy of physiolog...

Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures.

Postoperative pneumonia, a prevalent complication arising from lower limb fracture surgery, can significantly prolong hospitalization periods and elev...

Evaluation of MRI-based synthetic CT for lumbar degenerative disease: a comparison with CT.

Patients with lumbar degenerative disease typically undergo preoperative MRI combined with CT scans, but this approach introduces additional ionizing ...

Adaptive neuro-fuzzy inference system optimization of natural rubber latex modified concrete's mechanical Properties.

The study investigates the optimization of Natural Rubber Latex Modified Concrete (NRLMC) using an Adaptive Neuro-Fuzzy Inference System (ANFIS) to en...

Radiomics and machine learning for osteoporosis detection using abdominal computed tomography: a retrospective multicenter study.

OBJECTIVE: This study aimed to develop and validate a predictive model to detect osteoporosis using radiomic features and machine learning (ML) approa...

Ultrasound-based machine learning model to predict the risk of endometrial cancer among postmenopausal women.

BACKGROUND: Current ultrasound-based screening for endometrial cancer (EC) primarily relies on endometrial thickness (ET) and morphological evaluation...

Deep learning image reconstruction and adaptive statistical iterative reconstruction on coronary artery calcium scoring in high risk population for coronary heart disease.

OBJECTIVE: Deep learning image reconstruction (DLIR) technology effectively improves the image quality while maintaining spatial resolution. The impac...

Mid-FTIR and machine learning for predicting fig leaf macronutrients content.

Predicting leaf mineral composition is critical for monitoring plant health and optimizing agricultural practices. This study combines Fourier-transfo...

In silico prediction of drug-induced cardiotoxicity with ensemble machine learning and structural pattern recognition.

Drug-induced cardiotoxicity poses a significant risk to human health, and reliable predictive models are needed for safety assessment. In this study, ...

Unveiling the role of microbial rease in ureolysis-induced calcium carbonate precipitation, Its mechanistic insights, and emerging applications.

Urease (urea amidohydrolase, EC 3.5.1.5) is a metalloenzyme that catalyzes the hydrolysis of urea into ammonia and carbamate, playing a pivotal role i...

Artificial intelligence for predicting the risk of bone fragility fractures in osteoporosis.

Osteoporosis is widespread with a high incidence rate, resulting in fragility fractures which are a major contributor to mortality among the elderly. ...

Axial Skeletal Assessment in Osteoporosis Using Radiofrequency Echographic Multi-spectrometry: Diagnostic Performance, Clinical Utility, and Future Directions.

Osteoporosis, a prevalent skeletal disorder, necessitates accurate and accessible diagnostic tools for effective disease management and fracture preve...

Investigating the metastability of amorphous calcium carbonate by droplet microfluidics experiments using machine learning.

Amorphous calcium carbonate (ACC) plays an important role in the crystallization pathways of calcite and its polymorphs influencing many natural and a...

Predicting neuronal firing from calcium imaging using a control theoretic approach.

Calcium imaging techniques, such as two-photon imaging, have become a powerful tool to explore the functions of neurons and the connectivity of their ...