Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well established that it depends on pre- and postsynaptic activity. However, models that rely solely on pre- and postsynaptic activity for synaptic changes have, ...
Artificial intelligence and machine learning approaches have become increasingly applied in the field of echocardiography to streamline diagnostic and prognostic assessments, and to support treatment decisions. Artificial intelligence and machine lea...
The prevalence of prediabetes is rapidly increasing, and this can lead to an increased risk for individuals to develop type 2 diabetes and associated diseases. Therefore, it is necessary to develop nutritional strategies to maintain healthy glucose l...
Premature all-cause mortality is high in patients receiving peritoneal dialysis (PD). The accurate and early prediction of mortality is critical and difficult. Three prediction models, the logistic regression (LR) model, artificial neural network (AN...
The objectives of current investigation are (1) to find out wavelength of maximum absorbance (λ) for combined cyclosporin A and etodolac solution followed by selection of mobile phase suitable for the RP-HPLC method, (2) to define analytical target p...
Recent advances in sequencing and barcoding technologies have enabled researchers to simultaneously profile gene expression, chromatin accessibility, and/or protein levels in single cells. However, these multiomic techniques often pose technical and ...
Post-traumatic stress disorder (PTSD) is characterized by complex, heterogeneous symptomology, thus detection outside traditional clinical contexts is difficult. Fortunately, advances in mobile technology, passive sensing, and analytics offer promisi...
Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer ce...
Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. Herein we present the development of a deep learning model by clinicians without coding, which predicts reporte...
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