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Skin whitening potential of extracts from discarded cabbage outer leaves
Optimizing electrical stimulation parameters to enhance visual cortex activation in retina degeneration rats
Development and validation of a Japanese version of the multimorbidity treatment burden questionnaire
Study on dynamic and static load characteristics and rockburst induced mechanism under the influence of fault
Considering the bonding shear strength and flexural performance of bamboo scrimber bonded with magnesium oxysulfates adhesives of different properties
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Systems consolidation reorganizes hippocampal engram circuitry
The nonlinear relationship between built environment and cycling propensity for different travel purposes − based on extreme gradient boosting decision tree
Discovery of natural scaffolds as HER2 inhibitors for breast cancer: virtual screening, molecular dynamics, and biological characterization with selectivity profiling
Abstract Nature offers potential therapeutic candidates for breast cancer (BC), and targeting HER2 signaling presents a promising approach, leveraging the successful history of anticancer drug discovery. Using a structure-based virtual screening workflow, a comprehensive library of natural products (NPs) was screened for potential HER2 binding. Five of these NPs were selected for in-depth biological validation against BC. Biochemically, oroxin B, liquiritin, ligustroflavone, and mulberroside A suppressed HER2 catalysis with nanomolar potency. Binding mode studies of NPs revealed their binding patterns, providing valuable SAR insights for effective HER2 inhibition. Further cellular assays revealed that the top NPs have preferential anti-proliferative effects towards HER2 over-expressing BC cells, with notable selectivity indices. Liquiritin exhibited promising anti-migratory activity in two cellular motility models, while other tested hits primarily inhibited cancer cell growth with minimal effects on metastasis. Liquiritin and oroxin B stood out as validated hits, revealing the most promising profiles. ADME predictions and MD simulations positioned liquiritin as a more promising HER2 inhibitor than oroxin B, despite oroxin B’s higher ranking in rigid docking studies. On the molecular level, liquiritin significantly inhibited HER2 phosphorylation and expression in BC cells. Liquiritin demonstrated notable selectivity for HER family proteins when tested against various kinases, highlighting its potential as a pan-HER inhibitor hit for future development. Further in vivo assessment is necessary to support the hit-to-lead promotion of liquiritin.
Phytochemical profiling and antioxidant capacity of Achillea Santolina at different phenological stages
Deep learning models for deriving optimised measures of fat and muscle mass from MRI
Abstract Fat and muscle mass are potential biomarkers of wellbeing and disease in oncology, but clinical measurement methods vary considerably. Here we evaluate the accuracy, precision and ability to track change for multiple deep learning (DL) models that quantify fat and muscle mass from abdominal MRI. Specifically, subcutaneous fat (SF), intra-abdominal fat (VF), external muscle (EM) and psoas muscle (PM) were evaluated using 15 convolutional neural network (CNN)-based and 4 transformer-based deep learning model architectures. There was negligible difference in the accuracy of human observers and all deep learning models in delineating SF or EM. Both of these tissues had excellent repeatability of their delineation. VF was measured most accurately by the human observers, then by CNN-based models, which outperformed transformer-based models. In distinction, PM delineation accuracy and repeatability was poor for all assessments. Repeatability limits of agreement determined when changes measured in individual patients were due to real change rather than test-retest variation. In summary, DL model accuracy and precision of delineating fat and muscle volumes varies between CNN-based and transformer-based models, between different tissues and in some cases with gender. These factors should be considered when investigators deploy deep learning methods to estimate biomarkers of fat and muscle mass.