Therapeutic dose prediction of α5-GABA receptor modulation from simulated EEG of depression severity.

Treatment for major depressive disorder (depression) often has partial efficacy and a large portion of patients are treatment resistant. Recent studies implicate reduced somatostatin (SST) interneuron inhibition in depression, and new pharmacology boosting this inhibition via positive allosteric modulators of α5-GABAA receptors (α5-PAM) offers a promising effective treatment. However, testing the effect of α5-PAM on human brain activity is limited, meriting the use of detailed simulations.

Genotes - a 'just-in-time' genomics education resource co-designed with clinicians.

Background: Powerful new genomic technologies are transforming the way healthcare is delivered, shaping medical practice across all specialties. In this rapidly changing landscape, there is an urgent need to equip the clinical workforce with knowledge and skills to navigate the new healthcare terrain. Co-design of healthcare resources with end users is increasingly gaining traction as a method of ensuring that educational content and delivery are tailored to users' needs, increasing likelihood of use and resulting in better outcomes for patients.

Machine Learning for the Genomic Prediction of Growth Traits in a Composite Beef Cattle Population.

The adoption of genomic selection is prevalent across various plant and livestock species, yet existing models for predicting genomic breeding values often remain suboptimal. Machine learning models present a promising avenue to enhance prediction accuracy due to their ability to accommodate both linear and non-linear relationships. In this study, we evaluated four machine learning models-Random Forest, Support Vector Machine, Convolutional Neural Networks, and Multi-Layer Perceptrons-for predicting genomic values related to birth weight (BW), weaning weight (WW), and yearling weight (YW), and compared them with other conventional models-GBLUP (Genomic Best Linear Unbiased Prediction), Bayes A, and Bayes B.