B cells expressing authentic naive human VRC01-class BCRs can be recruited to germinal centers and affinity mature in multiple independent mouse models.

Animal models of human antigen-specific B cell receptors (BCRs) generally depend on "inferred germline" sequences, and thus their relationship to authentic naive human B cell BCR sequences and affinities is unclear. Here, BCR sequences from authentic naive human VRC01-class B cells from healthy human donors were selected for the generation of three BCR knockin mice. The BCRs span the physiological range of affinities found in humans, and use three different light chains (VK3-20, VK1-5, and VK1-33) found among subclasses of naive human VRC01-class B cells and HIV broadly neutralizing antibodies (bnAbs). The germline-targeting HIV immunogen eOD-GT8 60mer is currently in clinical trial as a candidate bnAb vaccine priming immunogen. To attempt to model human immune responses to the eOD-GT8 60mer, we tested each authentic naive human VRC01-class BCR mouse model under rare human physiological B cell precursor frequency conditions. B cells with high (HuGL18HL) or medium (HuGL17HL) affinity BCRs were primed, recruited to germinal centers, and they affinity matured, and formed memory B cells. Precursor frequency and affinity interdependently influenced responses. Taken together, these experiments utilizing authentic naive human VRC01-class BCRs validate a central tenet of germline-targeting vaccine design and extend the overall concept of the reverse vaccinology approach to vaccine development.

Artificial intelligence in healthcare: a primer for medical education in radiomics.

The application of artificial intelligence (AI) to healthcare has garnered significant enthusiasm in recent years. Despite the adoption of new analytic approaches, medical education on AI is lacking. We aim to create a usable AI primer for medical education. We discuss how to generate a clinical question involving AI, what data are suitable for AI research, how to prepare a dataset for training and how to determine if the output has clinical utility. To illustrate this process, we focused on an example of how medical imaging is employed in designing a machine learning model. Our proposed medical education curriculum addresses AI's potential and limitations for enhancing clinicians' skills in research, applied statistics and care delivery.

The application of artificial intelligence (AI) to healthcare has generated increasing interest in recent years; however, medical education on AI is lacking. With this primer, we provide an overview on how to understand AI, gain exposure to machine learning (ML) and how to develop research questions utilizing ML. Using an example of a ML application in imaging, we provide a practical approach to understanding and executing a ML analysis. Our proposed medical education curriculum provides a framework for healthcare education which we hope will propel healthcare institutions to implement ML laboratories and training environments and improve access to this transformative paradigm.