Parameterization of the stress-strain relation for modeling wave propagation in nearly incompressible transversely isotropic materials.

The stress-strain relation in a transversely isotropic (TI) material is described by five independent parameters. In the incompressible limit, only three parameters are required to describe shear wave propagation. Existing material parameterization models are not ideal for the analysis of wave propagation in the nearly incompressible TI (NITI) regime due to difficult-to-interpret parameters, complicated forms of the stiffness matrix elements, or the lack of five independent parameters.

When Black and White Turns Gray: Navigating the Ethical Challenges of Implementing Shared Infant Feeding Decisions for Persons Living with Human Immunodeficiency Virus in the United States.

In 2023, US guidelines for feeding perinatally human immunodeficiency virus (HIV)-exposed infants were revised to encourage collaborative decision-making in lieu of categorical proscription of breastfeeding. This change advances autonomy and health equity for persons living with HIV in the United States, for the first time supporting those who prioritize the maternal and infant benefits of breastfeeding in the setting of effective, well-established HIV risk mitigation. The authors review key moral dilemmas facing clinicians and patients who must navigate the reversal of longstanding dogma against breastfeeding and provide recommendations for implementation of a new ethical paradigm.

Evaluation of the Master's in Genomic Medicine framework: A national, multiprofessional program to educate health care professionals in NHS England.

Purpose: Genomic medicine is revolutionizing health care but requires health care professionals to update their understanding of genomics and its application to clinical practice for successful implementation. To meet this need, Health Education England developed the Master's in Genomic Medicine, a national multiprofessional program to increase genomic literacy in the National Health Service workforce. This study summarizes an evaluation of the program, which will inform its future development.

Multiparametric Ultrasound Imaging of Prostate Cancer Using Deep Neural Networks.

Objective: A deep neural network (DNN) was trained to generate a multiparametric ultrasound (mpUS) volume from four input ultrasound-based modalities (acoustic radiation force impulse [ARFI] imaging, shear wave elasticity imaging [SWEI], quantitative ultrasound-midband fit [QUS-MF], and B-mode) for the detection of prostate cancer.

Methods: A DNN was trained using co-registered ARFI, SWEI, MF, and B-mode data obtained in men with biopsy-confirmed prostate cancer prior to radical prostatectomy (15 subjects, comprising 980,620 voxels). Data were obtained using a commercial scanner that was modified to allow user control of the acoustic beam sequences and provide access to the raw image data.