Whole personhood in medical education: Visual thinking strategy, close reading, and creative practice with a diversity and equity lens.

Purpose: To create and implement a Whole Personhood in Medical Education curriculum including Visual Thinking Strategies (VTS), close reading, and creative practice that features creative works by BIPOC, persons with disability, and/or LGBTQ + individuals that aligns with educational competencies.

Materials And Methods: Curriculum design by an interdisciplinary team made up of physician educators, medical sociologist, digital collection librarian, and art museum educators. Prospective single arm intervention study at a single site academic teaching hospital.

Expanding the Scope of Narrative Medicine by Emphasizing Stories from Minoritized Communities: A Novel Facilitator Training Program.

Background: Narrative medicine (NM) emphasizes the vital role healthcare stories play in conveying patients' experiences and expanding health professionals' reflective capacity. Though predicated on inclusivity, social justice, and equality, NM programs do not tend to include communities with marginalized health narratives due to a paucity of trained facilitators.

Objective: To evaluate the impact of a novel virtual NM facilitator training intended to expand NM programming to minoritized communities.

Exquisite selectivity of griselimycin extends to beta subunit of DNA polymerases from Gram-negative bacterial pathogens.

Griselimycin, a cyclic depsidecapeptide produced by Streptomyces griseus, is a promising lead inhibitor of the sliding clamp component of bacterial DNA polymerases (β-subunit of Escherichia coli DNA pol III). It was previously shown to inhibit the Mycobacterium tuberculosis β-clamp with remarkably high affinity and selectivity - the peptide lacks any interaction with the human sliding clamp. Here, we used a structural genomics approach to address the prospect of broader-spectrum inhibition, in particular of β-clamps from Gram-negative bacterial targets.

Accelerated Phase Contrast MRI with Use of Resolution Enhancement Generative Adversarial Neural Network.

Background: Cardiovascular magnetic resonance (CMR) phase-contrast is used to quantify blood flow. We sought to develop a complex-difference reconstruction for inline super-resolution of phase-contrast (CRISPFlow) to accelerate phase-contrast imaging.

Methods: CRISPFlow was built on the super-resolution generative adversarial network.

Late Gadolinium Enhancement CMR with Generative AI.

Background: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging enables imaging of scar/fibrosis and is a cornerstone of most CMR imaging protocols. CMR imaging can benefit from image acceleration; however, image acceleration in LGE remains challenging due to its limited signal-to-noise ratio. In this study, we sought to evaluate a rapid 2D LGE imaging protocol using a generative artificial intelligence (AI) algorithm with inline reconstruction.