A comparison of the accuracy and feasibility of a low-cost mobile application versus higher-cost handheld 3D scanner for digital ear prosthetics.

Background: Facial prosthetics are an important means to rehabilitate patients with congenital or acquired facial defects. However, with a time-consuming manual workflow and workforce shortage, access to facial prosthetics is limited in Australia and worldwide, especially for rural and remote patients. Optical 3D scanning has been increasingly integrated in digitizing data.

Transcriptional profile of the rat cardiovascular system at single-cell resolution.

We sought to characterize cellular composition across the cardiovascular system of the healthy Wistar rat, an important model in preclinical cardiovascular research. We performed single-nucleus RNA sequencing (snRNA-seq) in 78 samples in 10 distinct regions, including the four chambers of the heart, ventricular septum, sinoatrial node, atrioventricular node, aorta, pulmonary artery, and pulmonary veins, which produced 505,835 nuclei. We identified 26 distinct cell types and additional subtypes, with different cellular composition across cardiac regions and tissue-specific transcription for each cell type.

Metacognition facilitates theory of mind through optimal weighting of trait inferences.

The ability to represent and infer accurately others' mental states, known as Theory of Mind (ToM), has been theorised to be associated with metacognitive ability. Here, we considered the role of metacognition in mental state inference through the lens of a recent theoretical approach to explaining ToM, the 'Mind-space' framework. The Mind-space framework posits that trait inference, representation of the qualities of the mind giving rise to the mental state, is important in forming accurate mental state inferences.

QUEST-AI: A System for Question Generation, Verification, and Refinement using AI for USMLE-Style Exams.

The United States Medical Licensing Examination (USMLE) is a critical step in assessing the competence of future physicians, yet the process of creating exam questions and study materials is both time-consuming and costly. While Large Language Models (LLMs), such as OpenAI's GPT-4, have demonstrated proficiency in answering medical exam questions, their potential in generating such questions remains underexplored. This study presents QUEST-AI, a novel system that utilizes LLMs to (1) generate USMLE-style questions, (2) identify and flag incorrect questions, and (3) correct errors in the flagged questions.