- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

Dopaminergic PET to SPECT domain adaptation: a cycle GAN translation approach.

Purpose: Dopamine transporter imaging is routinely used in Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) diagnosis. While [C]CFT PET is prevalent in Asia with a large APS database, Europe relies on [I]FP-CIT SPECT with limited APS data. Our aim was to develop a deep learning-based method to convert [C]CFT PET images to [I]FP-CIT SPECT images, facilitating multicenter studies and overcoming data scarcity to promote Artificial Intelligence (AI) advancements.

Preclinical development of lyophilized self-replicating RNA vaccines for COVID-19 and malaria with improved long-term thermostability.

Messenger RNA (mRNA) vaccines against COVID-19 have demonstrated high efficacy and rapid deployment capability to target emerging infectious diseases. However, the need for ultra-low temperature storage made the distribution of LNP/mRNA vaccines to regions with limited resources impractical. This study explores the use of lyophilization to enhance the stability of self-replicating mRNA (repRNA) vaccines, allowing for their storage at non-freezing temperatures such as 2-8 °C or room temperature (25 °C).

Imaging differentiation of solid pseudopapillary neoplasms and neuroendocrine neoplasms of the pancreas.

Purpose: The present study aimed to compare the computed tomography (CT) and magnetic resonance imaging (MRI) features of solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine neoplasms (pNENs).

Method: Lesion imaging features of 39 patients with SPNs and 127 patients with pNENs were retrospectively extracted from 104 CT and 91 MRI scans.

Results: Compared to pNEN patients, SPN patients were significantly younger (mean age 51.