Bariatric surgery prior to pancreas transplantation: a retrospective matched case-control study.

Background: The clinical impact of bariatric surgery (BS) prior to pancreas transplantation (PTx) is unclear.

Setting: University of Minnesota Hospital, Minneapolis, MN.

Methods: This was a single center retrospective case-controlled study of all patients January 1, 1998 and May 1, 2024 with a history of BS prior to PTx.

Vitrification and rapid rewarming of precision-cut liver slices for pharmacological and biomedical research.

Background And Aims: High-throughput in vitro pharmacological toxicity testing is essential for drug discovery. Precision-cut liver slices (PCLS) provide a robust system for screening that is more representative of the complex 3D structure of the whole liver than isolated hepatocytes. However, PCLS are not available as off-the-shelf products, significantly limiting their translational potential.

Impact of a national dementia research consortium: The Canadian Consortium on Neurodegeneration in Aging (CCNA).

The Canadian Consortium on Neurodegeneration in Aging (CCNA) was created by the Canadian federal government through its health research funding agency, the Canadian Institutes for Health Research (CIHR), in 2014, as a response to the G7 initiative to fight dementia. Two five-year funding cycles (2014-2019; 2019-2024) have occurred following peer review, and a third cycle (Phase 3) has just begun. A unique construct was mandated, consisting of 20 national teams in Phase I and 19 teams in Phase II (with research topics spanning from basic to clinical science to health resource systems) along with cross-cutting programs to support them.

SMART-PET: a Self-SiMilARiTy-aware generative adversarial framework for reconstructing low-count [18F]-FDG-PET brain imaging.

Introduction: In Positron Emission Tomography (PET) imaging, the use of tracers increases radioactive exposure for longitudinal evaluations and in radiosensitive populations such as pediatrics. However, reducing injected PET activity potentially leads to an unfavorable compromise between radiation exposure and image quality, causing lower signal-to-noise ratios and degraded images. Deep learning-based denoising approaches can be employed to recover low count PET image signals: nonetheless, most of these methods rely on structural or anatomic guidance from magnetic resonance imaging (MRI) and fails to effectively preserve global spatial features in denoised PET images, without impacting signal-to-noise ratios.