Carboxyl-terminal sequences in APOA5 are important for suppressing ANGPTL3/8 activity.

Apolipoprotein AV (APOA5) lowers plasma triglyceride (TG) levels by binding to the angiopoietin-like protein 3/8 complex (ANGPTL3/8) and suppressing its capacity to inhibit lipoprotein lipase (LPL) catalytic activity and its ability to detach LPL from binding sites within capillaries. However, the sequences in APOA5 that are required for suppressing ANGPTL3/8 activity have never been defined. A clue to the identity of those sequences was the presence of severe hypertriglyceridemia in two patients harboring an APOA5 mutation that truncates APOA5 by 35 residues ("APOA5Δ35"). We found that wild-type (WT) human APOA5, but not APOA5Δ35, suppressed ANGPTL3/8's ability to inhibit LPL catalytic activity. To pursue that finding, we prepared a mutant mouse APOA5 protein lacking 40 C-terminal amino acids ("APOA5Δ40"). Mouse WT-APOA5, but not APOA5Δ40, suppressed ANGPTL3/8's capacity to inhibit LPL catalytic activity and sharply reduced plasma TG levels in mice. WT-APOA5, but not APOA5Δ40, increased intracapillary LPL levels and reduced plasma TG levels in Apoa5-/- mice (where TG levels are high and intravascular LPL levels are low). Also, WT-APOA5, but not APOA5Δ40, blocked the ability of ANGPTL3/8 to detach LPL from cultured cells. Finally, an antibody against a synthetic peptide corresponding to the last 26 amino acids of mouse APOA5 reduced intracapillary LPL levels and increased plasma TG levels in WT mice. We conclude that C-terminal sequences in APOA5 are crucial for suppressing ANGPTL3/8 activity in vitro and for regulating intracapillary LPL levels and plasma TG levels in vivo.

Is Automatic Tumor Segmentation on Whole-Body 18F-FDG PET Images a Clinical Reality?

The integration of automated whole-body tumor segmentation using 18F-FDG PET/CT images represents a pivotal shift in oncologic diagnostics, enhancing the precision and efficiency of tumor burden assessment. This editorial examines the transition toward automation, propelled by advancements in artificial intelligence, notably through deep learning techniques. We highlight the current availability of commercial tools and the academic efforts that have set the stage for these developments. Further, we comment on the challenges of data diversity, validation needs, and regulatory barriers. The role of metabolic tumor volume and total lesion glycolysis as vital metrics in cancer management underscores the significance of this evaluation. Despite promising progress, we call for increased collaboration across academia, clinical users, and industry to better realize the clinical benefits of automated segmentation, thus helping to streamline workflows and improve patient outcomes in oncology.

Extracting value from total-body PET/CT image data - the emerging role of artificial intelligence.

The evolution of Positron Emission Tomography (PET), culminating in the Total-Body PET (TB-PET) system, represents a paradigm shift in medical imaging. This paper explores the transformative role of Artificial Intelligence (AI) in enhancing clinical and research applications of TB-PET imaging. Clinically, TB-PET's superior sensitivity facilitates rapid imaging, low-dose imaging protocols, improved diagnostic capabilities and higher patient comfort. In research, TB-PET shows promise in studying systemic interactions and enhancing our understanding of human physiology and pathophysiology. In parallel, AI's integration into PET imaging workflows-spanning from image acquisition to data analysis-marks a significant development in nuclear medicine. This review delves into the current and potential roles of AI in augmenting TB-PET/CT's functionality and utility. We explore how AI can streamline current PET imaging processes and pioneer new applications, thereby maximising the technology's capabilities. The discussion also addresses necessary steps and considerations for effectively integrating AI into TB-PET/CT research and clinical practice. The paper highlights AI's role in enhancing TB-PET's efficiency and addresses the challenges posed by TB-PET's increased complexity. In conclusion, this exploration emphasises the need for a collaborative approach in the field of medical imaging. We advocate for shared resources and open-source initiatives as crucial steps towards harnessing the full potential of the AI/TB-PET synergy. This collaborative effort is essential for revolutionising medical imaging, ultimately leading to significant advancements in patient care and medical research.

Tirzepatide modulates the regulation of adipocyte nutrient metabolism through long-acting activation of the GIP receptor.

Tirzepatide, a glucose-dependent insulinotropic polypeptide/glucagon-like peptide 1 receptor (GIPR/GLP-1R) agonist, has, in clinical trials, demonstrated greater reductions in glucose, body weight, and triglyceride levels compared with selective GLP-1R agonists in people with type 2 diabetes (T2D). However, cellular mechanisms by which GIPR agonism may contribute to these improved efficacy outcomes have not been fully defined. Using human adipocyte and mouse models, we investigated how long-acting GIPR agonists regulate fasted and fed adipocyte functions. In functional assays, GIPR agonism enhanced insulin signaling, augmented glucose uptake, and increased the conversion of glucose to glycerol in a cooperative manner with insulin; however, in the absence of insulin, GIPR agonists increased lipolysis. In diet-induced obese mice treated with a long-acting GIPR agonist, circulating triglyceride levels were reduced during oral lipid challenge, and lipoprotein-derived fatty acid uptake into adipose tissue was increased. Our findings support a model for long-acting GIPR agonists to modulate both fasted and fed adipose tissue function differentially by cooperating with insulin to augment glucose and lipid clearance in the fed state while enhancing lipid release when insulin levels are reduced in the fasted state.