SUMMARY Adipose-resident invariant natural killer T (iNKT) cells are key players in metabolic regulation. iNKT cells are innate lipid sensors, and their activation, using their prototypic ligand α-galactosylceramide (αGalCer), induces weight loss and restores glycemic control in obesity. Here, iNKT activation induced fibroblast growth factor 21 (FGF21) production and thermogenic browning of white fat. Complete metabolic analysis revealed that iNKT cell activation induced increased body temperature, V02, VC02, and fatty acid oxidation, without affecting food intake or activity. FGF21 induction played a major role in iNKT cell-induced weight loss, as FGF21 null mice lost significantly less weight after αGalCer treatment. The glucagon-like peptide 1 (GLP-1) receptor agonist, liraglutide, also activated iNKT cells in humans and mice. In iNKT-deficient mice, liraglutide promoted satiety but failed to induce FGF21, resulting in less weight loss. These findings reveal an iNKT cell-FGF21 axis that defines a new immune-mediated pathway that could be targeted for glycemic control and weight regulation.

Trial recruitment and outcome measures are affected by inter-observer variation in UC activity indices, and endoscopic scoring was the component most susceptible to variation.

Testicular cancer is the most common cancer in men aged from 15 to 34 years. Lymphovascular invasion refers to the presence of tumours within endothelial-lined lymphatic or vascular channels, and has been shown to have prognostic significance in testicular germ cell tumours. In non-seminomatous tumours, lymphovascular invasion is the most powerful prognostic factor for stage 1 disease. For the pathologist, searching multiple slides for lymphovascular invasion can be highly time-consuming. The aim of this retrospective study was to develop and assess an artificial intelligence algorithm that can identify areas suspicious for lymphovascular invasion in histological digital whole slide images. Areas of possible lymphovascular invasion were annotated in a total of 184 whole slide images of haematoxylin and eosin (H&E) stained tissue from 19 patients with testicular germ cell tumours, including a mixture of seminoma and non-seminomatous cases. Following consensus review by specialist uropathologists, we trained a deep learning classifier for automatic segmentation of areas suspicious for lymphovascular invasion. The classifier identified 34 areas within a validation set of 118 whole slide images from 10 patients, each of which was reviewed by three expert pathologists to form a majority consensus. The precision was 0.68 for areas which were considered to be appropriate to flag, and 0.56 for areas considered to be definite lymphovascular invasion. An artificial intelligence tool which highlights areas of possible lymphovascular invasion to reporting pathologists, who then make a final judgement on its presence or absence, has been demonstrated as feasible in this proof-of-concept study. Further development is required before clinical deployment.