Generation of an Ihh-mKate2-Dre knock-in mouse line.

Indian hedgehog (Ihh), a member of the Hh family, plays important roles in vertebrate development and homeostasis. To improve our understanding of the function of Ihh-expressing cells and their progeny as well, we generate an Ihh-mKate2tomm20 -Dre knock-in mouse line that can label Ihh-positive cells with a fluorescence protein mKate2 and trace Ihh-positive cells and their progeny via Dre-mediated recombination. Consistent with previous reports, we verified Ihh expression in hypertrophic chondrocytes of growth plate and granulosa cells of ovarian follicles by mKate2 immunostaining, and meanwhile confirmed Dre activity in these cells via a Dre reporter mouse line Rosa26-confetti2. We also found, for the first time, that Ihh can mark some cell types, including retinal ganglion cells, Purkinje cells, and gallbladder epithelial cells. Taken together, the Ihh-mKate2tomm20 -Dre mouse is a genetic tool for examining the precise expression profile of Ihh and tracing Ihh-expressing cells and their progeny.

Identification of the metaphyseal skeletal stem cell building trabecular bone.

Skeletal stem cells (SSCs) that are capable of self-renewal and multipotent differentiation contribute to bone development and homeostasis. Several populations of SSCs at different skeletal sites have been reported. Here, we identify a metaphyseal SSC (mpSSC) population whose transcriptional landscape is distinct from other bone mesenchymal stromal cells (BMSCs). These mpSSCs are marked by Sstr2 or Pdgfrb+Kitl-, located just underneath the growth plate, and exclusively derived from hypertrophic chondrocytes (HCs). These HC-derived mpSSCs have properties of self-renewal and multipotency in vitro and in vivo, producing most HC offspring postnatally. HC-specific deletion of Hgs, a component of the endosomal sorting complex required for transport, impairs the HC-to-mpSSC conversion and compromises trabecular bone formation. Thus, mpSSC is the major source of BMSCs and osteoblasts in bone marrow, supporting the postnatal trabecular bone formation.

[Spatial and temporal expression pattern of somatostatin receptor 2 in mouse].

Somatostatin (SST) is an inhibitory polypeptide hormone that plays an important role in a variety of biological processes. Somatostatin receptor 2 (SSTR2) is the most widely expressed somatostatin receptor. However, the specific cell types expressing Sstr2 in the tissues have not been investigated. In this study, we detected the expression pattern of SSTR2 protein in mouse at different development stages, including the embryonic 15.5 days and the postnatal 1, 7, 15 days as well as 3 and 6 months, by multicolour immunofluorescence analyses. We found that Sstr2 was expressed in some specific cells types of several tissues, including the neuronal cells and astrocytes in the brain, the mesenchymal cells, the hematopoietic cells, the early hematopoietic stem cells, and the B cells in the bone marrow, the macrophages, the type Ⅱ alveolar epithelial cells, and the airway ciliated cells in the lung, the epithelial cells and the neuronal cells in the intestine, the hair follicle cells, the gastric epithelial cells, the hematopoietic stem cells and the nerve fibre in the spleen, and the tubular epithelial cells in the kidney. This study identified the specific cell types expressing Sstr2 in mouse at different developmental stages, providing new insights into the physiological function of SST and SSTR2 in several cell types.

Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence.

BACKGROUND: The sensitivity of endoscopy in diagnosing chronic atrophic gastritis is only 42%, and multipoint biopsy, despite being more accurate, is not always available. AIMS: This study aimed to construct a convolutional neural network to improve the diagnostic rate of chronic atrophic gastritis. METHODS: We collected 5470 images of the gastric antrums of 1699 patients and labeled them with their pathological findings. Of these, 3042 images depicted atrophic gastritis and 2428 did not. We designed and trained a convolutional neural network-chronic atrophic gastritis model to diagnose atrophic gastritis accurately, verified by five-fold cross-validation. Moreover, the diagnoses of the deep learning model were compared with those of three experts. RESULTS: The diagnostic accuracy, sensitivity, and specificity of the convolutional neural network-chronic atrophic gastritis model in diagnosing atrophic gastritis were 0.942, 0.945, and 0.940, respectively, which were higher than those of the experts. The detection rates of mild, moderate, and severe atrophic gastritis were 93%, 95%, and 99%, respectively. CONCLUSION: Chronic atrophic gastritis could be diagnosed by gastroscopic images using the convolutional neural network-chronic atrophic gastritis model. This may greatly reduce the burden on endoscopy physicians, simplify diagnostic routines, and reduce costs for doctors and patients.