Effect of Type 2 Diabetes and Impaired Glucose Tolerance on Digestive Enzymes and Glucose Absorption in the Small Intestine of Young Rats.

The reactions of intestinal functional parameters to type 2 diabetes at a young age remain unclear. The study aimed to assess changes in the activity of intestinal enzymes, glucose absorption, transporter content (SGLT1, GLUT2) and intestinal structure in young Wistar rats with type 2 diabetes (T2D) and impaired glucose tolerance (IGT). To induce these conditions in the T2D (n = 4) and IGT (n = 6) rats, we used a high-fat diet and a low dose of streptozotocin. Rats fed a high-fat diet (HFD) (n = 6) or a standard diet (SCD) (n = 6) were used as controls. The results showed that in T2D rats, the ability of the small intestine to absorb glucose was higher in comparison to HFD rats (p < 0.05). This was accompanied by a tendency towards an increase in the number of enterocytes on the villi of the small intestine in the absence of changes in the content of SGLT1 and GLUT2 in the brush border membrane of the enterocytes. T2D rats also showed lower maltase and alkaline phosphatase (AP) activity in the jejunal mucosa compared to the IGT rats (p < 0.05) and lower AP activity in the colon contents compared to the HFD (p < 0.05) and IGT (p < 0.05) rats. Thus, this study provides insights into the adaptation of the functional and structural parameters of the small intestine in the development of type 2 diabetes and impaired glucose tolerance in young representatives.

First Trimester video Saliency Prediction using CLSTMU-NET with Stochastic Augmentation.

In this paper we develop a multi-modal video analysis algorithm to predict where a sonographer should look next. Our approach uses video and expert knowledge, defined by gaze tracking data, which is acquired during routine first-trimester fetal ultrasound scanning. Specifically, we propose a spatio-temporal convolutional LSTMU-Net neural network (cLSTMU-Net) for video saliency prediction with stochastic augmentation. The architecture design consists of a U-Net based encoder-decoder network and a cLSTM to take into account temporal information. We compare the performance of the cLSTMU-Net alongside spatial-only architectures for the task of predicting gaze in first trimester ultrasound videos. Our study dataset consists of 115 clinically acquired first trimester US videos and a total of 45, 666 video frames. We adopt a Random Augmentation strategy (RA) from a stochastic augmentation policy search to improve model performance and reduce over-fitting. The proposed cLSTMU-Net using a video clip of 6 frames outperforms the baseline approach on all saliency metrics: KLD, SIM, NSS and CC (2.08, 0.28, 4.53 and 0.42 versus 2.16, 0.27, 4.34 and 0.39).

Vascular region-specific changes in arterial tone in rats with type 2 diabetes mellitus: Opposite responses of mesenteric and femoral arteries to acetylcholine and 5-hydroxytryptamine.

AIMS: Type 2 diabetes mellitus (T2DM) ranks in the top 10 causes of mortality worldwide. The key factor of T2DM vascular complications is endothelial dysfunction. It is characterized by the vessels motor activity disruption and endothelium-derived factors imbalance. The blood vessels morphological and molecular heterogeneity greatly affects the changes occurring in T2DM. Therefore, we conducted a comparative study of vascular bed changes occurring in T2DM. MAIN METHODS: Male Wistar rats were fed a high-fat diet for 20 weeks, followed by a single streptozotocin injection (20 mg/kg). T2DM was confirmed with an oral glucose tolerance test. KEY FINDINGS: A dose-dependent contraction study showed an increase in third-order mesenteric arterioles response to serotonin but not to phenylephrine. These vessels also exhibited a decrease in acetylcholine-dependent relaxation and an increase in guanylate cyclase function. At the same time, the femoral arteries showed a tendency for increased acetylcholine-dependent relaxation. The blood plasma analysis revealed low bioavailable nitric oxide and high levels of endothelin-1 and ROS. SIGNIFICANCE: This knowledge, in conjunction with the features of the T2DM course, can allow further targeted approaches development for the prevention and treatment of vascular complications occurring in the disease.

First Trimester Gaze Pattern Estimation Using Stochastic Augmentation Policy Search for Single Frame Saliency Prediction.

While performing an ultrasound (US) scan, sonographers direct their gaze at regions of interest to verify that the correct plane is acquired and to interpret the acquisition frame. Predicting sonographer gaze on US videos is useful for identification of spatio-temporal patterns that are important for US scanning. This paper investigates utilizing sonographer gaze, in the form of gaze-tracking data, in a multimodal imaging deep learning framework to assist the analysis of the first trimester fetal ultrasound scan. Specifically, we propose an encoderdecoder convolutional neural network with skip connections to predict the visual gaze for each frame using 115 first trimester ultrasound videos; 29,250 video frames for training, 7,290 for validation and 9,126 for testing. We find that the dataset of our size benefits from automated data augmentation, which in turn, alleviates model overfitting and reduces structural variation imbalance of US anatomical views between the training and test datasets. Specifically, we employ a stochastic augmentation policy search method to improve segmentation performance. Using the learnt policies, our models outperform the baseline: KLD, SIM, NSS and CC (2.16, 0.27, 4.34 and 0.39 versus 3.17, 0.21, 2.92 and 0.28).