Bacterial translocation occurs in ESRD patients and is associated with microinflammation in end stage renal disease.

ESRD, rather than hemodialysis, primarily contributes to BT and microinflammation in ESRD patients. Hemodialysis may exaggerate microinflammation in ESRD patients to some extent.

Gut microbiome dysbiosis occurs and bacteria translocate to the systemic and lymph circulation, thereby contributing to microinflammation in experimental uremia.

Tripartite motif-containing 14 (TRIM14) is a member of the TRIM protein family which has been implicated in several critical processes and is dysregulated in human cancers in a cancer-specific trend. However, its expression and function in human gastric cancer (GC) are still largely unknown. In this study, we confirmed for the first time that TRIM14 mRNA and protein were upregulated in GC tissues and cell lines as determined by qRT-PCR and western blot analysis. Clinical data disclosed that high TRIM14 expression was significantly associated with aggressive prognostic features, including advanced TNM stage and lymph node metastasis. In regards to 5-year survival, TRIM14 served as a potential prognostic marker for GC. Notably, TRIM14 promoted migration, invasion as measured by Transwell and epithelial-to-mesenchymal transition (EMT) as determined by western blot analysis and immunofluorescence (IF) in vitro and in vivo. Moreover, TRIM14 induced protein kinase B (AKT) pathway activation, and inhibition of AKT reversed the TRIM14-induced promotive effects on cell migration, invasion and EMT progression. Furthermore, we demonstrated that TRIM14 expression was regulated by miR-195-5p. miR-195-5p exerted an inhibitory role in GC migration and invasion. Finally, we confirmed that alteration of TRIM14 expression abolished the effects of miR-195-5p on GC cells. Conclusively, our results demonstrated that TRIM14 functions as an oncogene in regulating EMT and metastasis of GC via activating AKT signaling, which was regulated by miR-195-5p, supporting its potential utility as a therapeutic target for GC.

Backgrounds: Conventional ultrasound manual scanning and artificial diagnosis approaches in breast are considered to be operator-dependence, slight slow and error-prone. In this study, we used Automated Breast Ultrasound (ABUS) machine for the scanning, and deep convolutional neural network (CNN) technology, a kind of Deep Learning (DL) algorithm, for the detection and classification of breast nodules, aiming to achieve the automatic and accurate diagnosis of breast nodules. Methods: Two hundred and ninety-three lesions from 194 patients with definite pathological diagnosis results (117 benign and 176 malignancy) were recruited as case group. Another 70 patients without breast diseases were enrolled as control group. All the breast scans were carried out by an ABUS machine and then randomly divided into training set, verification set and test set, with a proportion of 7:1:2. In the training set, we constructed a detection model by a three-dimensionally U-shaped convolutional neural network (3D U-Net) architecture for the purpose of segment the nodules from background breast images. Processes such as residual block, attention connections, and hard mining were used to optimize the model while strategies of random cropping, flipping and rotation for data augmentation. In the test phase, the current model was compared with those in previously reported studies. In the verification set, the detection effectiveness of detection model was evaluated. In the classification phase, multiple convolutional layers and fully-connected layers were applied to set up a classification model, aiming to identify whether the nodule was malignancy.Results: Our detection model yielded a sensitivity of 91% and 1.92 false positive subjects per automatically scanned imaging. The classification model achieved a sensitivity of 87.0%, a specificity of 88.0% and an accuracy of 87.5%.Conclusions: Deep CNN combined with ABUS maybe a promising tool for easy detection and accurate diagnosis of breast nodule.