No abstract

MicroRNAs (miRNAs) play important roles in transcriptional regulation by targeting the 3'-UTR of target genes which participate in various biological processes. We aimed to investigate the potential role of miR-28-5p in the process of ovarian cancer development through regulating N4BP1. We found that the mRNA expression level of miR-28-5p was significantly increased in ovarian cancer tissues in comparison with adjacent ovarian tissues by qRT-PCR (P<0.0001). We established that miR-28-5p promoted the progression of ovarian cancer cell proliferation using colony forming assay and MTT assay. Wound healing assay and the migration and invasion assay showed that miR-28-5p accelerated the migration and invasion abilities of ovarian cancer cells. Simultaneously, we showed that miR-28-5p promoted ovarian cancer cell cycle, and inhibited apoptosis by flow cytometry in vitro. Furthermore, the results showed that miR-28-5p promoted the growth of ovarian tumor by tumor formation assay in vivo. The results of western blot analysis indicated that miR-28-5p promoted the protein expression level of F-actin. Western blot analysis also demonstrated that miR-28-5p promoted the progress of epithelial-mesenchymal transition (EMT) in ovarian carcinoma cells. In addition, we found that miR-28-5p downregulated N4BP1 mRNA and protein expression by qRT-PCR and western blot analysis in human ovarian cancer. Therefore, our study indicated that miR-28-5p promoted the progression of ovarian cancer cell cycle, proliferation, migration and invasion, inhibited apoptosis, and induced the process of EMT through inhibition of N4BP1 in vitro. Moreover, miR-28-5p promoted the growth of ovarian tumor in vivo.

A series of macrocyclic analogues were designed and synthesized based on the cocrystal structure of small molecule plasma kallikrein (pKal) inhibitor, , with the pKal protease domain. This led to the discovery of a potent macrocyclic pKal inhibitor, with an IC of 2 nM for one olefinic isomer and 42.3 nM for the other olefinic isomer.

No abstract

Endometriosis affects 6-10% of healthy women of reproductive age. Therefore, it is important to study the molecular mechanism by which endometriosis develops. This study examined whether aberrant expression of LINC01541 contributes to the pathogenesis of endometriosis. Human endometrial stromal cells (ESCs) were stimulated with 10 nmol/L of 17β-Estradiol (17β-E2) to simulate ectopic cells found in endometriosis. Next, the levels of proteins related to the epithelialmesenchymal transition (EMT), cell invasion, and metastasis were investigated. The effects of LINCO1541 silencing and overexpression were also examined in ESCs. Cell proliferation and apoptosis were detected by cell counting kit-8 and flow cytometry assays, respectively. ESCs stimulated with 17β-E2 displayed increased levels of N-Cadherin and vimentin expression, but decreased levels of E-Cadherin expression. 17β-E2 promoted the migration and invasion of ESCs, and those affects were partially reversed by overexpression of LINC01541. Furthermore, silencing of LINC01541 attenuated apoptosis and promoted the EMT of ESCs, while overexpression of LINC01541 stimulated cell apoptosis, increased the levels of caspase 3 protein, and decreased the levels of B cell leukemia/lymphoma 2 protein. Overexpression of LINC01541 also decreased the expression of vascular endothelial growth factor A (VEGFA) by repressing the Wnt/β-catenin pathway. Our, results suggest that LINC01541 can inhibit the EMT process, metastasis of ESCs, and VEGFA expression by regulating the Wnt/β-catenin pathway, which may play an important role in the pathogenesis of endometriosis.

This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

By the merits of self-stability and low energy consumption, high temperature superconducting (HTS) maglev has the potential to become a novel type of transportation mode. As a key index to guarantee the lateral self-stability of HTS maglev, guiding force has strong non-linearity and is determined by multitudinous factors, and these complexities impede its further researches. Compared to traditional finite element and polynomial fitting method, the prosperity of deep learning algorithms could provide another guiding force prediction approach, but the verification of this approach is still blank. Therefore, this paper establishes 5 different neural network models (RBF, DNN, CNN, RNN, LSTM) to predict HTS maglev guiding force, and compares their prediction efficiency based on 3720 pieces of collected data. Meanwhile, two adaptively iterative algorithms for parameters matrix and learning rate adjustment are proposed, which could effectively reduce computing time and unnecessary iterations. And according to the results, it is revealed that, the DNN model shows the best fitting goodness, while the LSTM model displays the smoothest fitting curve on guiding force prediction. Based on this discovery, the effects of learning rate and iterations on prediction accuracy of the constructed DNN model are studied. And the learning rate and iterations at the highest guiding force prediction accuracy are 0.00025 and 90000, respectively. Moreover, the K-fold cross validation method is also applied to this DNN model, whose result manifests the generalization and robustness of this DNN model. The imperative of K-fold cross validation method to ensure universality of guiding force prediction model is likewise assessed. This paper firstly combines HTS maglev guiding force prediction with deep learning algorithms considering different field cooling height, real-time magnetic flux density, liquid nitrogen temperature and motion direction of bulk. Additionally, this paper gives a convenient and efficient method for HTS guiding force prediction and parameter optimization.

No abstract