Mediator complex subunit 19 regulates the proliferation, migration and invasion of human breast cancer cells

Purpose: To investigate the therapeutic implication of mediator complex subunit 19 (Med19) in breast cancer cells. Methods: The mRNA expression of Med19 was assayed using qRT-PCR. Cell viability was determined with 3-(4,5-dimethylthiazol-2-yl)2,5-diphenyl tetrazolium bromide (MTT) assay, while 4′,6-diamidino-2- phenylindole (DAPI) and annexin V/propidium iodide (PI) assays were used for determination of apoptosis. Wound healing and Transwell assays were used for the determination of cell migration and invasion. Western blotting analysis was used for assay of protein expression levels. Results: The results showed that Med19 was significantly (p < 0.05) upregulated in human breast cancer cell lines, relative to normal cells. The up-regulations ranged from 3.7-fold in UACC-2087 cells to 6.4-fold in BT-20 cells. Moreover, Med19 silencing caused significant decrease in the proliferation of BT-20 breast cancer cells (p < 0.05). The inhibition of cell proliferation was due to the induction of apoptosis, as was evident in increased Bax/Bcl-2 ratio. Annexin V/PI staining revealed 6 % apoptosis in si-NC-transfected, and about 13.30 % in si-Med19-transfected BT-20 cells. Wound healing and Transwell assays revealed that the invasion of BT-20 breast cancer cells significantly decreased upon Med19 silencing. Conclusion: Med19 regulates the proliferation, migration and invasion of human breast cancer cells. Thus, Med19 may be beneficial in the treatment of breast cancer

LPI Radar Signal Recognition Based on Dual-Channel CNN and Feature Fusion

The accuracy of low probability of intercept (LPI) radar waveform recognition is an important and challenging problem in electronic warfare. Aiming at the problem of the difficulty in feature extraction and the low recognition rates of the LPI radar signal under a low signal-to-noise ratio, and inspired by the symmetry theory, we propose a new approach for the LPI radar signal recognition method based on a dual-channel convolutional neural network (CNN) and feature fusion. Our new approach contains three main modules: the preprocessing module that converts the LPI radar waveforms into two-dimensional time-frequency images using the Choi–Williams distribution (CWD) transformation and performs image binarization, the feature extraction module that extracts different features obtained from the images, and the recognition module that utilizes a multi-layer perceptron (MLP) network to fuse these features and distinguish the type of LPI radar signals. In the feature extraction module, a two-channel CNN model is proposed that extracts Histogram of Oriented Gradients (HOG) features and deep features from time-frequency images, respectively. Finally, the recognition module recognizes the radar signals using a Softmax classifier based on the fused features from two channels. The experimental results from 12 types of LPI radar signals prove the superiority and robustness of the proposed model. Its overall recognition rate reaches 97% when the signal-to-noise ratio is −6 dB

LPI Radar Signal Recognition Based on Dual-Channel CNN and Feature Fusion

The accuracy of low probability of intercept (LPI) radar waveform recognition is an important and challenging problem in electronic warfare. Aiming at the problem of the difficulty in feature extraction and the low recognition rates of the LPI radar signal under a low signal-to-noise ratio, and inspired by the symmetry theory, we propose a new approach for the LPI radar signal recognition method based on a dual-channel convolutional neural network (CNN) and feature fusion. Our new approach contains three main modules: the preprocessing module that converts the LPI radar waveforms into two-dimensional time-frequency images using the Choi–Williams distribution (CWD) transformation and performs image binarization, the feature extraction module that extracts different features obtained from the images, and the recognition module that utilizes a multi-layer perceptron (MLP) network to fuse these features and distinguish the type of LPI radar signals. In the feature extraction module, a two-channel CNN model is proposed that extracts Histogram of Oriented Gradients (HOG) features and deep features from time-frequency images, respectively. Finally, the recognition module recognizes the radar signals using a Softmax classifier based on the fused features from two channels. The experimental results from 12 types of LPI radar signals prove the superiority and robustness of the proposed model. Its overall recognition rate reaches 97% when the signal-to-noise ratio is −6 dB

Immunohistochemical Study of NR2C2, BTG2, TBX19, and CDK2 Expression in 31 Paired Primary/Recurrent Nonfunctioning Pituitary Adenomas

This study investigated potential markers for predicting nonfunctioning pituitary adenoma (NFPA) invasion and recurrence by high-throughput tissue microarray analyses. We retrospectively studied two groups of patients: 60 nonrecurrent NFPA cases that included noninvasion and invasion subtypes and 43 recurrent cases that included primary NFPA. A total of 31 paired patient samples were evaluated (12 patients with one surgery and 31 who had undergone two operations, with both tumors analyzed). Expressions of nuclear receptor subfamily 2 group C member 2 (NR2C2), B cell translocation gene 2, T-box-19 (TBX19), and cyclin-dependent kinase 2 (CDK2) in surgically resected specimens were assessed by immunohistochemistry. The relationships between marker expression and clinical characteristics including age, sex, tumor volume, and follow-up time were analyzed. Tumor volume and invasion as well as follow-up time were significantly associated with invasion and recurrence (P < 0.01). Of the 60 nonrecurrent samples, 15/41 and 13/19 showed high NR2C2 expression in the noninvasion and invasion groups, respectively (χ2 =5.287, P = 0.021). NR2C2 was also overexpressed in 43 primary recurrent cases (χ2 =5.433, P = 0.02), whereas CDK2 (χ2 = 11.242, P = 0.001) and TBX19 (χ2 = 4.875, P = 0.027) were downregulated. In the 31 paired samples, NR2C2 was more highly expressed in the recurrent as compared to the primary tumor. High NR2C2 expression was associated with NFPA invasion, recurrence, and progression, while TBX19 and CDK2 were associated with NFPA recurrence

A model of metabolic syndrome and related diseases with intestinal endotoxemia in rats fed a high fat and high sucrose diet.

We sought develop and characterize a diet-induced model of metabolic syndrome and its related diseases.The experimental animals (Spague-Dawley rats) were randomly divided into two groups, and each group was fed a different feed for 48 weeks as follows: 1) standard control diet (SC), and 2) a high sucrose and high fat diet (HSHF). The blood, small intestine, liver, pancreas, and adipose tissues were sampled for analysis and characterization.Typical metabolic syndrome (MS), non-alcoholic fatty liver disease (NAFLD), and type II diabetes (T2DM) were common in the HSHF group after a 48 week feeding period. The rats fed HSHF exhibited signs of obesity, dyslipidemia, hyperglycaemia, glucose intolerance, and insulin resistance (IR). At the same time, these animals had significantly increased levels of circulating LPS, TNFα, and IL-6 and increased ALP in their intestinal tissue homogenates. These animals also showed a significant reduction in the expression of occluding protein. The HSHF rats showed fatty degeneration, inflammation, fibrosis, cirrhosis, and lipid accumulation when their liver pathologies were examined. The HSHF rats also displayed increased islet diameters from 12 to 24 weeks, while reduced islet diameters occurred from 36 to 48 weeks with inflammatory cell infiltration and islet fat deposition. The morphometry of adipocytes in HSHF rats showed hypertrophy and inflammatory cell infiltration. HSHF CD68 analysis showed macrophage infiltration and significant increases in fat and pancreas size. HSHF Tunel analysis showed significant increases in liver and pancreas cell apoptosis.This work demonstrated the following: 1) a characteristic rat model of metabolic syndrome (MS) can be induced by a high sucrose and high fat diet, 2) this model can be used to research metabolic syndrome and its related diseases, such as NAFLD and T2DM, and 3) intestinal endotoxemia (IETM) may play an important role in the pathogenesis of MS and related diseases, such as NAFLD and T2DM

The changes of weight, Lee's index, liver index and fat percentages in different groups per month.

<p>A. Comparison of rats. B. Comparison of body weight. C. Comparison of liver index. D. Comparison of fat percentages. E. Comparison of Lee's index. SC: standard chow group; HSHF: high sugar and high fat diet group; ^a p<0.05 vs normal control; ^b p<0.05 vs 12^th week group; ^c p<0.05 vs 24^th week group; ^b p<0.05 vs 36^th week group.</p

Observation of pancreas apoptosis.

<p>A. Pancreas in SC group. B. Pancreas in 12 weeks in HSHF group. C. Pancreas in 24 weeks in HSHF group. D. Pancreas in 36 weeks in HSHF group. E. Pancreas in 48 weeks in HSHF group (HE staining, ×100). F. Percentage of apoptosis in pancreas(%).^a p<0.05 vs normal control; ^b p<0.05 vs 12^th week group; ^c p<0.05 vs 24^th week group; ^d p<0.05 vs 36^th week group.</p

Sudan staining for pancreas.

<p>A. pancreas in 12 weeks in HSHF group; B. pancreas in 24 weeks in HSHF group; C. pancreas in 36 weeks in HSHF group; D. pancreas in 48 weeks in HSHF group (Sudan staining, ×100).</p

HE staining for intestine lesion.

<p>A. Normal morphology of intestine in standard chow group. B. Intestine lesion at 12 weeks in HSHF group. C. Intestine lesion at 24 weeks in HSHF group. D. Intestine lesion at 36 weeks in HSHF group. E. Intestine lesion at 48 weeks in HSHF group (×100).</p

HE staining for histological examination of fat.

<p>A. fat in SC group. B. fat in 12 weeks in HSHF group. C. fat in 24 weeks in HSHF group. D. fat in 36 weeks in HSHF group. E. fat in 48 weeks in HSHF group (HE staining, ×100). F. comparison of adipocyte areas. ^a p<0.05 vs normal control; ^b p<0.05 vs 12^th week group; ^c p<0.05 vs 24^th week group; ^d p<0.05 vs 36^th week group.</p