The present study aimed to assess the protective effect of epigallocatechingallate (EGCG) against myocardial injury in a mouse model of heart failure and to determine the mechanism underlying regulation of the transforming growth factor-β1/mothers against decapentaplegic homolog 3 (TGF-β1/Smad3) signaling pathway. Mouse models of heart failure were established. Alterations in ejection fraction, left ventricular internal diastolic diameter (LVIDd) and left ventricular internal systolic diameter (LVIDs) were measured by echocardiography. Pathological alterations of myocardial tissue were determined by hematoxylin and eosin, and Masson staining. The levels of serum brain natriuretic peptide (BNP), N-terminal-proBNP, interleukin (IL)-1β, IL-6, tumor necrosis factor-α, malondialdehyde, superoxide dismutase and glutathione peroxidase were detected with ELISA. Expression of collagen I, collagen III were detected by western blotting and reverse transcription quantitative polymerase chain reaction. Transforming growth factor-β1 (TGF-β1), Smad3, phosphorylated (p)-Smad3, apoptosis regulator BAX (Bax), caspase-3 and apoptosis regulator Bcl2 in mouse cardiac tissue were measured by western blotting. P-smad3 and TGF-β1 were measured by immunofluorescence staining. EGCG reversed the alterations in LVIDd and LVIDs induced by establishment of the model of heart failure, increased ejection fraction, inhibited myocardial fibrosis, attenuated the oxidative stress, inflammatory and cardiomyocyte apoptosis and lowered the expression levels of collagen I and collagen III. Following treatment with TGF-β1 inhibitor, the protective effect of EGCG against heart failure was attenuated. The results of the present study demonstrated that EGCG can inhibit the progression and development of heart failure in mice through inhibition of myocardial fibrosis and reduction of ventricular collagen remodeling. This protective effect of EGCG is likely mediated through inhibition of TGF-β1/smad3 signaling pathway.

The core objective of image steganalysis is to explore the presence of weak image steganographic signals.Extracting effective steganographic signal features will play an essential role in digital image steganalysis. However, existing networks rely more on spatial rich model kernels or random learnable kernels to obtain noise residuals during the stage of steganographic signal features extraction. In this paper, we proposed a JPEG steganalysis network which based on denoising network and attention module, mainly including a noise extract block, a noise analysis block, and a judgment block. Specifically, a professional denoising convolutional neural network is first introduced in noise extract block to obtain better steganalysis features. The noise analysis block is integrated with the attention module to finely extract the steganographic signals hidden in the complex texture regions, which is quite effective in improving the signal-to-noise ratio of the stego signal. The judgment block is primarily a classifier to distinguish between cover images and stego images. Comprehensive experiments show a significant improvement in performance over the stateof-the-art steganalysis scheme. Moreover, the proposed network has better generalization capability than the compared steganalysis network for the case of

Steganalysis is a detection technology against steganography that embeds secret data into digital media carriers. The selection channel, which indicates the embedding details of steganography, is well recognized in boosting the detection performance of image steganalysis. However, nearly all the selection channels are constructed in a hand-crafted manner, even when they are incorporated into end-to-end deep steganalytic networks, for which the embedding rate and steganographic algorithms also need to be predetermined. Such

A total of 107 health care professionals, including 88 medical doctors and 19 pharmacists, participated in the study. The median (25th, 75th) knowledge score of the respondents was 8 (7, 11) out of a maximum possible score of 17. Most of the respondents exhibited positive attitude towards implementation of PV system in the healthcare sector. Of all, 24 (22.4%) respondents did not know the actual cause of death among the patients taking Isosorbide 5-mononitrate. The ADRs reporting and monitoring was not practiced routinely owing to multiple factors. ConClusions: After about three years of Isosorbide 5-mononitrate tragedy, our respondents still exhibited poor knowledge, and there was lack of PV related practices. However, positive attitude towards implementation of PV system is good omen.