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Purpose Type 2 diabetes mellitus (T2DM) is a complex genetic disease associated with genetic and environmental factors. Previous studies have shown that changes in the gut microbiota may affect the development of host metabolic diseases and promote the progression of T2DM. Tang-ping-san (TPS) decoction can effectively treat T2DM. However, its specific mechanisms must be evaluated. Patients and Methods In the present study, we established an animal model of T2DM using a high‑fat diet (HFD) with intraperitoneal injection streptozotocin injection. Results The therapeutic effect of TPS decoction on T2DM in mice was initially evaluated. TPS decoction was found to improve hyperglycemia, hyperlipidemia, insulin resistance, and pathological liver, pancreatic, and colon changes. Moreover, it reduced the pro-inflammatory cytokine levels. Based on 16SrRNA sequencing, TPS decoction reduced the Firmicutes/Bacteroidetes ratio at the phylum level. At the genus level, it increased the relative abundances of Akkermansia, Muribaculaceae , and the Eubacterium coprostanoligenes group and decreased the relative abundance of Fusobacterium, Escherichia coli, Dubosiella , and Helicobacter . Conclusion TPS decoction improves T2DM and liver function and reduces the risk of hyperglycemia, hyperlipidemia, insulin resistance, pathological organ changes, and inflammatory reactions. The mechanism of TPS decoction in T2DM can be correlated with the reversal of gut microbiota dysfunction and repair of the intestinal mucosal barrier.

Objectives In this study, we focused on the function of nuclear factor E2–related factor 2 (Nrf2) in acute pancreatitis (AP), which has been shown to have protective effects in gliomas, hepatocytes, and astrocytes. Methods Acute pancreatitis cell line and animal model were induced by administration of lipopolysaccharide and cerulein into the cell supernatant or intraperitoneal injection. Oxidative stress status was evaluated by measuring the level of amylase, C-reactive protein, malondialdehyde, superoxide dismutase, and myeloperoxidase. Morphological alterations in the pancreas were evaluated by hematoxylin-eosin staining, the wet-to-dry weight ratio, and the pathology injury scores. Western blot, reverse transcription-polymerase chain reaction, and immunofluorescence staining were performed to analyze the expression of Nrf2, Heme oxygenase 1, and NAD(P)H: quinone oxidoreductase 1. Results Overexpression of Nrf2 inhibits oxidative stress and inflammatory responses by inducting the expression of superoxide dismutase as well as reducing the level of amylase, malondialdehyde, and myeloperoxidase in the AR42J rat pancreatic acinar cells in AP. Importantly, overexpression of Nrf2 displayed the same protective effect in vivo. Data from an AP rat model showed that Nrf2 could relieve pancreatic damage. Conclusions These results indicated that Nrf2 has a protective role in lipopolysaccharide and cerulein-induced cytotoxicity, providing potential therapeutic strategies for the treatment of AP.

In paper "Robust reversible data hiding scheme based on two-layer embedding strategy" published in INS recently, Kumar et al. proposed an Robust Reversible Data Hiding (RRDH) scheme based on two-layer embedding. Secret data is embedded into Most Significant Bit (MSB) planes and sorting strategy based on local complexity is adopted to reduce distortion. However, Kumar et al.'s RDH scheme is not as robust as stated and can not be called RRDH. In this comment, we give a brief description of Kumar et al.'s RDH scheme at first and then show robust testing experiments by analysing the changes in each bit plane after Joint Photographic Experts Group (JPEG) compression and the changes in pixel value ordering.

Some diseases, particularly cardiovascular disease, will change the shape and structure of retinal vessels. Observation and detection of retinal vessels play an important role in the diagnosis of diseases. Traditional diagnosis of the retinal vessels that ophthalmologist perform under artificial visual attending. Image segmentation based on Markov random field is a method based on statistical theory, which takes into account the correlation between the local pixels, uses the prior knowledge effectively, has fewer model parameters and is easy to be combined with other methods etc., so this method has been widely researched and applied in the field of image segmentation. This paper which mainly studied the Markov random field is how to specific apply to image segmentation, and the iterated conditional mode and the traditional segmentation (clustering) algorithm segmented and compared in the medical retinal vessel image. The method of MRF can effectively restrain the noise in the vessel segmentation.

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Because of the powerful data processing ability of FPGA, the fast interpolation algorithm is used for Bayer format data which comes from CMOS sensor MT9M011 to convert to RGB image format. In the RGB color space to YCbCr space conversion stage,using color space conversion formula, combined with the characteristics of FPGA, realize the conversion of RGB to YCbCr. Finally, correctness is verified by the experimental results which use SignalTap II embedded logic analyzer.