miRNA-29a reverses P-glycoprotein-mediated drug resistance and inhibits proliferation via up-regulation of PTEN in colon cancer cells

Colon cancer is a serious malignant type of cancer in the world. Acquisition of multi-drug resistance (MDR) during chemotherapy is still a controversial challenge during cancer treatment. Accordingly, detection of safe and impressive MDR-reversing targets such as microRNAs (miRNAs/miRs) can play critical role in cancer treatment. Here, the functional effects of miR-29a in chemo-resistant colon cancer cells is scrutinized. The effect of doxorubicin (DOX) on cell proliferation after miR-29a transfection has been evaluated using MTT assay in HT29 and HT29/DOX cells. Rhodamine123 (Rh123) assay is used to identify the activity of common drug efflux through membrane transporters P-glycoprotein (P-gp). P-gp and PTEN mRNA/protein expression levels were measured by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and Western blot analyses. Flow cytometry was employed to the investigation of apoptosis. ANOVA followed by Bonferroni's and Sidak's tests were used to compare the data from different groups. Thus, it was shown that miRNA-29a overexpression considerably inhibited the HT29/DOX viability. miR-29a significantly down-regulated P-gp expression and activity in HT29/DOX cells and declined drug resistance through elevation of intracellular DOX. Furthermore, upon miRNA-29a transfection, PTEN expression could be restored in resistant cells. These results have indicated that miR-29a target PTEN ultimately P-gp, which is downstream of PTEN, inhibit drug resistance, proliferation, and apoptosis through PI3K/Akt pathway. As a result, miR-29a overexpression is led to enhance the sensitivity of HT29/DOX cells to DOX-treatment by targeting P-gp. MiR-29a might proffer a novel promising candidate for colon cancer therapeutics during chemotherapy. © 202

Image steganography based on color palette transformation in color space

In this paper, we present a novel image steganography method which is based on color palette transformation in color space. Most of the existing image steganography methods modify separate image pixels, and random noise appears in the image. By proposing a method, which changes the color palette of the image (all pixels of the same color will be changed to the same color), we achieve a higher user perception. Presented comparison of stegoimage quality metrics with other image steganography methods proved the new method is one of the best according to Structural Similarity Index (SSIM) and Peak Signal Noise Ratio (PSNR) values. The capability is average among other methods, but our method has a bigger capacity among methods with better SSIM and PSNR values. The color and pixel capability can be increased by using standard or adaptive color palette images with smoothing, but it will increase the embedding identification possibilityThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.The research had no specific funding and was implemented as a master thesis in Šiauliai Univesity with the supervisor from Vilnius Gediminas Technical Universit

Privacy Intelligence: A Survey on Image Sharing on Online Social Networks

Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also led to an increased risk of privacy invasion. The recent image leaks from popular OSN services and the abuse of personal photos using advanced algorithms (e.g. DeepFake) have prompted the public to rethink individual privacy needs when sharing images on OSNs. However, OSN image sharing itself is relatively complicated, and systems currently in place to manage privacy in practice are labor-intensive yet fail to provide personalized, accurate and flexible privacy protection. As a result, an more intelligent environment for privacy-friendly OSN image sharing is in demand. To fill the gap, we contribute a systematic survey of 'privacy intelligence' solutions that target modern privacy issues related to OSN image sharing. Specifically, we present a high-level analysis framework based on the entire lifecycle of OSN image sharing to address the various privacy issues and solutions facing this interdisciplinary field. The framework is divided into three main stages: local management, online management and social experience. At each stage, we identify typical sharing-related user behaviors, the privacy issues generated by those behaviors, and review representative intelligent solutions. The resulting analysis describes an intelligent privacy-enhancing chain for closed-loop privacy management. We also discuss the challenges and future directions existing at each stage, as well as in publicly available datasets.Comment: 32 pages, 9 figures. Under revie