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Rizvi, Syed Zaki Husain,Shah, Fawad Ali,Khan, Namrah,Muhammad, Iftikhar,Ali, Khan Hashim,Ansari, Muhammad Mohsin,Din, Fakhar ud,Qureshi, Omer Salman,Kim, Kyoung-Won,Choe, Yeong-Hwan,Kim, Jin-Ki,Zeb, A Elsevier 2019 International journal of pharmaceutics Vol.560 No.-
<P><B>Abstract</B></P> <P>The objective of current study was to develop solid lipid nanoparticles-loaded with simvastatin (SIM-SLNs) and investigate their <I>in vivo</I> anti-hyperlipidemic activity in poloxamer-induced hyperlipidemia model. Nano-template engineering technique was used to prepare SIM-SLNs with palmityl alcohol as lipid core and a mixture of Tween 40/Span 40/Myrj 52 to stabilize the core. The prepared SIM-SLNs were evaluated for physicochemical parameters including particle diameter, surface charge, morphology, incorporation efficiency, thermal behaviour and crystallinity. <I>In vitro</I> release profile of SIM-SLNs in simulated gastric and intestinal fluids was evaluated by using dialysis bag technique and anti-hyperlipidemic activity was assessed in hyperlipidemia rat model. SIM-SLNs revealed uniform particle size with spherical morphology, zeta potential of −24.9 mV and high incorporation efficiency (∼85%). Thermal behaviour and crystallinity studies demonstrated successful incorporation of SIM in the lipid core and its conversion to amorphous form. SIM-SLNs demonstrated a sustained SIM release from the lipid core of nanoparticles. SIM-SLNs significantly reduced the elevated serum lipids as indicated by ∼3.9 and ∼1.5-times decreased total cholesterol compared to those of untreated control and SIM dispersion treated hyperlipidemic rats. In conclusion, SIM-SLNs showed a great promise for improving the therapeutic outcomes of SIM via its effective oral delivery.</P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
Muhammad, Wazir,Hussain, Ayaz,Shah, Syed Ali Raza,Shah, Jalal,Bhutto, Zuhaibuddin,Thaheem, Imdadullah,Ali, Shamshad,Masrour, Salman International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.11
Recently, image super-resolution techniques used in convolutional neural networks (CNN) have led to remarkable performance in the research area of digital image processing applications and computer vision tasks. Convolutional layers stacked on top of each other can design a more complex network architecture, but they also use more memory in terms of the number of parameters and introduce the vanishing gradient problem during training. Furthermore, earlier approaches of single image super-resolution used interpolation technique as a pre-processing stage to upscale the low-resolution image into HR image. The design of these approaches is simple, but not effective and insert the newer unwanted pixels (noises) in the reconstructed HR image. In this paper, authors are propose a novel single image super-resolution architecture based on synchronized depthwise separable convolution with Dense Skip Connection Block (DSCB). In addition, unlike existing SR methods that only rely on single path, but our proposed method used the synchronizes path for generating the SISR image. Extensive quantitative and qualitative experiments show that our method (SDCN) achieves promising improvements than other state-of-the-art methods.
LDCSIR: Lightweight Deep CNN-based Approach for Single Image Super-Resolution
Muhammad, Wazir,Shaikh, Murtaza Hussain,Shah, Jalal,Shah, Syed Ali Raza,Bhutto, Zuhaibuddin,Lehri, Liaquat Ali,Hussain, Ayaz,Masrour, Salman,Ali, Shamshad,Thaheem, Imdadullah International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.spc12
Single image super-resolution (SISR) is an image processing technique, and its main target is to reconstruct the high-quality or high-resolution (HR) image from the low-quality or low-resolution (LR) image. Currently, deep learning-based convolutional neural network (CNN) image super-resolution approaches achieved remarkable improvement over the previous approaches. Furthermore, earlier approaches used hand designed filter to upscale the LR image into HR image. The design architecture of such approaches is easy, but it introduces the extra unwanted pixels in the reconstructed image. To resolve these issues, we propose novel deep learning-based approach known as Lightweight deep CNN-based approach for Single Image Super-Resolution (LDCSIR). In this paper, we propose a new architecture which is inspired by ResNet with Inception blocks, which significantly drop the computational cost of the model and increase the processing time for reconstructing the HR image. Compared with the other state of the art methods, LDCSIR achieves better performance in terms of quantitively (PSNR/SSIM) and qualitatively.
LDCSIR: Lightweight Deep CNN-based Approach for Single Image Super-Resolution
Muhammad, Wazir,Shaikh, Murtaza Hussain,Shah, Jalal,Shah, Syed Ali Raza,Bhutto, Zuhaibuddin,Lehri, Liaquat Ali,Hussain, Ayaz,Masrour, Salman,Ali, Shamshad,Thaheem, Imdadullah International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.12
Single image super-resolution (SISR) is an image processing technique, and its main target is to reconstruct the high-quality or high-resolution (HR) image from the low-quality or low-resolution (LR) image. Currently, deep learning-based convolutional neural network (CNN) image super-resolution approaches achieved remarkable improvement over the previous approaches. Furthermore, earlier approaches used hand designed filter to upscale the LR image into HR image. The design architecture of such approaches is easy, but it introduces the extra unwanted pixels in the reconstructed image. To resolve these issues, we propose novel deep learning-based approach known as Lightweight deep CNN-based approach for Single Image Super-Resolution (LDCSIR). In this paper, we propose a new architecture which is inspired by ResNet with Inception blocks, which significantly drop the computational cost of the model and increase the processing time for reconstructing the HR image. Compared with the other state of the art methods, LDCSIR achieves better performance in terms of quantitively (PSNR/SSIM) and qualitatively.