Dr. Hari Singh Gour University

About: Dr. Hari Singh Gour University is a education organization based out in Saugor, Madhya Pradesh, India. It is known for research contribution in the topics: Drug delivery & Computer science. The organization has 1120 authors who have published 1315 publications receiving 29511 citations. The organization is also known as: Dr Harisingh Gour Vishwavidyalaya & Sagar University.

Preparation and pharmacodynamic evaluation of liposomes of indomethacin.

Abstract: The side effects of indomethacin, such as ulceration of the kidney and central nervous system (CNS) toxicity, limit its use as a drug for rheumatoid arthritis. Encapsulation of this drug in liposom...

Proultraflexible lipid vesicles for effective transdermal delivery of levonorgestrel: development, characterization, and performance evaluation.

Abstract: The present investigation aimed at formulation, performance evaluation, and stability studies of new vesicular drug carrier system protransfersomes for transdermal delivery of the contraceptive agent, levonorgestrel. Protransfersome gel (PTG) formulations of levonorgestrel were prepared and characterized for vesicle shape, size, entrapment efficiency, turbidity, and drug permeation across rat skin and were evaluated for their stability. The system was evaluated in vivo for biological assay of progestational activity including endometrial assay, inhibition of the formation of corpora lutea, and weight gain of uterus. The effects of different formulation variables (type of alcohol, type and concentration of surfactant) on transdermal permeability profile were assessed. The optimized PTG formulation showed enhanced in vitro skin permeation flux of 15.82±0.37 μg/cm2/hr as compared to 0.032±0.01 μg/cm2/hr for plain drug solution. PTG also showed good stability and after 2 months of storage there was no change in liquid crystalline nature, drug content, and other characteristic parameters. The peak plasma concentration of levonorgestrel (0.139±0.05 μg/mL) was achieved within 4 hours and maintained until 48 hours by a single topical application of optimized PTG formulation. In vivo performance of the PTG formulation showed increase in the therapeutic efficacy of drug. Results indicated that the optimized protransfersomal formulation of levonorgestrel had better skin permeation potential, sustained release characteristic, and better stability than proliposomal formulation.

Development and characterization of emulsomes for sustained and targeted delivery of an antiviral agent to liver.

Abstract: In this study we developed emulsomes, a novel lipoidal vesicular system with an internal solid fat core surrounded by a phospholipid bilayer. Plain emulsomal formulations composed of solid lipid (trilaurin or tristearin), cholesterol and soya phosphatidylcholine and stearylamine containing cationic emulsomes loaded with an antiviral drug (zidovudine) were prepared by a simple cast film method followed by sonication to produce emulsomes of nanometric size range. All different types of formulations were optimized for lipid ratios and characterized in-vitro for shape, morphology, size and in-vitro drug release profile. Emulsomal formulations displayed a sufficiently slow drug release profile (12-15% after 24 h). In-vivo organ distribution studies in rats demonstrated better uptake of emulsomal formulations by the liver cells. Further, a significantly higher (P < 0.05) liver concentration of drug was estimated over a period of 24 h for cationic emulsomes than for plain neutral emulsomes. We concluded that cationic emulsomes could fuse with the endosomal membrane due to charge-charge interaction and were released in the cytoplasm before lysosomal degradation and could sustain drug release over a prolonged period. The proposed cationic emulsome-based system showed excellent potential for intracellular hepatic targeting and the strategy could play a vital role in the effective treatment of life-threatening viral infections, such as hepatitis, HIV and Epstein-Barr virus infection.

Prediction of COVID-19 Using Genetic Deep Learning Convolutional Neural Network (GDCNN)

Abstract: Rapid spread of Coronavirus disease COVID-19 leads to severe pneumonia and it is estimated to create a high impact on the healthcare system. An urgent need for early diagnosis is required for precise treatment, which in turn reduces the pressure in the health care system. Some of the standard image diagnosis available is Computed Tomography (CT) scan and Chest X-Ray (CXR). Even though a CT scan is considered a gold standard in diagnosis, CXR is most widely used due to widespread, faster, and cheaper. This study aims to provide a solution for identifying pneumonia due to COVID-19 and healthy lungs (normal person) using CXR images. One of the remarkable methods used for extracting a high dimensional feature from medical images is the Deep learning method. In this research, the state-of-the-art techniques used is Genetic Deep Learning Convolutional Neural Network (GDCNN). It is trained from the scratch for extracting features for classifying them between COVID-19 and normal images. A dataset consisting of more than 5000 CXR image samples is used for classifying pneumonia, normal and other pneumonia diseases. Training a GDCNN from scratch proves that, the proposed method performs better compared to other transfer learning techniques. Classification accuracy of 98.84%, the precision of 93%, the sensitivity of 100%, and specificity of 97.0% in COVID-19 prediction is achieved. Top classification accuracy obtained in this research reveals the best nominal rate in the identification of COVID-19 disease prediction in an unbalanced environment. The novel model proposed for classification proves to be better than the existing models such as ReseNet18, ReseNet50, Squeezenet, DenseNet-121, and Visual Geometry Group (VGG16).