Surface Functionalized Lipid Nanoparticles in Promoting Therapeutic Outcomes: An Insight View of the Dynamic Drug Delivery System.

Compared to the conventional approach, nanoparticles (NPs) facilitate a non-hazardous, non-toxic, non-interactive, and biocompatible system, rendering them incredibly promising for improving drug delivery to target cells. When that comes to accomplishing specific therapeutic agents like drugs, peptides, nucleotides, etc., lipidic nanoparticulate systems have emerged as even more robust. They have asserted impressive ability in bypassing physiological and cellular barriers, evading lysosomal capture and the proton sponge effect, optimizing bioavailability, and compliance, lowering doses, and boosting therapeutic efficacy. However, the lack of selectivity at the cellular level hinders its ability to accomplish its potential to the fullest. The inclusion of surface functionalization to the lipidic NPs might certainly assist them in adapting to the basic biological demands of a specific pathological condition. Several ligands, including peptides, enzymes, polymers, saccharides, antibodies, etc., can be functionalized onto the surface of lipidic NPs to achieve cellular selectivity and avoid bioactivity challenges. This review provides a comprehensive outline for functionalizing lipid-based NPs systems in prominence over target selectivity. Emphasis has been put upon the strategies for reinforcing the therapeutic performance of lipidic nano carriers' using a variety of ligands alongside instances of relevant commercial formulations.

CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection.

Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect on the global economy and health. A positive chest X-ray of infected patients is a crucial step in the battle against COVID-19. Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data. Because the outbreak is recent, it is difficult to gather a significant number of radiographic images in such a short time. Therefore, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN. In addition, we demonstrate that the synthetic images produced from CovidGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By adding synthetic images produced by CovidGAN,the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to more robust systems of radiology.