Polymeric Nanovaccine Delivery System for Influenza Vaccine

Vaccines are the most efficient and cost-effective method for preventing illnesses caused by infectious pathogens. Even though the great success of vaccines over decades, the development of safe and robust vaccines is still essential for emerging new pathogens, re-evolving old pathogens, and improving the insufficient protection given by existing vaccines. One of the most critical strategies for developing effective new vaccines is selecting and using a suitable adjuvant or immune stimulant. Immunologic adjuvants are essential for improving vaccine potency by enhancing the immune response of vaccine antigens. The amount of antigen could be spared with improved potency, especially during mass vaccinations in pandemics. In the past, our laboratory had discovered a plant-based novel toll-like-receptor-4 agonist (adjuvant), inulin acetate (InAc), and showed that a particulate delivery system using InAc is a potent vaccine delivery system that produces strong humoral and cell-mediated immunity, which was tested in mouse models. The study in this dissertation investigated the application of nanoparticles prepared with inulin acetate nanoparticles (InAc-NPs) for dual functionality: as a delivery system and vaccine adjuvant for enhancing mucosal and systemic immunity in mice and pigs. The rationale behind selecting InAc-NPs is their established ability to stimulate strong systemic immunity and a clear understanding of their activation mechanisms. In chapter II, we have established through subcutaneous vaccinations in swine for the first time that inulin acetate nanoparticles (InAc-NPs) could generate high levels of systemic antibodies (IgG) by using influenza antigens the extracellular domain of matrix protein 2 (M2e), and the influenza virus\u27s surface membrane protein, Hemagglutinin (HA) protein. InAC-NPs, as a vaccine delivery system, protected the antigen from degradation during the storage and efficiently delivered it to swine macrophages (in-vitro). The antibodies induced by InAc-NPs have a strong affinity and avidity to bind to HA. These antibodies potentially prevent the virus from entering the host cell. The study introduced inulin acetate (InAc) as a vaccine adjuvant in swine for subcutaneous vaccine delivery. Chapter III, for the first time, established the efficacy of InAc-NPs as a vaccine delivery system in a mouse for oral vaccines using influenza peptide (Inf-A) as a model antigen. Importantly, InAc-NPs carrying the Inf-A produced higher mucosal and systemic antibodies than unadjuvanted antigens in mice. InAc-NPs activated mouse macrophages to secrete pro-inflammatory cytokines such as Interleukin-6 (IL-6) and macrophage activation marker nitric oxide (NO). In conclusion, we have demonstrated the capability of InAc-NPs as a robust vaccine delivery and adjuvant platform for parenteral and oral vaccines that offer strong systemic and mucosal immunity, which will have substantial implications in fighting several viral diseases in humans and animals (pigs) in future

The Flavonoid Metabolite 2,4,6-Trihydroxybenzoic Acid Is a CDK Inhibitor and an Anti-Proliferative Agent: A Potential Role in Cancer Prevention

Flavonoids have emerged as promising compounds capable of preventing colorectal cancer (CRC) due to their anti-oxidant and anti-inflammatory properties. It is hypothesized that the metabolites of flavonoids are primarily responsible for the observed anti-cancer effects owing to the unstable nature of the parent compounds and their degradation by colonic microflora. In this study, we investigated the ability of one metabolite, 2,4,6-trihydroxybenzoic acid (2,4,6-THBA) to inhibit Cyclin Dependent Kinase (CDK) activity and cancer cell proliferation. Using in vitro kinase assays, we demonstrated that 2,4,6-THBA dose-dependently inhibited CDKs 1, 2 and 4 and in silico studies identified key amino acids involved in these interactions. Interestingly, no significant CDK inhibition was observed with the structurally related compounds 3,4,5-trihydroxybenzoic acid (3,4,5-THBA) and phloroglucinol, suggesting that orientation of the functional groups and specific amino acid interactions may play a role in inhibition. We showed that cellular uptake of 2,4,6-THBA required the expression of functional SLC5A8, a monocarboxylic acid transporter. Consistent with this, in cells expressing functional SLC5A8, 2,4,6-THBA induced CDK inhibitory proteins p21Cip1 and p27Kip1 and inhibited cell proliferation. These findings, for the first time, suggest that the flavonoid metabolite 2,4,6-THBA may mediate its effects through a CDK- and SLC5A8-dependent pathway contributing to the prevention of CRC

Monocular Depth Estimation using Transfer learning-An Overview

Depth estimation is a computer vision technique that is critical for autonomous schemes for sensing their surroundings and predict their own condition. Traditional estimating approaches, such as structure from motion besides stereo vision similarity, rely on feature communications from several views to provide depth information. In the meantime, the depth maps anticipated are scarce. Gathering depth information via monocular depth estimation is an ill-posed issue, according to a substantial corpus of deep learning approaches recently suggested. Estimation of Monocular depth with deep learning has gotten a lot of interest in current years, thanks to the fast expansion of deep neural networks, and numerous strategies have been developed to solve this issue. In this study, we want to give a comprehensive assessment of the methodologies often used in the estimation of monocular depth. The purpose of this study is to look at recent advances in deep learning-based estimation of monocular depth. To begin, we'll go through the various depth estimation techniques and datasets for monocular depth estimation. A complete overview of multiple deep learning methods that use transfer learning Network designs, including several combinations of encoders and decoders, is offered. In addition, multiple deep learning-based monocular depth estimation approaches and models are classified. Finally, the use of transfer learning approaches to monocular depth estimation is illustrated

CNN Based Monocular Depth Estimation

In several applications, such as scene interpretation and reconstruction, precise depth measurement from images is a significant challenge. Current depth estimate techniques frequently provide fuzzy, low-resolution estimates. With the use of transfer learning, this research executes a convolutional neural network for generating a high-resolution depth map from a single RGB image. With a typical encoder-decoder architecture, when initializing the encoder, we use features extracted from high-performing pre-trained networks, as well as augmentation and training procedures that lead to more accurate outcomes. We demonstrate how, even with a very basic decoder, our approach can provide complete high-resolution depth maps. A wide number of deep learning approaches have recently been presented, and they have showed significant promise in dealing with the classical ill-posed issue. The studies are carried out using KITTI and NYU Depth v2, two widely utilized public datasets. We also examine the errors created by various models in order to expose the shortcomings of present approaches which accomplishes viable performance on KITTI besides NYU Depth v2