Eliciting a robust immune response at mucosal sites is critical in preventing the entry of mucosal pathogens such as influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This ...task is challenging to achieve without the inclusion of a strong and safe mucosal adjuvant. Previously, inulin acetate (InAc), a plant-based polymer, is shown to activate toll-like receptor-4 (TLR4) and elicit a robust systemic immune response as a vaccine adjuvant. This study investigates the potential of nanoparticles prepared with InAc (InAc-NPs) as an intranasal vaccine delivery system to generate both mucosal and systemic immune responses. InAc-NPs (∼250 nm in diameter) activated wild-type (WT) macrophages but failed to activate macrophages from TLR4 knockout mice or WT macrophages when pretreated with a TLR4 antagonist (lipopolysaccharide-RS (LPS-RS)), which indicates the selective nature of a InAc-based nanodelivery system as a TLR4 agonist. Intranasal immunization using antigen-loaded InAc-NPs generated ∼65-fold and 19-fold higher serum IgG1 and IgG2a titers against the antigen, respectively, as compared to PLGA-NPs as a delivery system. InAc-NPs have also stimulated the secretion of sIgA at various mucosal sites, including nasal-associated lymphoid tissues (NALTs), lungs, and intestine, and produced a strong memory response indicative of both humoral and cellular immune activation. Overall, by stimulating both systemic and mucosal immunity, InAc-NPs laid a basis for a potential intranasal delivery system for mucosal vaccination.
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 p21
and p27
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.
Inflammatory Bowel Diseases (IBD) is a debilitating condition that affects ~70,000 new people every year and has been described as “an expensive disease with no known cure”. In addition, IBD ...increases the risk of developing colon cancer. The current therapeutics for IBD focus on the established disease where the immune dysfunction and bowel damage have already occurred but do not prevent or delay the progression. The current work describes a polymer-based anti-inflammatory technology (Ora-Curcumin-S) specifically targeted to the luminal side of the colon for preventing and/or treating IBD. Ora-Curcumin-S was prepared by molecular complexation of curcumin with a hydrophilic polymer Eudragit® S100 using co-precipitation method. Curcumin interacted with the polymer non-covalently and existed in an amorphous state as demonstrated by various physicochemical techniques. Ora-Curcumin-S is a polymer-drug complex, which is different than solid dispersions in that the interactions are retained even after dissolving in aqueous buffers. Ora-Curcumin-S was >1000 times water soluble than curcumin and importantly, the enhanced solubility was pH-dependent, which was observed only at pHs above 6.8. In addition, around 90% of Ora-Curcumin-S was stable in phosphate buffer, pH 7.4 and simulated intestinal fluid after 24 h, in contrast to 10–20% unformulated curcumin. Ora-Curcumin-S inhibited Monophosphoryl Lipid-A and E. coli induced inflammatory responses in dendritic cells and cells over expressing Toll-Like Receptor-4 (TLR-4) suggesting that Ora-Curcumin-S is a novel polymer-based TLR-4 antagonist. Preliminary pharmacokinetics in mice showed targeted delivery of soluble curcumin to the colon lumen without exposing to the systemic circulation. Furthermore, Ora-Curcumin-S significantly prevented colitis and associated injury in a mouse model of ulcerative colitis estimated using multiple preclinical parameters: colonoscopy pictures, body weight, colon length, colon edema, spleen weight, pro-inflammatory signaling and independent pathological scoring. Overall, the outcome of this innovative proof-of-concept study provides an exciting and locally-targeted pathway for a dietary therapeutic option for IBD patients to help limit colonic inflammation and thus susceptibility to colitis-associated colorectal cancer.
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Inflammatory bowel disease (IBD) is a chronic gut disorder that also elevates the risk of colorectal cancer (CRC). The global incidence and severity of IBD are rising, yet existing therapies often ...lead to severe side effects. Curcumin offers potent anti-inflammatory and chemotherapeutic properties. However, its clinical translation is hindered by rapid metabolism, as well as poor water solubility and stability, which limits its bioavailability. To address these challenges, we developed OC-S, a water-soluble and colon-targeted curcumin formulation that protects against colitis in mice. The current study advances OC-S as a dietary supplement by establishing its stability and compatibility with various commercial dietary products. Further, OC-S exhibited specific binding to inflamed colon tissue, potentially aiding in targeted drug retention at the inflammation site in colitis with diarrhea symptoms. We further investigated its efficacy in vivo and in vitro using a murine model of colitis and tumoroids from APCmin mice. OC-S significantly reduced colitis severity and pro-inflammatory cytokine expression compared with curcumin, even at very low doses (5 mg/kg/day). It also demonstrated higher anti-proliferative activity in CRC cells and colon cancer tumoroids vs. curcumin. Overall, this study demonstrated that OC-S effectively targets and retains water-soluble curcumin at the inflamed colon sites, while showing promise in addressing both colitis and colorectal cancer, which potentially paves the way for OC-S to advance into clinical development as a dietary product for both IBD and CRC.
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's 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.
Manual tumor diagnosis from magnetic resonance images (MRIs) is a time-consuming procedure that may lead to human errors and may lead to false detection and classification of the tumor type. ...Therefore, to automatize the complex medical processes, a deep learning framework is proposed for brain tumor classification to ease the task of doctors for medical diagnosis. Publicly available datasets such as Kaggle and Brats are used for the analysis of brain images. The proposed model is implemented on three pre-trained Deep Convolution Neural Network architectures (DCNN) such as AlexNet, VGG16, and ResNet50. These architectures are the transfer learning methods used to extract the features from the pre-trained DCNN architecture, and the extracted features are classified by using the Support Vector Machine (SVM) classifier. Data augmentation methods are applied on Magnetic Resonance images (MRI) to avoid the network from overfitting. The proposed methodology achieves an overall accuracy of 98.28% and 97.87% without data augmentation and 99.0% and 98.86% with data augmentation for Kaggle and Brat's datasets, respectively. The Area Under Curve (AUC) for Receiver Operator Characteristic (ROC) is 0.9978 and 0.9850 for the same datasets. The result shows that ResNet50 performs best in the classification of brain tumors when compared with the other two networks.
CNN Based Monocular Depth Estimation Swaraja, K.; Naga Siva Pavan, K.; Suryakanth Reddy, S. ...
E3S Web of Conferences,
01/2021, Letnik:
309
Journal Article, Conference Proceeding
Recenzirano
Odprti dostop
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.
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.
In this paper, firefly optimization is applied to balance robustness and imperceptibility in image watermarking. First of all, the original image is partitioned into 4 × 4 blocks, and later Redundant ...Discrete Wavelet Transform (RDWT) and Schur applied for obtaining transform coefficients. The highly correlated pixels in the first column are collected from each 4 × 4 block and are amended by blindly concealing the watermark using quantization index modulation. These quantized values are amended with the randomized matrix and quantization factor randomly selected from the firefly algorithm to obtain the best performance in terms of robustness without compromising the quality of the image. Experimental results show that the proposed scheme maintains improved imperceptibility, and the watermark can still extract from a seriously distorted image.
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