Particulate carrier systems are promising drug delivery vehicles for subunit vaccination as they can enhance and direct the type of T cell response. In order to develop vaccines with optimal immunogenicity, a thorough understanding of parameters that could affect the strength and quality of immune responses is required. Pathogens have different dimensions and stimulate the immune system in a specific way. It is therefore not surprising that physicochemical characteristics of particulate vaccines, such as particle size, shape, and rigidity, affect multiple processes that impact their immunogenicity. Among these processes are the uptake of the particles from the site of administration, passage through lymphoid tissue and the uptake, antigen processing and activation of antigen-presenting cells. Herein, we systematically review the role of the size, shape and rigidity of particulate vaccines in enhancing and skewing T cell response and attempted to provide a "roadmap" for rational vaccine design.
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http://dx.doi.org/10.1016/j.jconrel.2016.05.033 | DOI Listing |
BMC Plant Biol
January 2025
Plant Production Department, College of Food and Agricultural Sciences, King Saud University, P.O. Box. 2460, Riyadh, 11451, Saudi Arabia.
Background: The present research work was done to evaluate the anatomical differences among selected species of the family Bignoniaceae, as limited anatomical data is available for this family in Pakistan. Bignoniaceae is a remarkable family for its various medicinal properties and anatomical characterization is an important feature for the identification and classification of plants.
Methodology: In this study, several anatomical structures were examined, including stomata type and shape, leaf epidermis shape, epidermal cell size, and the presence or absence of trichomes and crystals (e.
Sci Rep
January 2025
School of Safety Science and Engineering (School of Emergency Management), Nanjing University of Science and Technology, Nanjing, 210094, People's Republic of China.
Powder-based fire extinguishing agents have become a kind of promising substitutes for halon extinguishing agents in civil aircrafts. However, their storage lifespan, significantly influenced by the thermal aging, emerges as a crucial yet overlooked aspect for aviation use. This study investigates the effects of thermal aging cycles on various parameters of ordinary dry powder extinguishing agent (ODPEA) and novel superhydrophobic and oleophobic ultra-fine dry powder extinguishing agent (SHOU DPEA), including surface microscopic morphology, D90 (the diameter at which 90% of the cumulative volume of particles are equal to or smaller than this value), chemical structure, hydrophobic and oleophobic angles, flowability, extinguishing time and effectiveness.
View Article and Find Full Text PDFInsights Imaging
January 2025
Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.
Objective: To assess the utility of clinical and MRI features in distinguishing ovarian clear cell carcinoma (CCC) from adnexal masses with ovarian-adnexal reporting and data system (O-RADS) MRI scores of 4-5.
Methods: This retrospective study included 850 patients with indeterminate adnexal masses on ultrasound. Two radiologists evaluated all preoperative MRIs using the O-RADS MRI risk stratification system.
Gut
January 2025
Barts Cancer Institute, Queen Mary University of London, London, UK
Background: The risk of developing advanced neoplasia (AN; colorectal cancer and/or high-grade dysplasia) in ulcerative colitis (UC) patients with a low-grade dysplasia (LGD) lesion is variable and difficult to predict. This is a major challenge for effective clinical management.
Objective: We aimed to provide accurate AN risk stratification in UC patients with LGD.
Nano Lett
January 2025
Electron Microscopy Center, Empa - Swiss Federal Laboratories for Materials Science and Technology, Überlandstrasse 129, 8600 Dübendorf, Switzerland.
The computational cost of simulating scanning transmission electron microscopy (STEM) images limits the curation of large enough data sets to train accurate and robust machine learning networks for deep feature extraction from atomically resolved STEM images. For nanoparticle size estimation in particular, a diverse data set is essential due to the large variations in size, shape, crystallinity, orientation, and dynamical diffraction effects in experimental data. To address this, we train a 3D convolutional neural network to predict STEM images from voxelized atomic models, achieving a 100x speed-up compared to traditional multislice simulations while maintaining high image quality.
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