The coronavirus 2019 (COVID-19) pandemic is causing serious impact in the world, safe and effective vaccines and medicines are the best method to combat the disease. The receptor-binding domain (RBD)...

The tumor-associated antigen mucin 1 (MUC1) is an attractive target of antitumor vaccine, but its weak immunogenicity is a big challenge for the development of vaccine. In order to enhance immune responses against MUC1, herein, we conjugated small molecular toll-like receptor 7 agonist (TLR7a) to carrier protein BSA via MUC1 glycopeptide to form a three-component conjugate (BSA-MUC1-TLR7a). Furthermore, we combined the three-component conjugate with Alum adjuvant to explore their synergistic effects. The immunological studies indicated that Alum adjuvant and built-in TLR7a synergistically enhanced anti-MUC1 antibody responses and showed Th1-biased immune responses. Meanwhile, antibodies elicited by the vaccine candidate effectively recognized tumor cells and induced complement-dependent cytotoxicity. In addition, Alum adjuvant and built-in TLR7a synergistically enhanced MUC1 glycopeptide-specific memory CD8+ T-cell immune responses. More importantly, the vaccine with the binary adjuvant can significantly inhibit tumor growth and prolong the survival time of mice in the tumor challenge experiment. This novel vaccine construct provides an effective strategy to develop antitumor vaccines.

Developing a novel and potent adjuvant with great biocompatibility for immune response augmentation is of great significance to enhance vaccine efficacy. In this work, we prepared a long-term stable, pH-sensitive, and biodegradable Mn 3 (PO 4 ) 2 ·3H 2 O nanoparticle (nano-MnP) by simply mixing MnCl 2 /NaH 2 PO 4 /Na 2 HPO 4 solution for the first time and employed it as an immune stimulant in the bivalent COVID-19 protein vaccine comprised of wild-type S1 (S1-WT) and Omicron S1 (S1-Omicron) proteins as antigens to elicit a broad-spectrum immunity. The biological experiments indicated that the nano-MnP could effectively activate antigen-presenting cells through the cGAS–STING pathway. Compared with the conventional Alum-adjuvanted group, the nano-MnP-adjuvanted bivalent vaccine elicited approximately 7- and 8-fold increases in IgG antibody titers and antigen-specific IFN-γ secreting T cells, respectively. Importantly, antisera of the nano-MnP-adjuvanted group could effectively cross-neutralize the SARS-CoV-2 and its five variants of concern (VOCs) including Alpha, Beta, Gamma, Delta, and Omicron, demonstrating that this bivalent vaccine based on S1-WT and S1-Omicron proteins is an effective vaccine design strategy to induce broad-spectrum immune responses. Collectively, this nano-MnP material may provide a novel and efficient adjuvant platform for various prophylactic and therapeutic vaccines and provide insights for the development of the next-generation manganese adjuvant.

B-rep model can provide effective topological data of the solid model for feature recognition analysis. The vertex, edge, and face are fundamental elements in the B-rep model. Each of these elements only records the topological data of its neighboring elements. The loop is another essential element linking all edges corresponding to a face. An outer loop always exists to record the external boundary of the face. Additional inner loops may also exist to record the inner boundaries because of holes. The loop concept can be employed in feature recognition for searching edges and faces related to the target feature. However, the current data structure of the loop in the B-rep model does not provide enough information for use in feature recognition. Therefore, a new type of loop, hereafter called a virtual loop, is necessary to fulfill the task. The aim of this study was to define and record the virtual loop in the B-rep model, and to develop algorithms in accordance with the virtual loop for the recognition of several types of features.The primary limitation of the current loop data in the B-rep model is that they are restricted to a face only. Whenever a face is sliced or divided into several faces, a new set of B-rep data must be established; all the newly generated faces are considered individual faces. In addition, the loop data are modified accordingly to reflect the new face status. Often in CAD models, a feature can be recognized in accordance with specific contours surrounding the target feature. For examples, features are generally classified as concave (e.g., features such as holes and pockets) or convex (e.g., features such as bosses, extrusions, and ribs). Finding a contour corresponding to the target feature may not be the only possible method for feature recognition, but it certainly facilitates the recognition of complex features. Many practical features do not lie on only a single face; rather, they usually lie on multiple faces, which is beyond the scope of the current loop data in the B-rep model. AbstractLoops are critical elements in boundary representation (B-rep) models because they link all edges corresponding to a face. Loops can be used in feature recognition for identifying depressions or protrusions. In real 3D CAD models, however, features typically lie across multiple faces, which is beyond the data structure of current B-rep models. This study presents a virtual loop concept to account for all loop types used in CAD models, and develops algorithms for recognizing them. In accordance with the complexity of the recognition algorithm, this study defines three types of loop: single, virtual, and multivirtual. A single loop is the current loop recorded in the B-rep model. A virtual loop lies across faces that are at least G 1 continuous. Finally, a multivirtual loop lies across faces that are either G 0 or G 1 continuous. The proposed loop structure provides a more complete data structure for recognizing various types of features in feature-recognition modules. Several realistic CAD models ar...