Aluminum hydroxide and immunostimulatory glycolipid adjuvant combination for enhanced COVID-19 subunit vaccine immunogenicity.

Protein-based subunit vaccines like RBD-Fc are promising tools to fight COVID-19. RBD-Fc fuses the receptor-binding domain (RBD) of the SARS-CoV-2 virus spike protein with the Fc region of human IgG1, making it more immunogenic than RBD alone. Earlier work showed that combining RBD-Fc with iNKT cell agonists as adjuvants improved neutralizing antibodies but did not sufficiently enhance T cell responses, a limitation RBD-Fc vaccines share with common adjuvants. Here we demonstrate that aluminum hydroxide combined with α-C-GC, a C-glycoside iNKT cell agonist, significantly improved the RBD-Fc vaccine's induction of RBD-specific T-cell responses. Additionally, aluminum hydroxide with α-GC-CPOEt, a phosphonate diester derivative, synergistically elicited more robust neutralizing antibodies. Remarkably, modifying αGC with phosphate (OPO3H2) or phosphonate (CPO3H2) to potentially enhance aluminum hydroxide interaction did not improve efficacy over unmodified αGC with aluminum hydroxide. These findings underscore the straightforward yet potent potential of this approach in advancing COVID-19 vaccine development and provide insights for iNKT cell-based immunotherapy.

Conformationally Restricted Analogues of α-Galactosylceramide as Adjuvant in COVID-19 Subunit Vaccine.

iNKT cells are a type of T lymphocyte that recognizes glycolipid antigens presented by CD1d protein. αGC is an agonistic glycolipid that activates iNKT cells and triggers immune modulatory cytokine responses, making it a promising vaccine adjuvant. To find more potent immunostimulating glycolipids, we prepared 4,6-O-galactosyl conformationally restricted analogues of αGC. Mice vaccinated with the SARS-CoV-2 RBD-Fc vaccine adjuvanted with these newly developed glycolipids produced robust anti-RBD antibody responses, comparable to those achieved with αGC. Importantly, we also found that omitting αGC, α-C-GalCer (Th1-type agonist), or C20:2 (Th2-type agonist) from the booster vaccine had negligible impact on antibody and cellular responses, potentially reducing the frequency of adjuvant use required to maintain potent immune responses.

Inhomogeneous antibiotic distribution in sediment profiles in anthropogenically impacted lakes: Source apportionment, fate drivers, and risk assessment.

Antibiotic residues in lake ecosystems have been widely reported; however, the vertical distribution of antibiotics in lake sediment profiles have rarely been examined. This study systematically revealed the vertical distribution pattern, sources, and risks of antibiotics in sediments of four typical agricultural lakes in central China. Nine of 33 target antibiotics were detected with a total concentration range of 39.3-18,250.6 ng/g (dry weight), and the order of average concentration was erythromycin (1447.4 ng/g) > sulfamethoxazole (443.7 ng/g) > oxytetracycline (62.6 ng/g) > enrofloxacin (40.7 ng/g) > others (0.1-2.1 ng/g). The middle-layer sediments (9-27 cm) had significantly higher antibiotic detected number and concentration than those in the top layer (0-9 cm) and bottom layer (27-45 cm) (p < 0.05). Correlation analysis showed that significant relationships existed between antibiotic concentrations and the octanol-water partition coefficients (Kow) of antibiotics (p < 0.05). Redundancy analysis indicated that Pb, Co, Ni, water content, and organic matter (p < 0.05) jointly affected the distribution of antibiotics in sediment profiles. Risk assessment showed that the highest potential ecological and resistance selection risks of antibiotics occurred in the middle-layer sediments, and oxytetracycline, tetracycline, and enrofloxacin had the most extensive potential risks in the sediment profiles. Additionally, the positive matrix factorization model revealed that human medical wastewater (54.5%) contributed more antibiotic pollution than animal excreta (45.5%) in sediment. This work highlights the inhomogeneous distribution of antibiotics in sediment profiles and provides valuable information for the prevention and control of antibiotic contamination in lakes.

Occurrence, Risk, and Source of Heavy Metals in Lake Water Columns and Sediment Cores in Jianghan Plain, Central China.

Heavy metal pollution in lakes is an issue that endangers ecosystems worldwide; however, the vertical properties of heavy metals in the water columns and sediment cores of lakes have been rarely evaluated simultaneously. This study revealed the pollution, risks, and sources of heavy metals from surface water to deep sediments in four typical shallow lakes located in central China. The results showed that the concentrations of heavy metals, except Hg, had insignificant stratification in the water column. Heavy metals had three vertical profiles in sediment cores, i.e., the concentrations of As, Hg, Cd, Pb, and Mn in the surface sediment (0-9 cm) were higher than that in the bottom sediment (9-45 cm) (p < 0.05), the concentrations of Cr, Co, Fe, and Ni in the bottom sediment were higher than the surface sediment (p < 0.05), and the concentrations of Cu and Zn had no significant stratification. The Nemerow pollution index showed that heavy metal pollution dominated by Hg reached slight-moderate levels, and had higher levels in surface water than that in bottom water (p < 0.05). The Nemerow integrated risk index showed that the heavy metals had moderate-extreme potential ecological risks (Cd contributed 43.4%) in the sediments, and the ecological risk in surface sediment was significantly higher than that in bottom sediment (p < 0.01). Principal component analysis revealed that agriculture, transportation, and chemical industry were the major sources of heavy metals in water and surface sediments, while agriculture and steel-making were the primary sources in bottom sediments. This study provides valuable data and insight for the control of heavy metal pollution in lakes with high human activity loads.

A New iNKT-Cell Agonist-Adjuvanted SARS-CoV-2 Subunit Vaccine Elicits Robust Neutralizing Antibody Responses.

Adjuvants are essential components of vaccines. Invariant natural killer T (iNKT) cells are a distinct subset of T cells that function to bridge the innate and adaptive immunities and are capable of mediating strong and rapid responses to a range of diseases, including cancer and infectious disease. An increasing amount of evidence suggests that iNKT cells can help fight viral infection. In particular, iNKT-secreting IL-4 is a key mediator of humoral immunity and has a positive correlation with the levels of neutralizing antibodies. As iNKT cell agonists, αGC glycolipid (α-galactosylceramide, or KRN7000) and its analogues as vaccine adjuvants have begun to provide vaccinologists with a new toolset. Herein we found that a new iNKT-cell agonist αGC-CPOEt elicited a strong cytokine response with increased IL-4 production. Remarkably, after three immunizations, SARS-CoV-2 RBD-Fc adjuvanted by αGC-CPOEt evoked robust neutralizing antibody responses that were about 5.5-fold more than those induced by αGC/RBD-Fc and 25-fold greater than those induced by unadjuvanted RBD-Fc. These findings imply that αGC-CPOEt could be investigated further as a new COVID-19 vaccine adjuvant to prevent current and future infectious disease outbreaks.

A high-resolution trajectory data driven method for real-time evaluation of traffic safety.

Real-time safety evaluation is essential for developing proactive safety management strategy and improving the overall traffic safety. This paper proposes a method for real-time evaluation of road safety, in which traffic states and conflicts are combined to explore the internal relationship based on high-resolution trajectory data. In order to assess the real-time traffic safety at a lane level, the trajectory data of the HighD dataset from Germany are utilized to collect lane-based dataset. A surrogate safety measure, time-to-collision (TTC) index, is used for the conflict identification. A binary logistic regression model is employed to quantify the relationship between traffic states and conflicts. Moreover, machine learning methods, including support vector machine, decision tree, random forest, and gradient boosting decision tree, are applied for real-time evaluation. A total of 24 models are trained using the selected four classifier algorithms, and random forest achieves the best performance with 0.85 of the overall accuracy. The results show that the conflict risk can be well estimated by the proposed method. The findings of this study contribute to the high-precision evaluation of real-time traffic safety and the development of proactive safety management.