A Polymer-Based Antigen Carrier Activates Two Innate Immune Pathways for Adjuvant-Free Subunit Vaccines.

The activation of multiple Pattern Recognition Receptors (PRRs) has been demonstrated to trigger inflammatory responses and coordinate the host's adaptive immunity during pathogen infections. The use of PRR agonists as vaccine adjuvants has been reported to synergistically induce specific humoral and cellular immune responses. However, incorporating multiple PRR agonists as adjuvants increases the complexity of vaccine design and manufacturing. In this study, we discovered a polymer that can activate both the Toll-like receptor (TLR) pathway and cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway. The polymer was then conjugated to protein antigens, creating an antigen delivery system for subunit vaccines. Without additional adjuvants, the antigen-polymer conjugates elicited strong antigen-specific humoral and cellular immune responses. Furthermore, the antigen-polymer conjugates, containing the Receptor Binding Domain (RBD) of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Spike Protein or the Monkeypox Antigen M1R as the antigens, were found to induce potent antigen-specific antibodies, neutralizing antibodies, and cytotoxic T cells. Immunization with M1R-polymer also resulted in effective protection in a lethal challenge model. In conclusion, this vaccine delivery platform offers an effective, safe, and simple strategy for inducing antigen-specific immunity against infectious diseases.

CO2-assisted 'Weathering' of Steel Slag-Derived Calcium Silicate Hydrate: A Generalized Strategy for Recycling Noble Metals and Constructing SiO2-Based Nanocomposites.

The spent adsorbent loaded by toxic metals is a solid hazardous waste which could cause significant secondary pollution due to potential possible additional release of metal ions. Therefore, the main subject is direct reutilization of spent adsorbents which can further economically and realistically offer new features, like recycling metal adsorbed, or formation of functional SiO2-based nanocomposites. The nanoporous structure and negative surface charges enable steel slag-derived amorphous calcium silicate hydrate (CSH) to retain effectively the incoming metal ions (e. g. Au3+, Ag+, Pd2+, Fe3+, Co2+, Ni2+, Cu2+, Zn2+, Ce3+, Y3+, and Gd3+) by chemisorption. Sparked by natural carbonation 'weathering', which ultimately sequestrates atmospheric CO2 by alkaline silicate minerals to leach calcium from mineral matrix, the decalcification reactions of metal-bearing CSH results in successful recovery of noble metals (Ag, Au, Pd) upon NaOH etching the resultant SiO2 support. Further, SiO2-based heterostructures, containing nanocrystalline metals (e. g. Au0, Ag0, Pd0, Fe0, Co0, Ni0, Cu0, and Zn0) or rare-earth oxides (e. g. CeO2, Y2O3, and Gd2O3), are formed after reduction in H2/Ar (5 vol% H2) flow, which is also very important for the multipurpose immobilization of diverse hybrid materials on SiO2 surface (e. g. Cu0-Ag0@SiO2, Cu0-CeO2@SiO2, and Cu0-Ag0-CeO2@SiO2).

Predicting physical stability of solid dispersions by machine learning techniques.

Amorphous solid dispersion (SD) is an effective solubilization technique for water-insoluble drugs. However, physical stability issue of solid dispersions still heavily hindered the development of this technique. Traditional stability experiments need to be tested at least three to six months, which is time-consuming and unpredictable. In this research, a novel prediction model for physical stability of solid dispersion formulations was developed by machine learning techniques. 646 stability data points were collected and described by over 20 molecular descriptors. All data was classified into the training set (60%), validation set (20%), and testing set (20%) by the improved maximum dissimilarity algorithm (MD-FIS). Eight machine learning approaches were compared and random forest (RF) model achieved the best prediction accuracy (82.5%). Moreover, the RF models revealed the contribution of each input parameter, which provided us the theoretical guidance for solid dispersion formulations. Furthermore, the prediction model was confirmed by physical stability experiments of 17ß-estradiol (ED)-PVP solid dispersions and the molecular mechanism was investigated by molecular modeling technique. In conclusion, an intelligent model was developed for the prediction of physical stability of solid dispersions, which benefit the rational formulation design of this technique. The integrated experimental, theoretical, modeling and data-driven AI methodology is also able to be used for future formulation development of other dosage forms.