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The COVID-19 pandemic caused by SARS-CoV-2 sparked intensive research into the development of effective vaccines, 50 of which have been approved thus far, including the novel mRNA-based vaccines developed by Pfizer and Moderna. Although limiting the severity of the disease, the mRNA-based vaccines presented drawbacks, such as the cold chain requirement. Moreover, antibody levels generated by these vaccines decline significantly after 6 months. These vaccines deliver mRNA encoding the full-length spike (S) glycoprotein of SARS-CoV-2, but must be updated as new strains and variants of concern emerge, creating a demand for adjusted formulations and booster campaigns. To overcome these challenges, we have developed COVID-19 vaccine candidates based on the highly conserved SARS CoV-2, 809-826 B-cell peptide epitope (denoted 826) conjugated to cowpea mosaic virus (CPMV) nanoparticles and bacteriophage Qβ virus-like particles, both platforms have exceptional thermal stability and facilitate epitope delivery with inbuilt adjuvant activity. We evaluated two administration methods: subcutaneous injection and an implantable polymeric scaffold. Mice received a prime–boost regimen of 100 μg per dose (2 weeks apart) or a single dose of 200 μg administered as a liquid formulation, or a polymer implant. Antibody titers were evaluated longitudinally over 50 weeks. The vaccine candidates generally elicited an early Th2-biased immune response, which stimulates the production of SARS-CoV-2 neutralizing antibodies, followed by a switch to a Th1-biased response for most formulations. Exceptionally, vaccine candidate 826-CPMV (administered as prime-boost, soluble injection) elicited a balanced Th1/Th2 immune response, which is necessary to prevent pulmonary immunopathology associated with Th2 bias extremes. While the Qβ-based vaccine elicited overall higher antibody titers, the CPMV-induced antibodies had higher avidity. Regardless of the administration route and formulation, our vaccine candidates maintained high antibody titers for more than 50 weeks, confirming a potent and durable immune response against SARS-CoV-2 even after a single dose. 

Living tissues with high cell density and high resolution can be 3D printed. 

The pharmacology and toxicology of a broad variety of therapies and chemicals have significantly improved with the aid of the increasing in vitro models of complex human tissues. Offering versatile and precise control over the cell population, extracellular matrix (ECM) deposition, dynamic microenvironment, and sophisticated microarchitecture, which is desired for the in vitro modeling of complex tissues, 3D bio-printing is a rapidly growing technology to be employed in the field. In this review, we will discuss the recent advancement of printing techniques and bio-ink sources, which have been spurred on by the increasing demand for modeling tactics and have facilitated the development of the refined tissue models as well as the modeling strategies, followed by a state-of-the-art update on the specialized work on cancer, heart, muscle and liver. In the end, the toxicological modeling strategies, substantial challenges, and future perspectives for 3D printed tissue models were explored.

 

Abstract Digital light processing (DLP)-based three-dimensional (3D) printing technology has the advantages of speed and precision comparing with other 3D printing technologies like extrusion-based 3D printing. Therefore, it is a promising biomaterial fabrication technique for tissue engineering and regenerative medicine. When printing cell-laden biomaterials, one challenge of DLP-based bioprinting is the light scattering effect of the cells in the bioink, and therefore induce unpredictable effects on the photopolymerization process. In consequence, the DLP-based bioprinting requires extra trial-and-error efforts for parameters optimization for each specific printable structure to compensate the scattering effects induced by cells, which is often difficult and time-consuming for a machine operator. Such trial-and-error style optimization for each different structure is also very wasteful for those expensive biomaterials and cell lines. Here, we use machine learning to learn from a few trial sample printings and automatically provide printer the optimal parameters to compensate the cell-induced scattering effects. We employ a deep learning method with a learning-based data augmentation which only requires a small amount of training data. After learning from the data, the algorithm can automatically generate the printer parameters to compensate the scattering effects. Our method shows strong improvement in the intra-layer printing resolution for bioprinting, which can be further extended to solve the light scattering problems in multilayer 3D bioprinting processes.