The most prevalent form of bioprinting—extrusion bioprinting—can generate structures from a diverse range of materials and viscosities. It can create personalized tissues that aid in drug testing and cancer research when used in combination with natural bioinks. This paper reviews natural bioinks and their properties and functions in hard and soft tissue engineering applications. It discusses agarose, alginate, cellulose, chitosan, collagen, decellularized extracellular matrix, dextran, fibrin, gelatin, gellan gum, hyaluronic acid, Matrigel, and silk. Multi-component bioinks are considered as a way to address the shortfalls of individual biomaterials. The mechanical, rheological, and cross-linking properties along with the cytocompatibility, cell viability, and printability of the bioinks are detailed as well. Future avenues for research into natural bioinks are then presented.

3D bioprinting has tremendous potential to revolutionize the field of regenerative medicine by automating the process of tissue engineering. A significant number of new and advanced bioprinting technologies have been developed in recent years, enabling the generation of increasingly accurate models of human tissues both in the healthy and diseased state. Accordingly, this technology has generated a demand for smart bioinks that can enable the rapid and efficient generation of human bioprinted tissues that accurately recapitulate the properties of the same tissue found in vivo. Here, we define smart bioinks as those that provide controlled release of factors in response to stimuli or combine multiple materials to yield novel properties for the bioprinting of human tissues. This perspective piece reviews the existing literature and examines the potential for the incorporation of micro and nanotechnologies into bioinks to enhance their properties. It also discusses avenues for future work in this cutting-edge field.

Current treatments for neurodegenerative diseases aim to alleviate the symptoms experienced by patients; however, these treatments do not cure the disease nor prevent further degeneration. Improvements in current disease-modeling and drug-development practices could accelerate effective treatments for neurological diseases. To that end, 3D bioprinting has gained significant attention for engineering tissues in a rapid and reproducible fashion. Additionally, using patient-derived stem cells, which can be reprogrammed to neural-like cells, could generate personalized neural tissues. Here, adipose tissue-derived mesenchymal stem cells (MSCs) were bioprinted using a fibrin-based bioink and the microfluidic RX1 bioprinter. These tissues were cultured for 12 days in the presence of SB431542 (SB), LDN-193189 (LDN), purmorphamine (puro), fibroblast growth factor 8 (FGF8), fibroblast growth factor-basic (bFGF), and brain-derived neurotrophic factor (BDNF) to induce differentiation to dopaminergic neurons (DN). The constructs were analyzed for expression of neural markers, dopamine release, and electrophysiological activity. The cells expressed DN-specific and early neuronal markers (tyrosine hydroxylase (TH) and class III beta-tubulin (TUJ1), respectively) after 12 days of differentiation. Additionally, the tissues exhibited immature electrical signaling after treatment with potassium chloride (KCl). Overall, this work shows the potential of bioprinting engineered neural tissues from patient-derived MSCs, which could serve as an important tool for personalized disease models and drug-screening.

We report wastewater surveillance of the spread of SARS-CoV-2 based upon 24-h composite influent samples taken weekly from four wastewater treatment plants (WWTP) on Vancouver Island, BC, Canada between January 3, 2021 and July 10, 2021. Samples were analyzed by reverse transcription quantitative polymerase chain reaction targeting the N1 and N2 gene fragments of SARS-CoV-2 and a region of the replication associate protein of the pepper mottle mosaic virus (PMMoV) serving as endemic control. Only a small proportion of samples had quantifiable levels of N1 or N2. Overall case rates are weakly correlated with the concentration (gene copies/L) and with the flux of viral material influent to the WWTP (gene copies/day); the latter accounts for influent flow variations. Poisson multimodal rank correlation accounts for differences between the four WWTP and shows a significant correlation with a significant positive intercept. Receiver operator characteristics (ROC) analysis confirms a cut-off of cases based on amplified/not-amplified experimental data. At the optimal cut point of 19 (N1) or 17 (N2) cases/week/100,000 the sensitivity and specificity is about 75% for N1 and 67% for N2.