An important unsolved challenge in tissue engineering has been the inability to replicate the geometry and function of vascular networks and blood vessels. Here, we engineer a user-defined 3D microfluidic vascular channel using 3D printing-enabled hydrogel casting. First, a hollow L-shaped channel is developed using a template casting process. In this process, murine 10T1/2 cells are encapsulated within gelatin methacrylate (GelMA) hydrogel using UV photocrosslinking, and upon removal of the template results in a hollow channel within GelMA. Second, human umbilical vein endothelial cells (HUVECs) were cultured within the channel and immunostaining was used to visualize endothelial monolayers. Third, diffusion/permeability studies on endothelialized channels were carried out to demonstrate the barrier function of HUVEC monolayer. Taken together, we develop a facile, cytocompatible and rapid approach to engineer a user-defined multicellular vascular chip that could be potentially useful in developing new vascular model systems.

Despite the promise of stem cell engineering and the new advances in bioprinting technologies, one of the major challenges in the manufacturing of large scale bone tissue scaffolds is the inability to perfuse nutrients throughout thick constructs. Here, we report a scalable method to create thick, perfusable bone constructs using a combination of cell-laden hydrogels and a 3D printed sacrificial polymer. Osteoblast-like Saos-2 cells were encapsulated within a gelatin methacrylate (GelMA) hydrogel and 3D printed polyvinyl alcohol pipes were used to create perfusable channels. A custom-built bioreactor was used to perfuse osteogenic media directly through the channels in order to induce mineral deposition which was subsequently quantified via micro-CT. Histological staining was used to verify mineral deposition around the perfused channels, while COMSOL modeling was used to simulate oxygen diffusion between adjacent channels. This information was used to design a scaled-up construct containing a 3D array of perfusable channels within cell-laden GelMA. Progressive matrix mineralization was observed by cells surrounding perfused channels as opposed to random mineral deposition in static constructs. Micro-CT confirmed that there was a direct relationship between channel mineralization within perfused constructs and time within the bioreactor. Furthermore, the scalable method presented in this work serves as a model on how large-scale bone tissue replacement constructs could be made using commonly available 3D printers, sacrificial materials, and hydrogels.

When humans assemble into face-to-face social networks, they create an extended environment that permits exposure to the microbiome of other members of a population. Social network interactions may thereby also shape the composition and diversity of the microbiome at individual and population levels. Here, we use comprehensive social network and detailed microbiome sequencing data in 1,098 adults across 9 isolated villages in Honduras to investigate the relationship between social network structure and microbiome composition. Using both species-level and strain-level data, we show that microbial sharing occurs between many relationship types, notably including non-familial and non-household connections. Using strain- sharing data alone, we can confidently predict a wide variety of relationship types (AUC ~0.73). This strain-level sharing extends to second-degree social connections in a network, suggesting the importance of the extended network with respect to microbiome composition. We also observe that socially central individuals are more microbially similar to the overall village than those on the social periphery. Finally, we observe that clusters of microbiome species and strains occur within clusters of people in the village social networks, providing the social niches in which microbiome biology and phenotypic impact are manifested.

Sociocentric network maps of entire populations, when combined with data on the nature of constituent dyadic relationships, offer the dual promise of advancing understanding of the relevance of networks for disease transmission and of improving epidemic forecasts. Here, using detailed sociocentric data collected over 4 years in a population of 24 702 people in 176 villages in Honduras, along with diarrhoeal and respiratory disease prevalence, we create a social-network-powered transmission model and identify super-spreading nodes as well as the nodes most vulnerable to infection, using agent-based Monte Carlo network simulations. We predict the extent of outbreaks for communicable diseases based on detailed social interaction patterns. Evidence from three waves of population-level surveys of diarrhoeal and respiratory illness indicates a meaningful positive correlation with the computed super-spreading capability and relative vulnerability of individual nodes. Previous research has identified super-spreaders through retrospective contact tracing or simulated networks. By contrast, our simulations predict that a node’s super-spreading capability and its vulnerability in real communities are significantly affected by their connections, the nature of the interaction across these connections, individual characteristics (e.g. age and sex) that affect a person’s ability to disperse a pathogen, and also the intrinsic characteristics of the pathogen (e.g. infectious period and latency). This article is part of the theme issue ‘Data science approach to infectious disease surveillance’.