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Radiation therapy, one of the most effective therapies to treat cancer, is highly toxic to healthy tissue. The delivery of radiation at ultra-high dose rates, FLASH radiation therapy (FLASH), has been shown to maintain therapeutic anti-tumor efficacy while sparing normal tissues compared to conventional dose rate irradiation (CONV). Though promising, these studies have been limited mainly to murine models. Here, we leveraged enteroids, three-dimensional cell clusters that mimic the intestine, to study human-specific tissue response to radiation. We observed enteroids have a greater colony growth potential following FLASH compared with CONV. In addition, the enteroids that reformed following FLASH more frequently exhibited proper intestinal polarity. While we did not observe differences in enteroid damage across groups, we did see distinct transcriptomic changes. Specifically, the FLASH enteroids upregulated the expression of genes associated with the WNT-family, cell-cell adhesion, and hypoxia response. These studies validate human enteroids as a model to investigate FLASH and provide further evidence supporting clinical study of this therapy.

Insight Box Promising work has been done to demonstrate the potential of ultra-high dose rate radiation (FLASH) to ablate cancerous tissue, while preserving healthy tissue. While encouraging, these findings have been primarily observed using pre-clinical murine and traditional two-dimensional cell culture. This study validates the use of human enteroids as a tool to investigate human-specific tissue response to FLASH. Specifically, the work described demonstrates the ability of enteroids to recapitulate previous in vivo findings, while also providing a lens through which to probe cellular and molecular-level responses to FLASH. The human enteroids described herein offer a powerful model that can be used to probe the underlying mechanisms of FLASH in future studies.

 

The classical house allocation problem involves assigning n houses (or items) to n agents according to their preferences. A key criteria in such problems is satisfying some fairness constraints such as envy-freeness. We consider a generalization of this problem wherein the agents are placed along the vertices of a graph (corresponding to a social network), and each agent can only experience envy towards its neighbors. Our goal is to minimize the aggregate envy among the agents as a natural fairness objective, i.e., the sum of the envy value over all edges in a social graph. When agents have identical and evenly-spaced valuations, our problem reduces to the well-studied problem of linear arrangements. For identical valuations with possibly uneven spacing, we show a number of deep and surprising ways in which our setting is a departure from this classical problem. More broadly, we contribute several structural and computational results for various classes of graphs, including NP-hardness results for disjoint unions of paths, cycles, stars, or cliques; we also obtain fixed-parameter tractable (and, in some cases, polynomial-time) algorithms for paths, cycles, stars, cliques, and their disjoint unions. Additionally, a conceptual contribution of our work is the formulation of a structural property for disconnected graphs that we call separability which results in efficient parameterized algorithms for finding optimal allocations.