Gastrointestinal (GI) discomfort is a hallmark of most gut disorders and represents an important component of chronic visceral pain¹. For the growing population afflicted by irritable bowel syndrome, GI hypersensitivity and pain persist long after tissue injury has resolved². Irritable bowel syndrome also exhibits a strong sex bias, afflicting women three times more than men¹. Here, we focus on enterochromaffin (EC) cells, which are rare excitable, serotonergic neuroendocrine cells in the gut epithelium^3-5. EC cells detect and transduce noxious stimuli to nearby mucosal nerve endings^3,6 but involvement of this signalling pathway in visceral pain and attendant sex differences has not been assessed. By enhancing or suppressing EC cell function in vivo, we show that these cells are sufficient to elicit hypersensitivity to gut distension and necessary for the sensitizing actions of isovalerate, a bacterial short-chain fatty acid associated with GI inflammation^7,8. Remarkably, prolonged EC cell activation produced persistent visceral hypersensitivity, even in the absence of an instigating inflammatory episode. Furthermore, perturbing EC cell activity promoted anxiety-like behaviours which normalized after blockade of serotonergic signalling. Sex differences were noted across a range of paradigms, indicating that the EC cell-mucosal afferent circuit is tonically engaged in females. Our findings validate a critical role for EC cell-mucosal afferent signalling in acute and persistent GI pain, in addition to highlighting genetic models for studying visceral hypersensitivity and the sex bias of gut pain.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.