Caregiver survey in glioblastoma focused on cognitive dysfunction: development and results from a multicenter study

Aim: This study aimed to develop a cognitive dysfunction (CD) focused questionnaire to evaluate caregiver burden in glioblastoma. Materials & methods: The survey was developed from stakeholder consultations and a pilot study, and disseminated at eight US academic cancer centers. Caregivers self-reported caring for an adult with glioblastoma and CD. Results: The 89-item survey covered demographics, CD symptoms and caregiver burden domains. Among 185 caregivers, most were white, educated females and reported memory problems as the most common CD symptom. An exposure-effect was observed, with increase in number of CD symptoms significantly associated with greater caregiver burden. Conclusion: This questionnaire could guide caregiver interventions and be adapted for use longitudinally, in community cancer settings, and in patients with brain metastases.

Federated learning enables big data for rare cancer boundary detection

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.

Washing methanogenic cells with the liquid fraction from a Mars soil simulant and water mixture

Certain methanogens have been shown to grow on a Mars soil simulant following a washing procedure using a carbonate buffer. In experiments where liquid fractions from the soil simulant and water mixtures were used in place of the buffer, two out of three of the species demonstrated significantly greater methane production compared to the buffer.