Cancer is a leading cause of disease burden globally, with more than 19·3 million cases and 10 million deaths recorded in 2020. Research is crucial to understanding the determinants of cancer and the effects of interventions, and to improving outcomes. We aimed to analyse global patterns of public and philanthropic investment in cancer research.

Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes and relaxation of mitigation measures leave many US communities at risk for surges of COVID-19 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop a framework to generate simple classification rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. This framework uses a simulation model of SARS-CoV-2 transmission and COVID-19 hospitalizations in the US to train classification decision trees that are robust to changes in the data-generating process and future uncertainties. These generated classification rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We show that these classification rules present reasonable accuracy, sensitivity, and specificity (all ≥ 80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19. Our proposed classification rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations.

Multiplexed protein analysis platforms are a novel and efficient way to characterize biomarkers in a variety of biological samples. Few studies have compared protein quantitation and reproducibility of results across platforms. We utilize a novel nasosorption technique to collect nasal epithelial lining fluid (NELF) from healthy subjects, and compare the detection of proteins in NELF across three commonly used platforms.

To evaluate the effectiveness of the Enhanced Primary Healthcare (EnPHC) interventions on process of care and intermediate clinical outcomes among type 2 diabetes patients.