Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data.

Despite the remarkable advances in cancer diagnosis, treatment, and management that have occurred over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires patient-specific information integrated into an appropriate mathematical model of tumor response.

A data assimilation framework to predict the response of glioma cells to radiation.

We incorporate a practical data assimilation methodology into our previously established experimental-computational framework to predict the heterogeneous response of glioma cells receiving fractionated radiation treatment. Replicates of 9L and C6 glioma cells grown in 96-well plates were irradiated with six different fractionation schemes and imaged via time-resolved microscopy to yield 360- and 286-time courses for the 9L and C6 lines, respectively. These data were used to calibrate a biology-based mathematical model and then make predictions within two different scenarios.

Latent trajectories of anxiety and depressive symptoms among adults in early treatment for nonmedical opioid use.

Background: Understanding the course of anxiety and depressive symptoms in early opioid use disorder (OUD) treatment may inform efforts to promote positive early treatment response and reduce treatment attrition.

Methods: Persons in treatment for nonmedical opioid use were identified from 86 addiction treatment facilities. Growth mixture modeling was used to identify trajectories of anxiety and depression symptoms during the first month of treatment among individuals who screened positive for depression (N = 3016) and/or anxiety (N = 2779) at intake.

Optimal Control Theory for Personalized Therapeutic Regimens in Oncology: Background, History, Challenges, and Opportunities.

Optimal control theory is branch of mathematics that aims to optimize a solution to a dynamical system. While the concept of using optimal control theory to improve treatment regimens in oncology is not novel, many of the early applications of this mathematical technique were not designed to work with routinely available data or produce results that can eventually be translated to the clinical setting. The purpose of this review is to discuss clinically relevant considerations for formulating and solving optimal control problems for treating cancer patients.

Search for lepton-flavor-violating decays of the neutral kaon.

The Fermilab KTeV experiment has searched for lepton-flavor-violating decays of the K(L) meson in three decay modes. We observe no events in the signal region for any of the modes studied, and we set the following upper limits for their branching ratios at the 90% C.L.