A Case of Fulminant Cerebral Edema Leading to Death After Chimeric Antigen Receptor T-Cell Therapy.

Chimeric antigen receptor (CAR) T-cell therapy has transformed treatment of refractory B-cell malignancies; however, treatment puts patients at risk for side effects secondary to the amplified immune response it induces. Fulminant cerebral edema (FCE) is one of the rarest, yet most devastating side effects following CAR T-cell therapy. Due to this rarity, FCE has not been well characterized and the risk factors associated with its development are not fully understood.

Successful Treatment of Refractory Post-Transplant Lymphoproliferative Disorder With Chimeric Antigen Receptor T-Cell Therapy in a Heart Transplant Recipient.

Post-transplant lymphoproliferative disorders (PTLDs) are opportunistic malignancies that complicate the success of hematopoietic stem cell or solid organ transplantation. These disorders often arise post-transplant due to the immunosuppression required for minimizing the risk of rejection of donor tissue. First-line treatment of these disorders includes limiting immunosuppression when permissible.

Forecasting seasonal influenza with a state-space SIR model.

Seasonal influenza is a serious public health and societal problem due to its consequences resulting from absenteeism, hospitalizations, and deaths. The overall burden of influenza is captured by the Centers for Disease Control and Prevention's influenza-like illness network, which provides invaluable information about the current incidence. This information is used to provide decision support regarding prevention and response efforts.

Epidemic Forecasting is Messier Than Weather Forecasting: The Role of Human Behavior and Internet Data Streams in Epidemic Forecast.

Mathematical models, such as those that forecast the spread of epidemics or predict the weather, must overcome the challenges of integrating incomplete and inaccurate data in computer simulations, estimating the probability of multiple possible scenarios, incorporating changes in human behavior and/or the pathogen, and environmental factors. In the past 3 decades, the weather forecasting community has made significant advances in data collection, assimilating heterogeneous data steams into models and communicating the uncertainty of their predictions to the general public. Epidemic modelers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zika virus infection and Ebola virus disease.

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge.

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season.

Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s).