Yamana, T. | Search bioRxiv

bioRxiv2024

Tertiary lymphoid structure as a therapeutic target of Crohns disease: modulation by TNFα blockade

Inflammatory diseases, such as Crohns disease (CD), are primarily treated with therapies aimed at alleviating inflammation. Notably, biological agents like TNF inhibitors have transformed the management of these conditions. Previous research has indicated that TNF inhibitors affect the cells in the lamina propria. However, this study demonstrates that infliximab, a type of TNF inhibitor, reduces the presence of tertiary lymphoid structures (TLS) and alleviates inflammation of CD by targeting follicular dendritic cells and macrophages within these structures. TLS could also be a potential target for various biological agents used to treat inflammatory diseases beyond CD. Furthermore, our study suggests that the status of TLS may serve as a predictor for the response to TNF inhibitor treatment. Our research could pave the way for new treatment strategies and the advancement of personalized medicine for inflammatory diseases.

immunology↗

bioRxiv2018

Forecasting seasonal influenza in the U.S.: A collaborative multi-year, multi-model assessment of forecast performance

Influenza infects an estimated 9 to 35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multi-institution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the US for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of 7 targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the US, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1, 2 and 3 weeks ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision-making.

epidemiology↗