ABSTRACT
INTRODUCTION: Here, we explored methods to generate anti-tumor bone marrow-derived macrophages (BMDM) and how delivery of the BMDM at early tumor sites could impact disease progression. METHODS: BMDM treated with IFN-γ, sCD40L, poly(I:C), and a combination of the three were assessed. RESULTS: Treatment with sCD40L had no significant impact on the BMDM. Treating BMDM with IFN-γ impacted IL-1ß, MHC Class II, and CD80 expression. While poly(I:C) treatment had a greater impact on the BMDM than IFN-γ when assessed by the in vitro assays, the BMDM treated with poly (I:C) had mixed results in vivo where they decreased growth of the EMT6 tumor, did not impact growth of the 168 tumor, and enhanced growth of the 4T1 tumor. The combination of poly(I:C), IFN-γ, and sCD40L had the greatest impact on the BMDM in vitro and in vivo. Treatment with all three agonists resulted in increased IL-1ß, TNF-α, and IL-12 expression, decreased expression of arginase and mrc, increased phagocytic activity, nitrite production, and MHC Class II and CD80 expression, and significantly impacted growth of the EMT6 and 168 murine mammary carcinoma models. DISCUSSION: Collectively, these data show that treating BMDM with poly(I:C), IFN-γ, and sCD40L generates BMDM with more consistent anti-tumor activity than BMDM generated with the individual agonists.
ABSTRACT
Compact and automated sensing systems are needed to monitor plant health for NASA's controlled-environment space crop production. A new hyperspectral system was designed for early detection of plant stresses using both reflectance and fluorescence imaging in visible and near-infrared (VNIR) wavelength range (400-1000 nm). The prototype system mainly includes two LED line lights providing VNIR broadband and UV-A (365 nm) light for reflectance and fluorescence measurement, respectively, a line-scan hyperspectral camera, and a linear motorized stage with a travel range of 80 cm. In an overhead sensor-to-sample arrangement, the stage translates the lights and camera over the plants to acquire reflectance and fluorescence images in sequence during one cycle of line-scan imaging. System software was developed using LabVIEW to realize hardware parameterization, data transfer, and automated imaging functions. The imaging unit was installed in a plant growth chamber at NASA Kennedy Space Center for health monitoring studies for pick-and-eat salad crops. A preliminary experiment was conducted to detect plant drought stress for twelve Dragoon lettuce samples, of which half were well-watered and half were under-watered while growing. A machine learning method using an optimized discriminant classifier based on VNIR reflectance spectra generated classification accuracies over 90% for the first four days of the stress treatment, showing great potential for early detection of the drought stress on lettuce leaves before any visible symptoms and size differences were evident. The system is promising to provide useful information for optimization of growth environment and early mitigation of stresses in space crop production.
