The Use of Glucarpidase in an Open-Label Treatment Protocol as Adjunctive Treatment for a Patient with Delayed Methotrexate Elimination

Presented at: ASHP Clinical MidYear Meeting in Las Vegas. Background Methotrexate (MTX) Cytotoxic agent that competitively inhibits dihydrofolate reductase (DHFR), the intracellular enzyme responsible for converting folic acid to reduced folate inhibitors, necessary for DNA synthesis Used since 1948 in the treatment of various malignancies and as a disease-modifying agent in rheumatoid arthritis and psoriasis High-dose mexthotrexate (HDMTX) began in 1960s solely or in combination with other chemotherapeutic agents Methotrexate Toxicity Almost exclusively cleared through the kidneys Precipitation of drug occurs in the renal tubules Prolonged elevations of systemic MTX concentrations results in potential serious toxicity Increased use of HDMTX resulted in recognizable toxicities Myelosuppression Mucositis Nephrotoxicity Acute hepatitis Fatal toxicity à secondary to renal failure or sepsis Prevention of Methotrexate Toxicity Hydration Alkalinization of urine Sodium bicarbonate administration for urine pH ≥ 7 Leucovorin Counteracts cellular damage caused by MTX as it is converted to tetrahydrofolate, a precursor of DNA synthesis Does NOT reduce the amount of circulating MTX Glucarpidase (Voraxaze®) An enzyme produced in Escherichia Coli that hydrolyzes the carboxyl terminal glutamate from folic acid and its analogues, including MTX, resulting in inactive metabolites Offers an alternative to rapidly reducing the amount of MTX in systemic circulation Evaluated in 3 clinical studies à produced a clinically important reduction (CIR) in MTX concentrations in majority of patients (72/116, 62%) Most frequently reported adverse events: allergic reaction and non-allergic paraesthesi

Value of Manual Plant Identification in an Age of Drone Technology

In our research, we are training and optimizing a learning algorithm to predict plant species using drone and plant survey data. Drones collect images to produce both RGB photos and multi-spectral data that can be utilized in a variety of ways depending on application. Complimenting the drone imagery, surveying collects basic plant characteristics and GPS locations. When both of these are then given to a learning algorithm as training data it has more information than only a picture to distinguish plants. We have seen promising success in the past getting past the proof of concept. This work serves as an example of how manual plant Identification is still commonly used. With the development of AI, it’s likely that in the near future we could be identifying plants with photos alone. For now, researchers still rely on traditional methods to support these emerging technologies

Comparison of Image Processing Methods for Better Point Clouds of Sagebrush

Accurate and comprehensive monitoring, where information can be collected across multiple scales and be spatially referenced on a continual basis, is needed to create better models for sagebrush restoration efforts. By using remote sensing techniques like unoccupied aerial vehicles (UAV), researchers can collect in an afternoon flight equivalent data to field-based methods that would take days to accumulate. In this study, we used Agisoft Metashape software to process UAV imagery taken at the Soda common garden in 2019 and again in 2020. Our objective was to test the impacts of several image processing parameters on final products including point clouds. We found that changes to the parameters in Agisoft Metashape did not produce any large differences in point cloud products. However, we did find a noticeable difference in the quality of images from flights in June 2019 and September 2020. Because the images were taken at different times of year, we found the software had difficulty detecting the sagebrush in the 2020 images due to the lack of leaves, and the longer shadows cast in the fall, which resulted in a lower quality point cloud. Based on these results, our next steps will focus on testing other parameters to improve the final products generated from UAS flights in both spring and fall seasons

Drone Imaging for the Future, the Better Option for Local Scientific Advancement

Unoccupied aerial vehicles (UAVs) can capture imagery of vegetation across landscapes. Imagery can then be processed for ecological insights using two programs. The first program is Agisoft Metashape where images are loaded in from the UAV flight and stitched together to create a 3D point cloud, orthomosaic picture, and a digital surface model (DSM). The other program is QGIS where I load in the products from Agisoft and manipulate them to put into maps and view the data collected. Currently, we are focused on the imagery map and DSM to differentiate the spatial layout of sagebrush at a site with landscape heterogeneity in topography and genetic diversity of sagebrush. Sites 1 and 4 are compared because they are at opposite ends of an elevation and precipitation gradient. Site 1 is a higher elevation averaging 1843 meters, lower overall temperatures, more annual precipitation, and primarily consists of mountain sage. These qualities allow for a lusher view, larger trees, and the sagebrush is less abundant fighting for space among the other species that take hold. Site 4 has a lower elevation averaging 1622 meters, higher temperatures, less yearly precipitation, and primarily consists of Wyoming big sage. Sagebrush can grow more efficiently in a wider space without the tall trees which compete for sunlight, water, and nutrients. Now that we have these data, more drone flights will be done at varying times of year to track the vegetation over time and later compare the growth and survival rates of sagebrush. Sites like Castle Rock with high genetic diversity can function as living laboratories for understanding biodiversity. UAVs present an opportunity to study biodiverse patterns at scales that match the extent of biophysical variation, including topography

Drone Imagery Enables Fine-Scale Detection of Sagebrush Dieback During a Summer Heatwave

Sagebrush species are an essential part of the western desert ecosystem and influence local recreation and employment. These shrubs help prevent erosion, capture water, sustain wild animal populations, and more. Climate change imposes a significant threat to these populations as we see an increasing number of wildfires, less precipitation, and more high heat summer days. This project focuses on the latter issue by comparing imagery taken at Castle Rocks State Park, in southern Idaho, over the summer of 2021 during an unprecedented heatwave. We used drones to gather aerial imagery in June and September, and then I stitched together the multiple overlapping images to create one large image with high resolution. By outlining individual shrubs, I extracted the green band of the color images and calculated the average Green Leaf Index (GLI) values as an indicator of greenness for each shrub. Greenness directly corresponds to the photosynthetic activity and health of a plant. After comparing the images from June and September, I found that 72% lost values, and of these shrubs, there was an average loss of 10.8%. The other 28% of shrubs gained an average of 5%. This location is one site out of four along an elevation gradient with several sagebrush subspecies. The next step is to find the change of GLI at all four Castle Rocks sites from June and September to get a broader range of data analysis over the 2021 heatwave. These methods allow us to identify resilient shrubs that have retained their greenness during this heatwave, and we can target those shrubs to collect seeds for restoration efforts. The advantage of drone data is to create a map that spans hundreds or thousands of individual shrubs, which is more data than we could collect on the ground. This research brings vital information to the conversation of where and how to allocate tight budget funds to conserve the sagebrush steppe that so many of us depend on. It also shows the impact one hot and dry summer can have on this slow-growing plant

Predicting Sagebrush Flowering with Machine Learning

The purpose of this project is to test if we can predict which sagebrush will flower by utilizing a combination of drone imagery, remote sensing, and machine learning. Data are collected via plotted drone flights at sites in which wildfires changed the landscape and affected sagebrush growth and development. These sites are analyzed to see how sagebrush recover from fire. The Drone flights, in combination with precisely geo-referenced locations of individual plants measured in the field, allow predictions of flowering using GIS related software. Data collected in the field gives accurate positions, high resolution imagery, elevation maps, 3D images, and spectral imagery, which is useful for analyzing vegetation. These datasets are added to a GIS application. Tools within said application allow for filtering of sagebrush from other vegetation and the ground. The size and shape of the sagebrush, its UV reflectance, and data collection from the field of which shrubs are already flowering, will be the training data for a machine learning algorithm. The expected result from feeding this data is for the algorithm to be able to predict which sagebrush will flower and which will not to some degree

Using Drone Imagery to Understand Plant Species Recovery in Idaho and Peru

Drones played a crucial role in both my research projects this summer. Drone imagery was used to better understand plant species recovery in Idaho and Peru. In Idaho, we traveled to burn spots, areas that have been ravaged by recent fires, and flew drones overhead to capture aerial photos of our keystone species, Artemisia, the sagebrush. In Peru, drones were flown in areas that had begun to see more human impact, such as agriculture and trails. The species we analyzed in Peru was the native bracken fern, Pteridium esculentum, and a few woody species. My poster aims to connect both research projects through the importance of drone imaging and will showcase the benefits it can bring, as well as what limitations we were faced with