Annually approximately 1.5 million Americans suffer from a traumatic brain injury (TBI) increasing the risk of developing a further neurological complication later in life [1-3]. The molecular drivers of the subsequent ensuing pathologies after the initial injury event are vast and include signaling processes that may contribute to neurodegenerative diseases such as Alzheimer’s Disease (AD). One such molecular signaling pathway that may link TBI to AD is necroptosis. Necroptosis is an atypical mode of cell death compared with traditional apoptosis, both of which have been demonstrated to be present post-TBI [4-6]. Necroptosis is initiated by tissue necrosis factor (TNF) signaling through the RIPK1/RIPK3/MLKL pathway, leading to cell failure and subsequent death. Prior studies in rodent TBI models report necroptotic activity acutely after injury, within 48 hours. Here, the study objective was to recapitulate prior data and characterize MLKL and RIPK1 cortical expression post-TBI with our lab’s controlled cortical impact mouse model. Using standard immunohistochemistry approaches, it was determined that the tissue sections acquired by prior lab members were of poor quality to conduct robust MLKL and RIPK1 immunostaining assessment. Therefore, the thesis focused on presenting the staining method completed. The discussion also expanded on expected results from these studies regarding the spatial distribution necroptotic signaling in this TBI model.
Maternal morbidity and mortality rates in the United States continues to rise, with a wide range of contributing factors such as mental illness, cardiovascular disease and systemic inequality. This metastudy provides a holistic view of the research that has been published on the issue of U.S. maternal healthcare from 2000-2022. The patterns of publications on specific topics over time can tell us what is perceived as a current major cause by physicians, public leaders, researchers, and the public. A deeper dive into systemic inequality as a cause of maternal morbidity and mortality highlights it as a major contributor to these high rates, but that progress is slowly being made through the implementation of detection and prevention tactics, as well as accessible prenatal programs and care.
In the past decade, numerous methods have been developed to analyze in-vivo calcium imaging data that involves complex techniques such as overlapping signals segregation and motion artifact correction. The hypothesis used to detect calcium signal is the spatiotemporal sparsity of calcium signal, and these methods are unable to identify the passive cells that are not actively firing during the time frame in the video. Statistics regarding the percentage of cells in each frame of view can be critical for the analysis of calcium imaging data for human induced pluripotent stem cells derived neurons and astrocytes.
The objective of this research is to develop a simple and efficient semi-automated pipeline for analysis of in-vitro calcium imaging data. The region of interest (ROI) based image segmentation is used to extract the data regarding intensity fluctuation caused by calcium concentration changes in each cell. It is achieved by using two approaches: basic image segmentation approach and a machine learning approach. The intensity data is evaluated using a custom-made MATLAB that generates statistical information and graphical representation of the number of spiking cells in each field of view, the number of spikes per cell and spike height.
damage, immune system activation, impaired protein function, or aberrant DNA methylation. In the case of DNA methylation, I demonstrate that inhibiting DNA methylation dynamics can impair long-term memory formation, while the nurse-to- forager transition is not altered. These experiments could serve as the bases for and reference groups of studies testing the effects of metal or metalloid toxicity on DNA methylation. Each potential mechanism provides an avenue for investigating how neural function is influenced by the physiological status of non-neural organs. And from an ecological perspective, my results highlight the need for environmental policy to consider sublethal effects in determining safe environmental toxin loads for honey bees and other insect pollinators.