oringham's blog

Social determinants play a large role in accessibility of resources that promote health and wellness. These include circumstances such as place of birth and residency, occupation, race, ethnicity, age, sexual orientation, gender, and disabilities. It has been seen in the past that specific social groups are not provided with the same medical treatment that people belonging to other social groups are provided with. An example of this is homosexuals affected with HIV/AIDS during the HIV/AIDS epidemic in the United States during the 1980s and early 1990s, in which homosexuals were denied medical counsel from majority of doctors due to fear and homophobia. Another example includes black and white segregation, in which people of color were not allowed to achieve the same education, shop at the same stores, and overall were unable to live at a higher standard of living due to civic restrictions, fostering decreased mental and physical health. In developed countries, it is often stigma, fear, discrimination and social segregation and condemnation that lead to inequities in access to healthcare resources.

Health inequities at the community, city, country and global level have a large impact on the overall wellness and quality of life of all populations – large and small – around the world. These differences in health among certain populations can be caused by a variety social, governmental, and economical factors that put select groups of people at an inherent disadvantage in gaining access to important healthcare and wellness resources. These inequities, although greatly rooted socially and systemically within our society, are avoidable and require extensive and important measures in order to circumvent them. In order enact change and avoid detrimental outcomes from health inequity, we must investigate where these differences in health arise from, from social, scientific and civic perspectives. 

There are many issues regarding food safety and the current methods and infrastructure in place to track food products through the journey from farm to store shelf are not sufficient. There is no standardized system for tracking food and many processors handle it differently. When there are issues with contamination, lengthy and resource intensive traceback investigations must occur. Some distributors keep records in closed databases or on paper meaning they are not readily accessible or publicly available. Frank Yiannas, Walmart VP of food safety, mentioned in an interview that after giving staff a randomly selected package of mangoes, it required almost a full seven days to trace them back to the source.  In seven days, hundreds of thousands of people can be significantly harmed by a contaminated product.

Foodborne illness carries substantial health and economic consequences. The World Health Organization (WHO) estimates that food related illness is responsible for 600 million illness and 400,000 deaths annually. The Center for Disease Control and Prevention (CDC) believes that foodborne disease affects 179 million Americans annually. Additionally, Robert Scharff, an economist for the Food and Drug Administration (FDA), estimates that foodborne illness costs the US fifty to ninety billion dollars each year. This is an ongoing problem that has not been appropriately addressed and we see outbreaks every year such as the papaya salmonella instance this year and Chipotle's E.coli outbreak two years ago.

It is possible rate of cellular movement will follow the predictions made in our hypothesis, or that it will not. The average rate at which cells migrate in order to heal the wound could be roughly the same throughout the entire process. Additionally, there could be a large discrepancy in rates of cells based on their position relative to the wound, in which case studying average rate of movement over time would not be an effective way to capture cellular movement rate.

              Displacement patterns in cellular movement across the wound could follow the predictions made in our hypothesis, or could demonstrate large differences in total displacement over time, based on a cells location relative to the wound. It is possible that cells closer to the wound will move a greater distance over time than cells further away from the wound.

LLC-Pk1 parental cells will be cultured and plated in dishes. These cells will then be allowed to grow and divide until they are at confluent levels. Once the cells are confluent, a scratch assay will be conducted in which a small portion of the cells are scraped from the plate, creating a small and consistent gap between two large patches of cells. The cells are then washed twice with HBS and submerged in non-CO_2­ media. They are then observed under phase microscopy at 10X magnification in a time-lapse fashion. The migration of the cells into the “wound” will then be captured and analyzed. Rate of cellular migration and length of time it took for the wound to close will be parameters analyzed. 

Cell migration is a fundamental and crucial component in the survival and maintenance of multicellular organisms. Organisms rely on cellular migration during embryonic development and immune responses in order to correctly organize and heal tissues within the body. Investigation into the mechanisms by which cells migrate is essential in understanding these important processes.

              One way in which cell migration dynamics can be studied is via a scratch (or wound) assay. It is done in-vitro, but greatly mimics, a cells migratory pattern in-vivo. Cells that naturally form monolayers, such as epithelial cells, are exemplary to use in this type of assay as the cells behave analogously in-vitro. The assay is done by scraping a small portion of the layer of cells off of the plate, creating a gap in between cells that were once touching in the monolayer, mimicking an epithelial “wound”. The cells are then visualized and allowed to migrate into the space where cells were scraped off of the plate, thus closing the gap and “healing the wound”. The migratory pattern that cells exhibit during this time is studied and analyzed for important parameters, such as rate and total displacement. Investigating these factors of cell migration in epithelial cells is important in order to better understand mechanisms behind the healing of epithelial abrasions, which affect a large majority of organisms.

              We predict that rate of cellular movement will not be constant but will have a steady increase in rate over time as the wound healing process goes on, and slow as the wound is close to healed. The change in rate over time will be graphically reminiscent of a normal curve. Additionally. we predict that all migrating cells will have a similar average displacement over time as they fill the wound.

Cells treated with different stimuli demonstrate different patterns of cytosolic calcium fluctuations due to the nature of the stimuli interaction with the cell. ATP stimulated cells demonstrate oscillatory calcium concentrations due to the negative feedback loops of the signaling pathway that allow calcium influx and export to be halted and resumed on a continuum. Bradykinin stimulated cells are stimulated by G-protein receptors, which when stimulated commence a signaling cascade which opens calcium ion channels on the plasma membrane of the cell. The nature of G-protein coupled receptors can be variable with respect to response, so this delayed and segmented response of fluo-4 is expected. Vasopressin stimulated cells exhibited no change in cytosolic concentration over time, despite common scientific literature stating an oscillatory response is expected. This lack of response in the cells to vasopressin could be due to insufficient time of cells observed, in which the cytosolic calcium did in fact fluctuate, but later in time and was not visualized in the time lapse. Another reason for this disappearance in fluo-4 intensity fluctuation could be due to inadequate addition of the vasopressin stimulus, so that cells were unable to react to the vasopressin.

Three additional samples of live LLC-Pk1 cells were treated with fluo-4. Control group cells were submerged in HBS buffer, while ATP and bradykinin experimental groups were submerged in calcium free HBS buffer, eliminating significant extracellular calcium. Cells were then treated with respective stimuli, and sub sequentially treated with ionomycin. Time lapse images were taken at 10X magnification for 270 s (1 frame every 2s) to capture the cellular response to the stimuli and ionomycin via fluorescent activity (Figure 4). Fluorescence intensity versus time data reveal patterns of fluorescence intensity (and therefore, cytosolic calcium level) fluctuation based on specific stimuli introduced to the extracellular space (Figure 2). Control sample (ATP/ionomycin treated cells in HBS) exhibited an oscillating fluctuation in fluorescence intensity over time (Figure 3A) and did not demonstrate a relative fluo-4 intensity increase after ionomycin treatment (Table 1). Bradykinin experimental cells   demonstrated no response over time after being treated with stimuli, whereas a majority of ATP experimental cells exhibited a response (0/101, 152/154, respectively, Table 2). However, after ionomycin introduction, both bradykinin and ATP experimental cells experienced an increase in fluorescence intensity (800, 500, respectively, Table 2) for the remainder of the time lapse imaging (32s, Table 2). Differences in control and ATP experimental groups are noted in the duration of initial calcium spike (12 s, 8 s, respectively, Table 2), avg. time to first peak of cells responding to initial stimulus (42 s, 32 s, respectively, Table 2) and increase in intensity at peak of response to the stimuli (240, 850, respectively, Table 2).

Four samples of live LLC-Pk1 cells were treated with fluo-4 (a calcium sensitive fluorophore). Each sample was then exposed to a different stimulus while submerged in HBS buffer, Time lapse images were taken at 10X magnification for 120 s (1 frame every 2s) to capture the cellular response to the stimuli via fluorescent activity (Figure 3). Fluorescence intensity versus time data reveal patterns of fluorescence intensity (and therefore, cytosolic calcium level) fluctuation based on specific stimuli introduced to the extracellular space (Figure 1). Cells treated with HBS (control group) expectedly demonstrate no significant fluctuation in fluorescence intensity overtime, and therefore were not able to quantitatively analyzed any further (Figure 1), Cells treated with bradykinin appear to have a delayed rise in fluorescence intensity over time (44s, Table 1) whereas ATP treated cells appear to have an immediate and oscillating fluctuation in fluorescence intensity over time (3s, 18s, respectively, Table 1). Only a small number of cells responded to bradykinin introduction with an increase in fluorescence intensity, while a majority of the cells treated with ATP demonstrated a response (11/154, 97/99, respectively, Table 1). Additionally, the relative increase of fluorescence intensity in cells treated with bradykinin appears to be much larger than that of cells treated with ATP (589, 331, respectively, Table 1). Vasopressin treated cells demonstrated no fluorescent fluctuation over time, and therefore were not able to be quantitatively analyzed any further (Figure 1). Reasons for this absence of an expected response are detailed in the discussion.