Category: College of Engineering and Computing

College of Engineering and Computing Making and Creating

Controlled Syringe Pump Extrusion to Create Hydrogel Gradients

Author(s): Elizabeth Clark

Mentor(s): Remi Veneziano, Bioengineering

Abstract

The primary objective of this project is to build upon my previous research where I developed a method to create hydrogel gradients. Hydrogels are comprised of polymer(s) suspended in water. A gradient is the change from one concentration of to another. I used 10% gelatin weight ratio to deionized water mixed with dye. The gelatin was heated and stirred until dissolved and then was split into two portions and dyed two different colors and then placed into syringes while at around 45 degrees. By using a specialized nozzle, I could feed two syringes into one nozzle that has a static mixer at the tip to ensure the gelatin was evenly mixed. Gelatin is a liquid at higher temps (40-50 degrees C) and sets at lower temperatures so the gradients were extruded on a chilled metal plate so the gelatin would set almost immediately. Depending on how fast one syringe extruded versus the other I could change the color and even mix them. After creating several gradients by hand I utilized a syringe pump to have even extrusion rates. The syringe pump was utilized by alternating which pump was extruding so a colleague had to move the plate. This has applications in bioprinters and rather than having somebody move the plate manually the bioprinter will move the plate or the extruder. These results build on the potential of bioprinting gradients for use in bioprinters in regenerative medicine and other bioengineering applications.

Audio Transcript

Hello, my name is Elizabeth Clark and I’m a bioengineering student and my research project was built up on my previous research project which is creating hydrogen gradient as many cellular functions and processes utilize gradient in the human body.

So for keywords and background, hydrogels are comprises a polymers in water a gradient is the change of concentration so in this case in a line and will be represented by the changing color. Gelatin is the hydrogel I used. I use the 10% concentration so 10 mL of water I would use 1 g of gelatin and a syringe pump is the tool that I use that allows for the extrusion rate and you can program different extrusion rates.

So this slide just shows briefly the set up I used in my previous research project and I modified it slightly for the gelatin. Two syringes are being fed into a custom static mixer and extruded by hand. I only did this a few times just to ensure that a different hydrogel would work. Gelatin is a liquid at warmer temperatures so around 40 to 50°C and when placed on a cooler surface, in this case of metal plate that is chilled, it would sit almost immediately set.
I have with the syringe pump and it will alter the color by which one is extruded.

Here’s a video of that. When I wanted to change the color I would just pause one syringe pump and start to extrude on the other and then flip it. And as you can see on the right is the gradient that was just created from the video

Special thanks to Oscar for funding this project as well as my mentor Dr. Remi Veneziano, as well as the other people listed. Thank you.

Laser-Induced Graphene–Nanoparticle Platforms for Plasmonic Enhanced Photosensing

Author(s): Ali Kabli

Mentor(s): Pilgyu Kang, Mechanical Engineering

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Abstract

This project explores the potential for enhancing the performance of Laser-Induced Graphene (LIG) using metallic nanoparticles (NPs) as a platform for fabricating photosensors with enhanced sensitivity. The main question being addressed is can a Laser-Induced Graphene–Palladium nanoparticle (LIG-PdNP) nanocomposite enhance sensor sensitivity through plasmonic and interfacial effects? Research has been conducted in the past regarding the use of LIG as the functional material in a photosensor, and the rationalization behind using these metallic NPs in a nanocomposite material is to improve the sensitivity of the sensor by improving the photoresponsivity. This is due to the introduction of plasmonic effects from the NPs, which allows for the photocurrent to flow more efficiently. The main novelty behind this particular project’s approach lies in the synthesis of the nanocomposite, where classic means would have the NPs deposited on the LIG surface creating point contacts. The synthesis technique being explored here involved a one-step synthesis via precursors and a polymer substrate, which creates a “seamless interface” between the components of the functional material. This interface allows for the electrons to flow freely between the LIG and NPs, enhancing the photoresponsivity of the device. Two devices were compared, one with 0wt% of PdNPs, and another with 30wt% PdNPs in order to observe any improvements in the performance of the devices when hit with a blue laser (448.2nm wavelength). Future research regarding this project includes using NPs with higher plasmonic effects such as gold or silver, as well as refining the geometric footprint and patter of the sensor itself to increase performance further.

Audio Transcript

Hello everyone, my name is Ali Kabli and today I’m going to present to you my undergraduate research project, Laser Induced Graphene Nanoparticle Platforms for Plasmonic Enhanced Photosensing. This project was advised by Dr. Pilgyu Kang from the Department of Mechanical Engineering.

So to give a brief background and introduction, past research has been done by Dr. Kang and his group, utilizing laser-induced graphene, or LIG, as a sensing element in photosensors. Now, these sensors operate based on the premise of photosensitivity. You basically shine a laser of some known wavelength at the sensor, which will induce some photocurrent. The change in photocurrent can be observed and used for sensing purposes. We want to improve the sensitivity of these devices by introducing metallic nanoparticles, or NPs, to increase the plasmonic effects and photoresponsivity of these devices. Now, for the purposes of this project, the specific nanoparticles that were used were palladium. However, any metal that has known plasmonic effects can be used.

For the purposes of this presentation, or project, we proposed a novel nanocomposite synthesis technique, which resulted in a seamless interface between the LIG and the nanoparticles. Traditional methods would have you deposit these nanoparticles on the surface of the LIG, or whatever substrate you’re using, which results in a point contact between the particles and the bulk surface. The downside to this is the fact that that point contact doesn’t allow for the most efficient flow of electrons. However, through a one-step synthesis technique using precursors and polymer substrate, we are able to integrate these nanoparticles within the surface of the laser-induced graphene itself, allowing the electrons to flow seamlessly.

So, the main question that we were answering with this research project was, can a laser-induced graphene palladium nanoparticle nanocomposite enhance sensor sensitivity through plasmonic and interfacial effects? The plasmonic effects, once again, coming from the fact that we’re using these metallic nanoparticles, and the interfacial effects coming from the seamless interface through our unique synthesis technique.

The methods and procedure for this project involved the actual synthesis of our nanocomposite using the one-step technique. Then we would fabricate the photosensor device using the synthesized nanocomposite. It should be mentioned that the scale of this sensor was 500 millimeters by 500 milliliters, which is actually quite large given the nanoscale. It’s very, very large. So that may have resulted in the data being slightly skewed, which is an improvement that we will go over at the end of this presentation. Then we collected optical data regarding the photoresponsivity of the device by hooking it up to an optical testing apparatus where we would shine a laser on and off at known intervals. The laser’s wavelength was known for the purposes of this project. We were using a blue laser, 448.2 nanometers of wavelength, and we would plot the resulting photocurrent as a function of time. The long-term goals of this project are to one day harness these nanocomposites as a platform for plasmonically enhanced PEC or photoelectrochemical gas sensors.

Now here’s just a brief snapshot of the results. We see on the left side a comparison between the photocurrent resulting from a 30 weight percent nanoparticle nanocomposite and on the right side we have the photocurrent resulting from just pure LIG. As you can see the scale on the left side is in microamps, and the scale on the right side is in nanoamps, which means that we were able to show a drastic improvement, three orders of magnitude to be exact.

In conclusion, the experiment was a huge success in proving that plasmonic effects could enhance the sensitivity of these devices. However, more work is still needed in the future. We can refine the geometry and footprint of the sensor itself so that it’s a lot smaller than 500 by 500 millimeters. We can also test other nanoparticles with known greater plasmonic effects, such as gold or silver. And we can also play around with different laser parameters, focusing the laser’s beam more, increasing the wavelength, etc.

Some acknowledgements. Of course, my advisor, Dr. Pilgyu Kang, Graham Harper, who aided in data collection on this project, and Philip Acatrinei, for being an indispensable help in data collection and in setting up the experiment itself. He actually programmed the software that we were using to collect the data. So without him, this project would not have been possible. Thank you.

College of Engineering and Computing College of Humanities and Social Science Honors College Making and Creating OSCAR Undergraduate Research Scholars Program (URSP) - OSCAR Winners

A Robotic Cat for Examining Camera Clarity and Privacy in Human–Robot Interaction

Author(s): Alexia De Costa

Mentor(s): Eileen Roesler, Department of Psychology

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Abstract

This project presents the Bioinspired Automated Robotic Cat (BARC), a functional companion robot designed to support research in human–robot interaction and privacy-aware design. BARC features camera-based facial detection, expressive gaze behaviors, audio responses, and various soft and rigid materials to mimic a household cat. Because camera systems can enhance interaction while raising privacy concerns, the ongoing study compares peoples’ responses under two conditions: a clear, high-quality camera filter and a blurred, low-clarity camera filter. Using surveys and observation of touch behavior, the study examines how camera clarity shapes engagement and perceived privacy, informing the design of social robots that are effective while respecting user comfort.

Audio Transcript

Have you ever wondered what a robot actually sees when it looks at you?
Today, social and service robots are becoming increasingly common, and many rely on cameras for facial recognition and user engagement. But as useful as cameras are, they also raise important questions: Do they make people feel watched? Can a robot feel friendly while still respecting privacy?

These questions lie at a key intersection in human–robot interaction, that robots need perception to understand us, yet high-resolution sensing can make people uncomfortable. So I wanted to explore a central challenge: can we reduce privacy concerns without making interactions less enjoyable? And does being transparent about what a robot sees change how people feel?

To investigate this, I designed and built a robot cat from scratch called BARC, the Bioinspired Automated Robotic Cat. BARC is part engineering platform and part research tool. It can switch between two controlled camera conditions: a clear, high-quality camera filter and a blurred, low-clarity filter that still allows for partial facial detection. These interchangeable physical filters let me directly compare how different levels of sensing clarity influence interaction.

BARC is also designed to feel expressive and lifelike. It uses camera-based facial detection for gaze behavior, animated OLED eyes, a speaker for cat-like sounds, and soft and rigid materials that mimic the look and feel of a household cat. Through surveys and observations of touch behavior, my ongoing study explores how these two camera conditions shape user engagement and perceived privacy.

To create BARC, I began with feline anatomical references, studying limb placement, joint spacing, and overall proportions, to inspire the CAD model for the chassis. I laser-cut the acrylic components and assembled them using screws and tab-and-slot joints for a sturdy, lightweight frame.

At the heart of the robot is a Raspberry Pi 4, which handles perception and behavioral control.

A camera provides the main sensory input for facial detection.

Two OLED displays animate expressive eyes that track the user once a face is detected, giving the illusion of attention and social presence.

A speaker and amplifier generate a range of cat sounds, from meows to purrs to alarmed yowls.

An accelerometer-gyroscope detects movement, such as being picked up or shaken, so BARC can respond appropriately.

Servos are controlled by a PCA9685 driver, animate the limbs, jaw, head, and tail.

All behaviors are programmed in Python and organized in a state machine with modes such as Idle, Seeking Attention, Interacting, and Startled. BARC transitions between these states based on sensory input and probability, helping interactions feel natural rather than scripted.

To examine how camera clarity influences engagement and privacy perceptions, BARC serves as a fully capable research platform. Seventy-two participants are currently part of a single-blind study with two groups:

Group 1: interacts with BARC using a clear camera filter

Group 2: interacts with BARC using a blurred, privacy-preserving filter

The physical filter is noticeable, so using filters in both groups keeps the robot visually consistent. That way, any differences we see are truly due to what the robot can or can’t perceive.

Participants interact with BARC, complete a survey measuring constructs such as Perceived Sociability and Perceived Enjoyment, and then are shown a live camera feed so they can see the actual resolution of the robot’s vision. Afterward, they complete a second survey measuring perceived privacy, perceived surveillance, disturbance, and attitudes about robots.

The hypotheses are:
1: No difference in sociability, enjoyment, or touch behavior.
2: The filtered-camera group will report higher perceived privacy.
3: The clear-camera group will report higher perceived surveillance.

This interdisciplinary project connects mechanical engineering, psychology, and human-robot interaction to better understand how people perceive robotic sensing. BARC’s expressiveness, biological inspiration, and controlled camera conditions make it a powerful research platform.

By comparing clear versus filtered camera views, this research explores whether privacy concerns come from what the robot actually sees, or what users believe it sees. Ultimately, the goal is to guide the design of future social robots that remain engaging and respectful of user’s privacy

Special thanks to Dr. Eileen Roesler (Psychology) and Dr. Daigo Shishika (Mechanical Engineering) for their invaluable mentorship. Thank you to Katya Schafer for assistance with data collection, and to Dr. Karen Lee and OSCAR for their support and funding, which made this project possible.

Thank you!

Cells, Individuals, and Community College of Engineering and Computing OSCAR Undergraduate Research Scholars Program (URSP) - OSCAR

Pain, Medication Use and Biomarker Associations in Individuals with Polycystic Ovary Syndrome (PCOS) : Insights from the All of Us Research Program

Author(s): Jannatul Nayeem

Mentor(s): Jenny Phan, CASSBI

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Abstract

Background:
Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder often accompanied by chronic pain, yet the biological and social determinants of this pain remain underexplored. Understanding how stress-related biomarkers and healthcare access interact in shaping pain experiences may reveal mechanisms underlying health disparities in PCOS populations.

Objective:
This study examined associations between inflammatory and neuroendocrine biomarkers (C-reactive protein (CRP), cortisol, and body mass index (BMI)) and pain burden among individuals with PCOS, while exploring the moderating role of healthcare access and insurance coverage.

Methods:
Using data from the All of Us Research Program, 2,160 adults with PCOS (identified by ICD-9/10 codes) were analyzed. Pain burden was measured through pain-related diagnoses and pain medication dosage. Biomarker distributions were winsorized, log-transformed, and analyzed via multivariate regression models adjusting for age, race, socioeconomic status, and healthcare variables.

Results:
Pain burden alone was not significantly associated with higher CRP, cortisol, or BMI levels. However, healthcare access moderated these relationships: participants with greater barriers to care exhibited elevated inflammation and BMI with increasing pain, whereas those with adequate access showed flatter or reduced biomarker trends.

Conclusions:
Findings suggest that chronic pain and stress responses in PCOS may be shaped more by social and contextual factors than biological burden alone. Enhancing healthcare accessibility and equity could mitigate stress-related physiological outcomes and improve pain management for individuals with PCOS.

Audio Transcript

0:01 Hello, my name is Jannatul Nayeem. I am a student researcher with the B&LAB, um, at George Mason. I’m working directly with, um, Dr.
0:13 Jenny, um, in her our static load study. Which is the body’s biological stress response, and how it relates to menstrual disorders and chronic pain.
0:23 Um, from that study, I wanted to dive deeper into PCOS, and look at pain medication use and biomarker. Or associations and individuals with that disorder.
0:36 Umm, for methodology, I started off by using, umm, the all of us data set, uhh, database. Umm, it has a ton of data on- individuals, uhh, with all sorts of diseases and, umm, information from their doctor visits, umm, patient records, and also, umm, some survey questions that, the program itself asks
1:06 those participants, umm, and so through that, through that database, I was able to find what 2160 individuals with PCOS, and dive deeper into, uhh, their- biomarkers, uh, specifically for this, I’m using, umm, 3 biomarkers as predictors for inflammation and stress.
1:30 I’m using BMI, C-reactive protein, and cortisol. And then, umm, for- for their outcome variable, I use pain diagnosis along with their medication usage, umm, for medication usage, umm, I accounted for, umm, how many medications they’re taking.
1:52 And, and also what the dosage was for that medication, umm, and then some co-variates, such moderators that, sorry, some co-variates that I used was age, race, uhh, and SES index, and then for moderators, umm, I looked at healthcare access, uh, specifically insurance insurance status and access to care
2:19 . there. And so, I’m going to zoom into the results that I had, umm, hopefully in the video it resumes in two.
2:33 umm, but for my results, I found that, umm, pain alone didn’t start- we predict inflammation or stress, but limited access to care did.
2:45 Individuals with more barriers, such as lack of insurance, showed higher inflammation and BMI with pain, suggesting that health equity plays a critical role in PCOS pain.
2:56 Um, while my insurance data was limited, uh, because, uh, that not many people answered those questions, um, there’s still show some support that the idea- that stress biology and pain in PCOS are influenced by social environment and not just physiology.
3:22 Um, right now, I am continuing this study, um, to- or they’re deep in my understanding and advocate for equitable pain care in PCOS populations.
3:36 Umm, and so one thing I want to focus on more is, uh, imp- moving, um, how we, uh, state these questions, because I do feel like how the, uh, question is stated about access to care and insurance status is pretty sensitive.
3:58 So how can we go about it to, change the way, um, someone feels about answering those type of questions. Um, and so yeah, that was my study.
4:09 Thank you for listening.

College of Engineering and Computing OSCAR

A Machine-Learning Approach for Studying Disinfectant By-Products (DBPs)

Author(s): Isaac Amouzou

Mentor(s): Ben Seiyon Lee, Department of Statistics

Disinfectant byproducts or DBPs for short are chemical compounds that form when disinfectants in water (ex: chlorine) react with natural organic matter. Chronic DBP exposure can cause significant negative health effects, such as bladder cancer, colon cancer, and pregnancy complications. DBP exposure is difficult to measure so a surrogate is needed to model DBP exposure. In the previous spring we developed statistical models that could accurately model the relationship between specific DBPs (selected using a feature selection method called lasso regression), categorical variables and toxic DBP classes when taking into account the public water system of origin. This project developed a classification framework for detecting at-risk public water systems using a machine learing method known as feed-forward neural networks. We also created an interactive tool for allowing anyone regardless of prior experience to explore the data used for the project, perform predictions using the previously developed models on the DBP classes, and evaluate the models with the DBP classes using either ICR data that contained data from over 13,000 measurements from 295 different public water systems or from data given by the user.

hello I am Isaac Amouzou and this is my project a machine learning approach for studying disinfectant byproducts or dvps disinfectant byproducts or chemical compounds that form when disinfectants in water react with natural organic matter some negative health effects that can come from chronic disinfectant byproduct exposure are bladder cancer colon cancer or pregnancy complications highly disinfectant water sources can lead to DBP exposure to ensure drinking water safety it is imperative that DBP levels and public water sources be properly monitored

dbp exposure is difficult to measure directly so surrogates have been used for example trihalomethanes or THMs are easier to measure and it was believed that THM concentrations are proportional to concentrations of other dbp classes a past study examined the link between THMs and a dbp class Haloacetonitriles or Hans the data consisted of over 9500 measurements from 248 Public Water Systems the result was that thms could only explain 30% of the variance in HANconcentrations

previously in Spring We examined four DBP classes using statistical models for this current project our goals were to create a framework to classify at-risk Public Water Systems given a DBP threshold using machine learning methods and to create a publicly available interactive tool to explore our data to generate and download predictions and evaluate model performance our data is from the information collection request database from the Environmental Protection Agency the data set contains over 13 000 measurements from 295 Public Water Systems

this is our framework for classifying at-risk Public Water Systems for class the classification we use a neural network model in this section we prepare the data to be processed and passed into the neural network for classification here we created a threshold to classify at-risk water systems and to prepare the DBP target class haa9 a specific threshold we used here was 72 micrograms per liter and we did 1.5 standard deviations off that limit so any public water system that is above 72 minus one and a half standard deviations will be classified as at risk and any below will be classified as save here we split the data into training and validation sets this is done because when we want to train our new network model we want to split up the validation sets so we can train with the specific training set and then only validate with data that hasn’t been seen before

here we want to prepare our data loader and batch data and batching is essential for stochastic gradient descent or SGD because the model updates its perimeters after processing each batch instead of updating after processing the entire data set this introduces Randomness in the update process and can lead to faster convergence and better generalization

this is the structure of a feed forward neural network a feed fowardr neural network is consisted of an input layer one or more hidden layers and then to an output layer each layer in the neural network will consist of a neuron which is a node that can take input and pass output a neuron also performs a computation with weights and biases before passing the computation through an activation function which is a function that adds non-linearity to a neural network which helps it the model learn complex relationships some common activation function are relu tanh or softMax here is the training and validation procedure for our neural network

here’s our neural network structure we have 326 inputs and two outputs with where the two outputs are either at risk which is represented by one meaning above the threshold or zero which means not at risk or below the threshold we have one hidden layer of 164 neurons and we have selected railroad as our activation function

here are the metrics we will use we have Precision which is sometimes known as positive predictive rate it is a measure for evaluating how well a model is at classifying positive or in this case at risk Public Water Systems and as well as recall which is sometimes known as true positive rate which is a measure for evaluating situations when for all cases where the data or at-risk Public Water Systems can be identified as positive and how accurate the model is at classifying them as positive

here we have an Epoch which is a single pass through the entire training data set and in the process of turning this model we have we use 18 epochs this is the final evaluation section and here we have a recall score of 0.72 of 72 percent the Precision score of 0.79 or 79 in an accuracy score of 0.83 or 83 which is just checking out how accurate a model was at classification

this interactive tool was made using R shiny the first panel shows a map of the Public Water Systems being used here on the all tab this denotes all public water systems for each DBP class when hovering over a specific marker you will be able to see the average concentrations of these DBP classes the public water system ID and the public water system name when you click on a marker you’ll be presented with a link to the EPA water system report on the public the specific public water system as well as the icr water system report

when you select a DBP from the drop down you it will you’ll be shown all public water systems that reported that specific DBP level and it will denote how close they are to a risk threshold with red being above the risk threshold yellow being within one standard deviation of the risk threshold and green being more than one standard deviation away from the risk threshold

on the predict panel if you have data that you would like to generate predictions for you can upload it as a CSV file like so and the dashboard will automatically remove any rows that have missing DBP or categorical values you can select which model you would like to predict with as well as which DBP class you would like to predict and generate your predictions here you can see the predicted value of the DBP class HKS or haloketones and the actual value although to generate predictions you do not need the actual value and if you want to download the data you can just simply click this button and download it as well

on the evaluation panel similarly you will also have to select a model that you wish to evaluate as well as which class you would like to evaluate the model on and again I’ll select hello ketones and you have two options here if you have data that you wish to test the model on yourself you can input the data as well as a similar process before and I’ll also remove any missing rows this time you actually will need the actual the class value in order to evaluate how well the model performs you’ll still have to click the evaluate button and you will get these metrics that appear the average prediction error is the average difference between the predicted DBP and the actual DBP the average percentage prediction error is the average percentage difference between the predictions and the actual DBP MSE or mean squared error is assesses the average square difference between the observed and predicted values rmsc is the square root of MSE so it’s called root mean squared error and while MSE measures the units that are the square of the target variable rmsc measures this in the same units as the target variable and here adjusted r squared is the proportion of the DBP class with the model can explain and I could also evaluate to see that a linear mix model as well if I wanted to use the icr data I can input seed which allows for random selection and reproducibility of results I can also input the training proportion which is how much of the data set the model will use for training so in this case 0.7 or 70% of the data will be used for training and I can evaluate here

the metrics are going to be the same but instead they are calculated on a test set which is kept separate from the training set here we also again have the test average prediction error which is the average prediction error on the test set the average percentage prediction error on the test set as well and MSE and RMSE on the test set too the new metrics we have here are a conditional r squared which is the proportion of the DBP class and in this case HKS are haloketones that the model can explain and the margin r squared which is the proportion of the DBP class that the fix effects or variables without taking into account public water system levels can explain and this is an interactive tool that you can use to explore the data as well as predict and evaluate the models using the models using neural networks we have created a framework to classify water systems that fall above or below a risk threshold we have created an interactive tool that will allow the users to examine the data that was used predict with and evaluate using the models and we have we will we plan to continue to develop and improve our classification framework and interactive tool we also plan to implement imputation to take into account the missing records for certain DBP classes

and these are citations and thank you for listening to my presentation

College of Engineering and Computing Honors College OSCAR Summer Team Impact Project

Optimizing Sensitivity of Methods for Detecting Analytes Released During Inflammation

Author(s): Joelle Nguyen

Mentor(s): Caroline Hoemann, Bioengineering

During inflammation, there are microparticles that are released from the formation of blood clots. From these microparticles, bioactive lipids such as eicosanoids, which can be further categorized as prostaglandin, thromboxanes, and leukotrienes, are released. There are also proteins such as LOX-1, known to participate in the pathogenesis of atherosclerotic cardiovascular disease and lung cancer and is expressed by neutrophils in patients with COVID-19. Soluble LOX-1 protein (sLOX-1) was found to become elevated in blood plasma of patients with diabetes, atherosclerosis, non-small cell lung cancer and acute myocardial infarction. The clinical challenge is that these biomarkers are difficult to detect because they are at low concentrations in the bloodstream. The goal of my project is to develop more sensitive methods to detect these biomarkers to aid in the diagnosis, monitoring, and progression of diseases.

This project involved creating a lipidomic and proteomic profile of human blood plasma and serum samples. All samples were from de-identified healthy donors under IRB-approved protocols with written informed consent. Liquid Chromatography – Mass Spectrometry (LCMS) is the ideal method of creating lipidomic profiles of lipid analytes due to its high specificity and sensitivity. Plasma samples’ lipids contents were extracted using 2:1:1 v/v chloroform/methanol/water or 3:2 v/v hexane/isopropanol. These extraction methods were compared for their sensitivity in detecting certain analytes, and it was found that 3:2 v/v hexane/isopropanol was the better extraction method. Nanotraps, made of various types of nanoparticles, were utilized to bind to sLOX-1 in sample and deplete them for analysis using ELISA, where it was discovered that the majority of sLOX-1 fraction was depleted by the Nanotraps in citrated plasma/serum with recombinant sLox-1 but not much in human blood serum alone. Future sample preparation modifications are needed before LC-MS analyses of Nanotrap-associated sLOX-1.

Hello my name is Joelle Nguyen and this is my project on “Optimizing the Sensitivity of Methods for Detecting Analytes Released During Inflammation.” To start with an introduction on the problem I’m trying to address with my project. The biomarkers involved in inflammation and found in the blood are difficult to detect at low concentrations so it’s necessary to develop assays and lab techniques that have enough sensitivity to detect these biomarkers. These biomarkers can include bioactive lipid mediators such as thromboxanes, prostaglandin, and leukotrienes which form from the oxidation of arachidonic acid, and there are biomarkers categorized as proteins such as the sLox-1 receptor. Specific eicosanoids as seen in this diagram here including thromboxane and HETE species are pro-inflammatory mediators that can play a role in platelet aggregation or inflammatory responses. sLox-1 receptors are elevated in blood plasma of patients with diabetes, atherosclerosis, non-small cell lung cancer and acute myocardial infarction. Detecting these biomarkers can aid in the diagnosis and monitoring of diseases as well as their progression. The hypothesis tested was that methods can be developed to improve the sensitivity of inflammatory biomarker detection by LCMS for lipids and proteins.

So to move on to the methodology of my project, there were two approaches. One was comparing two different extraction methods for the lipid analytes found in blood plasma samples for a lipidomic mass spectrometry analysis, and the other was examining the extent of depletion using nanotraps on the sLox-1 protein receptor in plasma/serum samples for a proteomic mass spectrometry analysis. To go into more detail on the lipidomic experiment, I had compared 2:1:1 v/v chloroform/methanol/water with 3:2 v/v hexane/isopropanol as potential extraction solvents. To compare these methods, I analyzed the peak areas of lipid analytes from duplicate citrated plasma samples and used JMP to provide a descriptive statistical analysis. For the proteomic experiment, there were six dyed nanoparticles used as Nanotraps to deplete sLox-1 in plasma/serum samples and an ELISA test was done to analyze the concentration of depleted sLox-1. The samples were not analyzed with mass spectrometry quite yet but the ELISA results provided future directions for sample preparation and LCMS analysis.

Here are the results for the lipidomic profile made using LCMS. The experiment had two different blood donors, and for this donor here you can see that there is a higher peak area for lipid analytes extracted by hexane on the JMP graph to the left and visually to the right you see that 3:2 v/v hexane/isopropanol has a higher peak area for the specific lipid analytes called 4-HNE-H2O. The same can be said for the second donor in my experiment where again the hexane extracted analytes had a higher peak area both on the JMP plot and the mass spectra graph for 4 HNE-H2O. Now for the mass spectrometry proteomics experiment, there was the ELISA data collected which showed that not a lot of sLox-1 depleted from the nanotraps, only around 20% or less for serum with free sLox-1. The figure to the right shows that citrated blood plasma combined with recombinant sLox-1 protein had a much higher depletion of sLox-1 with 90-100% depleted.

Therefore based on the results of my experiments, the 3:2 v/v hexane/isopropanol is the better solvent to use for extracting lipids from citrated blood plasma samples for a lipidomic profile. For the proteomic experiment, the nanotraps shown in the excel bar graphs showed that they did not interfere with ELISA detection of sLox-1 in control samples. The nanotraps only depleted 0 to 20% of free sLox-1 in serum and depleted 40-100% of recombinant sLox-1 in citrated plasma. The next steps for my project would include doing more experimental trials with both extraction methods to see that 3:2 v/v hexane/isopropanol produces reproducible data. There could also be future modifications to plasma/serum sample preparation to improve sLox-1 binding to the nanotraps.

Thank you for listening and I’d like to acknowledge Dr. Hoemann, Dr. Girgis, Dr. Karen Lee, Dr. Luchini, Rayan Ibrahim Alhammad, and Julia Leonard for their help throughout this project.

College of Engineering and Computing OSCAR

Patient Specific Three-dimensional Biomechanical Eye Models from Magnetic Resonance Imaging

Author(s): SuJung Rodriguez

Mentor(s): Qi Wei, Department of Bioengineering

The purpose of this research project was to create a three-dimensional, patient-specific biomechanical model of the eye from magnetic resonance imaging. Previously, a pipeline was developed which took two MRI datasets, data collected in the coronal and sagittal view, as inputs and created a model of the eyeball and extraocular muscles (EOMs) as the output. One limitation of this pipeline is that oftentimes, data in the sagittal view will be unavailable. This is because data collection is very lengthy and uncomfortable for patients. Therefore, this pipeline needs to generate a model when only coronal data is available without sacrificing accuracy. To accomplish this task, we artificially generated sagittal data using data in the coronal view and fed it into our pipeline. We discovered that four of the EOMs, the rectus muscles, did not require sagittal data to be generated. The pipeline was modified to not expect sagittal data when modeling the rectus muscles. We then modeled the inferior oblique and found that it was only visible in a few slices of the artificially generated sagittal data. As a result, we could not see the origin point and had to hardcode it in based on what is documented in literature. When trying to model the last muscle, the superior oblique (SO), we found that it was not visible in our artificially generated sagittal data. We will have to hardcode in where the SO inserts on the eyeball and interpolate a line to follow the path we expect to see the SO follow. Once all muscles have been modeled, we will use this model to simulate surgical outcomes and provide ophthalmologists with a quantitative way to assess surgical outcomes.

Hi! My name is SuJung Rodriguez, and I will be presenting my work on 3D biomechanical eye modeling.

So the objective was to create a patient-specific three-d biomechanical model of the eye and the corresponding extraocular muscles

Some background on why we would want to model those muscles. So those muscles are responsible for eye movement, as you can see. Here you have 4 rectus muscles which are responsible for moving your eyes up down, left, right. Then you have your oblique muscles which are responsible for your eye being able to twist. So any deviations in those muscles from healthy physiology can cause vision disorders, a common one, being strabismus. That’s a case where your eyes cannot look at the same target at the same time. So modeling those muscles can provide valuable insight when planning treatment. And currently, when ophthalmologists are trying to treat vision disorders, they have no quantitative measures. So they go in and decide which muscles to recess, and how much to recess them by based on their own intuition and experience. So, being able to actually model those muscles, would be able to give them a quantitative measure and improve surgical outcomes

Some previous work that was done.

So Bassam Mutawak developed a pipeline which uses 2 MRI data sets to generate a patient-specific model of the eyeball and extracurricular muscles. Basically, those 2 MRI data sets, they’re collected in 2 different views. So you have what’s called the coronal view and the sagittal view. So in each data set, those extraocular muscles are traced. They’re sent into the pipeline, and an iterative closest point registration algorithm is used to register points from the 2 data sets. Basically what it does is it takes your points in the coronal view, and then it looks for points in the sagittal view that are the closest to those points, and then it computes the amount of translation and rotation necessary to have those 2 different points be aligned. Those 2 data sets be aligned. The traces of muscles are then plotted, and these centroids are computed, and from there the muscle paths are generated.

So here’s what those datasets look like. This is what the data in the coronal view looks like. As you can see, these dark bands are the extraocular muscles. Here’s what data in the sagittal view looks like. Again, those dark bands you see are the muscles. And here’s the model that gets generated.

So my task was to make this pipeline work when it only has one data set the coronal view available. The reason why is data collection is actually very long and uncomfortable for patients. So most of the times we are only able to get one view. We focus on the coronal view. The only issue is that the inferior oblique and superior oblique, their tendons are only reliably imaged in the sagittal view.

So we focus on one eye. First, the oculus dextrous or OD. Because MRI data is actually a 3D volume, we can actually take our coronal data and re-slice it essentially so that we can artificially generate our own sagittal data and try to get the inferior oblique and the tendon of the superior oblique. We then model the rectus muscles, the oblique muscles, and while we’re doing that, we’re checking against our ground truth measure, which is the model that was generated when we had data from the coronal and sagittal view available.

So to model the rectus muscles, the rectus muscles actually do not require sagittal data to be modeled. As you can see here, these green traces are the data from the coronal view. The black traces you see here are from the sagittal view. So you just modify the code as needed. And basically just making sure that this pipeline isn’t expecting 2 data sets when it’s only going to be getting one

Next is to model the interior oblique. So here we trace it in the coronal view. It’s this muscle here. You see here the point where it’s connecting to the socket. That’s actually the origin point which will be important for later. And here we also trace it in the sagittal view. As you can see, it’s this little black dot here. So we re-register the points from the generated sagittal data that we’ve created to the points in the coronal data. We hard code in the origin point based on values found in literature. The reason we do this is because, as you can see in our artificially generated sagittal data, the top of the eyeball gets cut off, and the inferior oblique is pretty far back. So it’s only visible in anterior slices, and it’s just not visible in our artificially generated satchel data. It’s only there in 2 or 3 image slices. So, after we hardcode in our origin point, we plot the coronal traces of the inferior oblique muscle, and compare it to the origin point that we hard-coded in. If the origin point that we hard-coded in is still very far from that point we see where it’s connecting to the socket, then we adjust it again.

So, as you can see here in order to get our hard-coded origin point from literature, we take one model that was created by Dr. Wei and Bassam Mutawak. That was more generalized. It wasn’t data-driven, patient-specific. It was based on what we expect to find in the literature. And then we take the model that I’ve generated, as you can see here where it is, data-driven and line them up. So as you can see here, these green, these big green asterisks are from Dr. Wei’s model, and then these actual like muscle paths, are from mine. We line them up so as you can see here, because there just was not a lot of data to build it off of the inferior oblique isn’t going in the direction we needed to. It’s starting to point out. And we take this last point that you see here, and hard code that in as our origin point. We then rerun the model, plot the data on the coronal views, see if it’s close to that origin point. We see where it’s joining the socket and make an adjustment. And this is the model that we finally generate. So, as you can see here, it matches up what we see in our ground truth, measure, and the shape also looks as we expected it to, and when we check the muscle lengths they line up with what we see in literature.

Next step is to model the superior oblique. So because the superior oblique tendon is only visible in very anterior MRI images. It’s very difficult to model. When we artificially generate our sagittal data, it’s not visible in any slices. So a possible solution is to hardcode in where it inserts on the eyeball and fit a line to wrap around the eyeball, so that we’re still seeing that shape that we expect to.

So our future directions are to wrap up the superior oblique modeling. Then the model is done, so we’ll send it to a dynamic simulation environment, and what we’re going to do is we’re going to simulate how these patients were seeing before they had any surgery. And then, because we have these surgeon’s nodes, we have the notes of like which muscles they recessed, how much they recessed them by and how their vision was after, and we again in that dynamic simulator basically mimic what those surgeons did, and then see if the way the eye is moving is the same as what the surgeon documented. If that’s not the case and the model is not working, we would make adjustments as needed. But the end goal ultimately is to be able to send these to a dynamic simulation environment and simulate surgical outcomes

And for acknowledgments, thank you to my mentor, Dr. Wei, at the Department of Bioengineering, Dr. Demer for providing us with MRI images. Bassam Mutawak, who created the original pipeline and has helped, and OSCAR GMU for supporting this project.

College of Engineering and Computing OSCAR

Mechanical and Surface Characterization of 3D Printed PLA-HA Composites

Author(s): Muhammad Sardar

Mentor(s): Shaghayegh Bagheri, Department of Mechanical Engineering

Numerous studies in the field have commonly relied on a singular mixing method for composite formulation, without delving into the comparison of various mixing techniques. Consequently, the primary objective of this research is to identify the most optimal manufacturing process for formulating FDM printed PLA/HA composite structures, achieved through a comprehensive comparison of four distinct mixing methods. These methods include the utilization of a magnetic stirrer, and speed mixer, which will be systematically examined for fabricating PLA/HA material. In order to arrive at a sound conclusion, this study will conduct thorough mechanical and tribological characterizations of the FDM printed PLA/HA structures, aiming to ascertain the best methodology for fabricating PLA infused with 3% wt of HA. By presenting these findings, this research aims to contribute significantly to the collective knowledge surrounding FDM printing and the formulation of PLA-based composites. The data obtained will offer valuable insights for the advancement of this innovative field.

Hello everyone and welcome to my project on mechanical and surface characterization of PLA/HA composites.
The main objective of this research is to find an optimal manufacturing method for PLA/HA composite, based on the comparison of three mixing methods. We have seen that previous studies have only focused on the weight percent of Hydroxyapatite in the PLA samples, but no studies have been found yet to focus on the manufacturing method and how it affects the mechanical characterization and surface area of the sample. So, for that purpose, we have proposed three manufacturing methods that includes magnetic stirring, dry speed mix and wet speed mix.
Now, the dry and wet speed mix are not that different, except, the wet speed uses an organic compound called dichloromethane or DCM, which exists in a liquid state at room temperature. Once the material is ready, it is then used for filament extrusion.
This is machine, called extruder where the raw material is fed into which melts these pellets at about 210 Celsius and we can see the filament is coming out on the other side. This filament is then inserted into a 3D printer to print the sample.
This is the next step called indentation, where the machine applies and gradually increases the load to access the hardness and elasticity of the sample. This step is then later on key to analyze the tribology and surface properties of the sample.
From Electron Microscope images, we can see the indentation that was done on the surface with a closer view on the right side.
Apart from the SEM images, we have also done something called EDS, or electron dispersive spectroscopy. Basically, what it does is the electron beam in the electron microscope emits high energy electromagnetic waves, or x-rays, on the surface of sample which results in ejection of innermost electrons and hence, we can see what the chemical composition of the sample is.
As expected, we have seen an abundance of carbon followed by oxygen and phosphorus. The gold is only present because the sample was gold coated before doing EDS to prevent it from burning. All the colored areas indicate the presence of their respective element, and we are also given a spectrum as to the percentage of each element is present.
I think it is important to mention here about the limitation of our Scanning Electron Microscope as it cannot detect hydrogen. We have seen some cases where the percent composition is not adding up to 100% so it’s probably most likely that this is hydrogen unless we know that our sample does not have any trace amount of hydrogen in it.
PLA/HA composites have plethora applications in the real world from aerospace to modern medicine to especially in bioengineering where it is used in bone fracture repair, which is the motivation of our research.
Now this concludes my video and if you have any questions about my research, don’t forget to comment down below and I will reach out to you as soon as possible. Thank you very much!

College of Engineering and Computing OSCAR

Effect of Inflammation on lipid nanoparticle performance

Author(s): Peter Touma

Mentor(s): Caroline Hoemann, Bioengineering

The purpose of this study is to determine the inhibition constant (Ki) of beta glycerol phosphate as an inhibitor of alkaline phosphatase using 4-Methylumbelliferyl phosphate (4-MUP) fluorescent dye. Alkaline phosphatase (ALP) is an enzyme responsible for the hydrolysis of organic phosphate esters present in extracellular space. In other words, it removes phosphate groups from other molecules, such as nucleotides and proteins. The tissue-nonspecific isozyme of ALP (TNAP) is commonly found in bone. When the ALP enzyme hydrolyzes the pyrophosphates and phosphate esters present, inorganic phosphate is supplied to the bone tissue, enhancing mineralization. Beta glycerol phosphate is a substrate of ALP, typically found in the liver. ALP activity can be visualized with many different fluorescent techniques, such as using 4-Methylumbelliferyl phosphate dye. An ALP stock solution of 20 U/mL was used, as were stock solutions of 200 mM and 10 mM for beta-GP and 4-MUP, respectively. These solutions were serially diluted into working solutions. The working solutions consisted of a 0.1 U/mL of ALP, which would be used for each well containing ALP in the 96 well plate, as well as 0.05 mM, 0.1 mM, 0.5 mM, 1mM, and 2 mM for beta-GP, and 20 ÂµM ,50 ÂµM, 100 ÂµM, and 200 ÂµM for 4-MUP, which would be varied in wells according to the plate layout. By conducting a plate reading, the fluorescent dye was able to detect enzyme activity when different concentrations of the beta-GP inhibitor were added. A trend showing that an increase in beta-GP concentration was directly related to a decrease in ALP activity was observed, confirming that beta-GP does act as inhibitor to ALP. The inhibition constant (Ki) found was 240.29 ÂµM. This value is like that found in related literature. ALP activity was also found to be higher at pH 9.25 than at neutral pH.

Hello, my name is Peter Touma and my project is using beta glycerol phosphate as an inhibitor of alkaline phosphatase. The purpose of this study was to determine the inhibition constant or Ki of beta glycerol phosphate as an inhibitor of ALP using the 4-Mup fluorescent dye at an alkaline and neutral PH. This is being done to see if beta GP can act as an inhibitor of a op at physiological Ph as well as to see how great the inhibition is when different concentrations of beta GP are used. ALP enzymes are found naturally in the bloodstream liver bone and the kidney ALP is also responsible for the hydrolysis of organic phosphate ethers present in the extracellular space. And beta GP is a subtrate found in bone tissues and promotes mineralization. In blood coagulation. Beta GP could inhibit ALP activity towards other organic phosphates as well. The hypothesis of this study was that beta GP would act as a competitive inhibitor with the form EP substrate for calf intestinal alkaline phosphatase. Solutions were placed accordingly to the plate layout with column six and seven containing the positive and negative controls respectively. The same plate layout was used for each PH tested. Stock solutions were made for the A LP. Beta GP and four Mup. And then serial dilutions were made using these and placed in the 96 world plate accordingly. Stock solution to ALP was 20 units per milliliter. While for four Mup it was 10 milli molar and Beta GP had a 200 milli molar stock solution solutions included a 0.4 unit per milliliter working solution. A LP which had a final concentration of 0.1 units per milliliter in the plate. Beta GP had working solutions of 0.1 milli molar, 0.512 and 0.05 milli molar as well. Or MEP had working solutions of 20 micro molar, 50 micro molar, 100 micro molar and 200 micro molar LB plots are made for each concentration of beed GP that was used using these plots. The KM could be found by obtaining the X intercept. The Km found in each line bid plot were used to generate a Dixon plot. This was done by dividing the CAM found which is known as the cam apparent by the true Km which is the KM when there is no inhibitor added the Dixon plot generated can then be used to find the K I of beta GP. By taking the inverse of the slope. Mila’s mentum plots show the relationship between the concentration of a substrate and the rate of which enzyme reaction is taking place, he remade Mila’s mentum plots at ph seven and 9.25. We see that there’s a clear increase in innovation with an increase in beta GP concentration in both of these plots at both Ph’s. ALP activity as expected was also higher at ph 9.25 compared to neutral PH. But the enzyme activity was still present in neutral Ph and could be inhibited by beta GP in dose dependent manners. The K I found in the study was 2 40 micro molar. This is very similar to the K I found in literature where it was 250 micro molar. We used the PH of 8.39 0.25 and seven. In our study, the K I was not obtained at neutral and alkaline ph. But the A OP activity was seen to be higher alkaline Ph than at neutral Ph beta GP was also shown to have a large inhibitory effect on ALP. And higher concentrations of beta GP did show to have larger inhibition as the change in initial velocity was larger at lower concentrations of beta GP. Thank you for listening. I hope you enjoyed my presentation.

College of Engineering and Computing Schar School of Policy and Government

Promoting public transportation in No. virginia

Author(s): Minal Arunashalam, Nathan E Stolzenfeld, Sidarth Kumar

Mentor(s): Toni Farris, Carter School for Peace and Conflict Resolution

Using a lens of conflict analysis and resolution, this project synthesized works of authors such as Galtung, Gurr, Bruneau, and countless others to explore effectively solving a relevant conflict in society. The conflict discussed in this presentation specifically is focused on public transit and its place in society as a class conflict. As climate change and urban sprawl shift the focus of city planning offices, it is best for all parties involved to discuss equitable, accessible, and safe access to public transit. The presentation works through a social science-esque investigation of the stakeholders, interests (socially and psychologically), and best outcomes for those involved in the public transit conflict. We conclude with an advocacy plan that involves direct communication with state legislators in Virginia to increase funding or access to various WMATA, VRE, or regional public transit services. Outcomes of these communication channels could not be reached by the submission deadline, but hypothetical outcomes are discussed.

Transcript
Minal: Hello everyone, our project is promoting public transportation in Northern Virginia. My name is Minal, and I’m joined by Nathan and Sidarth. We believe there are numerous benefits to using public transportation. Firstly, it’s cost-effective. Secondly, walking to and from public transportation can increase physical activity, reducing air pollution, which can improve respiratory health and the health of our planet. Thirdly, public transportation promotes social interactions and community cohesion.
However, there are some cons to public transportation, such as traffic congestion, which leads to air pollution, contributing to climate change and health problems. Safety is another concern, as car accidents are a leading cause of death and injury. Lastly, lack of accessibility for those who can’t drive or afford a car poses a challenge in accessing services within the community.
Despite these benefits, there’s a lack of public transportation in Fairfax, Loudoun, and Prince William counties, making it difficult for residents to access the services they need. Therefore, we advocate for the expansion of public transportation in these areas.
Nathan: At the root of the issue of public transit is a class conflict divided between those who are pro-public Transit and those against public transit for certain reasons. The interests of the pro-public transit group are to get from point A to point B in the fastest, safest, and most economically viable way possible, and those needs are currently being met by cars in everyday life. However, public transit is cheaper in the long term, better for the environment, and most people used to the comfort and ease of their cars don’t care enough about global warming. So it’s easy to make the switch. On the pro-side, the government wants to keep the people happy because that’s what keeps them elected, while on the against side are mainly companies, corporations, and car manufacturers and dealers. The sales and use of cars and the taxation of those uses go towards the government. If car usage is reduced, then they don’t get that monetary value, and if car buying rates are reduced, then the manufacturers and dealers don’t get that value.
As for categorization, public transit’s categories are divided upon class lines, mainly, but then subdivided by race in certain scenarios like gentrification or historic minority neighborhoods, where public transit can be seen more. Those stratified citizens into different areas despite them having strong opinions or not on it. Car users are the middle level of leader X level of conflict, being kind of this neutral ground but holding a degree of power over Transit users who are at the grassroots level. Cost is the biggest complex factor because it actually costs a lot of money to build public transit infrastructure, even if it’s more economically viable for citizens.
In our study, positions have been talked about based on who has been violated, like their rights have been violated, or whose rights have been taken away. For public transit users, that’s their right to access, whereas car users have the right to safety. Our study has shown cognitive bias if you ask certain levels of things like the elements of dehumanization on the side. Still, to avoid dehumanizing and biased language in those tweets, we asked different types of questions such as utilitarian, neutral, and egalitarian methods.

Here’s our conflict map, it’s a lot of spaghetti and sadly we don’t have enough time to get into it, but there is a key describing the relationship between each of our parties as well as short descriptions of what the relationships are.
Sidarth: Our proposed solution was to expand the Metro Bus q bus or metro rail network into Fairfax, London, and Prince William counties. To achieve this goal, we developed an advocacy plan that involved sending letters to Virginia senators or other representatives from the U.S House, urging them to support the expansion of public transportation in these areas. Additionally, we encouraged other members of our community to do the same to make impactful change in that area of legislation.
However, we encountered two major challenges during the process. The first one was the speed of response from the representatives, who are often very busy, and as a result, we did not receive a conclusion. In the future, we aim to submit our letters much earlier to analyze the responses and follow-up. The second challenge was engagement. We attempted Outreach with every method available to us, but we did not have that many people to actually send letters. As a result, we plan to get involved with political organizations on campus to reach more people.
The impact of promoting public transportation and reducing car dependency is critical to improving the quality of life for all Virginians. By advocating for the expansion of public transport into these areas, we can help reduce traffic, improve air quality, and provide equitable transportation. We hope that more people will join us in this mission.

College of Engineering and Computing College of Humanities and Social Science College of Science Honors College

Syrian Refugee Crisis

Author(s): Alexander Bonilla, Jude Qader, Pilar Cerritos Gatto, Shree Mayuri Mahendramurthy

Mentor(s): Toni Farris, Honors College

At the beginning of our Honors 130: Identity, Community, and Difference: Resolving Identity Conflict class, we were tasked with finding a conflict in the world that we wanted to learn more about. Our group chose the Syrian Refugee Crisis as our topic. Throughout the semester, we analyzed different aspects of this conflict to gain a better understanding of the situation. We started this journey by discovering the cause of the conflict. Then decided who the major stakeholders were. In this case, the two primary groups were Turkish citizens and Syrian Refugees. We took the groups involved and analyzed their individual situations. This included recognizing the foundations of their group’s needs, values and wants. Based on the weekly readings in our Honors class, we began to discover the complexity of each group and how they were interconnected. Their positions within the conflict heavily relied on their foundations. After thoroughly obtaining unbiased and neutral information, we decided to take action. Our plan was to do something anonymous but impactful. We considered multiple different ideas. For example, blogs, letters, and videos but ultimately settled on creating a flyer. The flyers obtained a general synopsis of the information we had researched and then hung them up around the public bulletin boards on the George Mason University campus. Our goal for this project was to (1) broaden knowledge of and (2) advocate for change. We ultimately hope that our advocacy and presentation can change the lifestyles of those involved in the Syrian Refugee Crisis.

Hello. My name is Pilar Cerritos Gatto. At the beginning of this 2023 spring semester my Honors 130 class was tasked to research a conflict in the world today. My group chose to analyze the Syrian Refugee Crisis.

The Syrian Refugee Crisis was caused predominantly because of the Syrian Civil War. In 2011, many people fled from Syria to Turkey because of the border that the countries shared. At first Turkey was open to taking in refugees, with the thought that they would eventually return to Syria. However, the civil war is still currently on going and despite the number of refugees decreasing, there are still hundreds of refugees heading into Syria every year, adding to the surplus of people the country already holds. Causing a strain on Turkey’s resources, jobs, economy and opportunities.

The first step in our conflict anaylsis are positions, needs and interests. The primary stakeholders in this conflict are the Turkish citizens and the Syrian refugees. Each group has their own interests and needs in this conflict. The Syrian Refugees are in search of rebuilding or maintaining their lifestyle with access to jobs and resources. They have the right to humane treatment and safety. Turkish citizens want a prosperous nation and economy to ensure their wellbeing and lifestyle. They have a right to resources and their roles as citizens regardless of the increase in the refugee population.

The next step is categorization and complexity
Because of these interest and needs, the people within these groups form strong connections with each other. On top of that, the people within each group share backgrounds, experiences, traditions, and ethnicities etc. that form even stronger connections, creating an in-group. However, with the creation of an in-group there is always an out-group. A group that does not belong or that may stand in opposition to the in group. In this case the Syrian Refugee’s out-group is the Turkish citizens and the Turkish citizens out-group is the Syrian Refugees.

Each group has a different position because of these ingroups and outgroups. From the Syrian refugees perspective, they position themselves as victims due to the Syrian Civil war and the treatment from Turkish citizens. The Turkish citizens view themselves as heros, for taking in the refugees, but at the same time they have a genuine fear because of the struggling economy that, has worsened due to the pandemic, the 2023 earthquake and the 2022 Syrian attack on Istanbul, which caused a huge dislike of Syrian refugees.

Cognitive bias is also a concept that needs to be taken into consideration. This is when systematic errors influence a person’s judgement and decision-making. For example, if someone believes that refugees are a burden on society, they may be less likely to support policies that provide aid to them, despite any evidence that may arise. And vice versa for the Syrian point of view as well. These cognitive bias often lead to dehumanization.

Because of the intensity of the situation, dehumanization is evident in this conflict. The syrian refugee were originally dehumanized due to the Civil War. They lost their homes, loved ones, possessions, jobs, parts of their culture and identity. Which is an essence of a human being. Because of their loss, outsiders empathize with them. This empathy triggers a need to place blame, which often leads to the dehumanization of the Turkish citizens for their treatment of the refugees. Regardless of the postive or negative manner.

This is the conflict map that we created for the Syrian Refugee Crisis. It contains different groups/stakeholders, facts, conflicting viewpoints, events and other information displaying how they all connect with each other.

My group and I decided to use the knowledge and information gathered from our research to advocate for the people in this crisis. At the beginning we had various ideas of what we could to take action.

Our plan was to create flyers with information regarding the Turkish citizens and Syrian refugees. The primary purpose of this flyer was to share knowledge and information about the situation that people may not know about. As you can see the flyer contained some general background information, and information from both the Turkish Citizen and the Syrian refugee sides. As well as a QR code for more information and a link to get involved.

We did face some challenges when implementing our action plan. This included technological issues, reception of information, and location of the flyers in general. However, the biggest challenge was the notion to present the analysis of the conflict with an unbiased and neutral manner.

We hope that this presentation and our action plan provide detailed information to the students on campus and encourage them to advocate. We ultimately hope to change the lifestyles, minds, and opportunities for the people involved in this crisis.

This concludes the presentation of our project. This slide contains a QR code to find our conflict map. A QR code to the flyer that we created. And another QR code for more information and a link to get involved. Thank you for your time and I hope you have a wonderful day.

College of Engineering and Computing OSCAR

Effect of Inflammation on lipid nanoparticle performance

Author(s): Peter Touma

Mentor(s): Caroline Hoemann, Bioengineering

Characterization of nanoparticles was conducted on various samples for the purpose of analyzing the concentration of the samples and their particle counts. The samples studied were liposomes composed of phosphatidylcholine and phosphatidylserine (PSPC), and platelet microparticles. Nanoparticle characterization was conducted on these samples in order to better understand the results of surface plasmon resonance (SPR) analyses that were previously conducted on the same samples to test their binding ability to the Lox-1 protein. The data collected by SPR analysis showed that the PSPC sample had the greatest binding to the SPR sensor chip. However, the particle count or concentration of a sample is known to influence the accumulation of the sample. To measure particle count, samples were diluted in filtered deionized water and analyzed using a Zeta View instrument. The Zeta Viewer is equipped with a camera that collects up to 11 images at high magnification to analyze particle size and density. Results showed that PSPC had a mean concentration of 7E+13 particles per mL with a mean diameter of 213.7 nm and polydispersity of 209.5 nm – 217.1 nm (range). Platelet microparticles were found to have a mean concentration of 9.93E+10 particles per mL with a mean diameter of 176.9 nm and polydispersity of 176 nm – 178.5 nm. These results indicate that PSPC liposomes had a 1000-fold particle count compared to the platelet microparticles. Future experiments will require Zeta View analysis of the particle count and subsequent SPR tests with several dilutions, to calculate the binding constant of each sample type.

Hello, my name is Peter Touma, and today I’ll be sharing my undergraduate research with you all. I hope you enjoy. My research from this semester consisted of a nanoparticle analysis using a ZetaView instrument in one of the labs at the Manassas SciTech campus. Micro particles are nano sized extra cellular vesicles that are released from cells that are known to carry procoagulant surfaces with an anionic charge due to the presence of phosphatidylserine within them. The long term objective of the project is to measure the binding affinity of these micro particles to scavenger receptors, such as lectin-like oxidized lipoprotein receptor one, or simply called lox-1. To this end, the goal of the experiment was to measure the diameter and concentration of synthetically made lipid nanoparticles that contain phosphatidylserine and phosphatidylcholine, as well as platelet-derived particles from a human donor. To prepare the samples, liposomes containing phosphatidylcholine and phosphatidylserine were prepared and platelet microparticles were collected from a healthy consenting donor. The lipids were then suspended in PBS, extruded using varying filter sizes to form liposomes, and stored at 4 degrees Celsius until use in the experiment. The platelet microparticles were previously generated from another experiment using, again, a human donor. A citrated blood sample was centrifuged at 200 xg for five minutes at room temperature to first clear the sample’s red and white blood cells. Then the supernatant was centrifuged again, this time at a higher degree of 1500 xg for 10 minutes to pellet the platelets. These platelets were then washed and citrated isotonic saline and then resuspended in half the original volume of in isotonic saline with two millimolar CaCl2. The platelet samples were then pipetted into half milliliter aliquots and incubated for 30 minutes at 37 degrees Celsius in the presence of thrombin. The resulting platelet clot was then centrifuged again, and 25 microliters of the supernatant-containing platelet microparticles was frozen for use in this experiment. To conduct the experiment, the samples were first diluted in filtered water before injecting into the ZetaView machine. This is to ensure proper analysis, and it goes with the standard operating procedure of the machine. Four cycles were conducted using the microparticles, one of which was at first omitted but then later added back into the study to see a comparison with and without it. And three cycles were carried out using the PSPC liposomes. The initial cycle was recorded and used in the data, but the data was interpreted both with and without it to see the difference that or any possible difference that may arise. The purpose of collecting the data that we collected was the concentration in the particle size of particles. And this was just the overall particle count of our samples. Here we have three scatter plots, one depicting the platelets with cycle A, or the initial cycle, one of the platelets without the initial cycle, and one of the PSPC liposomes alone. Seen in the scatter plots, the PSPC liposomes had an overall larger diameter or size than the platelets. And the average particle size of the platelets seemed to increase when we removed the initial cycle from the analysis. Here, we have a table. Here in this table, we see, again, the mean particle size of our samples, as well as the concentration of our samples as well. And it’s followed by the dilution factors for each cycle, as well as the particles for frames and numbers of channels included for analysis. The number of channels included is important because the ZetaView uses 11 total channels, and not all of them are typically seen to be readable for the machine. Well, here we see that the liposomes actually had 1,000-fold higher concentration than the platelet micro particles at 7 times 10 to the 13th particles per milliliter. And we also see that the concentration of platelet microparticles increased, as did the particle size when removing the initial cycle. So using the ZetaView analysis, we obviously found out that the particle size and concentration of the PSPC liposomes was greater than the micro particles. The platelet micro particles also showed an increase in size and concentration when omitting the first cycle from the data, showing that the collected data initial cycle may be quite different than from the other cycles. With the measurements taken in this study, it will be beneficial to conduct further analysis and further analyze results from a previous SPR experiment or surface plasmid resonance so that we can better understand the binding affinities of both of these particles onto the lectin 1 protein, the lox1 protein. Typically, it’s best to conduct SPR analysis with similar particle counts and concentrations. So nanoparticle tracking analysis, such as this experiment, should usually be done prior to SPR and will be done prior to SPR for future experiments. That is all I had. I hope that you enjoyed it. Thank you for your time.