Platelets aggregate at the site of injury to stop bleeding, but disruptions to hemostasis can cause bleeding or thrombosis. Studying platelet-plug area and contractile force can predict whether bleeding or thrombosis is likely to occur. Microfluidic devices, composed of polydimethylsiloxane (PDMS), are used to study these biomechanics by inducing aggregation through shear flow. As blood flows through the device, the platelets pass over a rigid block in the channel which causes platelets to activate, deflecting a flexible post within the channel. This deflection is used to calculate the platelet forces based on the material properties of the PDMS. These microfluidic devices are single use and require a fabrication process that spans multiple days. Additionally, creating duplicate silicon master molds is a laborious and expensive process that necessitates cleanroom training. I am engineering and implementing a more efficient process for the fabrication of these devices, while limiting the use of the master mold that undergoes long-term wear from repeated uses. My focus is on improving the efficiency of the initial negative mold creation process by using a different material, urethane resin, to replicate the master, which allows us to make more negatives simultaneously without needing to fabricate another silicon master. To compare the devices produced using the onyx master with the silicon master, I am running three blood experiments, each with varying levels of antibodies, on two devices fabricated by the onyx and silicon masters. The aggregation sizes and forces are being observed between each of the experiments. I expect the results to be similar within a degree of certainty, proving the onyx master is equivalent to the silicon master and can be used to increase microfluidic device production and increase the availability of platelet biomechanics studies.

During a microbial infection, cytokine and interferon communication downstream of JAK-STAT signaling molecules are critical for the activation of pro- and anti-inflammatory gene programs. The prevailing model relies on JAKs (Janus Kinases) activation upon cytokine or interferon docking with its receptor at the membrane, which phosphorylates STATs (signal transducers and activators of transcription proteins) and leads to their translocation into the nucleus to either activate or repress the transcription of genes. In this study, we have identified the role of an endocytic pathway responsible for JAK-STAT activation upon interactions between the IL-10 family proteins and the cytokine receptor. Using skin epithelial cell lines, I have performed experiments to show that clathrin-mediated endocytosis is required for the signaling of cytokines that use the IL-10R2 receptor. According to my preliminary results, the inhibition of clathrin-mediated endocytosis using chemical inhibitor dynasore, prior to treatment with IL-26, results in complete shutdown of pSTAT3, pSTAT1, and pErk1/2. These downstream molecules are required for activation of inflammatory gene programming. We now propose to investigate the roles of other inhibitors within the endocytic pathway including chloroquine, bafilomycin A1, and a Rab7a inhibitor on subsequent JAK-STAT signaling transduction. In order to visualize these results, confocal microscopy studies will be used to investigate trafficking in these vacuoles, followed by colocalization studies of JAK proteins with receptors in early endosomes. Our studies suggest that a change in receptor mobilization controlled through the endosomal trafficking mechanism modulates JAK-STAT signaling and downstream gene expression. This proposal is a paradigm shift which challenges the prevailing dogma that IL-10R2 dependent cytokines can activate JAK-STATs at the cell membrane to induce inflammatory responses. Furthermore, this new signaling model has the potential to impact pharmaceutical interventions as the IL-10 family plays a critical role in inflammatory bowel diseases as well as fungal and bacterial infections.

Prediction of response to preoperative or ‘neoadjuvant’ chemotherapy (NAC) can help guide treatment strategies for patients with triple-negative breast cancer (TNBC), a highly aggressive subtype of breast cancer. Breast magnetic resonance imaging (MRI) can provide noninvasive measurements of the microenvironment across a tumor volume. We hypothesize that pre-treatment measurements from dynamic contrast-enhanced (DCE-) MRI reflecting tumor perfusion and vascular function are predictive of NAC response for TNBC patients. Women with TNBC who underwent pre-treatment MRI and NAC at our institution (2005-2019) were retrospectively identified. DCE-MRI was acquired at 2, 5, and 8 minutes after contrast injection. From DCE-MRI, whole tumor contrast kinetics measures including functional tumor volume (FTV), percent enhancement (PE) at 2 mins post-contrast and signal enhancement ratio (SER) were calculated, and hotspot measures of peak PE and peak SER (representing the highest mean PE and SER, respectively, for 3?3 voxel subregions) were determined. Imaging measurements were compared between those with complete pathologic response (pCR; no residual cancer present in the breast at surgery) and non-pCR patients with a two-tailed Student’s t-Test (p<0.05 considered significant). 95 women (median age: 49, range: 30-79 years) with TNBC were evaluated, of which 29 (31%) achieved pCR. FTV was significantly higher in non-pCR patients (21.1±28.1 cc) compared to pCR patients (8.6±11.3 cc, p<0.01). Peak SER was also higher in non-pCR patients (1.8±0.3) compared to pCR patients (1.7±0.3), trending toward significance (p=0.06). No significant differences between groups were observed in peak PE measures. Patients with lower pre-treatment tumor FTV and peak SER on DCE-MRI were more likely to achieve pCR after standard NAC. These findings indicate that baseline DCE-MRI measurements may help predict response and assist in optimizing treatment plans for TNBC patients, such as selecting more aggressive regimens incorporating immune checkpoint inhibitors or other novel agents in predicted non-responders.

Women with dense breasts have increased amounts of fibroglandular tissue (FGT) and are at higher risk of developing breast cancer. Quantitative measurement of FGT from magnetic resonance imaging (MRI) could provide more robust measurement of density, supplanting conventional qualitative radiologist assessments. Current quantitative methods involve manual selection of a signal intensity threshold, which can be time consuming and subjective. Fuzzy c-means (FCM) clustering is an automated approach to tissue segmentation, offering a reproducible process for quantifying FGT volume. The aim of this study is to evaluate the efficacy of the FCM clustering in identifying FGT compared to manual thresholding. Women (N=10) who underwent screening breast MRIs at our institution were evaluated in this preliminary study. Fat-suppressed T₁-weighted pre-contrast images acquired as part of their clinical breast MRI exams were used for FGT segmentation. Prior to segmentation, I cropped the images to include only the breast. FGT was then segmented two ways, 1) manually, using a signal intensity threshold that I chose and adjusted and 2) automatically, using existing lab software for FCM clustering. The Sørensen-Dice similarity coefficient was calculated between the manual and automatic segmentations for each patient to determine the degree of overlap. The concordance correlation coefficient (CCC) was calculated between automatic and manual segmentation volumes across the whole data set. Across the 10 patients, an average (± standard deviation) Dice coefficient of 0.81±0.04 was observed, indicating good spatial agreement between the manual and automatic segmentations. The CCC between the FGT volume from manual and automated segmentation was 0.89, demonstrating high correlation in volume estimates between the two methods. Fuzzy c-means clustering was determined to be an effective and efficient method of FGT segmentation in breast MRI data. Future work will evaluate the application of this technique in assessment of background parenchymal enhancement, a clinical marker of cancer risk.