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Browsing by Author "Gaines, Madelynn"
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Item CD68 Macrophage Expression in Normal Breast Tissue and Cancer(2019-04-19) Gaines, Madelynn; Jacobsen, Max; Temm, Connie; Sandusky, GeorgeBreast cancer is a common disease and is the second leading cause of death in women. This type of cancer is usually hormonally driven by estrogen, progesterone, and HER2. Macrophages play a large role in the tumor microenvironment (TME). The aim of this study was to investigate the percent of macrophages in 32 normal breast tissues, 66 normal adjacent tissue (NAT), and 82 breast cancer tissues using the CD68-specific biomarker. Tissue microarrays (TMA) were created, which are composed of 2-mm cores from multiple patients mounted onto a single slide. The breast tissue samples were fixed, processed, microtomed, and stained with CD68. Unstained slides were immunostained using the Dako FLEX system. The slides were imaged using the Aperio Whole Slide Imaging platform and the tissues were evaluated using the positive pixel count algorithm (a quantitative image analysis system). The positivity of macrophages in the tissue samples were reported as a percentage, and compared across the three groups. It was found that the CD68 positivity in the normal and breast cancer tissue were even, and the NAT was lower. However, the three groups had overlapping standard deviations. Because difference between the percentages of each group was minimal and the deviations overlapped, it was concluded that there is no statistical difference between the three groups.Item Polyphenic risk score shows robust predictive ability for long-term future suicidality(Discover Mental Health, 2022-06-13) Cheng, M; Roseberry, Kyle; Choi, Y; Quast, L; Gaines, Madelynn; Sandusky, George; Kline, JA; Bogdan, Paul; Niculescu, AlexanderSuicides are preventable tragedies, if risk factors are tracked and mitigated. We had previously developed a new quantitative suicidality risk assessment instrument (Convergent Functional Information for Suicidality, CFI-S), which is in essence a simple polyphenic risk score, and deployed it in a busy urban hospital Emergency Department, in a naturalistic cohort of consecutive patients. We report a four years follow-up of that population (n = 482). Overall, the single administration of the CFI-S was significantly predictive of suicidality over the ensuing 4 years (occurrence- ROC AUC 80%, severity- Pearson correlation 0.44, imminence-Cox regression Hazard Ratio 1.33). The best predictive single phenes (phenotypic items) were feeling useless (not needed), a past history of suicidality, and social isolation. We next used machine learning approaches to enhance the predictive ability of CFI-S. We divided the population into a discovery cohort (n = 255) and testing cohort (n = 227), and developed a deep neural network algorithm that showed increased accuracy for predicting risk of future suicidality (increasing the ROC AUC from 80 to 90%), as well as a similarity network classifier for visualizing patient's risk. We propose that the widespread use of CFI-S for screening purposes, with or without machine learning enhancements, can boost suicidality prevention efforts. This study also identified as top risk factors for suicidality addressable social determinants.