Experiential Learning Final Report: Forest City Film Festival and SASAH Internships

Experiential learning can be difficult to attain in a traditional classroom environment, where knowledge is transferred rather than experienced firsthand. Moreover, that challenge is especially amplified during a global pandemic; nevertheless, experiential learning is a critical component of a student’s education, and there are ways to achieve it. One solution is to extend the learning experience beyond the confines of the classroom, where students can get a taste of the real-world work environment and a clearer picture of what their future jobs might entail. To achieve meaningful experiential learning, I completed two 4-month internships at Western University and Forest City Film Festival. During and after the internships, I spent time reflecting on the valuable lessons I had learned through the experience. This report describes the main tasks and responsibilities I was assigned during both internships, outlines the main takeaways I gathered, and dissects my initial thoughts and responses to the various obstacles I faced, particularly in light of COVID-19. It also addresses how my internship experiences contributed to the planning process of my post-graduation career trajectory, especially considering how I might combine the education from both of my seemingly opposed majors, Arts and Humanities and Computer Science, into a singular role

Pre-transplant crossmatch-negative donor-specific anti-HLA antibody predicts acute antibody-mediated rejection but not long-term outcomes in kidney transplantation: an analysis of the Korean Organ Transplantation Registry

BackgroundPre-transplant donor-specific anti-human leukocyte antigen antibody (HLA-DSA) is a recognized risk factor for acute antibody-mediated rejection (ABMR) and allograft failure. However, the clinical relevance of pre-transplant crossmatch (XM)-negative HLA-DSA remains unclear.MethodsWe investigated the effect of XM-negative HLA-DSA on post-transplant clinical outcomes using data from the Korean Organ Transplantation Registry (KOTRY). This study included 2019 living donor kidney transplant recipients from 40 transplant centers in South Korea: 237 with HLA-DSA and 1782 without HLA-DSA.ResultsABMR developed more frequently in patients with HLA-DSA than in those without (5.5% vs. 1.5%, p<0.0001). Multivariable analysis identified HLA-DSA as a significant risk factor for ABMR (odds ratio = 3.912, 95% confidence interval = 1.831–8.360; p<0.0001). Furthermore, the presence of multiple HLA-DSAs, carrying both class I and II HLA-DSAs, or having strong HLA-DSA were associated with an increased incidence of ABMR. However, HLA-DSA did not affect long-term clinical outcomes, such as allograft function and allograft survival, patient survival, and infection-free survival.ConclusionPre-transplant XM-negative HLA-DSA increased the risk of ABMR but did not affect long-term allograft outcomes. HLA-incompatible kidney transplantation in the context of XM-negative HLA-DSA appears to be feasible with careful monitoring and ensuring appropriate management of any occurrence of ABMR. Furthermore, considering the characteristics of pre-transplant XM-negative HLA-DSA, the development of a more detailed and standardized desensitization protocol is warranted

Comparison of Target Features for Predicting Drug-Target Interactions by Deep Neural Network Based on Large-Scale Drug-Induced Transcriptome Data

Uncovering drug-target interactions (DTIs) is pivotal to understand drug mode-of-action (MoA), avoid adverse drug reaction (ADR), and seek opportunities for drug repositioning (DR). For decades, in silico predictions for DTIs have largely depended on structural information of both targets and compounds, e.g., docking or ligand-based virtual screening. Recently, the application of deep neural network (DNN) is opening a new path to uncover novel DTIs for thousands of targets. One important question is which features for targets are most relevant to DTI prediction. As an early attempt to answer this question, we objectively compared three canonical target features extracted from: (i) the expression profiles by gene knockdown (GEPs); (ii) the protein–protein interaction network (PPI network); and (iii) the pathway membership (PM) of a target gene. For drug features, the large-scale drug-induced transcriptome dataset, or the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 dataset was used. All these features are closely related to protein function or drug MoA, of which utility is only sparsely investigated. In particular, few studies have compared the three types of target features in DNN-based DTI prediction under the same evaluation scheme. Among the three target features, the PM and the PPI network show similar performances superior to GEPs. DNN models based on both features consistently outperformed other machine learning methods such as naïve Bayes, random forest, or logistic regression

Performance of a Potentially Invasive Species of Ornamental Seaweed <i>Caulerpa sertularioides</i> in Acidifying and Warming Oceans

Caulerpa, a (sub) tropical seaweed, is a notorious taxonomic group and an invasive seaweed worldwide. Similar to several species that have been introduced to benthic habitats through aquariums, Caulerpa sertularioides has also been introduced into Korean aquariums, although it is not native to the region. Thus, it is necessary to evaluate the potential of this species for invading domestic macroalgal habitats. Therefore, an indoor mesocosm experiment was conducted to examine the ecophysiological invasion risk of non-native seaweed C. sertularioides under various climate conditions and exposure to three future climate scenarios: acidification (doubled CO2), warming (5 °C increase from ambient temperature), and greenhouse (GR: combination of acidification and warming); additionally, we compared the invasion risk between future and present climates (control: 20 °C and 470 µatm CO2). High CO2 concentrations and increased temperatures positively affected the photosynthesis and growth of C. sertularioides. Photosynthesis and growth were more synergistically increased under GR conditions than under acidification and warming. Consequently, the performance of this potentially invasive species in the native macroalgal Korean habitat will be higher in the future in coastal environments. Therefore, proper management is required to prevent the geographic expansion of C. sertularioides in the Korean coastal ocean

Modeling of <i>FAN1</i>-Deficient Kidney Disease Using a Human Induced Pluripotent Stem Cell-Derived Kidney Organoid System

Karyomegalic interstitial nephritis (KIN) is a genetic kidney disease caused by mutations in the FANCD2/FANCI-Associated Nuclease 1 (FAN1) gene on 15q13.3, which results in karyomegaly and fibrosis of kidney cells through the incomplete repair of DNA damage. The aim of this study was to explore the possibility of using a human induced pluripotent stem cell (hiPSC)-derived kidney organoid system for modeling FAN1-deficient kidney disease, also known as KIN. We generated kidney organoids using WTC-11 (wild-type) hiPSCs and FAN1-mutant hiPSCs which include KIN patient-derived hiPSCs and FAN1-edited hiPSCs (WTC-11 FAN1+/−), created using the CRISPR/Cas9 system in WTC-11-hiPSCs. Kidney organoids from each group were treated with 20 nM of mitomycin C (MMC) for 24 or 48 h, and the expression levels of Ki67 and H2A histone family member X (H2A.X) were analyzed to detect DNA damage and assess the viability of cells within the kidney organoids. Both WTC-11-hiPSCs and FAN1-mutant hiPSCs were successfully differentiated into kidney organoids without structural deformities. MMC treatment for 48 h significantly increased the expression of DNA damage markers, while cell viability in both FAN1-mutant kidney organoids was decreased. However, these findings were observed in WTC-11-kidney organoids. These results suggest that FAN1-mutant kidney organoids can recapitulate the phenotype of FAN1-deficient kidney disease