Background: Sunitinib and pazopanib are orally-administered tyrosine kinase receptor inhibitors (TKIs) approved as first-line therapy for the treatment of metastatic renal cell carcinoma (mRCC). The IMDC criteria are a predictive prognostic model for patients with mRCC when stratified into three prognosis groups: favourable, intermediate and poor. We retrospectively compared the efficacy and safety of sunitinib and pazopanib as first-line therapy for patients with mRCC in our single institution database. Methods: Retrospective analysis was done to compare progression-free survival (PFS) and side effects of sunitinib and pazopanib as first-line therapy in patients with mRCC. Patients were stratified into prognosis groups according to IMDC criteria. Disease assessment was performed on measurable aspects of disease based on computed tomography or magnetic resonance imaging reports. Survival analysis was performed using the Kaplan-Meier method and Cox regression, with disease progression as the endpoint.Results: Data was obtained from 228 patients with mRCC who were treated with either pazopanib (n=57) or sunitinib (n=171). No significant difference in PFS was found between sunitinib and pazopanib (HR for disease progression or all-cause death, 1.10; 95%CI: 0.76-1.57, p=0.62). Median PFS time for patients receiving sunitinib was 9.4 months and for pazopanib, 8.5 months. Median PFS for patients with intermediate-risk disease was similar between groups (9.4 months vs. 9.2 months, respectively, p=0.93). However, patients treated with sunitinib experienced a greater number of side effects compared to pazopanib. Conclusions: Sunitinib and pazopanib are similarly efficacious as first-line therapy for mRCC. However, adverse events are lower with pazopanib.

Since people pay more attention to health issues, how to effectively diagnose potential diseases becomes gradually important in that most of us are not physical experts. This paper proposes a single collective app that offers solutions to both potential and diagnosed patients based on machine learning. Users can use the app to evaluate their status by uploading their documents. It is helpful to tell users the potential issues concerning health in advance.

For this project, I decided to relieve the tension of procrastination that commonly happens in students and adults. To find a solution to this, I created a program that uses Google Cloud Vision API (Optical Character Recognition) to detect the distracting forms of media such as Twitter, YouTube, and Facebook, and counts the number of times the user visits these websites. After a certain number of visits, the program sends a notification to remind the user to stay focused. If the user ignores the notification message while staying on the unapproved website, the program forces the tab to close. This application was applied to a small user study where a qualitative evaluation of the approach was conducted. After collecting data for two weeks, it concluded that the program was able to effectively reduce and limit the uses of online distractions, allowing the user to manage their time more efficiently by staying off websites they should not visit.