The coronavirus disease 2019 outbreak has become a huge challenge in the human sector for the past two years. The coronavirus is capable of mutating at a higher rate than other viruses. Thus, an approach for creating an effective vaccine is still needed to induce antibodies against multiple variants with lower side effects. Currently, there is a lack of research on designing a multiepitope of the COVID-19 spike protein for the Indonesian population with comprehensive immunoinformatic analysis. Therefore, this study aimed to design a multiepitope-based vaccine for the Indonesian population using an immunoinformatic approach. This study was conducted using the SARS-CoV-2 spike glycoprotein sequences from Indonesia that were retrieved from the GISAID database. Three SARS-CoV-2 sequences, with IDs of EIJK-61453, UGM0002, and B.1.1.7 were selected. The CD8+ cytotoxic T-cell lymphocyte (CTL) epitope, CD4+ helper T lymphocyte (HTL) epitope, B-cell epitope, and IFN-γ production were predicted. After modeling the vaccines, molecular docking, molecular dynamics, in silico immune simulations, and plasmid vector design were performed. The designed vaccine is antigenic, non-allergenic, non-toxic, capable of inducing IFN-γ with a population reach of 86.29% in Indonesia, and has good stability during molecular dynamics and immune simulation. Hence, this vaccine model is recommended to be investigated for further study.

In this study, we propose an automatic classification of three common types of lymphoma: (1) lymphoma, (2) benign lesion, and (3) carcinoma using lymphoma cell images magnified by 100x and by 400x. A comparative study was performed to find the best architecture to classify lymphoma cell images using the Keras library in Tensorflow. The architectures used in this study are ResNet50, MobileNetV1, and VGG16. Based on the accuracy of lymphoma classification for each architecture, the MobileNet model had the highest accuracy in all three classes at both 100x and 400x magnification levels, which suggests that MobileNet is the best model for lymphoma cell classification. This study can be later used as the base argument in modifying the MobileNet architecture further to get more accurate results in future similar studies.