In this study, the authors identify new potential targets to treat advanced diffuse large B-cell lymphoma after treatment relapse and loss of CD19 expression.

Wang and Gong developed a novel dynamic gene-searching algorithm called Dynamic Gene Search (DyGS) to create a gene panel for each of the 12 cancers with the highest annual incidence and death rate. The 12 gene panels the DyGS algorithm selected used only 3.5% of the original gene mutation pool, while covering every patient sample. About 40% of each gene panel is druggable, which indicates that the DyGS-generated gene panels can be used for early cancer detection as well as therapeutic targets in treatment methods.

In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.

In this article the authors created an interaction map of proteins involved in colorectal cancer to look for driver vs. non-driver genes. That is they wanted to see if they could determine what genes are more likely to drive the development and progression in colorectal cancer and which are present in altered states but not necessarily driving disease progression.

In this study, the authors investigated the time-dependent cytokine secretion ability of phyto-hemagglutinin (PHA)-activated T cells derived from human peripheral (PB) and cord blood (CB). They hypothesized that the anti-inflammatory cytokine, IL-10, and pro-inflammatory cytokine, TNFα, levels would be higher in PHA-activated T cells obtained from PB as compared to the levels obtained from CB and would decrease over time. Upon PHA-activation, the IL-10 levels were relatively high while the TNFα levels decreased, making these findings applicable in therapeutic treatments e.g., rheumatoid arthritis, psoriasis, and organ transplantation.

The authors investigate whether human blood cancers carrying mutations in DNA repair genes possess increased sensitivity to common chemotherapy drugs cisplatin or gemcitabine.

The authors looked at 5-FU resistant HeLa cells and the ability of an herbal extract to show anti-inflammatory properties.

Stem cells are at the forefront of research in regenerative medicine and cell therapy. Two essential properties of stem cells are self-renewal and potency, having the ability to specialize into different types of cells. Here, Park and Jeong took advantage of previously identified stem cell transcription factors associated with potency to differentiate umbilical cord mesenchymal stem cells (US-MSCs) from morphologically similar fibroblasts. Western blot analysis of the transcription factors Klf4, Nanog, and Sox2 revealed their expression was unique to US-MSCs providing insight for future methods of differentiating between these cell lines.

Osteosarcoma is a type of bone cancer that affects young adults and children. Early diagnosis of osteosarcoma is crucial to successful treatment. The current methods of diagnosis, which include imaging tests and biopsy, are time consuming and prone to human error. Hence, we used deep learning to extract patterns and detect osteosarcoma from histological images. We hypothesized that the combination of two different technologies (transfer learning and data augmentation) would improve the efficacy of osteosarcoma detection in histological images. The dataset used for the study consisted of histological images for osteosarcoma and was quite imbalanced as it contained very few images with tumors. Since transfer learning uses existing knowledge for the purpose of classification and detection, we hypothesized it would be proficient on such an imbalanced dataset. To further improve our learning, we used data augmentation to include variations in the dataset. We further evaluated the efficacy of different convolutional neural network models on this task. We obtained an accuracy of 91.18% using the transfer learning model MobileNetV2 as the base model with various geometric transformations, outperforming the state-of-the-art convolutional neural network based approach.

The authors compare RNA expression profiles across three human cell types following infection with MERS-CoV