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AI analysis of brain scans offers promise for helping doctors diagnose brain tumors. Haider and Drosis explore this field by developing machine learning models that classify brain scans as "cancer" or "non-cancer" diagnoses.

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.

The authors looked at genes and pathways that are enriched in glioblastoma multiforme.

The authors looked at biomarkers in glioblastoma patients they hypothesized to be correlated with survival rate. Ultimately they did not find hMSH2 or hMSH6, genes involved in mismatch repair, to be significantly associated with outcomes related to increased survival.

The authors found that treatment with AS20 suppressed phorbol 12-myristate 13-acetate (PMA) and 5-flurouracil (5-FU) induction of COX2 expression. We also observed AS20 treated cells showed DNA fragmentation in HeLa cells.

Here the authors used a free radical assay to characterize the antioxidant capacity of three types of coffee beans. They fond that Robusta coffee presented greater inhibition percentages than other species in their free radical assay, indicating higher antioxidant capacity.

The authors used RNA-seq datasets to assess the effect of CDK7 inhibition on transcriptional pathways in castration-resistant prostate cancer cells.

The authors combine fine needle aspiration biopsy and machine learning algorithms to develop a breast cancer detection method suitable for resource-constrained regions that lack access to mammograms.

The authors computationally screened potential ALDH1 inhibitors, for use as potential cancer therapeutics.

This study compares the performance of two deep learning models, U-Net and DeconvNet, for segmenting gliomas from MRI scans.