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The authors looked at previous studies to evaluate the ability to use serum levels of certain cytokines as biomarkers for pneumoconiosis.

In this study, the authors investigate the effect of a herbal formulation on Cyclooxygenase-2 (COX-2) expression in cancer cells. High levels of COX-2 correlates with worsened cancer outcomes and the authors hypothesize that the formulation will inhibit COX-2 levels.

In this study, the authors looked at a proto-oncogene, KRAS, and searched for molecules that are predicted to be able to bind to the inactive form of KRAS. They found that a modified version of Irbesartan, a derivative of benzimidazole, showed the best binding to inactive KRAS.

This manuscript explores the performance of five different machine learning models in classifying brain tumors from a dataset of MRI scans. The authors find that several of the models showed >90% accuracy. Thus, the authors suggest that machine learning models demonstrate potential for effective implementation in clinical settings, including as a diagnostic tool that can be used to complement the expertise of neuroradiologists.

Mesenchymal stem cells(MSCs) play a role in tumor formation by differentiating into cancer associated fibroblasts (CAFs) which enable metastasis of tumors. The process of conversion of MSCs into CAFs is not clear. In this study, authors tested the hypothesis that cancers cells secrete soluble factors that induce differentiation by culturing bone marrow mesenchymal stem cells in media conditioned by a breast cancer cell line.

Authors emphasize the challenges of manual tumor segmentation and the potential of deep learning models to enhance accuracy by automatically analyzing MRI scans.

The authors use machine learning on MRI images of brain tissue to predict tumor onset as an avenue for early detection of brain cancer.

This paper hypothesized that the tumor microenvironment mediates cancer’s response to oxidative stress by delivering extracellular vesicles to cancer cells. Breast and lung cancer cells were treated with EVs, reavealing that EVs extracted from oxidatively stressed adipocytes increased the cell proliferation of breast cancer cells. These findings present a novel way that the TME influences cancer progression.

Inefficient penetration of cancer drugs into the interior of the three-dimensional (3D) tumor tissue limits drugs' delivery. The authors hypothesized that the addition of phospholipase A2 (PLA2) would increase the permeability of the drug doxorubicin for efficient drug penetration. They found that 1 mM PLA2 had the highest permeability. Increased efficiency in drug delivery would allow lower concentrations of drugs to be used, minimizing damage to normal cells.

Glioblastoma Multiforme (GBM) is the most malignant brain tumor with the highest fraction of genome alterations (FGA), manifesting poor disease-free status (DFS) and overall survival (OS). We explored The Cancer Genome Atlas (TCGA) and cBioportal public dataset- Firehose legacy GBM to study DNA repair genes Activating Signal Cointegrator 1 Complex Subunit 3 (ASCC3) and Alpha-Ketoglutarate-Dependent Dioxygenase AlkB Homolog 3 (ALKBH3). To test our hypothesis that these genes have correlations with FGA and can better determine prognosis and survival, we sorted the dataset to arrive at 254 patients. Analyzing using RStudio, both ASCC3 and ALKBH3 demonstrated hypomethylation in 82.3% and 61.8% of patients, respectively. Interestingly, low mRNA expression was observed in both these genes. We further conducted correlation tests between both methylation and mRNA expression of these genes with FGA. ASCC3 was found to be negatively correlated, while ALKBH3 was found to be positively correlated, potentially indicating contrasting dysregulation of these two genes. Prognostic analysis showed the following: ASCC3 hypomethylation is significant with DFS and high ASCC3 mRNA expression to be significant with OS, demonstrating ASCC3’s potential as disease prediction marker.