Unsupervised machine learning models reveal predictive clinical markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation.

Background: Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested the use of various molecular biomarkers as predictors of patient prognosis or factors utilized for clinical decision-making. However, the accessibility of such molecular testing poses a constraint for various institutes requiring identification of low-cost predictive biomarkers to ensure equitable care.

Unsupervised machine learning models reveal predictive markers of glioblastoma patient survival using white blood cell counts prior to initiating chemoradiation.

Purpose: Glioblastoma is a malignant brain tumor requiring careful clinical monitoring even after primary management. Personalized medicine has suggested use of various molecular biomarkers as predictors of patient prognosis or factors utilized for clinical decision making. However, the accessibility of such molecular testing poses a constraint for various institutes requiring identification of low-cost predictive biomarkers to ensure equitable care.

[10]-Gingerol-Loaded Nanoemulsion and its Biological Effects on Triple-Negative Breast Cancer Cells.

The apoptotic, cytotoxic, and cytostatic activities for [10]-gingerol in triple-negative breast cancer cells (TNBCs) were already reported. However, despite these important antitumor activities, the compound has the disadvantage to have a hydrophobic characteristic, hindering in vivo administration. To surpass this issue, in this study we have created a [10]-gingerol-loaded nanoemulsion (10GNE) in order to increase the stability and solubility of the compound.