Prognostic Prediction of Cancer Based on Radiomics Features of Diagnostic Imaging: The Performance of Machine Learning Strategies.

Tumor phenotypes can be characterized by radiomics features extracted from images. However, the prediction accuracy is challenged by difficulties such as small sample size and data imbalance. The purpose of the study was to evaluate the performance of machine learning strategies for the prediction of cancer prognosis. A total of 422 patients diagnosed with non-small cell lung carcinoma (NSCLC) were selected from The Cancer Imaging Archive (TCIA). The gross tumor volume (GTV) of each case was delineated from the respective CT images for radiomic features extraction. The samples were divided into 4 groups with survival endpoints of 1 year, 3 years, 5 years, and 7 years. The radiomic image features were analyzed with 6 different machine learning methods: decision tree (DT), boosted tree (BT), random forests (RF), support vector machine (SVM), generalized linear model (GLM), and deep learning artificial neural networks (DL-ANNs) with 70:30 cross-validation. The overall average prediction performance of the BT, RF, DT, SVM, GLM and DL-ANNs was AUC with 0.912, 0.938, 0.793, 0.746, 0.789 and 0.705 respectively. The RF and BT gave the best and second performance in the prediction. The DL-ANN did not show obvious advantage in predicting prognostic outcomes. Deep learning artificial neural networks did not show a significant improvement than traditional machine learning methods such as random forest and boosted trees. On the whole, the accurate outcome prediction using radiomics serves as a supportive reference for formulating treatment strategy for cancer patients.

Noble metal catalyst detection in rocks using machine-learning: The future to low-cost, green energy materials?

Carbon capture and catalytic conversion to methane is promising for carbon-neutral energy production. Precious metals catalysts are highly efficient; yet they have several significant drawbacks including high cost, scarcity, environmental impact from the mining and intense processing requirements. Previous experimental studies and the current analytical work show that refractory grade chromitites (chromium rich rocks with Al2O3 > 20% and Cr2O3 + Al2O3 > 60%) with certain noble metal concentrations (i.e., Ir: 17-45 ppb, Ru: 73-178 ppb) catalyse Sabatier reactions and produce abiotic methane; a process which has not been investigated at the industrial scale. Thus, a natural source (chromitites) hosting noble metals might be used instead of concentrating noble metals for catalysis. Stochastic machine-learning algorithms show that among the various phases, the noble metal alloys are natural methanation catalysts. Such alloys form when pre-existing platinum group minerals (PGM) are chemically destructed. Chemical destruction of existing PGM results to mass loss forming locally a nano-porous surface. The chromium-rich spinel phases, hosting the PGM inclusions, are subsequently a second-tier support. The current work is the first multi-disciplinary research showing that noble metal alloys within chromium-rich rocks are double-supported, Sabatier catalysts. Thus, such sources could be a promising material in the search of low-cost, sustainable materials for green energy production.

Cell Cycle Arrest by a Natural Product via G2/M Checkpoint.

CKBM is a natural product that exhibits a novel anti-tumor activity through the induction of cell cycle arrest and apoptosis. We have investigated its effects on cell cycle regulation using a gastric cancer cell line, AGS. The effects of CKBM on cell proliferation, cell cycle regulation and apoptosis were analyzed using BrdU (5-bromo-2'-deoxyuridine) cell proliferation assay and flow cytometric analysis, respectively. Specific cellular protein expressions were measured using Western blot analysis. Flow cytometric analysis indicated that CKBM induced G2/M cell cycle arrest and apoptosis, whereas differential protein expressions of p21, p53 and 14-3-3sigma (stratifin) using Western blot analysis were enhanced. The differential expressions of p21, p53 and 14-3-3sigma in AGS cancer cells after CKBM treatment may play critical roles in the G2/M cell cycle arrest that blocks cell proliferation and induces apoptosis.