Classification Accuracy of Hepatitis C Virus Infection Outcome: Data Mining Approach.

BACKGROUND: The dataset from genes used to predict hepatitis C virus outcome was evaluated in a previous study using a conventional statistical methodology. OBJECTIVE: The aim of this study was to reanalyze this same dataset using the data mining approach in order to find models that improve the classification accuracy of the genes studied. METHODS: We built predictive models using different subsets of factors, selected according to their importance in predicting patient classification. We then evaluated each independent model and also a combination of them, leading to a better predictive model. RESULTS: Our data mining approach identified genetic patterns that escaped detection using conventional statistics. More specifically, the partial decision trees and ensemble models increased the classification accuracy of hepatitis C virus outcome compared with conventional methods. CONCLUSIONS: Data mining can be used more extensively in biomedicine, facilitating knowledge building and management of human diseases.

Synthesis and highly potent anti-inflammatory activity of licofelone- and ketorolac-based 1-arylpyrrolizin-3-ones.

Since NSAIDs are commonly used anti-inflammatory agents that produce adverse effects, there have been ongoing efforts to develop more effective and less toxic compounds. Based on the structure of the anti-inflammatory pyrrolizines licofelone and ketorolac, a series of 1-arylpyrrolizin-3-ones was synthesized. Also prepared was a series of substituted pyrroles, mimicking similar known anti-inflammatory agents. The anti-inflammatory activity of the test compounds was determined with a phorbol ester (TPA)-induced murine ear edema protocol. For the most active derivatives, 19b-c/20b-c, the anti-inflammatory effect was the same as that of the reference compound (indomethacin) and was dose-dependent. These compounds have an aryl ring at the C-1 position and a methoxycarbonyl group at the C-2 position of the pyrrolizine framework, which represent plausible pharmacophore groups with anti-inflammatory activity. The anti-inflammatory activity of 1-substituted analogs containing a five- or six-membered heterocycles was lower but still good, while that of the pyrroles was only moderate. Although the docking studies suggests that the effect of analogs 19a-c/20a-c is associated with the inhibition of cyclooxygenase-2, experimental assays did not corroborate this idea. Indeed, a significant inhibition of NO was found experimentally as a plausible mechanism of action.

Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.

BACKGROUND AND PURPOSE: Chest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose-volume constraints. MATERIALS AND METHODS: Twenty-five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain. Decision tree modeling was used to determine chest wall syndrome (CWS) thresholds for individual features. Significant features were determined using independent multivariate methods. These methods incorporate out-of-bag estimation using Random forests (RF) and bootstrapping (100 iterations) using decision trees. RESULTS: Univariate analysis identified rib dose to 1 cc < 4000 cGy (P = 0.01), chest wall dose to 30 cc < 1900 cGy (P = 0.035), rib Dmax < 5100 cGy (P = 0.05) and lung dose to 1000 cc < 70 cGy (P = 0.039) to be statistically significant thresholds for avoiding CWS. Subsequent multivariate analysis confirmed the importance of rib dose to 1 cc, chest wall dose to 30 cc, and rib Dmax. Using learning-curve experiments, the dataset proved to be self-consistent and provides a realistic model for CWS analysis. CONCLUSIONS: Using machine learning algorithms in this first of its kind study, we identify robust features and cutoffs predictive for the rare clinical event of CWS. Additional data in planned subsequent multicenter studies will help increase the accuracy of multivariate analysis.

An Efficient Strategy for Electroreduction Reactor Outlet Fractioning into Valuable Products.

In this work, two industrial dual-step pressure swing adsorption (PSA) processes were designed and simulated to obtain high-purity methane, CO2, and syngas from a gas effluent of a CO2 electroreduction reactor using different design configurations. Among the set of zeolites that was investigated using Monte Carlo and molecular dynamics simulations, NaX and MFI were the ones selected. The dual-PSA process for case study 1 is only capable of achieving a 90.5% methane purity with a 95.2% recovery. As for case study 2, methane is obtained with a 97.5% purity and 95.3% recovery. Both case studies can produce CO2 with high purity and recovery (>97 and 95%, respectively) and syngas with a H2/CO ratio above 4. Although case study 2 allows methane to be used as domestic gas, a much higher value for its energy consumption is observed compared to case study 1 (64.9 vs 29.8 W h molCH4-1).