Dysfunction of Nrf-2 in CF Epithelia Leads to Excess Intracellular H2O2 and Inflammatory Cytokine Production

Cystic fibrosis is characterized by recurring pulmonary exacerbations that lead to the deterioration of lung function and eventual lung failure. Excessive inflammatory responses by airway epithelia have been linked to the overproduction of the inflammatory cytokine IL-6 and IL-8. The mechanism by which this occurs is not fully understood, but normal IL-1β mediated activation of the production of these cytokines occurs via H2O2 dependent signaling. Therefore, we speculated that CFTR dysfunction causes alterations in the regulation of steady state H2O2. We found significantly elevated levels of H2O2 in three cultured epithelial cell models of CF, one primary and two immortalized. Increases in H2O2 heavily contributed to the excessive IL-6 and IL-8 production in CF epithelia. Proteomic analysis of three in vitro and two in vivo models revealed a decrease in antioxidant proteins that regulate H2O2 processing, by ≥2 fold in CF vs. matched normal controls. When cells are stimulated, differential expression in CF versus normal is enhanced; corresponding to an increase in H2O2 mediated production of IL-6 and IL-8. The cause of this redox imbalance is a decrease by ∼70% in CF cells versus normal in the expression and activity of the transcription factor Nrf-2. Inhibition of CFTR function in normal cells produced this phenotype, while N-acetyl cysteine, selenium, an activator of Nrf-2, and the overexpression of Nrf-2 all normalized H2O2 processing and decreased IL-6 and IL-8 to normal levels, in CF cells. We conclude that a paradoxical decrease in Nrf-2 driven antioxidant responses in CF epithelia results in an increase in steady state H2O2, which in turn contributes to the overproduction of the pro-inflammatory cytokines IL-6 and IL-8. Treatment with antioxidants can ameliorate exaggerated cytokine production without affecting normal responses

Using a Machine Learning Approach to Predict Outcomes after Radiosurgery for Cerebral Arteriovenous Malformations

Predictions of patient outcomes after a given therapy are fundamental to medical practice. We employ a machine learning approach towards predicting the outcomes after stereotactic radiosurgery for cerebral arteriovenous malformations (AVMs). Using three prospective databases, a machine learning approach of feature engineering and model optimization was implemented to create the most accurate predictor of AVM outcomes. Existing prognostic systems were scored for purposes of comparison. The final predictor was secondarily validated on an independent site’s dataset not utilized for initial construction. Out of 1,810 patients, 1,674 to 1,291 patients depending upon time threshold, with 23 features were included for analysis and divided into training and validation sets. The best predictor had an average area under the curve (AUC) of 0.71 compared to existing clinical systems of 0.63 across all time points. On the heldout dataset, the predictor had an accuracy of around 0.74 at across all time thresholds with a specificity and sensitivity of 62% and 85% respectively. This machine learning approach was able to provide the best possible predictions of AVM radiosurgery outcomes of any method to date, identify a novel radiobiological feature (3D surface dose), and demonstrate a paradigm for further development of prognostic tools in medical care

Predicting local malaria exposure using a Lasso-based two-level cross validation algorithm.

Recent studies have highlighted the importance of local environmental factors to determine the fine-scale heterogeneity of malaria transmission and exposure to the vector. In this work, we compare a classical GLM model with backward selection with different versions of an automatic LASSO-based algorithm with 2-level cross-validation aiming to build a predictive model of the space and time dependent individual exposure to the malaria vector, using entomological and environmental data from a cohort study in Benin. Although the GLM can outperform the LASSO model with appropriate engineering, the best model in terms of predictive power was found to be the LASSO-based model. Our approach can be adapted to different topics and may therefore be helpful to address prediction issues in other health sciences domains