Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy.

BACKGROUND: Post-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI). Electroencephalography aids early post-traumatic seizure diagnosis, but its optimal utility for PTE prediction remains unknown. We aim to evaluate the contribution of quantitative electroencephalograms to predict first-year PTE (PTE1). METHODS: We performed a multicentre, retrospective case-control study of patients with TBI. 63 PTE1 patients were matched with 63 non-PTE1 patients by admission Glasgow Coma Scale score, age and sex. We evaluated the association of quantitative electroencephalography features with PTE1 using logistic regressions and examined their predictive value relative to TBI mechanism and CT abnormalities. RESULTS: In the matched cohort (n=126), greater epileptiform burden, suppression burden and beta variability were associated with 4.6 times higher PTE1 risk based on multivariable logistic regression analysis (area under the receiver operating characteristic curve, AUC (95% CI) 0.69 (0.60 to 0.78)). Among 116 (92%) patients with available CT reports, adding quantitative electroencephalography features to a combined mechanism and CT model improved performance (AUC (95% CI), 0.71 (0.61 to 0.80) vs 0.61 (0.51 to 0.72)). CONCLUSIONS: Epileptiform and spectral characteristics enhance covariates identified on TBI admission and CT abnormalities in PTE1 prediction. Future trials should incorporate quantitative electroencephalography features to validate this enhancement of PTE risk stratification models.

Rational Design and Identification of Small-Molecule Allosteric Inhibitors of CD38.

CD38 is a multi-functional signaling enzyme that catalyzes the biosynthesis of two calcium-mobilizing second messengers: cyclic ADP-ribose and nicotinic acid adenine dinucleotide phosphate. It also regulates intracellular nicotinamide adenine dinucleotide (NAD) contents, associated with multiple pathophysiological processes such as aging and cancer. As such, enzymatic inhibitors of CD38 offer great potential in drug development. Here, through virtual screening and enzymatic assays, we discovered compound LX-102, which targets CD38 on the side opposite its enzymatic pocket with a binding affinity of 7.7 µm. It inhibits the NADase activity of CD38 with an IC50 of 14.9 µm. Surface plasmon resonance (SPR) and hydrogen/deuterium exchange and mass spectrometry experiments verified that LX-102 competitively binds to the epitope of the therapeutic SAR 650984 antibody in an allosteric manner. Molecular dynamics simulation was performed to demonstrate the binding dynamics of CD38 with the allosteric ligand. In summary, we established that the cavity to which SAR 650984 binds was an allosteric site and was accessible for the rational design of small chemical modulators of CD38. The lead compound LX-102 that we identified in this study could also be a useful tool for probing CD38 functions and promoting drug discovery.

Improving feature selection performance using pairwise pre-evaluation.

BACKGROUND: Biological data such as microarrays contain a huge number of features. Thus, it is necessary to select a small number of novel features to characterize the entire dataset. All combinations of the features subset must be evaluated to produce an ideal feature subset, but this is impossible using currently available computing power. Feature selection or feature subset selection provides a sub-optimal solution within a reasonable amount of time. RESULTS: In this study, we propose an improved feature selection method that uses information based on all the pairwise evaluations for a given dataset. We modify the original feature selection algorithms to use pre-evaluation information. The pre-evaluation captures the quality and interactions between two features. The feature subset should be improved by using the top ranking pairs for two features in the selection process. CONCLUSIONS: Experimental results demonstrated that the proposed method improved the quality of the feature subset produced by modified feature selection algorithms. The proposed method can be applied to microarray and other high-dimensional data.