Predictive Power of Interictal Epileptiform Discharges in Fitness-to-Drive Evaluation.

BACKGROUND AND OBJECTIVES: This study aimed to evaluate and predict the effects of interictal epileptiform discharges (IEDs) on driving ability using simple reaction tests and a driving simulator. METHODS: Patients with various epilepsies were evaluated with simultaneous EEGs during their response to visual stimuli in a single-flash test, a car-driving video game, and a realistic driving simulator. Reaction times (RTs) and missed reactions or crashes (miss/crash) during normal EEG and IEDs were measured. IEDs, as considered in this study, were a series of epileptiform potentials (>1 potential) and were classified as generalized typical, generalized atypical, or focal. RT and miss/crash in relation to IED type, duration, and test type were analyzed. RT prolongation, miss/crash probability, and odds ratio (OR) of miss/crash due to IEDs were calculated. RESULTS: Generalized typical IEDs prolonged RT by 164 ms, compared with generalized atypical IEDs (77.0 ms) and focal IEDs (48.0 ms) (p < 0.01). Generalized typical IEDs had a session miss/crash probability of 14.7% compared with a zero median for focal and generalized atypical IEDs (p < 0.01). Long repetitive bursts of focal IEDs lasting >2 seconds had a 2.6% miss/crash probabilityIED. Cumulated miss/crash probability could be predicted from RT prolongation: 90.3 ms yielded a 20% miss/crash probability. All tests were nonsuperior to each other in detecting miss/crash probabilitiesIED (zero median for all 3 tests) or RT prolongations (flash test: 56.4 ms, car-driving video game: 75.5 ms, simulator 86.6 ms). IEDs increased the OR of miss/crash in the simulator by 4.9-fold compared with normal EEG. A table of expected RT prolongations and miss/crash probabilities for IEDs of a given type and duration was created. DISCUSSION: IED-associated miss/crash probability and RT prolongation were comparably well detected by all tests. Long focal IED bursts carry a low risk, while generalized typical IEDs are the primary cause of miss/crash. We propose a cumulative 20% miss/crash risk at an RT prolongation of 90.3 ms as a clinically relevant IED effect. The IED-associated OR in the simulator approximates the effects of sleepiness or low blood alcohol level while driving on real roads. A decision aid for fitness-to-drive evaluation was created by providing the expected RT prolongations and misses/crashes when IEDs of a certain type and duration are detected in routine EEG.

Generation of LexA enhancer-trap lines in Drosophila by an international scholastic network.

Conditional gene regulation in Drosophila through binary expression systems like the LexA-LexAop system provides a superb tool for investigating gene and tissue function. To increase the availability of defined LexA enhancer trap insertions, we present molecular, genetic, and tissue expression studies of 301 novel Stan-X LexA enhancer traps derived from mobilization of the index SX4 line. This includes insertions into distinct loci on the X, II, and III chromosomes that were not previously associated with enhancer traps or targeted LexA constructs, an insertion into ptc, and seventeen insertions into natural transposons. A subset of enhancer traps was expressed in CNS neurons known to produce and secrete insulin, an essential regulator of growth, development, and metabolism. Fly lines described here were generated and characterized through studies by students and teachers in an international network of genetics classes at public, independent high schools, and universities serving a diversity of students, including those underrepresented in science. Thus, a unique partnership between secondary schools and university-based programs has produced and characterized novel resources in Drosophila, establishing instructional paradigms devoted to unscripted experimental science.

A machine-learning approach for predicting impaired consciousness in absence epilepsy.

Behavior during 3-4 Hz spike-wave discharges (SWDs) in absence epilepsy can vary from obvious behavioral arrest to no detectible deficits. Knowing if behavior is impaired is crucial for clinical care but may be difficult to determine without specialized behavioral testing, often inaccessible in practice. We aimed to develop a pure electroencephalography (EEG)-based machine-learning method to predict SWD-related behavioral impairment. Our classification goals were 100% predictive value, with no behaviorally impaired SWDs misclassified as spared; and maximal sensitivity. First, using labeled data with known behavior (130 SWDs in 34 patients), we extracted EEG time, frequency domain, and common spatial pattern features and applied support vector machines and linear discriminant analysis to classify SWDs as spared or impaired. We evaluated 32 classification models, optimized with 10-fold cross-validation. We then generalized these models to unlabeled data (220 SWDs in 41 patients), where behavior during individual SWDs was not known, but observers reported the presence of clinical seizures. For labeled data, the best classifier achieved 100% spared predictive value and 93% sensitivity. The best classifier on the unlabeled data achieved 100% spared predictive value, but with a lower sensitivity of 35%, corresponding to a conservative classification of 8 patients out of 23 as free of clinical seizures. Our findings demonstrate the feasibility of machine learning to predict impaired behavior during SWDs based on EEG features. With additional validation and optimization in a larger data sample, applications may include EEG-based prediction of driving safety, treatment adjustment, and insight into mechanisms of impaired consciousness in absence seizures.

The role of extracellular vesicles and PD-L1 in glioblastoma-mediated immunosuppressive monocyte induction.

BACKGROUND: Immunosuppression in glioblastoma (GBM) is an obstacle to effective immunotherapy. GBM-derived immunosuppressive monocytes are central to this. Programmed cell death ligand 1 (PD-L1) is an immune checkpoint molecule, expressed by GBM cells and GBM extracellular vesicles (EVs). We sought to determine the role of EV-associated PD-L1 in the formation of immunosuppressive monocytes. METHODS: Monocytes collected from healthy donors were conditioned with GBM-derived EVs to induce the formation of immunosuppressive monocytes, which were quantified via flow cytometry. Donor-matched T cells were subsequently co-cultured with EV-conditioned monocytes in order to assess effects on T-cell proliferation. PD-L1 constitutive overexpression or short hairpin RNA-mediated knockdown was used to determined the role of altered PD-L1 expression. RESULTS: GBM EVs interact with both T cells and monocytes but do not directly inhibit T-cell activation. However, GBM EVs induce immunosuppressive monocytes, including myeloid-derived suppressor cells (MDSCs) and nonclassical monocytes (NCMs). MDSCs and NCMs inhibit T-cell proliferation in vitro and are found within GBM in situ. EV PD-L1 expression induces NCMs but not MDSCs, and does not affect EV-conditioned monocytes T-cell inhibition. CONCLUSION: These findings indicate that GBM EV-mediated immunosuppression occurs through induction of immunosuppressive monocytes rather than direct T-cell inhibition and that, while PD-L1 expression is important for the induction of specific immunosuppressive monocyte populations, immunosuppressive signaling mechanisms through EVs are complex and not limited to PD-L1.