Epilepsy is one of the most common chronic neurological diseases. High-frequency oscillations (HFOs) have emerged as promising biomarkers for the epileptogenic zone. However, visual marking of HFOs is a time-consuming and laborious process. Several automated techniques have been proposed to detect HFOs, yet these are still far from being suitable for application in a clinical setting. Here, ripples and fast ripples from intracranial electroencephalograms were detected in six patients with intractable epilepsy using a convolutional neural network (CNN) method. This approach proved more accurate than using four other HFO detectors integrated in RIPPLELAB, providing a higher sensitivity (77.04% for ripples and 83.23% for fast ripples) and specificity (72.27% for ripples and 79.36% for fast ripples) for HFO detection. Furthermore, for one patient, the Cohen's kappa coefficients comparing automated detection and visual analysis results were 0.541 for ripples and 0.777 for fast ripples. Hence, our automated detector was capable of reliable estimates of ripples and fast ripples with higher sensitivity and specificity than four other HFO detectors. Our detector may be used to assist clinicians in locating epileptogenic zone in the future.

Objective: We proposed an improved automated high frequency oscillations (HFOs) detector that could not only be applied to various intracranial electrodes, but also automatically remove false HFOs caused by high-pass filtering. We proposed a continuous resection ratio of high order HFO channels and compared this ratio with each patient's post-surgical outcome, to determine the quantitative threshold of HFO distribution to delineate the epileptogenic zone (EZ).Methods: We enrolled a total of 43 patients diagnosed with refractory epilepsy. The patients were used to optimize the parameters for SEEG electrodes, to test the algorithm for identifying false HFOs, and to calculate the continuous resection ratio of high order HFO channels. The ratio can be used to determine a quantitative threshold to locate the epileptogenic zone.Results: Following optimization, the sensitivity, and specificity of our detector were 66.84 and 73.20% (ripples) and 69.76 and 66.13% (fast ripples, FRs), respectively. The sensitivity and specificity of our algorithm for removing false HFOs were 76.82 and 94.54% (ripples) and 72.55 and 94.87% (FRs), respectively. The median of the continuous resection ratio of high order HFO channels in patients with good surgical outcomes, was significantly higher than in patients with poor outcome, for both ripples and FRs (P < 0.05 ripples and P < 0.001 FRs).Conclusions: Our automated detector has the advantage of not only applying to various intracranial electrodes but also removing false HFOs. Based on the continuous resection ratio of high order HFO channels, we can set the quantitative threshold for locating epileptogenic zones.

Heat treatment, i.e., boiling or pasteurization, is the most widely recognized and practiced form of household water treatment. Considering recent advances in the development of light harvesting nanoparticles for solar-to-heat conversion, we envision that a nanomaterial enhanced water heating treatment system could obviate the need to use fuels or electricity to heat water by replacing the energy source with sunlight. In this study, we demonstrate that functional disinfection temperatures can be easily achieved with unconcentrated sunlight using a single layer interfacial photothermal film in direct contact with a tortuous flowing water channel. Photothermal films were fabricated by dispersing high concentrations of light harvesting nanoparticles, carbon black and Au nanorods, into a highly transparent curable polymer. Bench-scale 3-D printed reactors were employed to determine the effect of different parameters on reactor performance, such as channel height, retention time, flow rate, initial water temperature, and light intensity. Simulations demonstrate the scalability of the treatment system, predicting that a reactor footprint of 45 × 45 cm would be required for a photothermal treatment system that could produce 8 L of water per day with 8 h of sunlight at 1 Sun intensity.