Optimization of Ultrasound- and Microwave-Assisted Extraction for the Determination of Phenolic Compounds in Peach Byproducts Using Experimental Design and Liquid Chromatography-Tandem Mass Spectrometry.

The conversion of plant byproducts, which are phenolic-rich substrates, to valuable co-products by implementing non-conventional extraction techniques is the need of the hour. In the current study, ultrasound- (UAE) and microwave-assisted extraction (MAE) were applied for the recovery of polyphenols from peach byproducts. Two-level screening and Box-Behnken design were adopted to optimize extraction efficiency in terms of total phenolic content (TPC). Methanol:water 4:1% v/v was the extraction solvent. The optimal conditions of UAE were 15 min, 8 s ON-5 s OFF, and 35 mL g-1, while MAE was maximized at 20 min, 58 °C, and 16 mL g-1. Regarding the extracts' TPC and antioxidant activity, MAE emerged as the method of choice, whilst their antiradical activity was similar in both techniques. Furthermore, a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed and validated to determine chlorogenic acid and naringenin in byproducts' extracts. 4-Chloro-4'-hydroxybenzophenone is proposed as a new internal standard in LC-MS/MS analysis in foods and byproducts. Chlorogenic acid was extracted in higher yields when UAE was used, while MAE favored the extraction of the flavonoid compound, naringenin. To conclude, non-conventional extraction could be considered as an efficient and fast alternative for the recovery of bioactive compounds from plant matrices.

Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach.

Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson's disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications.

Dominant efficiency of nonregular patterns of subthalamic nucleus deep brain stimulation for Parkinson's disease and obsessive-compulsive disorder in a data-driven computational model.

OBJECTIVE: Almost 30 years after the start of the modern era of deep brain stimulation (DBS), the subthalamic nucleus (STN) still constitutes a standard stimulation target for advanced Parkinson's disease (PD), but the use of STN-DBS is also now supported by level I clinical evidence for treatment-refractory obsessive-compulsive disorder (OCD). Disruption of neural synchronization in the STN has been suggested as one of the possible mechanisms of action of standard and alternative patterns of STN-DBS at a local level. Meanwhile, recent experimental and computational modeling evidence has signified the efficiency of alternative patterns of stimulation; however, no indications exist for treatment-refractory OCD. Here, we comparatively simulate the desynchronizing effect of standard (regular at 130 Hz) versus temporally alternative (in terms of frequency, temporal variability and the existence of bursts or pauses) patterns of STN-DBS for PD and OCD, by means of a stochastic dynamical model and two microelectrode recording (MER) datasets. APPROACH: The stochastic model is fitted to subthalamic MERs acquired during eight surgical interventions for PD and eight surgical interventions for OCD. For each dynamical system simulated, we comparatively assess the invariant density (steady-state phase distribution) as a measure inversely related to the desynchronizing effect yielded by the applied patterns of stimulation. MAIN RESULTS: We demonstrate that high (130 Hz)-and low (80 Hz)-frequency irregular patterns of stimulation, and low-frequency periodic stimulation interrupted by bursts of pulses, yield in both pathologic conditions a significantly stronger desynchronizing effect compared with standard STN-DBS, and distinct alternative patterns of stimulation. In PD, values of the invariant density measure are proven to be optimal at the dorsolateral oscillatory region of the STN including sites with the optimal therapeutic window. SIGNIFICANCE: In addition to providing novel insights into the efficiency of low-frequency nonregular patterns of STN-DBS for advanced PD and treatment-refractory OCD, this work points to a possible correlation of a model-based outcome measure with clinical effectiveness of stimulation and may have significant implications for an energy- and therapeutically-efficient configuration of a closed-loop neuromodulation system.