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The present study aimed to investigate the acute effects and the mechanism of ketamine on nicotine-induced relaxation of the corpus cavernosum (CC) in mice. This study measured the intra-cavernosal pressure (ICP) of male C57BL/6 mice and the CC muscle activities using an organ bath wire myograph. Various drugs were used to investigate the mechanism of ketamine on nicotine-induced relaxation. Direct ketamine injection into the major pelvic ganglion (MPG) inhibited MPG-induced increases in ICP. D-serine/L-glutamate-induced relaxation of the CC was inhibited by MK-801 (N-methyl-D-aspartate (NMDA) receptor inhibitor), and nicotine-induced relaxation was enhanced by D-serine/L-glutamate. NMDA had no effect on CC relaxation. Nicotine-induced relaxation of the CC was suppressed by mecamylamine (a non-selective nicotinic acetylcholine receptor antagonist), lidocaine, guanethidine (an adrenergic neuronal blocker), Nw-nitro-L-arginine (a non-selective nitric oxide synthase inhibitor), MK-801, and ketamine. This relaxation was almost completely inhibited in CC strips pretreated with 6-hydroxydopamine (a neurotoxic synthetic organic compound). Ketamine inhibited cavernosal nerve neurotransmission via direct action on the ganglion and impaired nicotine-induced CC relaxation. The relaxation of the CC was dependent on the interaction of the sympathetic and parasympathetic nerves, which may be mediated by the NMDA receptor. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Hybridizing Chaotic and Quantum Mechanisms and Fruit Fly Optimization Algorithm with Least Squares Support Vector Regression Model in Electric Load Forecasting." The correction addresses a mistake in the legend for Tables 10 and 11, clarifying that the LS-SVR-CQGA model significantly outperforms the other models. The authors assure that this correction does not affect the scientific conclusions of the study. The original publication has been updated accordingly. [Extracted from the article]

Compared with a large power grid, a microgrid electric load (MEL) has the characteristics of strong nonlinearity, multiple factors, and large fluctuation, which lead to it being difficult to receive more accurate forecasting performances. To solve the abovementioned characteristics of a MEL time series, the least squares support vector machine (LS-SVR) hybridizing with meta-heuristic algorithms is applied to simulate the nonlinear system of a MEL time series. As it is known that the fruit fly optimization algorithm (FOA) has several embedded drawbacks that lead to problems, this paper applies a quantum computing mechanism (QCM) to empower each fruit fly to possess quantum behavior during the searching processes, i.e., a QFOA algorithm. Eventually, the cat chaotic mapping function is introduced into the QFOA algorithm, namely CQFOA, to implement the chaotic global perturbation strategy to help fruit flies to escape from the local optima while the population's diversity is poor. Finally, a new MEL forecasting method, namely the LS-SVR-CQFOA model, is established by hybridizing the LS-SVR model with CQFOA. The experimental results illustrate that, in three datasets, the proposed LS-SVR-CQFOA model is superior to other alternative models, including BPNN (back-propagation neural networks), LS-SVR-CQPSO (LS-SVR with chaotic quantum particle swarm optimization algorithm), LS-SVR-CQTS (LS-SVR with chaotic quantum tabu search algorithm), LS-SVR-CQGA (LS-SVR with chaotic quantum genetic algorithm), LS-SVR-CQBA (LS-SVR with chaotic quantum bat algorithm), LS-SVR-FOA, and LS-SVR-QFOA models, in terms of forecasting accuracy indexes. In addition, it passes the significance test at a 97.5% confidence level. [ABSTRACT FROM AUTHOR]

Glucagon-like peptide-1 (GLP-1) is easily degraded by dipeptidyl peptidase-4 (DPP-4) in the human body, limiting its therapeutic effect on type II diabetes. Therefore, improving GLP-1 receptor agonist (GLP-1RA) stability is a major obstacle for drug development. We analyzed human GLP-1, DPP-4, and GLP-1 receptor structures and designed three GLP-1RAs, which were introduced into fusion protein fragments and changed in the overall conformation. This modification effectively prevented GLP-1RAs from entering the DPP-4 active center without affecting GLP-1RAs' ability to bind to GLP-1R, the new GLP-1RA hypoglycemic effect lasting for >24 h. Through molecular modeling, molecular dynamics calculation, and simulation, possible tertiary structure models of GLP-1RAs were obtained; molecular docking with DPP-4 and GLP-1R showed access to the fusion protein. The overall conformational change of GLP-1RAs prevented DPP-4 binding, without affecting GLP-1RAs' affinity to GLP-1R. This study provides important drug design ideas for GLP-1RA development and a new example for application of structural biology-based protein design in drug development. [ABSTRACT FROM AUTHOR]