Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that is mainly characterized by cognitive deficits. Although many studies have been devoted to developing disease-modifying therapies, there has been no effective therapy until now. However, dietary interventions may be a potential strategy to treat AD. The ketogenic diet (KD) is a high-fat and low-carbohydrate diet with adequate protein. KD increases the levels of ketone bodies, providing an alternative energy source when there is not sufficient energy supply because of impaired glucose metabolism. Accumulating preclinical and clinical studies have shown that a KD is beneficial to AD. The potential underlying mechanisms include improved mitochondrial function, optimization of gut microbiota composition, and reduced neuroinflammation and oxidative stress. The review provides an update on clinical and preclinical research on the effects of KD or medium-chain triglyceride supplementation on symptoms and pathophysiology in AD. We also detail the potential mechanisms of KD, involving amyloid and tau proteins, neuroinflammation, gut microbiota, oxidative stress, and brain metabolism. We aimed to determine the function of the KD in AD and outline important aspects of the mechanism, providing a reference for the implementation of the KD as a potential therapeutic strategy for AD.
There are differences between the requirements for traffic network for traffic demand in daily and emergency situations. In order to evaluate how the network designed for daily needs can meet the surging demand for emergency evacuation, the concept of emergency reliability and corresponding evaluation method is proposed. This paper constructs a bilevel programming model to describe the proposed problem. The upper level problem takes the maximum reserve capacity multiplier as the optimization objective and considers the influence of reversible lane measures taken under emergency conditions. The lower level model adopts the combined traffic distribution/assignment model with capacity limits, to describe evacuees’ path and shelter choice behavior under emergency conditions and take into account the traits of crowded traffic. An iterative optimization method is proposed to solve the upper level model, and the lower level model is transformed into a UE assignment problem with capacity limits over a network of multiple origins and single destination, by adding a dummy node and several dummy links in the network. Then a dynamic penalty function algorithm is used to solve the problem. In the end, numerical studies and results are provided to demonstrate the rationality of the proposed model and feasibility of the proposed solution algorithms.
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