Drift Analysis with Fitness Levels for Elitist Evolutionary Algorithms.

The fitness level method is a popular tool for analyzing the hitting time of elitist evolutionary algorithms. Its idea is to divide the search space into multiple fitness levels and estimate lower and upper bounds on the hitting time using transition probabilities between fitness levels. However, the lower bound generated by this method is often loose. An open question regarding the fitness level method is what are the tightest lower and upper time bounds that can be constructed based on transition probabilities between fitness levels. To answer this question, we combine drift analysis with fitness levels and define the tightest bound problem as a constrained multi-objective optimization problem subject to fitness levels. The tightest metric bounds by fitness levels are constructed and proven for the first time. Then linear bounds are derived from metric bounds and a framework is established that can be used to develop different fitness level methods for different types of linear bounds. The framework is generic and promising, as it can be used to draw tight time bounds on both fitness landscapes with and without shortcuts. This is demonstrated in the example of the (1+1) EA maximizing the TwoMax1 function.

SHNE: Semantics and Homophily Preserving Network Embedding.

Graph convolutional networks (GCNs) have achieved great success in many applications and have caught significant attention in both academic and industrial domains. However, repeatedly employing graph convolutional layers would render the node embeddings indistinguishable. For the sake of avoiding oversmoothing, most GCN-based models are restricted in a shallow architecture. Therefore, the expressive power of these models is insufficient since they ignore information beyond local neighborhoods. Furthermore, existing methods either do not consider the semantics from high-order local structures or neglect the node homophily (i.e., node similarity), which severely limits the performance of the model. In this article, we take above problems into consideration and propose a novel Semantics and Homophily preserving Network Embedding (SHNE) model. In particular, SHNE leverages higher order connectivity patterns to capture structural semantics. To exploit node homophily, SHNE utilizes both structural and feature similarity to discover potential correlated neighbors for each node from the whole graph; thus, distant but informative nodes can also contribute to the model. Moreover, with the proposed dual-attention mechanisms, SHNE learns comprehensive embeddings with additional information from various semantic spaces. Furthermore, we also design a semantic regularizer to improve the quality of the combined representation. Extensive experiments demonstrate that SHNE outperforms state-of-the-art methods on benchmark datasets.

A Multiobjective Evolutionary Algorithm Based on Objective-Space Localization Selection.

This article proposes a simple yet effective multiobjective evolutionary algorithm (EA) for dealing with problems with irregular Pareto front. The proposed algorithm does not need to deal with the issues of predefining weight vectors and calculating indicators in the search process. It is mainly based on the thought of adaptively selecting multiple promising search directions according to crowdedness information in local objective spaces. Concretely, the proposed algorithm attempts to dynamically delete an individual of poor quality until enough individuals survive into the next generation. In this environmental selection process, the proposed algorithm considers two or three individuals in the most crowded area, which is determined by the local information in objective space, according to a probability selection mechanism, and deletes the worst of them from the current population. Thus, these surviving individuals are representative of promising search directions. The performance of the proposed algorithm is verified and compared with seven state-of-the-art algorithms [including four general multi/many-objective EAs and three algorithms specially designed for dealing with problems with irregular Pareto-optimal front (PF)] on a variety of complicated problems with different numbers of objectives ranging from 2 to 15. Empirical results demonstrate that the proposed algorithm has a strong competitiveness power in terms of both the performance and the algorithm compactness, and it can well deal with different types of problems with irregular PF and problems with different numbers of objectives.

Learning Task Relationships in Evolutionary Multitasking for Multiobjective Continuous Optimization.

Multiobjective multifactorial optimization (MO-MFO), rooted in a multitasking environment, is an emerging paradigm wherein multiple distinct multiobjective optimization problems are solved together. This article proposes an evolutionary multitasking algorithm with learning task relationships (LTR) for MO-MFO. In the proposed algorithm, a procedure of LTR is well designed. The decision space of each task is treated as a manifold, and all decision spaces of different tasks are jointly modeled as a joint manifold. Then, through solving a generalized eigenvalue decomposition problem, the joint manifold is projected to a latent space while keeping the necessary features for all tasks and the topology of each manifold. Finally, the task relationships are represented as the joint mapping matrix, which is composed of multiple mapping functions, and they are utilized for information transfer across different decision spaces during the evolutionary process. In the empirical experiments, the performance of the proposed algorithm is verified and compared with several state-of-the-art solvers for MO-MFO on three suites of MO-MFO test problems. Empirical results demonstrate that the proposed algorithm surpasses other competitors on most test instances, and can well tackle complicated MO-MFO problems which involve distinct optimization tasks with heterogeneous decision spaces.

A Neighborhood Regression Optimization Algorithm for Computationally Expensive Optimization Problems.

Expensive optimization problems arise in diverse fields, and the expensive computation in terms of function evaluation poses a serious challenge to global optimization algorithms. In this article, a simple yet effective optimization algorithm for computationally expensive optimization problems is proposed, which is called the neighborhood regression optimization algorithm. For a minimization problem, the proposed algorithm incorporates the regression technique based on a neighborhood structure to predict a descent direction. The descent direction is then adopted to generate new potential offspring around the best solution obtained so far. The proposed algorithm is compared with 12 popular algorithms on two benchmark suites with up to 30 decision variables. Empirical results demonstrate that the proposed algorithm shows clear advantages when dealing with unimodal and smooth problems, and is better than or competitive with other peer algorithms in terms of the overall performance. In addition, the proposed algorithm is efficient and keeps a good tradeoff between solution quality and running time.

Adaptive Estimation Distribution Distributed Differential Evolution for Multimodal Optimization Problems.

Multimodal optimization problems (MMOPs) require algorithms to locate multiple optima simultaneously. When using evolutionary algorithms (EAs) to deal with MMOPs, an intuitive idea is to divide the population into several small "niches," where different niches focus on locating different optima. These population partition strategies are called "niching" techniques, which have been frequently used for MMOPs. The algorithms for simultaneously locating multiple optima of MMOPs are called multimodal algorithms. However, many multimodal algorithms still face the difficulty of population partition since most of the niching techniques involve the sensitive niching parameters. Considering this issue, in this article, we propose a parameter-free niching method based on adaptive estimation distribution (AED) and develop a distributed differential evolution (DDE) algorithm, which is called AED-DDE, for solving MMOPs. In AED-DDE, each individual finds its own appropriate niche size to form a niche and acts as an independent unit to find a global optimum. Therefore, we can avoid the difficulty of population partition and the sensitivity of niching parameters. Different niches are co-evolved by using the master-slave multiniche distributed model. The multiniche co-evolution mechanism can improve the population diversity for fully exploring the search space and finding more global optima. Moreover, the AED-DDE algorithm is further enhanced by a probabilistic local search (PLS) to refine the solution accuracy. Compared with other multimodal algorithms, even the winner of CEC2015 multimodal competition, the comparison results fully demonstrate the superiority of AED-DDE.

Long-term stability and protection efficacy of the RBD-targeting COVID-19 mRNA vaccine in nonhuman primates.

Messenger RNA (mRNA) vaccine technology has shown its power in preventing the ongoing COVID-19 pandemic. Two mRNA vaccines targeting the full-length S protein of SARS-CoV-2 have been authorized for emergency use. Recently, we have developed a lipid nanoparticle-encapsulated mRNA (mRNA-LNP) encoding the receptor-binding domain (RBD) of SARS-CoV-2 (termed ARCoV), which confers complete protection in mouse model. Herein, we further characterized the protection efficacy of ARCoV in nonhuman primates and the long-term stability under normal refrigerator temperature. Intramuscular immunization of two doses of ARCoV elicited robust neutralizing antibodies as well as cellular response against SARS-CoV-2 in cynomolgus macaques. More importantly, ARCoV vaccination in macaques significantly protected animals from acute lung lesions caused by SARS-CoV-2, and viral replication in lungs and secretion in nasal swabs were completely cleared in all animals immunized with low or high doses of ARCoV. No evidence of antibody-dependent enhancement of infection was observed throughout the study. Finally, extensive stability assays showed that ARCoV can be stored at 2-8 °C for at least 6 months without decrease of immunogenicity. All these promising results strongly support the ongoing clinical trial.

The Association between Preterm Birth and Ambient Air Pollution Exposure in Shiyan, China, 2015-2017.

Shortening of the gestational duration has been found associated with ambient air pollution exposure. However, the critical exposure windows of ambient air pollution for gestational duration remain inconsistent, and the association between ambient air pollution and early term births (ETB, 37 to 38 weeks) has rarely been studied relative to preterm births (PTB, 28-37 weeks). A time-series study was conducted in Shiyan, a medium-sized city in China. Birth information was collected from the Shiyan Maternity and Child Health Hospital, and 13,111 pregnant women who gave birth between 2015 and 2017 were included. Data of the concentrations of air pollutants, including PM10, PM2.5, NO2, and SO2 and meteorological data, were collected in the corresponding gestational period. The Cox regression analysis was performed to estimate the relationship between ambient air pollution exposure and the risk of preterm birth after controlling the confounders, including maternal age, education, Gravidity, parity, fetal gender, and delivery mode. Very preterm birth (VPTB, 28-32 weeks) as a subtype of PTB was also incorporated in this study. The risk of VPTB and ETB was positively associated with maternal ambient air pollution exposure, and the correlation of gaseous pollutants was stronger than particulate matter. With respect to exposure windows, the critical trimester of air pollutants for different adverse pregnancy outcomes was different. The exposure windows of PM10, PM2.5, and SO2 for ETB were found in the third trimester, with HRs (hazard ratios) of 1.06 (95%CI: 1.04, 1.09), 1.07 (95%CI: 1.04, 1.11), and 1.28 (95%CI: 1.20, 1.35), respectively. However, for NO2, the second and third trimesters exhibited similar results, the HRs reaching 1.10 (95%CI: 1.03, 6.17) and 1.09 (95%CI: 1.03,1.15), respectively. This study extends and strengthen the evidence for a significant correlation between the ambient air pollution exposure during pregnancy and the risk of not only PTB but, also, ETB. Moreover, our findings suggest that the exposure windows during pregnancy vary with different air pollutants and pregnancy outcomes.

Large-Scale Evolution Strategy Based on Search Direction Adaptation.

The covariance matrix adaptation evolution strategy (CMA-ES) is a powerful evolutionary algorithm for single-objective real-valued optimization. However, the time and space complexity may preclude its use in high-dimensional decision space. Recent studies suggest that putting sparse or low-rank constraints on the structure of the covariance matrix can improve the efficiency of CMA-ES in handling large-scale problems. Following this idea, this paper proposes a search direction adaptation evolution strategy (SDA-ES) which achieves linear time and space complexity. SDA-ES models the covariance matrix with an identity matrix and multiple search directions, and uses a heuristic to update the search directions in a way similar to the principal component analysis. We also generalize the traditional 1/5th success rule to adapt the mutation strength which exhibits the derandomization property. Numerical comparisons with nine state-of-the-art algorithms are carried out on 31 test problems. The experimental results have shown that SDA-ES is invariant under search-space rotational transformations, and is scalable with respect to the number of variables. It also achieves competitive performance on generic black-box problems, demonstrating its effectiveness in keeping a good tradeoff between solution quality and computational efficiency.

Running Time Analysis of MOEA/D on Pseudo-Boolean Functions.

Decomposition-based multiobjective evolutionary algorithms (MOEAs) are a class of popular methods for solving the multiobjective optimization problems (MOPs), and have been widely studied in numerical experiments and successfully applied in practice. However, we know little about these algorithms from the theoretical aspect. In this paper, we present running time analysis of a simple MOEA with mutation and crossover based on the MOEA/D framework (MOEA/D-C) on five pseudo-Boolean functions. Our rigorous theoretical analysis shows that by properly setting the number of subproblems, the upper bounds of expected running time of MOEA/D-C obtaining a set of solutions to cover the Pareto fronts (PFs) of these problems are apparently lower than those of the one with mutation-only (MOEA/D-M). Moreover, to effectively obtain a set of solutions to cover the PFs of these problem, MOEA/D-C only needs to decompose these MOPs into several subproblems with a set of simple weight vectors while MOEA/D-M needs to find Ω(n) optimally decomposed weight vectors. This result suggests that the use of crossover in decomposition-based MOEA can simplify the setting of weight vectors for different problems and make the algorithm more efficient. This paper provides some insights into the working principles of MOEA/D and explains why some existing decomposition-based MOEAs work well in computational experiments.

Prevalence and associated factors of depression, anxiety, and stress among Hubei pediatric nurses during COVID-19 pandemic.

BACKGROUND: The COVID-19 pandemic is putting healthcare workers across the world in an unprecedented situation. The purpose of this study was to evaluate the levels of depression, anxiety, and stress among Hubei pediatric nurses during the COVID-19 pandemic and to analyze the potential factors associated with them. MATERIALS AND METHODS: A self-designed online questionnaire survey, which consisted of the demographic and selected features, the occupational protection knowledge, attitudes, and practices of COVID-19, and the Chinese version of Depression, Anxiety, and Stress Scale, were used to assess the levels of depression, anxiety, and stress among Hubei pediatric nurses during COVID-19 pandemic. The logistic regression analyses were performed to analyze the potential factors associated with depression, anxiety, and stress. RESULTS: A total of 617 pediatric nurses were included in the survey. A considerable proportion of pediatric nurses reported symptoms of depression (95 [15.4%]), anxiety (201 [32.6%]), and stress (111 [18.0%]). Results of multivariable logistic regression analyses indicated that the good occupational protection practices (for depression: OR = 0.455, 95%CI: 0.281 to 0.739; for anxiety: OR = 0.597, 95%CI: 0.419 to 0.851; for stress: OR = 0.269, 95%CI: 0.166 to 0.438) and the personal protective equipment (PPE) meeting work requirements (for depression: OR = 0.438, 95%CI: 0.246 to 0.778; for anxiety: OR = 0.581, 95%CI: 0.352 to 0.959; for stress: OR = 0.504, 95%CI: 0.283 to 0.898) were independent protective factors against depression, anxiety, and stress, respectively. Yet, working in an isolation ward or fever clinic was an independent risk factor associated with depression, anxiety, and stress, respectively (for depression: OR = 1.809, 95%CI: 1.103 to 2.966; for anxiety: OR = 1.864, 95%CI: 1.221 to 2.846; for stress: OR = 2.974, 95%CI: 1.866 to 4.741). Having suspected or confirmed COVID-19 patients in the departments (OR = 1.554, 95%CI: 1.053 to 2.294) and coming in contact with the patient's bodily fluids or blood (OR = 1.469, 95%CI: 1.031 to 2.095) were independent risk factors for anxiety, while >3 times of training for COVID-19 related information was an independent protective factor for depression (OR = 0.592, 95%CI: 0.360 to 0.974). Moreover, >10 years of working was an independent risk factor for stress (OR = 1.678, 95%CI: 1.075 to 2.618). CONCLUSION: During the COVID-19 outbreak, a considerable proportion of Hubei pediatric nurses had psychological problems. The pediatric nurses endorsing the higher number of risk factors should be given special attention and necessary psychological intervention. Improving the levels of PPE so as to meet the work requirements and intensifying occupational protection practices might help safeguard pediatric nurses from depression, anxiety, and stress.

Effect of polymeric support material on biofilm development, bacterial population, and wastewater treatment performance in anaerobic fixed-film systems.

This study evaluated the performance of high-density polyethylene (HDPE), acrylonitrile butadiene styrene (ABS), polycarbonate (PC), polyvinylchloride (PVC), polypropylene (PP), polyvinylidene fluoride (PVDF) and polymethyl methacrylate (acrylic) when used as a support media in anaerobic attached-growth wastewater treatment systems. A combination of physical and chemical (total solids, protein, phosphorus, ammonia, chemical oxygen demand) methods, environmental scanning electron microscopy (ESEM) and Live/Dead viability assay) and genetic sequencing over a period of 81 days was used to provide an in-depth understanding of the impact of different polymer materials on biofilm formation, bacteria population, and wastewater treatment performance. The results showed that hydrophobic polymeric materials (i.e., PP and PVDF) promoted initial cell adhesion and biofilm formation (<16 days) better than the hydrophilic (i.e., ABS and HDPE) polymeric materials. However, under longer-term and steady-state operation (after 81 days), the hydrophilic materials demonstrated larger mature biofilm quantities and better wastewater treatment performance. The sequencing data showed biofilm bacterial community structures of the ABS and HDPE to be significantly different compared to the other polymeric materials tested. The data showed a positive correlation as well between the phyla present on the ABS and HDPE and COD removal. These results suggest that the type of polymeric material play an important role in biofilm development, bacterial population diversity, and wastewater treatment performance for anaerobic fixed-film systems, and ABS and HDPE performed better than the widely used PVC in the industry.

Responses of microbial community to different concentration of perchlorate in the Qingyi River.

Perchlorate is a refractory and mobile contaminant that is wildly distributed in surface water, and due to its tremendous inhibitory effect on mammalian thyroid function, it has gained much attention in recent years. Numerous studies have focused on environmental detection of perchlorate, especially in water. However, less attention has been paid to the effects of perchlorate on the composition of the microbial community in rivers. Upstream of the Qingyi River, an important source of drinking water for local residents, there are two perchlorate manufacturers. In this study, we selected eight study sites from upstream to downstream of the Qingyi River, including sites located upstream and downstream of the perchlorate manufacturers. Our results indicated that perchlorate was detected in all sites except for QYR2, QYR3, and QYR10. The concentration of perchlorate in the five study sites was much higher than the reference dose proposed by the National Academy of Science, and ranged from 187 to 9647.00 µg/L. We utilized 16S rDNA high throughput sequencing to analyze changes in the composition of the microbial community, based on the Illumina 2 × 250 MiSeq platform. The results showed that, when microbial communities were exposed to high concentration of perchlorate, there was an increase in the ratio of Betaproteobacteria, Bacteroidetes, Actinobacteria, and Saccharibacteria in the microbial community along with a decrease in the ratio of Chloroflexi, Verrucomicrobia, and Gammaproteobacteria. Our study has provided a theoretical basis for the alteration of the microbial community caused by the perchlorate pollution, which maybe have a truly important impact on the quality of groundwater.

A Many-Objective Particle Swarm Optimizer With Leaders Selected From Historical Solutions by Using Scalar Projections.

The particle swarm optimizer (PSO), originally proposed for single-objective optimization problems, has been widely extended to other areas. One of them is multiobjective optimization. Recently, using the PSO to handle many-objective optimization problems (MaOPs) (i.e., problems with more than three objectives) has caught increasing attention from the evolutionary multiobjective community. In the design of a multiobjective/many-objective PSO algorithm, the selection of leaders is a crucial issue. This paper proposes an effective many-objective PSO where the above issue is properly addressed. For each particle, the leader is selected from a certain number of historical solutions by using scalar projections. In the objective space, historical solutions record potential search directions, and the leader is elected as the solution that is closest to the Pareto front in the direction determined by the nadir point and the point constructed by the objective vector of this particle. The proposed algorithm is compared with eight state-of-the-art many-objective optimizers on 37 test problems in terms of four performance metrics. The experimental results have shown the superiority and competitiveness of our proposed algorithm. The new algorithm is free of a set of weight vectors and can handle Pareto fronts with irregular shapes. Given the high performance and good properties of the proposed algorithm, it can be used as a promising tool when dealing with MaOPs.

Homophily Preserving Community Detection.

As a fundamental problem in social network analysis, community detection has recently attracted wide attention, accompanied by the output of numerous community detection methods. However, most existing methods are developed by only exploiting link topology, without taking node homophily (i.e., node similarity) into consideration. Thus, much useful information that can be utilized to improve the quality of detected communities is ignored. To overcome this limitation, we propose a new community detection approach based on nonnegative matrix factorization (NMF), namely, homophily preserving NMF (HPNMF), which models not only link topology but also node homophily of networks. As such, HPNMF is able to better reflect the inherent properties of community structure. In order to capture node homophily from scratch, we provide three similarity measurements that naturally reveal the association relationships between nodes. We further present an efficient learning algorithm with convergence guarantee to solve the proposed model. Finally, extensive experiments are conducted, and the results demonstrate that HPNMF has strong ability to outperform the state-of-the-art baseline methods.

Intraclass Correlations of Measured Magnetic Resonance Imaging Volumes of Laser Interstitial Thermal Therapy-Treated High-Grade Gliomas.

BACKGROUND AND OBJECTIVES: Laser interstitial thermal therapy (LITT) is a minimally invasive therapeutic option for the treatment of brain tumors. Previous studies have quantitatively followed the ablated volumes of high-grade gliomas. Reported treatment volumes range from 28% to 100%, with no reported interobserver analysis. Because these volumes are subjectively measured, it is necessary to establish concordance between clinicians. STUDY DESIGN/MATERIALS AND METHODS: Utilizing Brainlab tumor analysis software (Brainlab, Munich, Germany), five physician users traced out tumor volumes slice-by-slice on 10 treated tumors in eight patients. The participants were briefed with specific instructions and a demonstration on how to trace the enhancing borders of the tumor slice-by-slice. Volumes automatically calculated by the Brainlab software included preoperative, intraoperative ablation and postoperative enhancing volumes. Data regarding size, cystic appearance, pathology, previous surgery, and demographics were included. RESULTS: The intraclass correlation coefficient (ICC) for preoperative, intraoperative, and postoperative volumes was 0.92 (95% confidence interval, [CI] 0.81-0.97), 0.90 (0.77-0.96), and 0.89 (0.74-0.96), respectively. The overall ICC was 0.72 (0.50-0.87). ICC comparisons were also made for each pair of readers (neuroradiologist, neuro-oncologist, senior neurosurgery resident, neurosurgery junior resident) which resulted in pretreatment ICC scores of 0.97, 0.91, 0.66, 0.94; intratreatment scores of 0.97, 0.78, 0.90, 0.96; and posttreatment scores of 0.96, 0.81, 0.89, and 0.87. A Bland-Altman plot was also used to assess the differences in volumes. CONCLUSIONS: The ICC gives a composite of the consistency of measurements made by multiple observers measuring the same quantity. The overall ICC of 0.72 means there is good correlation between observers in our study between measured volumes. Lasers Surg. Med. © 2019 Wiley Periodicals, Inc.

A Decomposition-Based Many-Objective Artificial Bee Colony Algorithm.

In this paper, a decomposition-based artificial bee colony (ABC) algorithm is proposed to handle many-objective optimization problems (MaOPs). In the proposed algorithm, an MaOP is converted into a number of subproblems which are simultaneously optimized by a modified ABC algorithm. The hybrid of the decomposition-based algorithm and the ABC algorithm can make full use of the advantages of both algorithms. The former, with the help of a set of weight vectors, is able to maintain a good diversity among solutions, while the latter, with a fast convergence speed, is highly effective when solving a scalar optimization problem. Therefore, the convergence and diversity would be well balanced in the new algorithm. Moreover, subproblems in the proposed algorithm are handled unequally, and computational resources are dynamically allocated through specially designed onlooker bees and scout bees. The proposed algorithm is compared with five state-of-the-art many-objective evolutionary algorithms on 13 test problems with up to 50 objectives. It is shown by the experimental results that the proposed algorithm performs better than or comparably to other algorithms in terms of both quality of the final solution set and efficiency of the algorithms. Finally, as shown by the Wilcoxon signed-rank test results, the onlooker bees and scout bees indeed contribute to performance improvements of the algorithm. Given the high quality of solutions and the rapid running speed, the proposed algorithm could be a promising tool when approximating a set of well-converged and properly distributed nondominated solutions for MaOPs.

A Scalar Projection and Angle-Based Evolutionary Algorithm for Many-Objective Optimization Problems.

In decomposition-based multiobjective evolutionary algorithms, the setting of search directions (or weight vectors), and the choice of reference points (i.e., the ideal point or the nadir point) in scalarizing functions, are of great importance to the performance of the algorithms. This paper proposes a new decomposition-based many-objective optimizer by simultaneously using adaptive search directions and two reference points. For each parent, binary search directions are constructed by using its objective vector and the two reference points. Each individual is simultaneously evaluated on two fitness functions-which are motivated by scalar projections-that are deduced to be the differences between two penalty-based boundary intersection (PBI) functions, and two inverted PBI functions, respectively. Solutions with the best value on each fitness function are emphasized. Moreover, an angle-based elimination procedure is adopted to select diversified solutions for the next generation. The use of adaptive search directions aims at effectively handling problems with irregular Pareto-optimal fronts, and the philosophy of using the ideal and nadir points simultaneously is to take advantages of the complementary effects of the two points when handling problems with either concave or convex fronts. The performance of the proposed algorithm is compared with seven state-of-the-art multi-/many-objective evolutionary algorithms on 32 test problems with up to 15 objectives. It is shown by the experimental results that the proposed algorithm is flexible when handling problems with different types of Pareto-optimal fronts, obtaining promising results regarding both the quality of the returned solution set and the efficiency of the new algorithm.

Benzbromarone aggravates hepatic steatosis in obese individuals.

As a widely used anti-gout drug, benzbromarone has been found to induce hepatic toxicity in patients during clinical treatment. Previous studies have reported that benzbromarone is metabolized via cytochrome P450, thus causing mitochondrial toxicity in hepatocytes. In this study, we found that benzbromarone significantly aggravated hepatic steatosis in both obese db/db mice and high fat diet (HFD)-induced obese (DIO) mouse models. However, benzbromarone had less effect on the liver of lean mice. It was found that the expression of mRNAs encoding lipid metabolism and some liver-specific genes were obviously disturbed in benzbromarone-treated DIO mice compared to the control group. The inflammatory and oxidative stress factors were also activated in the liver of benzbromarone-treated DIO mice. In accordance with the in vivo results, an in vitro experiment using human hepatoma HepG2 cells also confirmed that benzbromarone promoted intracellular lipid accumulation under high free fatty acids (FFAs) conditions by regulating the expression of lipid metabolism genes. Importantly, prolonged treatment of benzbromarone significantly increased cell apoptosis in HepG2 cells in the presence of high FFAs. In addition, in benzbromarone-treated hyperuricemic patients, serum transaminase levels were positively correlated with patients' obesity level. CONCLUSION: This study demonstrated that benzbromarone aggravated hepatic steatosis in obese individuals, which could subsequently contribute to hepatic cell injury, suggesting a novel toxicological mechanism in benzbromarone-induced hepatotoxicity.

Amyloid beta: structure, biology and structure-based therapeutic development.

Amyloid beta peptide (Aß) is produced through the proteolytic processing of a transmembrane protein, amyloid precursor protein (APP), by ß- and γ-secretases. Aß accumulation in the brain is proposed to be an early toxic event in the pathogenesis of Alzheimer's disease, which is the most common form of dementia associated with plaques and tangles in the brain. Currently, it is unclear what the physiological and pathological forms of Aß are and by what mechanism Aß causes dementia. Moreover, there are no efficient drugs to stop or reverse the progression of Alzheimer's disease. In this paper, we review the structures, biological functions, and neurotoxicity role of Aß. We also discuss the potential receptors that interact with Aß and mediate Aß intake, clearance, and metabolism. Additionally, we summarize the therapeutic developments and recent advances of different strategies for treating Alzheimer's disease. Finally, we will report on the progress in searching for novel, potentially effective agents as well as selected promising strategies for the treatment of Alzheimer's disease. These prospects include agents acting on Aß, its receptors and tau protein, such as small molecules, vaccines and antibodies against Aß; inhibitors or modulators of ß- and γ-secretase; Aß-degrading proteases; tau protein inhibitors and vaccines; amyloid dyes and microRNAs.