Insulin Resistance/Sensitivity Measures as Screening Indicators of Metabolic-Associated Fatty Liver Disease and Liver Fibrosis.

BACKGROUND: Measures of insulin resistance (IR)/sensitivity (IS) are emerging tools to identify metabolic-associated fatty liver disease (MAFLD). However, the comprehensive assessment of the performance of various indicators is limited. Moreover, the utility of measures of IR/IS in detecting liver fibrosis remains unclear. AIMS: To evaluate the predictive ability of seventeen IR/IS and two beta cell function indices to identify MAFLD and liver fibrosis. METHODS: A cross-sectional study was conducted on individuals aged 25-75 years. Transient elastography was used to estimate liver stiffness and controlled attenuation parameter. The following measures were computed: homeostatic model assessment (HOMA/HOMA2) for IR, IS, and beta cell function; QUICKI; Bennett index; glucose/insulin; FIRI; McAuley index; Reynaud index; SPISE index; TyG; TyG-BMI; TyG-WC; TyG-WHtR; TG/HDL; and METS-IR. Subgroup analyses were performed according to age, gender, diabetes status, and body weight. RESULTS: A total of 644 individuals were included in our analysis. MAFLD and significant liver fibrosis were detected in 320 (49.7%) and 80 (12.4%) of the participants, respectively. All measures of IR/IS identified MAFLD and liver fibrosis. However, TyG-WC, TyG-BMI, and TyG-WHtR were the top three indicators that identified MAFLD. Measures that include insulin level in their mathematical calculation, namely, Raynaud index, HOMA-IR, HOMA 2-IR, FIRI, and QUICKI had the best performance in identifying liver fibrosis in the entire population, as well as among the study subgroups. CONCLUSIONS: TyG-WC, TyG-BMI, and TyG-WHtR were the best predictors of MAFLD. Insulin-based measures had better performances in the detection of advanced fibrosis. This was independent of age, gender, obesity, or diabetes status.

Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.

A new bio-inspired metaheuristic algorithm named the Pufferfish Optimization Algorithm (POA), that imitates the natural behavior of pufferfish in nature, is introduced in this paper. The fundamental inspiration of POA is adapted from the defense mechanism of pufferfish against predators. In this defense mechanism, by filling its elastic stomach with water, the pufferfish becomes a spherical ball with pointed spines, and as a result, the hungry predator escapes from this threat. The POA theory is stated and then mathematically modeled in two phases: (i) exploration based on the simulation of a predator's attack on a pufferfish and (ii) exploitation based on the simulation of a predator's escape from spiny spherical pufferfish. The performance of POA is evaluated in handling the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that POA has achieved an effective solution with the appropriate ability in exploration, exploitation, and the balance between them during the search process. The quality of POA in the optimization process is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that POA provides superior performance by achieving better results in most of the benchmark functions in order to solve the CEC 2017 test suite compared to competitor algorithms. Also, the effectiveness of POA to handle optimization tasks in real-world applications is evaluated on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. Simulation results show that POA provides effective performance in handling real-world applications by achieving better solutions compared to competitor algorithms.

Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.

In this paper, a new bio-inspired metaheuristic algorithm named the Kookaburra Optimization Algorithm (KOA) is introduced, which imitates the natural behavior of kookaburras in nature. The fundamental inspiration of KOA is the strategy of kookaburras when hunting and killing prey. The KOA theory is stated, and its mathematical modeling is presented in the following two phases: (i) exploration based on the simulation of prey hunting and (ii) exploitation based on the simulation of kookaburras' behavior in ensuring that their prey is killed. The performance of KOA has been evaluated on 29 standard benchmark functions from the CEC 2017 test suite for the different problem dimensions of 10, 30, 50, and 100. The optimization results show that the proposed KOA approach, by establishing a balance between exploration and exploitation, has good efficiency in managing the effective search process and providing suitable solutions for optimization problems. The results obtained using KOA have been compared with the performance of 12 well-known metaheuristic algorithms. The analysis of the simulation results shows that KOA, by providing better results in most of the benchmark functions, has provided superior performance in competition with the compared algorithms. In addition, the implementation of KOA on 22 constrained optimization problems from the CEC 2011 test suite, as well as 4 engineering design problems, shows that the proposed approach has acceptable and superior performance compared to competitor algorithms in handling real-world applications.

Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems.

In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates the natural behavior of lyrebirds in the wild is introduced. The fundamental inspiration of LOA is the strategy of lyrebirds when faced with danger. In this situation, lyrebirds scan their surroundings carefully, then either run away or hide somewhere, immobile. LOA theory is described and then mathematically modeled in two phases: (i) exploration based on simulation of the lyrebird escape strategy and (ii) exploitation based on simulation of the hiding strategy. The performance of LOA was evaluated in optimization of the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that the proposed LOA approach has high ability in terms of exploration, exploitation, and balancing them during the search process in the problem-solving space. In order to evaluate the capability of LOA in dealing with optimization tasks, the results obtained from the proposed approach were compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that LOA has superior performance compared to competitor algorithms by providing better results in the optimization of most of the benchmark functions, achieving the rank of first best optimizer. A statistical analysis of the performance of the metaheuristic algorithms shows that LOA has significant statistical superiority in comparison with the compared algorithms. In addition, the efficiency of LOA in handling real-world applications was investigated through dealing with twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. The simulation results show that LOA has effective performance in handling optimization tasks in real-world applications while providing better results compared to competitor algorithms.

Golf Optimization Algorithm: A New Game-Based Metaheuristic Algorithm and Its Application to Energy Commitment Problem Considering Resilience.

In this research article, we uphold the principles of the No Free Lunch theorem and employ it as a driving force to introduce an innovative game-based metaheuristic technique named Golf Optimization Algorithm (GOA). The GOA is meticulously structured with two distinctive phases, namely, exploration and exploitation, drawing inspiration from the strategic dynamics and player conduct observed in the sport of golf. Through comprehensive assessments encompassing fifty-two objective functions and four real-world engineering applications, the efficacy of the GOA is rigorously examined. The results of the optimization process reveal GOA's exceptional proficiency in both exploration and exploitation strategies, effectively striking a harmonious equilibrium between the two. Comparative analyses against ten competing algorithms demonstrate a clear and statistically significant superiority of the GOA across a spectrum of performance metrics. Furthermore, the successful application of the GOA to the intricate energy commitment problem, considering network resilience, underscores its prowess in addressing complex engineering challenges. For the convenience of the research community, we provide the MATLAB implementation codes for the proposed GOA methodology, ensuring accessibility and facilitating further exploration.

Non-alcoholic fatty liver disease and compromised endothelial function in people with type 2 diabetes.

INTRODUCTION: Nonalcoholic fatty liver disease (NAFLD) frequently coexists with type 2 diabetes mellitus (T2DM) and synergistically contributes to the development of atherosclerosis. Flow-mediated dilation (FMD) is a commonly used noninvasive test for assessing endothelial function. The main objective of this study was to explore FMD in patients with T2DM with and without NAFLD. METHODS: In this cross-sectional study, conducted on people with T2DM, NAFLD was defined as controlled attenuation parameter (CAP) score > 302 dB/m. Endothelial dysfunction was detected when arterial FMD of brachial artery was equal or less than 0.7%. Regression analyses were applied to assess factors associated with impaired FMD. RESULT: A total of 147 patients (72 with NAFLD and 75 without NAFLD) were included in the final analysis. Patients with NAFLD were more likely to develop FMD ≤ 7% (77.8% vs. 58.7%, P = 0.01). In multivariate analysis, NAFLD (OR = 2.581, 95% CI (1.18-5.62), P = 0.017) and hypertension (HTN) (OR = 3.114, 95% CI (1.31-7.35), P = 0.010) were associated with an increased risk of impaired FMD. However, female sex was associated with a decreased risk of impaired FMD (OR = 0.371, 95% CI (0.15-0.87), P = 0.024). CONCLUSION: NAFLD is associated with endothelial dysfunction in people with T2DM. This risk is comparable with the risk imposed by HTN, highlighting the importance of screening and management of NAFLD in these patients.

Cognitive impairment, depressive-like behaviors and hippocampal microglia activation following exposure to air pollution nanoparticles.

Air pollution particulate matter (PM) is a world risk factor that the effects of long-term exposure to these factors in terms of damage to cardiovascular and pulmonary function are well known, but little is known comparatively about the effects of PM on emotional and cognitive processes. Exposure to PM can adversely affect the central nervous system (CNS) by inflammatory pathways and activation of reactive oxygen species (ROS) associated with urban air pollution PM. Therefore, we investigated whether prolonged exposure to diesel exhaust particles (DEPs) affects hippocampal inflammatory cytokines and emotional and cognition responses. Male mice were exposed to DEPs for 6 and 12 weeks. DEP-exposed mice indicated more disorders in depressive-like responses and spatial memory and learning than in control groups. Pro-inflammatory cytokine expression in tge hippocampus was increased among mice exposed to DEPs. The number of activated microglia increased in the dentate gyrus (DG) and CA1 regions of the hippocampus in DEP-exposed mice. These results show that chronic exposure to DEPs can alter neurobehavioral and impair cognition. Generally, these findings reaffirm the importance of protecting from exposure to ambient PM2.5 and also advance our understanding of the toxic actions of air pollution nanoparticles.

Learning and memory disorders related to hippocampal inflammation following exposure to air pollution.

It has been demonstrated that sub-chronic exposure to air pollution containing nanoscale (˂100 nm) diesel exhaust particles (DEPs) may lead to excessive oxidative stress and neuro-inflammation in adult male mice. Hereby, we investigated the effects of DEPs on hippocampus-dependent spatial learning and neuro-inflammation and memory-related gene expression in male mice. In this study, we divided 48 adult NMRI male mice into control group VS. three exposure groups. Mice were exposed to 300-350 µg/m3 DEPs for 2, 5, and 7 h daily for 12 weeks. The Morris Water Maze (MWM) and Elevated Plus Maze device were used to examine anxiety, spatial memory and learning, respectively. The mRNAs expression of pro-inflammatory cytokines, N-methyl-D-aspartate (NMDA) receptor subunits, and glutaminase were studied in hippocampus (HI) by real-time RT-PCR. Besides, malondialdehyde (MDA) tests were used to determine the state of oxidative stress. After 5 and 7 h. of DEPs exposure, mRNA expression of NR2A and NR3B IL1α, IL1ß, TNFα, NMDA receptor subunits and MDA levels increased significantly (P < 0.05). Also, DEPs exposed mice for 2, 5, and 7 h. showed diminished entrance into open arms with short time spent there. Indeed, 5 and 7 h/day exposed mice required a longer time to reach the hidden platform. Sub-chronic exposure to DEPs increased oxidative stress markers, neuroinflammation, anxiety, impaired spatial learning and memory.

A New Two-Stage Algorithm for Solving Optimization Problems.

Optimization seeks to find inputs for an objective function that result in a maximum or minimum. Optimization methods are divided into exact and approximate (algorithms). Several optimization algorithms imitate natural phenomena, laws of physics, and behavior of living organisms. Optimization based on algorithms is the challenge that underlies machine learning, from logistic regression to training neural networks for artificial intelligence. In this paper, a new algorithm called two-stage optimization (TSO) is proposed. The TSO algorithm updates population members in two steps at each iteration. For this purpose, a group of good population members is selected and then two members of this group are randomly used to update the position of each of them. This update is based on the first selected good member at the first stage, and on the second selected good member at the second stage. We describe the stages of the TSO algorithm and model them mathematically. Performance of the TSO algorithm is evaluated for twenty-three standard objective functions. In order to compare the optimization results of the TSO algorithm, eight other competing algorithms are considered, including genetic, gravitational search, grey wolf, marine predators, particle swarm, teaching-learning-based, tunicate swarm, and whale approaches. The numerical results show that the new algorithm is superior and more competitive in solving optimization problems when compared with other algorithms.

Hippocampal inflammation and oxidative stress following exposure to diesel exhaust nanoparticles in male and female mice.

Air pollution exposure is among the most prevalent reasons for environmentally-induced oxidative stress and inflammation, both of which are involved in the development and progression of central nervous system (CNS) diseases. Ultrafine particles (UFPs) plays an important role in global air pollution and the diesel exhaust particles (DEPs) are the most important component in this regard. There are more than 40 toxic air pollutants in diesel exhaust (DE), which is one of the main constituents of an environmental pollutant and including particulate matter (PM) especially UFPs. Thus, in this study, adult female and male NMRI mice were exposed to DEPs (350-400 µg/m3) for 14 weeks (6 h per day and 5 days per week). After 14 weeks of exposure, expression of pro-inflammatory cytokines (IL-1α, IL-1ß, IL-6, TNF-α), nNOS, HO1, NR2A, and NR2B and malondialdehyde (MDA) level were analyzed in various brain regions such as the hippocampus (HI) and olfactory bulb (OB). Exposure to DEPs caused neuroinflammation and oxidative stress in female and male mice. That these effects observed in females were less pronounced than in male mice. The male mice emerged to be more susceptible significantly than the female mice to induced neuroinflammation following DEPs exposure. Also, our findings indicate that long term exposure to DEPs results in altered expression of hippocampal NMDA receptor subunits, and suggests that gender can play important role in the modulating susceptibility to neurotoxicity induced by DEPs exposure.