Epidemiology and Clinical Outcomes of Metabolic(Dysfunction)-associated Fatty Liver Disease

Metabolic(dysfunction)-associated fatty liver disease(MAFLD)is currently the most common chronic liver disease and affects at least a quarter of the global adult population.It has rapidly become one of the leading causes of hepatocellular carcinoma and cirrhosis in Western countries.In this review,we discuss the nomenclature and definition of MAFLD as well as its prevalence and incidence in different geographical regions.Although cardiovascular disease remains the leading cause of death in MAFLD patients,the proportion of patients dying from hepatic complications increases sharply as the disease progresses to advanced liver fibrosis and cirrhosis.In addition,patients with MAFLD are at increased risk of various extrahepatic cancers.Although a causal relationship between MAFLD and extrahepatic cancers has not been established,clinicians should recognize the association and consider cancer screening(e.g.,for colorectal cancer)as appropriate.

Non-invasive tests of non-alcoholic fatty liver disease

For the detection of steatosis,quantitative ultrasound imaging techniques have achieved great progress in past years.Magnetic resonance imaging proton density fat fraction is currently the most accurate test to detect hepatic steatosis.Some blood biomarkers correlate with non-alcoholic steatohepatitis,but the accuracy is modest.Regarding liver fibrosis,liver stiffness measurement by transient elastography(TE)has high accuracy and is widely used across the world.Magnetic resonance elastography is marginally better than TE but is limited by its cost and availability.Several blood biomarkers of fibrosis have been used in clinical trials and hold promise for selecting patients for treatment and monitoring treatment response.This article reviews new developments in the non-invasive assessment of non-alcoholic fatty liver disease(NAFLD).Accumulating evidence suggests that various non-invasive tests can be used to diagnose NAFLD,assess its severity,and predict the prognosis.Further studies are needed to determine the role of the tests as monitoring tools.We cannot overemphasize the importance of context in selecting appropriate tests.

Dynamic neighborhood genetic learning particle swarm optimization for high-power-density electric propulsion motor

To maximize the power density of the electric propulsion motor in aerospace application,this paper proposes a novel Dynamic Neighborhood Genetic Learning Particle Swarm Optimization(DNGL-PSO)for the motor design,which can deal with the insufficient population diversity and non-global optimal solution issues.The DNGL-PSO framework is composed of the dynamic neighborhood module and the particle update module.To improve the population diversity,the dynamic neighborhood strategy is first proposed,which combines the local neighborhood exemplar generation mechanism and the shuffling mechanism.The local neighborhood exemplar generation mechanism enlarges the search range of the algorithm in the solution space,thus obtaining highquality exemplars.Meanwhile,when the global optimal solution cannot update its fitness value,the shuffling mechanism module is triggered to dynamically change the local neighborhood members.The roulette wheel selection operator is introduced into the shuffling mechanism to ensure that particles with larger fitness value are selected with a higher probability and remain in the local neighborhood.Then,the global learning based particle update approach is proposed,which can achieve a good balance between the expansion of the search range in the early stage and the acceleration of local convergence in the later stage.Finally,the optimization design of the electric propulsion motor is conducted to verify the effectiveness of the proposed DNGL-PSO.The simulation results show that the proposed DNGL-PSO has excellent adaptability,optimization efficiency and global optimization capability,while the optimized electric propulsion motor has a high power density of 5.207 kW/kg with the efficiency of 96.12%.