Polymorphisms in adipokine genes, such as leptin (LEP), leptin receptor (LEPR), resistin (RETN), adiponectin (ADIPOQ), interleukin‐1β (IL‐1β), IL‐6 (IL‐6), and tumor necrosis factor‐α (TNF‐α) may be involved in the development of obesity. We conducted a systematic review of published evidence on the association between different adipokine genes and the risk of obesity. Librarian‐designed searches of PubMed and HuGeNet, review of reference lists from published reviews and content expert advice identified potentially eligible studies. The genotyping information and polymorphisms of different adipokine genes, numbers of genotyped cases and controls and frequencies of genotypes were extracted from 48 eligible studies included in this review. Twenty‐one polymorphisms each associated with obesity in at least one study were identified. Polymorphisms in the adipokine genes, LEP, LEPR, and RETN were not associated with obesity susceptibility, whereas ADIPOQ G276T (T vs. G: odds ratio (OR), 1.59; 95% confidence interval (CI), 1.39–1.81), IL‐1β C3953T (CC vs. CT+TT: OR, 1.61; 95% CI, 1.18–2.20), and TNF‐α G308A (GG vs. GA+AA: OR, 1.19; 95% CI, 1.02–1.39) polymorphisms were associated with an increased risk of obesity. The IL‐6 G174C polymorphism was also associated obesity when using allelic comparisons, the recessive genetic model and the dominant genetic model with OR (95% CI) of 1.95 (1.37–2.77), 1.44 (1.15–1.80), and 1.36 (1.16–1.59), respectively. No significant evidence of publication bias was present. However, these “null” results were underpowered due to a small pooled sample size, and analysis of additional case‐control studies with larger sample sizes should provide further clarifications.
<abstract> <p>Salp swarm algorithm (SSA) is a recently proposed, powerful swarm-intelligence based optimizer, which is inspired by the unique foraging style of salps in oceans. However, the original SSA suffers from some limitations including immature balance between exploitation and exploration operators, slow convergence and local optimal stagnation. To alleviate these deficiencies, a modified SSA (called VC-SSA) with velocity clamping strategy, reduction factor tactic, and adaptive weight mechanism is developed. Firstly, a novel velocity clamping mechanism is designed to boost the exploitation ability and the solution accuracy. Next, a reduction factor is arranged to bolster the exploration capability and accelerate the convergence speed. Finally, a novel position update equation is designed by injecting an inertia weight to catch a better balance between local and global search. 23 classical benchmark test problems, 30 complex optimization tasks from CEC 2017, and five engineering design problems are employed to authenticate the effectiveness of the developed VC-SSA. The experimental results of VC-SSA are compared with a series of cutting-edge metaheuristics. The comparisons reveal that VC-SSA provides better performance against the canonical SSA, SSA variants, and other well-established metaheuristic paradigms. In addition, VC-SSA is utilized to handle a mobile robot path planning task. The results show that VC-SSA can provide the best results compared to the competitors and it can serve as an auxiliary tool for mobile robot path planning.</p> </abstract>
The effect of scientific training on course learning in undergraduates is still controversial. In this study, we investigated the academic performance of undergraduate students with and without scientific training. The results show that scientific training improves students' test scores in general medical courses, such as biochemistry and molecular biology, cell biology, physiology, and even English. We classified scientific training into four levels. We found that literature reading could significantly improve students' test scores in general courses. Students who received scientific training carried out experiments more effectively and published articles performed better than their untrained counterparts in biochemistry and molecular biology examinations. The questionnaire survey demonstrated that the trained students were more confident of their course learning, and displayed more interest, motivation and capability in course learning. In summary, undergraduate academic performance is improved by scientific training. Our findings shed light on the novel strategies in the management of undergraduate education in the medical school. © 2017 by The International Union of Biochemistry and Molecular Biology, 45(5):379-384, 2017.
Hypertrophic obesity, characterized by an excessive expansion of subcutaneous adipocytes, causes chronic inflammation and insulin resistance. It is the primary feature of obesity in middle-aged and elderly individuals. In the adipose microenvironment, a high level of endoplasmic reticulum (ER) stress and changes in the extracellular vesicle (EV) composition of adipocytes may cause the senescence and restrained differentiation of progenitor cells of adipose, including adipose-derived mesenchymal stem cells (ASCs). In this study, a hypertrophic obesity mouse model was established, and the effects of adipocytes on the ER stress and senescence of ASCs were observed in a coculture of control ASCs and hypertrophic obesity mouse adipocytes or their derived EVs. The adipocytes of hypertrophic obesity mice were treated with GW4869 or an iron chelation agent to observe the effects of EVs secreted by adipocytes and their iron contents on the ER stress and senescence of ASCs. Results showed higher ER stress level and senescence phenotypes in the ASCs from the hypertrophic obesity mice than in those from the control mice. The ER stress, senescence phenotypes, and ferritin level of ASCs can be aggravated by the coculture of ASCs with adipocytes or EVs released by them from the hypertrophic obesity mice. GW4869 or iron chelator treatment improved the ER stress and senescence of the ASCs cocultured with EVs released by the adipocytes of the hypertrophic obesity mice. Our findings suggest that EV-mediated transmissible ER stress is responsible for the senescence of ASCs in hypertrophic obesity mice.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.