The effects of quercetin supplementation on lipid profiles and inflammatory markers among patients with metabolic syndrome and related disorders: A systematic review and meta-analysis of randomized controlled trials

Abstract

Aims: This systematic review and meta-analysis of randomized controlled trials (RCTs) was performed to determine the effect of quercetin administration on lipid profiles and inflammatory markers among patients with metabolic syndrome (MetS) and related disorders.

Methods: We searched systematically online databases including Cochrane Library, EMBASE, MEDLINE, and Web of Science to identify the relevant RCTs until November 2018. Q-test and I² statistics were applied to assess heterogeneity among included studies. Data were combined using fixed- or random-effects model and presented as standardized mean difference (SMD) with 95% confidence interval (CI).

Results: Out of 591 citations, 16 RCTs were included in the meta-analysis. The pooled findings showed that quercetin consumption significantly decreased total-cholesterol (SMD = ?0.98; 95% CI, ?1.48, ?0.49; p?<?0.001; I²: 94.0), LDL-cholesterol (SMD = ?0.88; 95% CI, ?1.35, ?0.41; p?<?0.001; I²: 92.7) and C-reactive protein (CRP) levels (?0.64; 95% CI, ?1.03, ?0.25; p?=?0.001; I²: 90.2). While, quercetin supplementation did not significantly affect triglycerides (TG) (SMD = ?0.32; 95% CI, ?0.68, 0.04; p?=?0.08; I²: 84.8), HDL-cholesterol (SMD = 0.20; 95% CI, ?0.20, 0.24; p?=?0.84; I²: 70.6), interleukin 6 (IL-6) (SMD = ?0.69; 95% CI, ?1.69, 0.31; p?=?0.17; I²: 94.5) and tumor necrosis factor-alpha (TNF-α) levels (SMD = ?0.06; 95% CI, ?0.25, 0.14; p?=?0.58; I²: 35.6)

Conclusions: In summary, the current meta-analysis demonstrated that quercetin supplementation significantly reduced total-cholesterol, LDL-cholesterol, and CRP levels, yet did not affect triglycerides, HDL-cholesterol, IL-6 and TNF-α among patients with MetS and related disorders.     

Removal of malachite green (a toxic dye) from water by cobalt ferrite silica magnetic nanocomposite: Herbal and green sol-gel autocombustion synthesis

Cobalt ferrite-silica nanocomposites were synthesized in the presence of various amounts of Salix alba bark extract via the self-propagating sol-gel process. The structure, morphology and magnetic property of nanostructures was investigated and the adsorbent ability for the removal of malachite green (MG) dye from water through CoFe₂O₄SiO₂ has been reported. The products were characterized by XRD, SEM, TEM, FTIR and VSM techniques. The samples that were synthesized in the presence of more extract (20 mg extract) revealed no trace of impurity in XRD pattern and indicated more spherical and agglomerated particles on SEM images; in contrast other samples indicated Co₃O₄ as impurity besides rough and irregular spherical shapes. TEM images for sample SA20 (15.41 ± 0.65 nm size) illustrated narrow size distribution which is consistent with the SEM analysis. The saturation magnetization values continuously increase with the increasing Salix alba bark extract amount and reaches to 2.89 emu/g for SA20 sample. Investigation on MG adsorption isotherm and kinetic onto the nanocomposites were carried out as well. The experimental data best fitted to the Langmuir model and revealed a monolayer adsorption capacity of 75.5 ± 1.21 mg g^?1. The adsorption kinetic was found to follow pseudo-second-order kinetic model. Consequently, prepared nanocomposite can be used as an effective magnetic adsorbent for the MG removal from water.     

A multi-objective optimization algorithm for feature selection problems

Feature selection (FS) is a critical step in data mining, and machine learning algorithms play a crucial role in algorithms performance. It reduces the processing time and accuracy of the categories. In this paper, three different solutions are proposed to FS. In the first solution, the Harris Hawks Optimization (HHO) algorithm has been multiplied, and in the second solution, the Fruitfly Optimization Algorithm (FOA) has been multiplied, and in the third solution, these two solutions are hydride and are named MOHHOFOA. The results were tested with MOPSO, NSGA-II, BGWOPSOFS and B-MOABC algorithms for FS on 15 standard data sets with mean, best, worst, standard deviation (STD) criteria. The Wilcoxon statistical test was also used with a significance level of 5% and the Bonferroni–Holm method to control the family-wise error rate. The results are shown in the Pareto front charts, indicating that the proposed solutions' performance on the data set is promising.

     

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