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Nanoparticles of zinc oxide (ZnO NPs), propolis, and the ZnO–propolis composite (ZnO-P NCs) have been synthesized using a biomimetic approach. Zeta potential analysis and Fourier-transform infrared spectroscopy (FT-IR) proved the formation and stability of nanomaterials. Findings using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), EDX-imaging, and transmission electron microscopy (TEM) demonstrated that the particle size of ZnO-P NCs was 9.70 nm. The antioxidant (DPPH radical scavenging) activity of synthesized nanomaterials was investigated. IC50 values of zinc oxide, propolis, and ZnO-P NCs nanoparticles were 2.75, 1.7, and 1.45 mg mL−1, respectively. In addition, their selectivity and anticancer activity for cancer cell lines (Hela and MCF-7) and human normal (W138) cell lines were investigated. ZnO-P NCs were highly effective against the cell line for breast cancer with an IC50 value of 18 µg/mL, indicating its anticancer-promising potent cytotoxicity in breast cancer treatment, and 23 µg/mL against cervical cancer. In addition, the higher observed safety, antioxidant, and anticancer activities for synthesized ZnO-P NCs confirmed the synergistic effect of this combination. It was obtained that the specific mechanisms underlying the synergy effect between zinc oxide nanoparticles and nanopropolis in their composite formulation varied depending on the preparation method, ratio, and concentration of the components. [ABSTRACT FROM AUTHOR]

Background: Abnormal cytokine production derived from specific polymorphisms can have effect on development of respiratory distress syndrome (RDS). Therefore, the present study aimed to determine whether polymorphisms of IL10 in preterm newborn are associated with RDS. Methods: A total of one hundred and one venous blood samples were collected from preterm neonates, and they were classified as 51 with no RDS and 50 with RDS. Grading of RDS, history of surfactant administration or ventilator was assessed in the diseased group. Genetic variant of IL10‐1082G/A (rs1800896) was genotyped by PCR‐RFLP. Results: The RDS group showed a higher prevalence of IL10‐1082 AA and lower prevalence of IL10‐1082 GG (p <.001). We found that the incidence of the allele G in the IL10‐1082 polymorphism was lower in the RDS group (24%) than the non‐RDS group (51%) (p <.001). Allele model (A vs. G): OR = 0.304, 95% CI: 0.166–0.554, p ≤.001; Dominant model (AA vs. AG + GG): OR = 0 0.470, 95% CI: 0.282–0.783, p =.04. More severe grades of RDS, need for surfactant and mechanical ventilation, were significantly associated with AA genotype when compared to AG + GG genotypes. IL10 (AG + GG) genotypes were considered as an independent predictor for lower risk of RDS within preterm neonates. Conclusion: IL10‐1082 A/A genotype associated with increased susceptibility to RDS. Also, A allele has been associated with increase severity of RDS in preterm neonates. Regression analysis revealed that IL10 AG + GG genotypes were considered as independent predictors for lower risk development of RDS within preterm neonates. [ABSTRACT FROM AUTHOR]

The task of mining association rules consists of two main steps. The first involves finding the set of all frequent itemsets. The second step involves testing and generating all high confidence rules among itemsets. This paper presents a comparative study of association rules algorithms on large databases. Five algorithms have been chosen for this comparative study. The Apriori, Close, FP-growth, Top-k rules, and TNR algorithms have been chosen because these are the most commonly used in the literature. Moreover, these algorithms differ in the number of dataset scanning which affects the performance. In addition, some of these algorithms generate redundant association rules while others don't. All these algorithms are implemented and compared on different datasets. Experimental results show that the FPGrowth algorithm has the best performance, while the TNR algorithm has the best generated non-redundant association rules, and the Top-k rules algorithm has the best performance when the minimum confidence is high. [ABSTRACT FROM AUTHOR]