TNF-α-induced osteoclastogenesis is central to post-menopausal and inflammatory bone loss, however, the effect of phytoestrogens on TNF-α-induced bone resorption has not been studied. The phytoestrogens genistein, daidzein, and coumestrol directly suppressed TNF-α-induced osteoclastogenesis and bone resorption. TRAP positive osteoclast formation and resorption area were significantly reduced by genistein (10(-7) M), daidzein (10(-5) M), and coumestrol (10(-7) M), which was prevented by the estrogen antagonist ICI 182,780. TRAP expression in mature TNF-α-induced osteoclasts was also significantly reduced by these phytoestrogen concentrations. In addition, in the presence of ICI 182,780 genistein and coumestrol (10(-5) -10(-6) M) augmented TNF-α-induced osteoclast formation and resorption. However, this effect was not observed in the absence of estrogen antagonist indicating that genistein's and coumestrol's ER-dependent anti-osteoclastic action normally negates this pro-osteoclastic effect. To determine the mechanism mediating the anti-osteoclastic action we examined the effect of genistein, coumestrol, and daidzein on caspase 3/7 activity, cell viability and expression of key genes regulating osteoclast differentiation and fusion. While anti-osteoclastic phytoestrogen concentrations had no effect on caspase 3/7 activity or cell viability they did significantly reduce TNF-α-induced c-fos and NFATc1 expression in an ER dependent manner and also inhibited NFATc1 nuclear translocation. Significant decreases in NFκB and DC-STAMP levels were also noted. Interestingly, constitutive c-fos expression prevented the anti-osteoclastic action of phytoestrogens on differentiation, resorption and NFATc1. This suggests that phytoestrogens suppress TNF-α-induced osteoclastogenesis via inhibition of c-fos-dependent NFATc1 expression. Our data provides further evidence that phytoestrogens have a potential role in the treatment of post-menopausal and inflammatory bone loss directly inhibiting TNF-α-induced resorption.
Steel fiber aluminum matrix composites were prepared by atomization technique. Different air atomization conditions were considered; which were atomization pressure and distance between sample and nozzle. Tensile stress properties were studied. XRF and XRD techniques were used to study the primary compositions and the structure of the raw materials and the atomized products. The tensile results showed that the best reported tensile strength observed for an atomization pressure equal to 4 mbar and sample to nozzle distance equal to 12 cm. Young modulus results showed that the best result occurred with an air atomization pressure equal to 8 mbar and sample to nozzle distance equal to 16cm
Anaerobic digestion (AD) is the most common process for dealing with primary and secondary wastewater sludge. In the present work, four pre-treatment methods (ultrasonic, chemical, thermal, and thermo-chemical) are investigated in Al-Rustumya Wastewater Treatment plant in order to find their effect on biogas production and volatile solid removal efficiency during anaerobic digestion.
Two frequencies of ultrasonic wave were used 30 KHz and 50 KHz during the pre-treatment. Sodium hydroxide was added in different amounts to give three pH values of 9, 10 and 11 in chemical pre-treating processes. The sludge was heated at 60oC and 80oC through thermal pre-treatment experiment. Also, the sludge was treated thermo-chemically at 80 oC and pH
In this work, yttrium oxide particles (powder) reinforced AL-Si matrix composites (Y2O3/Al-Si) and Chromium oxide particles reinforced AL-Si matrix composites (Cr2O3/AL-Si) were prepared by direct squeeze casting. The volume percentages of yttrium oxide used are (4, 8.1, 12.1, 16.1 vol %) and the volume percentages of the chromium oxide particles used are (3.1, 6.3, 9.4, 12.5 vol. %). The parameters affecting the preparation of Y2O3/Al-Si and Cr2O3/AL-Si composites by direct squeeze casting process were studied. The molten Al-Si alloy with yttrium oxide particles or with chromium oxide particles was stirred again using an electrical stirrer at speed 500 rpm and the molten alloy was poured into the squeeze die cavity. Th
... Show MoreKE Sharquie, AA Noaimi, AG Al-Ghazzi, Journal of Dermatology & Dermatologic Surgery, 2015 - Cited by 19
An agricultural waste (walnut shell) was undertaken to remove Cu(II) from aqueous solutions in batch and continuous fluidized bed processes. Walnut shell was found to be effective in batch reaching 75.55% at 20 and 200 rpm, when pH of the solution adjusted to 7. The equilibrium was achieved after 6 h of contacting time. The maximum uptake was 11.94mg/g. The isotherm models indicated that the highest determination coefficient belongs to Langmuir model. Cu (II) uptake process in kinetic rate model followed the pseudo-second-order with determination coefficient of 0.9972. More than 95% of the Cu(II) were adsorbed on the walnut shells within 6 h at optimum agitation speed of 800 rpm. The main functional groups responsible for biosorption of
... Show MoreSome of the issues that have become common in our society recently after the Americans entered our country and were rubbed by some security agencies: obtaining some information from children, and the serious consequences that may lead to the lives of innocent people, became common interrogation of some security agencies and rely on their words.
There are significant cases where their testimony needs to be heard, such as their presence in some places where incidents are not witnessed by others, such as schools or being witnesses to certain crimes.
I saw the study of this case in the light of Sharia and law
The region-based association analysis has been proposed to capture the collective behavior of sets of variants by testing the association of each set instead of individual variants with the disease. Such an analysis typically involves a list of unphased multiple-locus genotypes with potentially sparse frequencies in cases and controls. To tackle the problem of the sparse distribution, a two-stage approach was proposed in literature: In the first stage, haplotypes are computationally inferred from genotypes, followed by a haplotype coclassification. In the second stage, the association analysis is performed on the inferred haplotype groups. If a haplotype is unevenly distributed between the case and control samples, this haplotype is labeled
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