Abstract Introduction: MMP3 plays a crucial role in the process of bone erosion in the pathomechanism of rheumatoid arthritis (RA). It acts by removing the outer osteoid layer, which allows the osteoclasts to tightly connect and carry out the subsequent damage to the underlying bone. MMP3 can trigger the production of other MMPs like MMP-1, MMP-7, and MMP-9, it plays a pivotal role in the remodeling of connective tissues. Aim of the study: to assess the influence of MMP-3 serum levels and single-nucleotide polymorphisms of rs679620 in the rheumatoid arthritis patients' group in comparison to the control group. Subjects: eighty eight samples, 45 rheumatoid arthritis patients after being referred by their treating physician for regular RA

Chronic periodontitis (CP) is an inflammatory disease affecting tooth supporting structures in response to bacterial dental plaque causing irreversible tissue destruction. Cyclooxygenase-2 (COX-2) is an effective mediator in the pathogenesis of periodontitis. Polymorphisms in the COX-2 gene may contribute to its overexpression and increased disease susceptibility. To evaluate the association between -1195 A/G single nucleotide polymorphism (SNP) in the promotor area of the cyclooxygenase-2(COX-2) gene and severity of chronic periodontitis in a sample of Iraqi population. -1195A/ G COX-2 SNP was investigated in 70 chronic periodontitis (CP) cases and 30 healthy controls. CP cases composed of 2 subgroups (35 moderate CP cases and 35 severe CP

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo

This paper concerns is the preparation and characterization of a bidentate ligand [4-(5,5- dimethyl-3-oxocyclohex-1-enylamino)-N-(5-methylisoxazol-3-yl) benzene sulfonamide]. The ligand was prepared from fusing of sulfamethoxazole and dimedone at (140) ºC for half hour. The complex was prepared by refluxing the ligand with a bivalent cobalt ion using ethanol as a solvent. The prepared ligand and complex were identified using Spectroscopic methods. The proposed tetrahedral geometry around the metal ions studied were concluded from these measurements. Both molar ratio and continuous variation method were studied to determine metal to ligand ratio (M:L). The M to L ratio was found to be (1:1). The adsorption of cobalt complex was carried out

     Atenolol was used with ammonium molybdate to prove the efficiency, reliability and repeatability of the long distance chasing photometer (NAG-ADF-300-2) using continuous flow injection analysis. The method is based on reaction between atenolol and ammonium molybdate in an aqueous medium to obtain a dark brown precipitate. Optimum parameters was studied to increase the sensitivity for developed method. A linear range for calibration graph was 0.1-3.5 mmol/L for cell A and 0.3-3.5 mmol/L for cell B, and LOD 133.1680 ng/100 µL and 532.6720 ng/100 µL for cell A and cell B respectively with correlation coefficient (r) 0.9910 for cell A and 0.9901 for cell B, RSD% was lower than 1%, (n=8) for the determination of ate

Seventy five isolates of Saccharomyces cerevisiae were identified, they were isolated from different local sources which included decayed fruits and vegetables, vinegar, fermented pasta, baker yeast and an alcohol factory. Identification of isolates was carried out by cultural microscopical and biochemical tests. Ethanol sensitivity of the isolates showed that the minimal inhibitory concentration of the isolate (Sy18) was 16% and Lethal concentration was 17%. The isolate (Sy18) was most efficient as ethanol producer 9.36% (v/w). The ideal conditions to produce ethanol from Date syrup by yeast isolate, were evaluated, various temperatures, pH, Brix, incubation period and different levels of (NH4)2HP04. Maximum ethanol produced was 10

. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a