This experiment was conducted to evaluate the effect of foliar spraying with gibberellic acid concentrations (0,50,100)mg.L­¹ and proline acid concentrations (0, 25, 50)mg.L­¹ and their interactions on some growth parameters of pea plant using clay pots in the botanical garden of Biology Department College of Education for pure science Ibn –Al-Haitham ,Baghdad University, for the growing season 2012-2013 the experiment involved the studing of some growth parameters as plant?s height, dry weight, wt. of pods.plant­¹, biology yield and the concentration of some major elements (nitrogen, phosphorus and calcium) in plant?s seeds. The experiment was designed according to Completely Randomized Desig(CRD) with three replications. R

A review of comparative analytical methods for β-lactam antibiotics and heavy metals in pharmaceutical products and human biological matrices

A laboratory experiment has been carried out in the College of Science-University of Salahaddin to study the effect of different levels (0,5,10 and 15%) and sizes(250 and 1000µm) of walnut seeds residues and (160mg.kg^-1) phosphorus fertilization on the concentration of phosphorus availability and alkaline phosphatase activity in calcareous soil during 15 and 30 days period of incubation, the experimental design in factorial complet randomize design (C.R.D) with three replications. The results indicated that the application of different levels of walnut seed residues decreases the concentration of phosphorus availability and alkaline phosphatase activity, however the results revealed that combination between levels and sizes o

Apical periodontitis (AP) is the most prevalent chronic inflammatory disease of the teeth. Bone resorption dynamics in symptomatic and asymptomatic AP are still unrecognized. This study examined different inflammatory markers within gingival crevicular fluid, including matrix metalloproteinases 8 (MMP8), tissue inhibitors of metalloproteinases 1 (TIMP1), receptor activator of nuclear factor κB (RANK), its ligand (RANKL), and osteoprotegerin (OPG), to be used in comparing symptomatic apical periodontitis (SAP) and asymptomatic apical periodontitis (AAP) versus healthy teeth. Subjects with SAP, AAP, and a control group were recruited and GCF samples were collected by Periopaper strips. Clinical and radiographical measures were used f

Preparation of Carboxy Methylated mPEG-Block-(4-Dodecyl Anilide) Copolymers and Their Visco Metric and Surface Tension Properties in THF

Background: MicroRNAs (miRNAs) are small noncoding RNAs that post‐transcriptionally regulate gene expression by targeting specific mRNAs. The main objective of this study was measure the level of salivary (hsa-miR-200a, hsa-miR-125a and hsa- miR-93) in both oral squamous cell carcinoma and healthy controls to asses the association of them with age, gender and tumor grade materials and methods The level of three salivary microRNAs namely hsa-miR-200a, hsa-miR-125a and hsa- miR-93 were measured in saliva of patients with oral squamous cell carcinoma and healthy controls by using reveres transcription, preamplification and quantitative PCR also the general information from each patient including the age, sex and tumor grade were record

Simultaneous determination of Furosemide, Carbamazepine, Diazepam, and Carvedilol in bulk and pharmaceutical formulation using the partial least squares regression (PLS-1 and PLS-2) is described in this study. The two methods were successfully applied to estimate the four drugs in their quaternary mixture using UV spectral data of 84synthetic mixtures in the range of 200-350nm with the intervals Δλ=0.5nm. The linear concentration range were 1-20 μg.mL-1 for all, with correlation coefficient (R2) and root mean squares error for the calibration (RMSE) for FURO, CARB, DIAZ, and CARV were 0.9996, 0.9998, 0.9997, 0.9997, and 0.1128, 0.1292, 0.1868,0.1562 respectively for PLS-1, and for PLS-2 were 0.9995, 0.9999, 0.9997, 0.9998, and 0.1127, 0.

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F