Background: Many previous studies were concerned with
the relationship between gestational diabetes and the
development of vaginal candidiasis in pregnant women. In
this study, our aim is directed to uncover glucose tolerance
status in non-diabetic pregnant women inflicted with
candida albicans.
Methods: Thirty-four pregnant women with vaginal
condidiasis (as demonstrated by microscopy) were enrolled
in this study. The patients were nearly similar in their
anthropometric and demographic criteria with those of the
healthy pregnant women (control group, forty –two
women). Fasting plasma sugar and glucose tolerance test
were alone for all patients and control group.
Results: fasting plasma sugar was significantly higher in
the candidiasis-positive pregnant women in comparison to
those of candidiasis-negative subjects (5.09 mmol/L vs.
4.71, p <0.02). Plasma glucose level after 30 minutes of
performing oral glucose tolerance test was also significant
(8.47mmol/Lvs. 7.84, P <0.04). The same trend of
significance was noticed after 60 minutes and 120 minutes
of performing the corresponding test.
The results were (8.13 mmol/L vs. 7.10, P <0.02) and
(6.90mmol/L vs. 6.15, P<0.05) respectively.
Conclusion: the results reveal an impaired oral glucose
tolerance test in pregnant women with candida albicans
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The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv
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The preparation and characterization of the Cu (II), Co(II), Ni(II), Zn(II), Cd(II), and Hg(II) metal complexes of heterocyclic azo ligand 2-[(4`-sulphamide phenyl) azo] -4,5-diphenyl imidazole (4-SuBAI) have been studied by elemental analysis, FT-IR and UV-Vis Spectroscopic, magnetic moment and molar conductance methods. The analytical data showed that all chelate complexes were prepared with (metal-ligand) ratio of (1:2). The general formula of these complexes was [ML2X2]. nH2O [were L=2-[(4`-sulphamide phenyl) azo]-4,5-diphenyl imidazole and X=Cl, and the octahedral geometry were suggested for these complexes .
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2-(2-amino-5-nitro-phenylazo),-phenol was ready by grouping the diazonium salt of 2-aminophenol with 4-nitroaniline.Thegeometry of azo ligand(HL)was resolved on the origin of (C.H.N) analysis,1H and 13CNMR spectra, infrared spectra and UV–vis electronic absorption spectra. Dealing with the azo ligand produced with Rh+3 and La+3ataqueous ethanol for a 1:3 metal: ligand rate, and in perfect ph. The formation for compounds have been described by utilizing flame atomic, absorption,(C.H.N),Analyses, conductivity, infrared spectra and UV–vis spectral procedures. Nature in the produced compounds, have been studied, obey the ratio of mole and continuous, variance, manners, Beer's law, yielded up a concentration, rate (1×10-4- 3×10-4M),. High
... Show MoreAspartate aminotransferase was purified from urine and serum of patients with type 2 diabetes in a 2 steps procedure involving dialysis bag and sephadex G-25 gel filtration (column chromatography). The enzyme was purified 346.23 fold with 1467% yield and 3.46 fold with 142.85% yield in urine and serum of patients with type 2 diabetes respectively. The purified enzyme showed single peak. The results of this study revealed that AST activity of type 2 diabetes urine and serum increased significantly (p<0.001) compared with control group.
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