Objective: To evaluate two kinds of extraction (aqueous and ethanolic) for coriander using seeds, leaves and stems and
studying their antibacterial activity against nine different microorganisms.
Methodology: Coriander was selected to carry out this study. Seeds, leaves and stems were collected from local markets in
Baghdad then dried in shade for at least 10 days and grinded to fine powder. Aqueous hot extracts for 1hr. at (50
c) and
cold extracts for 24 hrs at (4
c) were performed by using seeds, leaves and stems then studied antibacterial effect against
nine different microorganisms by using well diffusion technique. Cold aqueous extracts of coriander seeds for 48 hrs. and
72 hrs and ethanolic extraction for 48hrs of seed, leaves and stems also performed.
Results: This study showed that hot aqueous extracts for 1hr. to all parts of coriander indicated no antibacterial activity,
while cold aqueous extract for 24hrs of coriander seeds had inhibitory effect for some tested bacteria, but leaves and stems
had not. Cold aqueous extract of seeds for 48hrs showed antibacterial activity for all tested bacteria but in 72hrs there was
no inhibitory effect. On the other hand, ethanolic extracts of seeds, leaves and stems for 48hrs had antibacterial activity and
the highest values for inhibition zone shown in Klebsiella pneumoniae and Proteus mirabilis.
Recommendations: The study recommends using coriander seeds extract as alternative medical therapy for
microorganisms which may resist conventional treatment. This study is a first step for further studies. It is necessary to use
various extraction methods to give active materials with high percentage, although different organic solvents to be used
with coriander plant to obtain extracts used for testing different kinds of microorganisms which have highly resistance to
conventional treatment.
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Abstract—Background: Polycystic ovary syndrome (PCOS) is a prevalent hormonal disorder affecting reproductive- age women, often linked to metabolic issues like insulin resistance. Objective: this study aimed to evaluate ornithine decarboxylase (ODC) and ferric reducing capacity (FRC) levels in women with PCOS, with assess the effects of metformin and Primolut N treatment on their levels. Subjects and Methods: A case− control study was conducted with 150 married Iraqi women, categorized into three groups: 50 healthy controls, 50 untreated PCOS, 50 treated PCOS. Blood samples were analyzed for ODC, FRC levels and hormonal profiles. Statistical analysis applied independent t-test, Pearson’s correlation, ROC curve. Results: The ODC level
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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This work describes the development of new spectrophotometric techniques for 3-aminophenol assessment. The first technique involves using benzidine in an alkaline solution to convert 3-aminophenol into a colored complex. The produced complex has a red color with an absorbance of 462 nm. Between the concentration range 5–14 μg mL−1, Beer's law is obeyed with a correlation coefficient (R2) of 0.99781, a limit of detection (LOD) of 0.0423 μg mL−1, and a limit of quantification (LOQ) of 0.1411 μg mL−1. The recovery was between 87.2–95.43%, the relative standard deviation (%RSD) was 2.40–3.31% and the molar absorptivity was 3.545 × 103 L mol−1 cm−1. Secondly, cloud point extraction (CPE) was used to determ
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Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
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In this work ester derivatives were synthesized by the reaction of imidazole derivatives (C1) with ethylchloroacetate in ethanol and NaOH to give the corresponding (C2) .While compound (C3) acetohydrazide was synthesized by the reaction of ester derivatives (C2) with hydrazine hydrat in ethanol. Compound (C3) from the reaction with different aromatic aldehydes in absolute ethanol gave the Schiff′s bases (C4,C5). The product compounds were characterized by FT-IR, U.V and 1HNMR spectra and the biological activities were studied as antibacterial.
The present study includes the effect of the ethanolic and aqueous extract of Piper nigrum against the third instar of Culex pipiens molestus (Diptera:Culicidae) in different concentrations: 1, 1.5 ppm of ethanolic extracts of Piper nigrum causing 100% mortality of larvae in the second day of treatment, while the concentrations 5% of aqueous extracts of Piper nigrum caused 100% mortality of larvae in the second day of treatment. The concentration 2.5% caused 100% mortality of larvae after four days of treatment. The current study also detected the effect of 0.1% concentration of Piper nigrum aqueous extract. The effect of this concentratio
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Abstract
This paper concerned with study the effect of a graphite micro powder mixed in the kerosene dielectric fluid during powder mixing electric discharge machining (PMEDM) of high carbon high chromium AISI D2 steel. The type of electrode (copper and graphite), the pulse current and the pulse-on time and mixing powder in kerosene dielectric fluid are taken as the process main input parameters. The material removal rate MRR, the tool wear ratio TWR and the work piece surface roughness (SR) are taken as output parameters to measure the process performance. The experiments are planned using response surface methodology (RSM) design procedure. Empirical models are developed for MRR, TWR and SR, using the analysis
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