Background: Clostridium perfringens enterotoxin (CPE), which is one of the most common cause’s foodborne illnesses and contribute to diarrhea that is associated with broadspectrum antibiotic treatment.
Objectives: This study focuses on diagnosis of Clostridium perfringens enterotoxin (CPE) from patients suffering from food poisoning and diarrhea associated with antibiotic treatment cases in stool samples and to determine the resistance of isolated against antibiotics.
Methods: Samples were taken during the period of first of June 2015 until the end of April 2016 from Baghdad hospitals. Enzyme Linked Immunosorbent Assay (ELISA) was used to detect Clostridium perfringens enterotoxin in stool samples. Api 20A kit and culture to confirm isolation and identification was used, disk diffusion was performed for antibiotic resistance.
Results: The infection cases increased among old adult age group, were (8.7%) and their age range was (64≥) years old,and children (5.3%) their age range was(15≤) years old. Overall positivity was (23%) in present studied groups and infection increased with causes of food poisoning (61.5%).
Conclusion: This study revealed that the majority percent from age ≥64year (8.7%) and this percent decreased under this age. The future advances research should explain the epidemiology of enterotoxigenic C. perfringens and also participate to the prevention of C. perfringens food poisoning outbreaks and other CPE-associated human diseases.
The research involves using phenol – formaldehyde (Novolak) resin as matrix for making composite material, while glass fiber type (E) was used as reinforcing materials. The specimen of the composite material is reinforced with (60%) ratio of glass fiber.
The impregnation method is used in test sample preparation, using molding by pressure presses.
All samples were exposure to (Co60) gamma rays of an average energy (2.5)Mev. The total doses were (208, 312 and 728) KGy.
The mechanical tests (bending, bending strength, shear force, impact strength and surface indentation) were performed on un irradiated and irrad
... Show MoreIn this research , we study the inverse Gompertz distribution (IG) and estimate the survival function of the distribution , and the survival function was evaluated using three methods (the Maximum likelihood, least squares, and percentiles estimators) and choosing the best method estimation ,as it was found that the best method for estimating the survival function is the squares-least method because it has the lowest IMSE and for all sample sizes
In this research , we study the inverse Gompertz distribution (IG) and estimate the survival function of the distribution , and the survival function was evaluated using three methods (the Maximum likelihood, least squares, and percentiles estimators) and choosing the best method estimation ,as it was found that the best method for estimating the survival function is the squares-least method because it has the lowest IMSE and for all sample sizes
The current issues in spam email detection systems are directly related to spam email classification's low accuracy and feature selection's high dimensionality. However, in machine learning (ML), feature selection (FS) as a global optimization strategy reduces data redundancy and produces a collection of precise and acceptable outcomes. A black hole algorithm-based FS algorithm is suggested in this paper for reducing the dimensionality of features and improving the accuracy of spam email classification. Each star's features are represented in binary form, with the features being transformed to binary using a sigmoid function. The proposed Binary Black Hole Algorithm (BBH) searches the feature space for the best feature subsets,
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