Background: - Aspergillus fumigatus is a ubiquitous and opportunistic fungal pathogen causing sever invasive aspergillosis, an important source of morbidity and mortality in
immunocompromised hosts (human and animals) .The present study was conducted to obtain more clarification about the toxicity and histopathological effects of this pathogen in albino mice
Material and Methods :- fourteen male mice( 6-10) weeks old ,( 20 – 28) gm weight , divided into two groups , test group ( n= 8) , intranasally infected with 25μl (710 conidia / ml normal
saline ) of A. fumigatus for 7 successive days , compared with uninfected group ( n=6) . the weight of all animals were measured at the beginning and at the end of the experiment , in
addition to histological study for lung , liver and kidney were performed , with estimation of enzyme activity of GOT ( Glutomic Oxaloacetic Transaminase),GPT (Glutamic Pyruvic
Transaminase) , ALP (Alkaline Phosphatase ) and Creatinine Kinase in homogenized liver and kidney tissues were studied by colorimetric method .
Results :- increased ( p< 0.05) in body weight , and organs weight ( lung , liver , kidney ) in infected mice with different histological changes were found in organs tissues of infected mice
especially in lung and liver , these changes were supported by variation in enzyme activities of GOT, GPT, ALP and Creatinin kinase in homogenized organs tissues
Conclusion: - normal host can suffer from systematic diseases after inhalation of infective forms of A. Fumigatus (conidia), and further work is needed to study the active components, which are responsible for pathogenesis of this opportunistic fungi
Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is s
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