Objective: To investigate and prove that aspirin
protects, or at least attenuates amikacin ototoxicity in
humans.
Method: This study was conducted in 60 patients that
completed all
requirements .The patients were divided into two
groups:
• Control group: receive placebo treatment.
• Drug–treated group: They receive aspirin
coated tablets (1.5gm/ day), 500mg 8 hourly.
Both groups had similar aspects regarding the gender,
age and weight. The duration of therapy was 7 days
and dosage of amikacin was 1gm/day (500mg 12
hourly).
Results: Comparison of Audiometry test in
Ear/Nose/Throat (E.N.T.) Department (Pure Tone
Audiometry) at 1000 Hertz (Hz), 2000 Hz, 4000 Hz,
and 8000 Hz showed significant differences between
mean Audiometry at 250Hz was significantly different
only at 8th and 15th day while at the frequency of 500Hz
the difference was significant at the 15thday only.
Conclusion: In present study, we had shown that
aspirin can protect the patients who are receiving
amikacin therapy from it' s ototoxicity.
The current research deals with spatial relations as a tool to link urban landmarks in a homogeneous composition with monumental sculptures, by identifying these landmarks and the extent of their impact on them, which constitutes an urgent need to evaluate the appropriate place and its effects on them, so that this analytical study is a critical approach adopted in artistic studies of monumental models in Arabcapitals .The current research came in four chapters, the first chapter of which dealt with the research problem, its importance and the need for it, then its objectives that were determined in revealing the spatial relations and their impact on
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Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybr
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