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Ouabain-Induced Auditory Nerve Degeneration in Congenic Ly5.1 Mice

journal of Otology 2011年第2期6卷 19-28页

作者： Lauren A.Kilpatrick devadoss j samuvel ZHU ju-hong Nancy Smythe LANG Hai-nan Department of Pathology and Laboratory Medicine Medical University of South Carolina Charleston SC 29425 United States Department of Otolaryngology - Heary & Neck Surgery Medical University of South Carolina Charleston SC 29425 United States

The Ly5.1 mouse,also termed ***-Ptprca Pepcb/Boyj,is a congenic strain widely used as a recipient in animal studies of bone marrow *** previous study documented that a majority of type I spiral ganglion neurons (SGNs) in the apical turns of Ly5.1 mice are unmyelinated and aggregate into neuronal clusters,similar to the spiral ganglion in the human ***,a well known Na-K ATPase inhibitor,has been shown to induce neuronal degeneration in a variety of neural tissues including the adult gerbil and CBA/Caj mouse spiral ***,functional and pathological changes of the auditory nerves in young-adult Ly5.1 mice were examined at 3,7 and 14 days after ouabain *** to observations in CBA/Caj mice,ouabain application selectively removed type I SGNs,resulting in an immense decline of the auditory nerve *** of glial cells was seen in the injured auditory nerves at 7 days after ouabain *** data indicate that the 'human-like' features of unmyelinated type I SGNs have no protective impact on the fate of SGNs after ouabain *** incorporating bromodeoxyuridine (BrdU) and expressing Sox2 were also counted in the auditory nerves of control and ouabain-treated *** number of Sox2+ glial cells significantly increased at 3 and 7 days ***,the highest density of BrdU+ cells appeared in the apical turn of the injured auditory nerve shortly after ouabain exposure,suggesting that the pattern of SGN loss at the apical turn in Ly5.1 mouse may have some impact on the reaction of non-neuronal cells in response to acute ototoxic drug exposure in the auditory nerve.

关键词： primary auditory nerve Ly5.1 mouse ouabain neural degeneration Sox2 BrdU glial cells

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Prediction of Cardiovascular Disease Using Machine Learning Technique—A Modern Approach

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Computers, Materials & Continua 2022年第4期71卷 855-869页

作者： Visvasam devadoss Ambeth Kumar Chetan Swarup Indhumathi Murugan Abhishek Kumar Kamred Udham Singh Teekam Singh Ramu Dubey Department of Computer Science&Engineering Panimalar Engineering CollegeAnna UniversityChennai600123India Department of Basic Science College of Science&Theoretical StudiesSaudi Electronic UniversityRiyadh-Male Campus 13316Saudi Arabia Department of Computer Science&IT JAIN(Deemed to be University)Bangalore560069India Department of Computer Science and Information Engineering National Cheng Kung UniversityTainan 701Taiwan Department of Mathematics Graphic Era Hill UniversityDehradun248002India Department of Mathematics J.C.Bose University of Science&TechnologyYMCAFaridabad121006India

Cardio Vascular disease(CVD),involving the heart and blood vessels is one of the most leading causes of death throughout the *** are several risk factors for causing heart diseases like sedentary lifestyle,unhealthy diet,obesity,diabetes,hypertension,smoking and consumption of alcohol,stress,hereditary factory *** cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human *** present,the highly stressful life with bad lifestyle activities causes heart disease at a very young *** main aim of this research is to predict the premature heart disease based on machine learning *** paper deals with a novel approach using the machine learning algorithm for predicting the cardiovascular disease at the premature stage *** Vector Machine(SVM)is used for segregating the CVD patients based on their symptoms and medical *** experimentation results by using the proposed method will facilitate the medical practitioners to provide suitable treatment for the patients on time.A sophisticated model has been developed with the current approach to examine the various stages of CVD and the performance metrics used have given effective and fruitful results as compared to other machine learning techniques.

关键词： Machine learning support vector machine classification cardiovascular disease

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