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Original research article:-
Rattan Deep Singh1, Juhi Mishra1, Anoop Kumar Dobriyal 2, *Aradhana1
1.Dept. of Biotechnology, S. Bhagwan Singh PG Institute of Biomedical Sciences and Research, Balawala, Dehradun, India.
2.Dept. of Zoology and Biotechnology, Hemwati Nandan Bahuguna Garhwal University, Pauri Campus, Pauri-Garhwal, India.
Abstract:- The present study is based on analysis of various physio-chemical properties of crude extract of lectins from selected Phaseolus vulgaris landraces. The haemagglutination and sugar inhibiting properties differ among the landraces. Sample from Dhanpoo and Darchula show maximum haemagglutinating activity (16HU). The minimum inhibitory concentration of sugar among cultivars was found in the range of 25-50 mM. The haemagglutinating activity of lectins was found to be stable upto temperature of 700C, above which there was no haemagglutinating activity. Similarly the lectin activity was found to be stable at pH range of 7-8, which means haemagglutination activity is negligible in too acidic and basic medium. The crude extract of lectins was subjected for partial purification through ammonium sulphate precipitation and SDS-PAGE analysis. The molecular weight of lectin was found to be 34kd, which is similar to commercially prepared Phytohaemagglutinin used as a standard.
Keywords:-Phytohaemagglutinin, Sugar inhibition, Haemagglutination titer.
Original research article:-
Tanuja Shailesh1, Dinesh Acharya U2, * Shailesh K R3
1,2.Dept. of CSE, MIT, Manipal University, Karnataka ,India.
3.Dept. of E & E, MIT, Manipal University, Karnataka , India.
Abstract:-Data mining has a wide use in the healthcare domain in areas such as diagnoses and patient management. One of the main concerns in the healthcare area is the measurement of flow of patients through hospitals and other health care facilities. For instance if the inpatient length of stay (LOS) can be predicted efficiently, the planning and management of hospital resources can be greatly enhanced. Hospital LOS of inpatients is frequently used as a proxy for measuring the consumption of hospital resources and therefore it is essential to develop accurate models for the prediction of inpatients LOS. In this paper we apply text mining techniques to pre-process the text data of electronic discharge summaries available in HTML format and apply traditional data mining techniques such as k-means clustering to analyze the textual information for classifying discharge summaries based on disease and patient’s length of stay in the hospital.
Keywords: Text mining, Discharge Summary, Regular Expression, feature extraction, Pattern recognition, length of stay, K-Means.