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Background: Diabetes mellitus is a chronic disease with microvascular and macrovascular complications. The pulmonary complications of diabetes mellitus have been poorly characterized. Pulmonary function tests (PFTs) are noninvasive tests that show how well the lungs are working. Objectives: The aim of this study is to compare pulmonary PFTs in type 2 diabetic and non diabetic patients undergoing elective major abdominal surgeries under general anesthesia. Materials & Methods: A total of 100 patients (50 diabetic & 50 non diabetic), undergoing elective major abdominal surgeries under general anesthesia were enrolled for the study. Pulmonary function tests were performed 60 minutes before and after the end of surgery. The PFTs recorded were FEV1, FVC, FEV1/FVC, FEF 25%, and Peak expiratory flow rate (PEFR). Results: Majority of the subjects (34% diabetic and 42% non diabetic) was 51-55 years age group, the mean age among diabetics was 52.9 ± 4.8 and non diabetic group were 51.3 ± 4.8 years. The male were slightly higher than female in both the groups. The mean age, gender and BMI of both the groups was not statistically different and both the groups were comparable at baseline. The pulmonary function test (FEV1, FVC, FEF 25%, and PEFR) were significantly reduced in diabetic subjects compared with non diabetic subject, except FEV1/FVC. Conclusion: The mean scores of FVC, FEV1, PEFR, and FEF 25% were significantly reduced among type 2 diabetic patients as compared to non-diabetic subjects, except FEV1/FVC ratio [ABSTRACT FROM AUTHOR]

Byline: C. Chandrakala, Gurusami. Tharini, M. Ananthi, R. Subha Sir, Alkaptonuria is an autosomal recessive disorder caused by deficiency of the enzyme homogentisic acid oxidase. [sup][1] We report a case [...]

Stock market data is a time-series data in which stock value varies depends on time. Prediction of the stock market is an endeavor to assess the future value of a company's stock rate which will increase the investor's profit. The accurate prediction of stock market analysis is still a challenging task. The proposed system predicts stock price of any company mentioned by the user for the next few days. Using the predicted stock price and datasets collected from various sources regarding a certain equity, the overall sentiment of the stock is predicted. The prediction of stock price is done by regression and candlestick pattern detection. The proposed system generates signals on the candlestick graph which allows to predict market movement to a sufficient level of accuracy so that the user is able to judge whether a stock is a 'Buy/Sell' and whether to short the stock or go long by delivery. The prediction accuracy of the stock exchange has analyzed and improved to 85% using machine learning algorithms. [ABSTRACT FROM AUTHOR]