Machine Learning Methods for Kidney Disease Screening

The number of people diagnosed with advanced stages of kidney disease has been rising every year. Early detection and constant monitoring are the only way to prevent severe kidney damage or kidney failure. Current test procedures require expensive consumables or several visits to the doctor, which results in many people foregoing regular testing. To address this problem, we propose a cost-effective teststrip-based testing system that can facilitate kidney health checks from the comfort of one’s home by using mobile phones. The specially designed teststrip facilitates a colorimetric reaction between alkaline picric acid and creatinine in a blood sample that has been applied to the teststrip. Our system uses state-of-the-art deep learning localization models to capture quality images of the teststrip using a cell phone, and then processes them using computer vision and machine learning techniques to predict the concentration of creatinine in the sample based on the change in color. The predicted creatinine concentration is then used to classify the severity of the kidney disease as normal, intermediate risk, or kidney failure. We thoroughly evaluate the effectiveness of our models, both in the localization and classification tasks, and find that our histogram of color-based, hybrid nearest neighbor methods outperform alternatives and exhibit good overall prediction performance

Prime Proof Protocol

Prime integers form the basis for finite field and elliptic curve cryptography, as well as many other applications. Provable prime generation guarantees primality and is more efficient than probabilistic generation, and provides components for an efficient primality proof. This paper details a protocol which takes in the proof components from the generation process, proves primality, and as an added benefit, supplies the user with a subgroup generator

ASSOCIATION OF SERUM URIC ACID AND LIPID PROFILE IN TYPE 2 DIABETIC PATIENTS WITH AND WITHOUT DIABETIC RETINOPATHY

Objectives: Diabetic retinopathy (DR) is one of the microvascular complications in type 2 diabetes (T2D).  Elevated serum uric acid (SUA) has been shown to play a significant role in diabetic neuropathy and nephropathy but there is little information on retinopathy. Therefore, the present study was aimed to investigate the SUA and lipid profile in T2D patients with and without DR and the association between SUA and severity of DR.Methods: The study was conducted in the ophthalmology OPD at Sri Lakshmi Narayana Institute of Medical Sciences. The presence of T2D was confirmed by investigating fasting blood glucose level (normal limit < 110 mg/dl) in all the individuals. DR was examined by detailed dilated fundoscopic examination. Based on the fundus examination, patients were divided in to diabetes with signs of DR and those without signs of DR. Age and sex matched healthy were taken as controls. Fasting blood sugar, SUA and lipid profile were investigated for these groups. Results: The study found elevated SUA and abnormal lipid profile in DR group when compared to non-DR and control groups. We also found the significant association between SUA and severity of DR particularly in males.Conclusion: We found a significant association between SUA and severity of DR in T2D patients. Further studies with large sample size are needed to establish the role of elevated SUA and the mechanism involved in the pathogenesis of retinopathy in diabetic patients. Regular measurements of SUA level could be advised to the diabetic patients for the early management