RAPHANUS SATIVUS LINN. A NEW ANTINOCICEPTIVE FOR DIABETIC NEUROPATHY IN RATS DETERMINED BY RANDALL SELITTO APPROACH

Objective: The objective of the study was to evaluate the antinociceptive effect of Raphanus sativus Linn. using Randall Selitto method. Methods: Streptozotocin, lard, casein, cholesterol, DL-methionine, yeast powder, quercetin, thiobarbituric acid, 2-nitrobenzoic acid (5, 5, Dithiobis), hematoxylin, and hydrogen peroxide were used. A diet rich in fat content was fed to the animals for a period of 2 weeks. After a stabilization period of 2weeks, the treatment period started and continued for a period of 8weeks. The nociceptive parameters were assessed once a week by Randall Selitto method and hot plate test. After treatment, the animals were sacrificed, and antioxidant parameters were assessed using sciatic nerve homogenate and histopathological analysis of sciatic nerve. Results: Treatment R. sativus extract (RSE 100 mg/kg and 200 mg/kg) appreciably declined the levels of blood glucose in a dose-dependent manner, and it was comparable with standard quercetin. A significant increase in pain threshold levels was observed by the treatment RSE in hot plate method after the 4th week compared to diabetic control, and it was consistent until the end of treatment (p<0.01, p<0.001). In Randall Selitto method RSE produced a significant increase in paw withdrawal threshold after the 4th week compared to diabetic control, and it was consistently increased until the end of treatment. RSE (100 and 200 mg/kg) significantly restored the levels of antioxidant enzymes and decreased lipid peroxidation in a dose-dependent fashion in comparison with the diabetic control group. RSE (100 mg/kg and 200 mg/kg) attenuated the nerve degeneration and axonal swelling along with quercetin. Conclusion: The findings from the current study showed the antinociceptive and antioxidant effect of R. sativus in neuropathic pain in diabetes

Multivariate Independence and k-sample Testing

With the increase in the amount of data in many fields, a method to consistently and efficiently decipher relationships within high dimensional data sets is important. Because many modern datasets are multivariate, univariate tests are not applicable. While many multivariate independence tests have R packages available, the interfaces are inconsistent and most are not available in Python. We introduce hyppo, which includes many state of the art multivariate testing procedures. This thesis provides details for the implementations of each of the tests within a test hyppo as well as extensive power and run-time benchmarks on a suite of high-dimensional simulations previously used in different publications. The documentation and all releases for hyppo are available at https://hyppo.neurodata.io

Simplest Streaming Trees

Decision forests, including random forests and gradient boosting trees, remain the leading machine learning methods for many real-world data problems, specifically on tabular data. However, current standard implementations only operate in batch mode, and therefore cannot incrementally update when more data arrive. Several previous works developed streaming trees and ensembles to overcome this limitation. Nonetheless, we found that those state-of-the-art algorithms suffer from a number of drawbacks, including poor performance on some problems and high memory usage on others. We therefore developed the simplest possible extension of decision trees we could think of: given new data, simply update existing trees by continuing to grow them, and replace some old trees with new ones to control the total number of trees. On three standard datasets, we illustrate that our approach, Stream Decision Forest (SDF), does not suffer from either of the aforementioned limitations. In a benchmark suite containing 72 classification problems (the OpenML-CC18 data suite), we illustrate that our approach often performs as well, and sometimes better even, than the batch mode decision forest algorithm. Thus, SDFs establish a simple standard for streaming trees and forests that could readily be applied to many real-world problems, including those with distribution drift and continual learning

A CLINICAL STUDY ON MANAGEMENT OF STRESS IN TYPE -2 DIABETES MELLITUS (MADHUMEHA) WITH ASHWAGANDHA (WITHANIA SOMNIFERA)

Extensive worldwide research on diabetes has established the complexities of the relationship between stress and diabetes. Evidence suggests that stress can affect diabetes, in terms of both its onset and exacerbation. Stress can have a deleterious effect on glycaemic control and can affect quality of life in diabetics. On the other hand emerging evidence strongly suggests that interventions that help individuals prevent or cope with stress can have an important positive effect on quality of life and glycaemic control.In this clinical study we have approached to assess the different stresses like Emotional burden, Physician related distress, Regimen related distress & Interpersonal distress in Type – 2 diabetic populations with Diabetes Distress Scale (DDS). Those diabetics having a considerable stress level (DDS Score > 3) were given treatment with Ashwagandha (Withania somnifera) capsules for six weeks and once again their stress level was reassessed.Ashwagandha significantly reduced the stress level and other stress related complains along with a better glycemic control as compared to placebo without any adverse effect. The positive effect of the drug also continued for another six weeks even after withdrawing the medication.Therefore Ashwagandha can offer comprehensive benefits as an adjuvant in Type – 2 diabetics suffering from stress

Retrospective analysis of endoscopic retrograde cholangio pancreatography (ERCP) procedures in a tertiary care centre in coastal Odisha

Background: ERCP is commonly performed for radiologic visualisation and therapeutic procedure for treating various pancreatico-biliary disorders. There is no comprehensive data available till date about ERCP procedures from Odisha. The aim of this study was to review the indications and complications of endoscopic retrograde cholangiopancreatography (ERCP) procedures in a tertiary care centre in Odisha.Methods: From July 2013 to December 2016, consecutive patients undergoing ERCP procedure were included in the study. Patients with any previous papillary intervention like papillotomy, sphincterotomy or stent placement were excluded from the study. Patients’ demographic characters, ERCP indications and post-ERCP complications were reviewed.Results: Three hundred and fourteen patients were included in the study. Among them male patients were 161 and females were 153. Mean age was 50.75 years and the age range was 18 to 82 years. Most common indications for ERCP was malignant obstructive jaundice (N = 171, 54%) and choledocholithiasis (N = 137, 43.6%). Post ERCP complications developed in 25 patients (8%). Pancreatitis was the most common post-ERCP complication.Conclusions: ERCP is a safe procedure. ERCP complications in our centre are similar to those reported from other centres

Nonpar MANOVA via Independence Testing

The $k$-sample testing problem tests whether or not $k$ groups of data points are sampled from the same distribution. Multivariate analysis of variance (MANOVA) is currently the gold standard for $k$-sample testing but makes strong, often inappropriate, parametric assumptions. Moreover, independence testing and $k$-sample testing are tightly related, and there are many nonparametric multivariate independence tests with strong theoretical and empirical properties, including distance correlation (Dcorr) and Hilbert-Schmidt-Independence-Criterion (Hsic). We prove that universally consistent independence tests achieve universally consistent $k$-sample testing and that $k$-sample statistics like Energy and Maximum Mean Discrepancy (MMD) are exactly equivalent to Dcorr. Empirically evaluating these tests for $k$-sample scenarios demonstrates that these nonparametric independence tests typically outperform MANOVA, even for Gaussian distributed settings. Finally, we extend these non-parametric $k$-sample testing procedures to perform multiway and multilevel tests. Thus, we illustrate the existence of many theoretically motivated and empirically performant $k$-sample tests. A Python package with all independence and k-sample tests called hyppo is available from https://hyppo.neurodata.io/.Comment: 15 pages main + 4 pages appendix, 9 figure

Learning sources of variability from high-dimensional observational studies

Causal inference studies whether the presence of a variable influences an observed outcome. As measured by quantities such as the "average treatment effect," this paradigm is employed across numerous biological fields, from vaccine and drug development to policy interventions. Unfortunately, the majority of these methods are often limited to univariate outcomes. Our work generalizes causal estimands to outcomes with any number of dimensions or any measurable space, and formulates traditional causal estimands for nominal variables as causal discrepancy tests. We propose a simple technique for adjusting universally consistent conditional independence tests and prove that these tests are universally consistent causal discrepancy tests. Numerical experiments illustrate that our method, Causal CDcorr, leads to improvements in both finite sample validity and power when compared to existing strategies. Our methods are all open source and available at github.com/ebridge2/cdcorr

Multivariate Independence and k-sample Testing

With the increase in the amount of data in many fields, a method to consistently and efficiently decipher relationships within high dimensional data sets is important. Because many modern datasets are multivariate, univariate tests are not applicable. While many multivariate independence tests have R packages available, the interfaces are inconsistent and most are not available in Python. We introduce hyppo, which includes many state of the art multivariate testing procedures. This thesis provides details for the implementations of each of the tests within a test hyppo as well as extensive power and run-time benchmarks on a suite of high-dimensional simulations previously used in different publications. The documentation and all releases for hyppo are available at https://hyppo.neurodata.io