Effect of essentiale in diabetic subjects with non-alcoholic fatty liver

Nonalcoholic fatty liver (NAFL) has been reported to be common among subjects with diabetes. However, there are not much therapeutic options for NAFL. In this open labeled clinical trial we studied the effect of Essentiale in diabetic subjects with NAFL. Twenty-eight type 2 diabetic patients attending the out-patient division of M.V. Diabetes Specialities Centre, Chennai and satisfying the inclusion criteria were recruited for the study. High resolution B mode ultrasonography was carried out for diagnosis of NAFL. Liver function markers [Alanine aminotransferase (ALT), Aspartate aminotransferase (AST) and Gamma Glutamyl transferase (GGT)] were measured. 22 out of the 28 patients (78.5%) were available for follow up. The mean age of the study subjects was 41±8 years and 50% were males. A significant reduction in all the liver enzymes were observed after Essentiale treatment (baseline vs. six months after treatment: ALT: 54.5± 29.6 IU/L vs. 37.1±18.7 IU/L, p< 0.05, AST: 38.0±18.0 IU/L vs. 27.6±12.4 IU/L, p< 0.05, GGT: 38.7±27.5 IU/L vs. 29.6±13.8 IU/L, p< 0.05). Ultrasound studies revealed that the hepatic echotexture improved after Essentiale treatment in 12/22 (54.5%) of the study subjects, while there was no change in 9/22 (40.9%), and it worsened in only one patient (4.5%). The study results suggest that Essentiale protects and improves liver function in diabetic subjects with NAFL. Prospective, blinded clinical trials are required to confirm these findings

Prevalence of Depression in a Large Urban South Indian Population — The Chennai Urban Rural Epidemiology Study (Cures – 70)

BACKGROUND: In India there are very few population based data on prevalence of depression. The aim of the study was to determine the prevalence of depression in an urban south Indian population. METHODS AND FINDINGS: Subjects were recruited from the Chennai Urban Rural Epidemiology Study (CURES), involving 26,001 subjects randomly recruited from 46 of the 155 corporation wards of Chennai (formerly Madras) city in South India. 25,455 subjects participated in this study (response rate 97.9%). Depression was assessed using a self-reported and previously validated instrument, the Patient Health Questionnaire (PHQ) - 12. Age adjustment was made according to the 2001 census of India. The overall prevalence of depression was 15.1% (age-adjusted, 15.9%) and was higher in females (females 16.3% vs. males 13.9%, p<0.0001). The odds ratio (OR) for depression in female subjects was 1.20 [Confidence Intervals (CI): 1.12-1.28, p<0.001] compared to male subjects. Depressed mood was the most common symptom (30.8%), followed by tiredness (30.0%) while more severe symptoms such as suicidal thoughts (12.4%) and speech and motor retardation (12.4%) were less common. There was an increasing trend in the prevalence of depression with age among both female (p<0.001) and male subjects (p<0.001). The prevalence of depression was higher in the low income group (19.3%) compared to the higher income group (5.9%, p<0.001). Prevalence of depression was also higher among divorced (26.5%) and widowed (20%) compared to currently married subjects (15.4%, p<0.001). CONCLUSIONS: This is the largest population-based study from India to report on prevalence of depression and shows that among urban south Indians, the prevalence of depression was 15.1%. Age, female gender and lower socio-economic status are some of the factors associated with depression in this population

Webometrics benefitting from web mining? An investigation of methods and applications of two research fields

Webometrics and web mining are two fields where research is focused on quantitative analyses of the web. This literature review outlines definitions of the fields, and then focuses on their methods and applications. It also discusses the potential of closer contact and collaboration between them. A key difference between the fields is that webometrics has focused on exploratory studies, whereas web mining has been dominated by studies focusing on development of methods and algorithms. Differences in type of data can also be seen, with webometrics more focused on analyses of the structure of the web and web mining more focused on web content and usage, even though both fields have been embracing the possibilities of user generated content. It is concluded that research problems where big data is needed can benefit from collaboration between webometricians, with their tradition of exploratory studies, and web miners, with their tradition of developing methods and algorithms

An Empirical Evaluation of Machine Learning Techniques for Crop Prediction

Agriculture is the primary source driving the economic growth of every country worldwide. Crop prediction, which is critical to agriculture, depends on the soil and environment. Nutrient levels differ from area to area and greatly influence in crop cultivation. Earlier, the tasks of crop forecast and cultivation were undertaken by farmers themselves. Today, however, crop prediction is determined by climatic variations. This is where machine learning algorithms step in to identify the most relevant crop for cultivation. This research undertakes an empirical analysis using the bagging, random forest, support vector machine, decision tree, Naïve Bayes and k-nearest neighbor classifiers to predict the most appropriate cultivable crop for certain areas, based on environment and soil traits. Further, the suitability of the classifiers is examined using a GitHub prisoners’ dataset. The experimental results of all the classification techniques were assessed to show that the ensemble outclassed the rest with respect to every performance metric

A Single-Center, Open, Comparative Study of the Effect of Using Self-Monitoring of Blood Glucose to Guide Therapy on Preclinical Atherosclerotic Markers in Type 2 Diabetic Subjects

Background: The aim of our study was to determine the effect of treatment based on preprandial and postprandial self-monitoring of blood glucose (SMBG) on the progression of carotid intima-medial thickness (CIMT) in noninsulin-treated type 2 diabetes mellitus (T2DM) subjects. Methods: In this 18-month prospective trial, we recruited subjects 18.70 years of age, treated with metformin and sulfonylurea, with a standardized hemoglobin A1c (HbA1c) level &#8804;9.0%. Subjects were randomized to use of fasting/preprandial (FP) SMBG results to adjust evening medication or use of postprandial (PP) SMBG results to adjust morning medication. The primary end point was change in CIMT; change in HbA1c was a secondary end point. Results: Of the 300 subjects randomized, 280 (140 in each group) completed all biochemical tests and CIMT analysis. Carotid intima-medial thickness was reduced significantly in PP subjects from 0.78 (&#177;0.15) mm to 0.73 (&#177;0.14) mm (p &lt; 0.005), but no significant CIMT reduction was seen in FP subjects. A significant reduction in HbA1c was also seen in the PP group (p &lt; 0.005) but not in the FP group 1 (p = 0.165). Significant improvements in body mass index (p = 0.038), waist circumference (p &#60; 0.001), systolic blood pressure (p = 0.008), and serum cholesterol (p = 0.02) were also seen in PP subjects but not in FP subjects. Conclusion: Use of postprandial SMBG data to adjust therapy was associated with a significant regression of carotid intima-medial thickening and a reduction in HbA1c in T2DM, whereas no significant improvement in these parameters was seen in subjects who used fasting/preprandial SMBG data for therapy adjustment