Common nuts (tree nuts and peanuts) are energy-dense foods that nature has gifted with a complex matrix of beneficial nutrients and bioactives, including monounsaturated and polyunsaturated fatty acids, high-quality protein, fiber, non-sodium minerals, tocopherols, phytosterols, and antioxidant phenolics. These nut components synergize to favorably influence metabolic and vascular physiology pathways, ameliorate cardiovascular risk factors and improve cardiovascular prognosis. There is increasing evidence that nuts positively impact myriad other health outcomes as well. Nut consumption is correlated with lower cancer incidence and cancer mortality, and decreased all-cause mortality. Favorable effects on cognitive function and depression have also been reported. Randomized controlled trials consistently show nuts have a cholesterol-lowering effect. Nut consumption also confers modest improvements on glycemic control, blood pressure (BP), endothelial function, and inflammation. Although nuts are energy-dense foods, they do not predispose to obesity, and in fact may even help in weight loss. Tree nuts and peanuts, but not peanut butter, generally produce similar positive effects on outcomes. First level evidence from the PREDIMED trial shows that, in the context of a Mediterranean diet, consumption of 30 g/d of nuts (walnuts, almonds, and hazelnuts) significantly lowered the risk of a composite endpoint of major adverse cardiovascular events (myocardial infarction, stroke, and death from cardiovascular disease) by ≈30% after intervention for 5 y. Impressively, the nut-supplemented diet reduced stroke risk by 45%. As they are rich in salutary bioactive compounds and beneficially impact various health outcomes, nuts can be considered natural pleiotropic nutraceuticals.

Educational Data Mining (EDM) is one of the crucial application areas of data mining which helps in predicting educational dropout and hence provides timely help to students. In Indian context, predicting educational dropouts is a major problem. By implementing EDM, we can predict the learning habits of the student. At present EDM has not been introduced at higher education level. Due to this we cannot recognize the genuine problems of students during their education. The objective of this analysis is to find the existing gaps in predicting educational dropout and find the missing attributes if any, which my further contribute for better prediction. After that we try to find the best attributes and DM techniques which are frequently used for dropout prediction. Based on the combination of missing attribute and best attribute of student data thus far, a new algorithm can be tested which may overcome the shortcomings of previous work done.

Background In orthotopic heart transplant recipients, surveillance with endomyocardial biopsy is crucial to detect acute cellular rejection (ACR) early. ACR is a common and serious complication of transplantation with substantial morbidity and mortality. Speckle tracking echocardiography with global longitudinal strain (GLS) assessment of the left ventricle has emerged as a possible noninvasive screening modality. We have conducted a systematic literature review and meta‐analysis to evaluate the role of GLS in diagnosing ACR. Methods The following databases were queried: PubMed, Cochrane Central Register of Controlled Trials (CENTRAL), Scopus, and Embase. We compiled all articles evaluating changes in GLS in comparison to endomyocardial biopsy in ACR dated prior to September 2019. Weighted mean differences (WMD) and 95% confidence intervals (CIs) were pooled by using a random effects model. In order to determine the risk of bias, we used the revised version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS‐2) tool. Results Twelve studies met inclusion criteria of which ten were chosen. These studies encompassed 511 patients and 1267 endomyocardial biopsies. There was a significant difference in GLS between patients who did and did not have ACR proven by biopsy (WMD = 2.18; 95% CI: 1.57‐2.78, P = <.001; I2 = 76%). The overall sensitivity for GLS in detecting ACR was 78% (CI: 63%‐90%, P = .123; I2 = 52.2%) while the overall specificity was 68% (CI: 50%‐83%, P = <.001; I2 = 88.3%). Conclusion Global longitudinal strain assessment of the left ventricle by speckle tracking echocardiography is useful in detecting ACR and could potentially reduce the burden of frequent endomyocardial biopsies in heart transplant recipients.

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