Reversible Full Adder Gate using Nano-technology

Volume 1 Issue 2 (April 2013

Evaluating the effect of Brainfood groups for people with mild cognitive impairment and mild dementia: preliminary mixed-methodology study

Background: Brainfood is a 5-week group intervention for people with mild cognitive impairment and mild dementia, promoting cognitive health through a Mediterranean-style diet, exercise, mindfulness and health self-management. Aims: To evaluate Brainfood acceptability and the feasibility of conducting a randomised controlled trial; in a single group study in two National Health Service (NHS) memory services. Method: Participants self-completed quantitative and semi-structured questionnaires. Recruitment, attendance and outcome completion were the primary outcomes. Results: In total, 30 of 59 people invited to Brainfood attended; of the 26 (87%) who completed baseline measures: 25 (96%) completed post-intervention quantitative measures, 16 (67%) qualitative questions and 21 (81%) attended ≥3/5 sessions. Compared with baseline, participants reported significantly higher quality of life, Mediterranean diet adherence and exercising more, up to 2 months after the groups. Participants valued the groups and felt enabled to improve their well-being. Conclusions: Brainfood was acceptable and feasible to implement in an NHS setting. Declaration of interest: A.B. and C.C. developed Brainfood - they hold a creative commons license for the manual and make it available to use for free to all. The manual evolves iteratively, but the manual used in this research study is provided in an online supplement

Prediction of Type 2 Diabetes Mellitus Using Soft Computing

Background: Type 2 Diabetes Mellitus (DM) is another pandemic of 21 century, and its control is of immense importance. Researchers developed many predictor models using soft computing techniques. The present study developed a prediction model for Type 2 DM using machine learning classifiers. The analysis excludes plasma glucose concentration and insulin concentration as predictors to explore relationships with other predictors. Background: Methods: This cross-sectional study enrolled 108 participants aged 25 to 67 years from SMS Medical College, Jaipur (Rajasthan, India), after approval from the ethics committee. The study developed a prediction model using machine learning techniques. The classifiers used in the application include decision trees, support vector machines, K-nearest neighbors, and ensemble learning classifiers. A total of 25 predictors were collected and underwent feature reduction. The response levels include diabetes mellitus, prediabetes, and no diabetes mellitus. The models were run using three predictors and a response variable. The prediction model with the best accuracy and area under the receiver operator characteristic curve was selected. Results: The features that vary among the three groups include age, WHR, biceps skinfold thickness, total lipids, phospholipids, triglycerides, total cholesterol, LDL, VLDL, and serum creatinine, and family history of DM. After feature reduction, the age, biceps skinfold thickness, and serum creatinine were run on the Classification learner application to predict the diabetic category. The best model was subspace discriminant with accuracy, sensitivity, specificity, and AUC under the ROC curve was 62.4%, 74%, 94%, and 0.70, respectively. Conclusion: The present study concludes that age, biceps skinfold thickness, and serum creatinine combination have higher specificity in predicting type 2 DM. The study emphasized the selection of appropriate predictors along with newer machine learning algorithms

Longevity of Oncidium varicosum (Orchidaceae) inflorescences treated with 1-methylciclopropene

Oncidium varicosum pertence à família Orchidaceae e atualmente vem sendo bastante comercializado pelo seu potencial como flor de corte. A espécie destaca-se ornamentalmente pelas inúmeras flores amarelas que compõem sua inflorescência grande e delicada. O hormônio etileno desempenha uma função importante nos processos relacionados com a senescência de flores cortadas e, especialmente para as orquídeas, são recomendados tratamentos antietileno para prolongar a vida de vaso. Entre os compostos mais usados recentemente como tratamento pós-colheita, destaca-se o 1-metilciclopropeno (1-MCP), considerado um eficiente inibidor da produção autocatalítica do etileno. A presente pesquisa objetivou avaliar o efeito de diferentes concentrações de 1-MCP (250ppb, 500ppb, 1000ppb) em relação a aspectos fisiológicos de inflorescências cortadas de Oncidium varicosum 'Samurai'. O tratamento com 1-MCP 1000ppb destacou-se dos demais, pois as flores apresentaram os maiores teores de água, de carboidratos solúveis e açúcares redutores, de carotenoides e taxas respiratórias menores, contribuindo assim para melhor qualidade e maior longevidade das inflorescências.Oncidium varicosum belongs to Orchidaceae family and nowadays it is commercialized on large scale due to its potential as cut flower. The species distinguishes decoratively in function of the high number of flowers golden yellow that compose its great and delicate inflorescence. The hormone ethylene performs an important function in the processes related with the senescence of cut flowers, and especially in relation to orchids anti-ethylene treatments are recommended to extend the vase life. Among chemicals used today for the postharvest treatment of flowers the 1-methylciclopropene (1-MCP) is an efficient inhibitor of autocatalytic production of ethylene. This research aimed to evaluate the effect of different concentrations of 1-methylciclopropene (Control and 1-MCP: 250ppb, 500ppb, 1000ppb) upon physiological aspects of cut inflorescences of Oncidium varicosum. The best treatment was 1-MCP 1000ppb and the flowers presented larger values of water content, soluble carbohydrates, reducing sugars, carotenoids and the respiration rates were lower. These results contributed to higher quality and longer life of inflorescences.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Universidade Estadual de São Paulo (UNESP)UNESPUniversidade Estadual de São Paulo (UNESP)UNES

Best herbs for managing diabetes: a review of clinical studies

Diabetes mellitus is a public health problem which leads to serious complications over time. Experimentally, many herbs have been recommended for treating diabetes. In most cases, however, the recommendations are based on animal studies and limited pieces of evidence exist about their clinical usefulness. This review focused on the herbs, the hypoglycemic actions of which have been supported by three or more clinical studies. The search was done in Google Scholar, Medline and Science Direct databases using the key terms diabetes, plants, herbs, glucose and patients. According to the clinical studies, Aegle marmelos, Allium cepa, Gymnema sylvestre, Momordica charantia, Ocimum sanctum, Nigella sativa, Ocimum sanctum, Panax quinquefolius, Salacia reticulate, Silybum marianum and Trigonella foenum-graecum have shown hypoglycemic and, in some cases, hypolipidemic activities in diabetic patients. Among them, Gymnema sylvestre, Momordica charantia, Silybum marianum and Trigonella foenum-graecum have acquired enough reputation for managing diabetes. Thus, it seems that physicians can rely on these herbs and advise for the patients to improve management of diabetes