187 research outputs found
Physical Activity Patterns Among School Children in India
No full textOBJECTIVE:
To assess the prevalence of physical activity and its relation with socio-demographic variables and eating habits among school-aged children in India.
METHODS:
The study incorporated secondary analysis of anthropometric measurements and questionnaires on lifestyle and dietary habits of 1,680 school children aged between 3 and 11 y, obtained while carrying out the OBEY-AD project. The inventory contained questions about several variables concerning to physical activity, educational background, lifestyles and eating habits for both children and parents. Questions were organized along specific contents, which could be informative topics, picture choices and multiple answers choices.
RESULTS:
Prevalence of inactivity was 21% and exhibited significant variations between cities. Physical activity was significantly associated to socio-economic status and consumption of fruits and vegetables. No association could be revealed with children's BMI.
CONCLUSIONS:
Health-promotion interventions aimed at improving healthy lifestyles in Indian children should focus on population strata with low socio-economic status
Development of Cardiovascular Disease Prediction System
No full textThis work presents the intelligent Cardiovascular Disease (CVD) prediction system based on machine learning, which uses Quantum Neural Network for machine learning. Early medical diagnosis of Heart disease is very important and should be performed accurately and efficiently. Unfortunately, the physicians don’t have the
enough time to analyze past history of patient in depth. This Intelligent system would enhance the medical care and reduce costs, by quick analysis of past data of patients with percentage of risk prediction. The accuracy of this intelligent system is significantly higher than other existing prognostic systems. All available Physical, Physiological, Clinical parameters have been considered in this study. The data of 815 Patients suffering with the symptoms of Heart disease has been collected from hospital and used for training and evaluation. Furthermore, the dataset of famous Framingham study consisting 5209 CVD patients’ data has been used for validation purpose. All the patients’ reports have been diagnosed and analysed by medical practitioner previously.
This system uses the Quantum Neural Network for machine learning. The results obtained have high degree of sensitivity and specificity that matches with the expert’s opinion with 98.5% accuracy. Such an expert system would be very useful when incorporated with other systems to provide diagnostic and predictive medical opinions in a timely manner. This system will work as an aid to physician for prognosis of heart disease. Using this system, medical practitioners may plan better medication and treatment strategy. The overall accuracy of this intelligent heart disease prognostic system is 98.5%, which is significantly higher than other existing approaches
Covariant formalism for the Berry connection due to gravity
No full textIt is well-known that Dirac particles gain geometric phase, namely Berry
phase, while moving in an electromagnetic field. Researchers have already shown
covariant formalism for the Berry connection due to an electromagnetic field. A
similar effect is expected to happen due to the presence of Gravity. We use WKB
approximation to develop a covariant formalism of Berry-like connection in the
presence of Einstein gravity, which can be further used to describe the
Berry-like phase or simply Berry phase. We also extend this formalism for
massless Dirac particles (Weyl particles).Then we further show that this
connection can be split into two parts, one of which vanishes when the metric
is spherically symmetric and thus can be linked to the Aharonov-Bohm-like
effect in the 3 + 1 formalism. At the same time, the other term can be related
to the Pancharatnam-Berry like effect.Comment: 6 pages; published in Scientific Voyag
Tax structure and economic growth: A study of selected Indian states
No full textThe present study examines the long-run and short-run relationship between tax structure and state-level growth performance in India for the period 1991-2016. The analysis in this paper is based on the model of Acosta-Ormaechea and Yoo (2012), and for the verification of the relationship between taxation and economic growth the panel regression method is used. With the use of 14 Indian states data, Panel Pool mean group estimation indicates that income tax and commodity-service tax have negative effects whilst property and capital transaction tax have a significant positive effect on state economic growth. This study finds 'U' shape relationship between tax structure and growth performance. Based on the analysis, we conclude that for faster growth of Indian states, policymakers should give more focus on property taxes along with the reduction in income taxes
Transforming growth factor-β1 (C509T, G800A, and T869C) gene polymorphisms and risk of ischemic stroke in North Indian population: A hospital-based case-control study
No full textBackground: Transforming growth factor-beta 1 (TGF-β1) is a multifunctional pleiotropic cytokine involved in inflammation and pathogenesis of cerebrovascular diseases. There is limited information on the association between variations within the TGF-β1 gene polymorphisms and risk of ischemic stroke (IS). The aim of this study was to investigate the association of the TGF-β1 gene (C509T, G800A, and T869C) polymorphisms, and their haplotypes with the risk of IS in North Indian population. Methods: A total of 250 IS patients and 250 age- and sex-matched controls were studied. IS was classified using the Trial of Org 10172 in Acute Stroke Treatment classification. Conditional logistic regression analysis was used to calculate the strength of association between TGF-β1 gene polymorphisms and risk of IS. Genotyping was performed using SNaPshot method. Results: Hypertension, diabetes, dyslipidemia, alcohol, smoking, family history of stroke, sedentary lifestyle, and low socioeconomic status were found to be associated with the risk of IS. The distribution of C509T, G800A and T869C genotypes was consistent with Hardy-Weinberg Equilibrium in the IS and control groups. Adjusted conditional logistic regression analysis showed a significant association of TGF-β1 C509T (odds ratio [OR], 2.1; 95% CI; 1.2–3.8;P= 0.006), G800A (OR, 4.4; 95% CI; 2.1–9.3;P< 0.001) and T869C (OR, 2.6; 95% CI; 1.5–4.5;P= 0.001) with the risk of IS under dominant model. Haplotype analysis showed that C509-A800-T869 and T509-G800-C869 haplotypes were significantly associated with the increased risk of IS. C509T and T869C were in strong linkage disequilibrium (D' =0.51, r2 = 0.23). Conclusion: Our results suggest that TGF-β1 polymorphisms and their haplotypes are significantly associated with the risk of IS in North Indian population
A Novel Heart Disease Prediction System Based on Quantum Neural Network Using Clinical Parameters
No full textAims: The diagnosis of Heart disease at earliest possible stage is very crucial to increase the chance of successful treatment and to reduce the mortality rate. The interpretation of cardiovascular disease is time-consuming and requires analysis by an expert physician. Thus there is a need of expert system which may provide quick and accurate prediction of Heart disease at early possible stage, without the help of physician.
Place and Duration of Study: The study was carried out during 2010 to 2013 in the vicinity of Yamuna Nagar, Haryana, India.
Methodology: The data used for this study consists of clinical values (Diabetes Mellitus, Low Density Lipoprotein, Triglycerides and High Density Lipoprotein) and has been collected from various Hospitals of 689 patients, who have symptoms of heart disease. All these cases are analyzed after careful scrutiny with the help of the Physicians. For training and evaluation purpose we have carefully predicted the level of heart disease by taking the help of Cardiologist/ Physician. The data consists of patients’ record with doctor’s predictions/ diagnosis.
Results: The obtained result of Heart disease prediction match with the expert physician’s opinion with 96.97% accuracy and shows high degrees of sensitivity and specificity.
Conclusion: The proposed Heart Disease Prediction System based on Quantum Neural Network gives the high degrees of accuracy in predicting the risk of cardiovascular diseases, are also the best results based on clinical factors. The result generated by this system has been evaluated and validated on data of patients with the Doctor’s diagnosis. This system will help the doctors to plan for a better medication and provide the patient with early diagnosis as it performs reasonably well even without retraining. Such an expert system may also prove useful in combination with other systems to providing diagnostic and predictive medical opinions in a timely manner
Major advances in amyotrophic lateral sclerosis in 2020
No full textResearch in amyotrophic lateral sclerosis (ALS), a neurodegenerative condition, has seen advancement in several key areas of research in 2020. These include a thrust to move the classification of ALS from a neuromuscular condition to a neurodegenerative condition, due to research suggesting involvement of cortical areas, and early cortical hyperexcitability. A new criterion for ALS, called the Gold Coast criterion, has been described. The Gold Coast criteria have removed the categories of possible, probable, and definite ALS, to make the diagnosis of ALS more inclusive and permit enhanced eligibility of patients in clinical trials for ALS. New biomarkers, both imaging and protein based, have been described. Advances in therapy have also occurred, with a large phase II clinical trial reporting benefits with sodium phenylbutyrate-taurursodiol
Semiclassical analysis of Dirac fields on curved spacetime
No full textWe present a semiclassical analysis for Dirac fields on an arbitrary
spacetime background and in the presence of a fixed electromagnetic field. Our
approach is based on a Wentzel-Kramers-Brillouin approximation, and the results
are analyzed at leading and next-to-leading order in the small expansion
parameter . Taking into account the spin-orbit coupling between the
internal and external degrees of freedom of wave packets, we derive effective
ray equations with spin-dependent terms. These equations describe the
gravitational spin Hall effect of localized Dirac wave packets. We treat both
massive and massless Dirac fields and show how a covariantly defined Berry
connection and the associated Berry curvature govern the semiclassical
dynamics. The gravitational spin Hall equations are shown to be particular
cases of the Mathisson-Papapetrou equations for spinning objects
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