An Efficient and Robust Tuple Timestamp Hybrid Historical Relational Data Model

This paper proposes a novel, efficient and robust tuple time stamped hybrid historical relational model for dealing with temporal data. The primary goal of developing this model is to make it easier to manage historical data robustly with minimal space requirements and retrieve it more quickly and efficiently. The model's efficiency and results were revealed when it was applied to an employee database. The proposed model's performance in terms of query execution time and space requirements is compared to a single relational data model. The obtained results show that the proposed model is approximately 20% faster than the conventional single relational data model. Memory consumption results also show that the proposed model's memory cost at different frequencies is significantly reduced, which is approximately 30% less than the single relational data model for a set of queries. Because net cost is strongly related to query execution time and memory cost, the suggested model's net cost is also significantly reduced. The proposed tuple timestamp hybrid historical model acts as generic, accurate and robust model. It provides the same functionality as previous versions, as well as hybrid functionality of previously proposed models, with a significant improvement in query execution speed and memory usage. This model is effective and reliable for the use in a wide range of temporal database fields, including insurance, geographic information systems, stocks and finance (e.g. Finacle in Banking), data warehousing, scientific databases, legal case histories, and medical records

Querying Capability Enhancement in Database Using Fuzzy Logic

We already know that Structured Query Language (SQL) is a very powerful tool. It handles data, which is crisp and precise in nature.but it is unable to satisfy the needs for data which is uncertain, imprecise, inapplicable and vague in nature. The goal of this work is to use Fuzzy techniques i.e linguistic expressions and degrees of truth whose result are presented in this paper. For this purpose we have developed the fuzzy generalized logical condition for the WHERE part of SQL. In this way, fuzzy queries are accessing relational databases in the same way as with SQL. These queries with linguistic hedges are converted into Crisp Query, by deploying an application layer over the Structured Query Languag

Evaluation of nutritive value, phytochemical screening, total phenolic content and in-vitro antioxidant activity of the seed of Prunus domestica L.

Prunus domestica L. is a member of the Rosaceae family that shows many biological activities including antioxidant, antimicrobial, antihaemolytic, anti-inflammatory, hepatoprotective activity and many other activities. In the current study, we evaluated nutritive value, phytochemical screening, total phenolic content and antioxidant activity by DPPH and FRAP method for the different extracts obtained by successive soxhlet extraction using the different solvents based on their polarity. Results show that it is a good source of energy. Phytochemical screening revealed the presence of many secondary metabolites which include alkaloids, carbohydrates, glycosides, protein, steroids and terpenoids, fixed oils and fat as well as phenolic compounds. The highest total phenolic content was found in the ethyl acetate fraction. Highest antioxidant activity by DPPH method is reported in ethyl acetate fraction (IC50 =1837.399±0.377µg/ml) while the ferric reducing antioxidant power was maximum for diethyl ether (56.032±0.985µM/ml FRAP value = 0.325±0.002)

Lack of research aptitude in medical education

Students are attracted towards the medical profession to become a doctor and not to be a researcher. According to a recent study there are about 1,00,000 undergraduate medical students in India at a given point of time, out of them only 0.9% of the students have shown research aptitude. During their training period of graduation in medical sciences, they are so much burdened with the work load of exams, practicals, ward duties and tutorials. In such an over burdened situation very few of them can think about research. A study had shown that training in research methodology received early in medical school helps students to develop a positive attitude towards research. So changes in the undergraduate and postgraduate curriculum are required to promote research among medical students

Retrospective derivation and validation of a search algorithm to identify extubation failure in the intensive care unit

BACKGROUND: Development and validation of automated electronic medical record (EMR) search strategies is important in identifying extubation failure in the intensive care unit (ICU). We developed and validated an automated search algorithm (strategy) for extubation failure in critically ill patients. METHODS: The EMR search algorithm was created through sequential steps with keywords applied to an institutional EMR database. The search strategy was derived retrospectively through secondary analysis of a 100-patient subset from the 978 patient cohort admitted to a neurological ICU from January 1, 2002, through December 31, 2011(derivation subset). It was, then, validated against an additional 100-patient subset (validation subset). Sensitivity, specificity, negative and positive predictive values of the automated search algorithm were compared with a manual medical record review (the reference standard) for data extraction of extubation failure. RESULTS: In the derivation subset of 100 random patients, the initial automated electronic search strategy achieved a sensitivity of 85% (95% CI, 56%-97%) and a specificity of 95% (95% CI, 87%-98%). With refinements in the search algorithm, the final sensitivity was 93% (95% CI, 64%-99%) and specificity increased to 100% (95% CI, 95%-100%) in this subset. In validation of the algorithm through a separate 100 random patient subset, the reported sensitivity and specificity were 94% (95% CI, 69%-99%) and 98% (95% CI, 92%-99%) respectively. CONCLUSIONS: Use of electronic search algorithms allows for correct extraction of extubation failure in the ICU, with high degrees of sensitivity and specificity. Such search algorithms are a reliable alternative to manual chart review for identification of extubation failure

Restriction on animal experimentation for medical education and research: pros and cons

Recently, a lot have been written and discussed about animal experiments and ethics. Still there is too much confusion among academicians and researchers about the future of use of animals in biomedical research and up to what extent their use in laboratory, research institutions, and medical colleges. This article highlighted and discussed about various aspects of this burning issue along with several pros and cons

Immunosuppressant effect of Boswellia serrata extract on CFA induced arthritis in rats

Background: Rheumatoid arthritis (RA) is an immune-mediated arthropathy, so for the treatment disease modifying antirheumatoid drugs are required. In this study we are evaluating the immunomodulatory property of Boswellia serrata extract (BSE) as an alternative medicine.Methods: Complete Freund’s adjuvant (CFA), 0.1ml was injected intradermally in the footpad of left hind paw in 36 Wistar rats to induce RA. Animals were divided into 6 groups. BSE in the doses of 45mg/kg, 90mg/kg and 180mg/kg was administered and cyclophosphamide as standard drug. Various parameters as body weight, paw thickness, ankle diameter, paw volume, arthritis index, TNF- α and histopathological changes were analyzed.Results: Marked reduction in paw thickness, ankle diameter, paw volume, arthritis index and an improved body weight was found in high dose BSE (180mg/kg) group but the effect was lesser than standard drug Cyclophosphamide.Conclusions: BSE has significant potential as an alternative medicine for treatment of autoimmune diseases like rheumatoid arthritis

Nationwide Analysis of The Outcomes and Mortality of Hospitalized COVID-19 Patients

Introduction: The Coronavirus disease 2019 (COVID-19) pandemic has affected people worldwide with the United States (US) with the largest number of reported cases currently. Previous studies in hospitalized COVID-19 patients have been limited by sample size. Methods: The National Inpatient Sample database which is the largest inpatient database in the US was queried in the year 2020 for the diagnosis of COVID-19 based on ICD-10-CM U07.1 and associated outcomes. Multivariate logistic regression analysis was used to identify predictors of mortality. STATA 16.0 was used for statistical analysis. Results: A weighted total of 1,678,995 hospitalizations for COVID-19 were identified. Median age of admitted patients with COVID-19 was 65 year (51-77) with 47.9% female and 49.2% White. Majority of the patients admitted were >65 years of age (49.3%). Hypertension and diabetes were the most common comorbidities (64.2% and 39.5%, respectively). Overall inpatient mortality was 13.2% and increasing to 55.9% in patients requiring mechanical ventilation. Trend of inpatient mortality was significantly decreasing over the year. Predictors of inpatient mortality included age, male sex, diabetes, chronic kidney disease, heart failure, arrythmia, obesity, and coagulopathy. Despite a lower proportion of patients admitted to hospital with COVID-19, Black, Hispanic, and Native Americans were at an increased adjusted odds of inpatient mortality. Disparity was also noted in income, with low median household income associated with higher risk of mortality. Conclusion: In the largest US cohort with >1.6 million hospitalized COVID-19 patients in 2020, overall inpatient mortality was 13.6% with significantly higher mortality in ventilated patients. Significant socioeconomic and racial disparities were present with minorities at higher odds of mortality