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- 저자 : Kakkar, Mayank (검색결과 19 건)
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Prevalence of dental anxiety in 10-14 years old children and its implications
Kakkar, Mayank,Wahi, Astha,Thakkar, Radhika,Vohra, Iqra,Shukla, Arvind Kumar The Korean Dental Society of Anesthsiology 2016 Journal of Dental Anesthesia and Pain Medicine Vol.16 No.3
Background: The aim of this study was to provide insight on dental fear amongst schoolchildren and evaluate the association between caries experience and fear of dental procedures. Methods: A sample size of 250 students (both sexes) of ages 10-14 years were enrolled in the study. Before dental examination, each participant was informed about the study and given the Children's Fear Survey Schedule - Dental Subscale (CFSS-DS) questionnaire. Children who scored greater than 38 were included in the 'with dental fear' group and those who scored less than 38 were assigned to the 'without dental fear' group. All oral check-ups were carried out on the school premises according to WHO criteria. Results: There were 105 children (42%) who experienced dental fear. As CFSS-DS scores increased, scores on the Decayed, Missing and Filled Surfaces Index (DMFS) also increased. Scores were highest on "injections" followed by "dentist drill" and "feeling of choking". Children were significantly less anxious about items of dental treatment if they had experienced that particular form of treatment. Female participants were found to be more dentally anxious than the male participants. Conclusions: The data revealed dental fear in 10-14 years old children and showed that dental fear scores decreased with increase in age and experience.
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Nidhi Kakkar 한국곤충학회 2010 Entomological Research Vol.40 No.6
A total of 8306 individuals as 29 species from 3 subfamilies (Hybosorinae, Aphodiinae, Scarabaeinae) were trapped by pitfall bait trapping. The maximum number of species of Scarabaeinae (68.97%) and number of individuals of Aphodiinae (56.84%) take part in the formation of a community. The number of species, number of individuals, diversity and dominance changes throughout the season. Seasonal prevalence of 82.76% of species and 50.81% of individuals was found in June. Aphodius campestris was found to be more attracted to the pitfall bait traps. The beetle community found in May shows the maximum Shannon's Diversity Index (2.46) but Margalef's Diversity Index was highest in August (3.06). Environmental factors play an important role in the occurrence and abundance of dung beetles. In the present study non-significant but positive correlations between temperature and species richness, abundance and diversity were found. Percent relative humidity showed a positive correlation with abundance only, and negative correlations with species richness and diversity.
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Diversity and seasonal fluctuations in dung beetle (Coleoptera) community in Kurukshetra, India
Nidhi KAKKAR,Sanjeev K. GUPTA 한국곤충학회 2010 Entomological Research Vol.40 No.3
Five pitfall traps baited with 150–200 g of fresh cattle dung were installed for 24 h at weekly intervals. A total of 991 dung beetles from 11 genera, 31 species and three subfamilies was obtained. The community was dominated by Oniticellus spinipes individuals by 32.3%. Ten species appeared only once during the collection period and species composition and dominance changed throughout the period. The overall pattern we detected in the organization of the dung beetle community is that the species richness, abundance and diversity rise in September and the 2nd week of October. The dung beetle community was found to be affected by season.
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Current Status of Stem Cell Treatment for Type I Diabetes Mellitus
Anupama Kakkar,Ashima Sorout,Mahak Tiwari,Pallavi Shrivastava,Poonam Meena,Sumit Kumar Saraswat,Supriya Srivastava,Rajan Datt,Siddharth Pandey 한국조직공학과 재생의학회 2018 조직공학과 재생의학 Vol.15 No.6
BACKGROUND: Diabetes mellitus is a major health concern in current scenario which has been found to affect people of almost all ages. The disease has huge impact on global health; therefore, alternate methods apart from insulin injection are being explored to cure diabetes. Therefore, this review mainly focuses on the current status and therapeutic potential of stem cells mainly mesenchymal stem cells (MSCs) for Type 1 diabetes mellitus in preclinical animal models as well as humans. METHODS: Current treatment for Type 1 diabetes mellitus mainly includes use of insulin which has its own limitations and also the underlying mechanism of diseases is still not explored. Therefore, alternate methods to cure diabetes are being explored. Stem cells are being investigated as an alternative therapy for treatment of various diseases including diabetes. Few preclinical studies have also been conducted using undifferentiated MSCs as well as in vitro MSCs differentiated into b islet cells. RESULTS: These stem cell transplant studies have highlighted the benefits of MSCs, which have shown promising results. Few human trials using stem cells have also affirmed the potential of these cells in alleviating the symptoms. CONCLUSION: Stem cell transplantation may prove to be a safe and effective treatment for patients with Type 1 diabetes mellitus.
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RELIABILITY AND PROFIT ANALYSIS OF A STANDBY UNIT SYSTEM WITH CORRELATED LIFE TIME IN AN INDUSTRY
M. K. Kakkar,J. Bhatti 장전수학회 2015 Advanced Studies in Contemporary Mathematics Vol.25 No.3
The main objective of this work is to present a reliability analysis of a cold standby unit system with variations in production and demand, As we know that Demand of a product in the market in uences the production of that product and also the business of the company or an organization. we have assumed that only one repair person is available in the whole system. Failure and Repair times of each unit are supposed to be dependent or correlated. By utilizing regenerative point technique, number of reliability attributes are obtained which are very vital and helpful to the framework planners, mechanical supervisors and industrial managers. Graphical behaviors of mean time to system failure and prot function have also been studied.
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Kanodia Anupam,Kakkar Aanchal,Verma Yash,Roy Diya,Verma Hitesh,Singh Chirom Amit,Monga Rabia,Jain Deepali,Thakar Alok,Sikka Kapil 대한청각학회 2023 Journal of Audiology & Otology Vol.27 No.2
Background and Objectives: Cholesteatomatous chronic otitis media acquires epithelial proliferation and differentiation characteristics, which render it able to erode the underlying bone and cause complications. We attempt to characterize the cholesteatoma epithelium by observing the expression of cytokeratins (such as 34ße12, CK17, and CK13) and Ki67 among patients with cholesteatoma with different aggressiveness as compared to disease-free controls.Subjects and Methods: In this prospective study (2017-2021), we enrolled all consenting consecutive patients with cholesteatomatous chronic otitis media. They were staged in accordance with the staging guidelines of the European Academy of Otology and Neurotology and the Japanese Otological Society. Bony external auditory canal (EAC) skin specimens of the patients undergoing tympanoplasty were chosen as controls. We did an immunohistochemical analysis of the cholesteatoma specimens and normal bony EAC controls by observing the expression of 34ße12, CK17, CK13, and Ki67 across the layers of the epithelium. Fisher’s exact test and chi-square test were used to evaluate any statistical significance between the cases and the controls, and the subgroups were made based on the clinical stage.Results: An increased expression of CK17 (<i>p</i><0.001), CK13 (<i>p</i><0.03), and Ki67 (<i>p</i><0.001) was observed in cholesteatoma specimens when compared to normal bony EAC controls. Also, there was a loss of expression of 34ße12 in a subset of cholesteatoma specimens, all of which showed full-thickness expression of CK13. There was no difference in the expression of cytokeratin among specimens from patients belonging to different subgroups based on clinical stage, age, sex, duration of ear symptoms, or type of hearing loss (conductive vs. sensorineural).Conclusions: The majority of cholesteatoma specimens significantly overexpressed CK17, CK13, and Ki67 when compared to normal bony EAC skin controls, while a subset showed loss of expression of 34ße12, which provides some insight into its pathogenesis.
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Software Module Fault Prediction using Convolutional Neural Network with Feature Selection
Rupali Sharma,Parveen Kakkar 보안공학연구지원센터 2016 International Journal of Software Engineering and Vol.10 No.12
Software plays a significant role in technological and economic development due to its utmost importance in day to day activities. A sequence of rigorous activities under certain constraints is followed to come up with reliable software. Various measures are taken during the process of software development to ensure high quality software. One such method is software module fault prediction for quality assurance to discover defects in the software prior to testing. It aids in predicting the software module faults earlier in the development of the software which predicts fault prone modules so that these can be given special attention to avoid any future risk which eventually curbs the testing along with maintenance cost and effort. The literature survey uncovers many findings that had never been focused like dimensionality reduction and feature selection based on individual feature importance which leads to increase in time complexity and chances of false information. This paper addresses these issues and proposes a supervised machine learning based software module fault prediction technique by implementing Convolutional Neural Network (CNN) as classifier model. Feature selection methods used are InfoGain and Correlation. The results obtained are compared with the existing method HySOM (SOM Clustering with Artificial Neural Network Classification) by considering three different feature sets (Fifteen features, Eighteen features and Twenty one features) of PC1 dataset from NASA. The comparative analysis is performed on the basis of accuracy, precision, recall and F1-measure. The results clearly show better performance of the proposed CNN based technique than HySOM. This paper will contribute towards improvement of quality assurance models utilized for software fault prediction by automating this process using machine learning which enhances True Positive Rate and reduces the detection error. This in turn will help project managers, testers and developers to locate and keep track of fault prone modules so that final software is more accurate, consistent and reliable without consuming much of the testing and maintenance resources.
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