Nicotine Replacement Therapy in Dental Settings: An Exploratory Survey in ‎Bangalore City, India

Background: Smoking and other forms of tobacco use remain the most significant worldwide public health problem. The dental practice is being identified as potential location for smoking cessation activity. Nicotine replacement therapy (NRT) promotes the chances of tobacco cessation, however, evidence related to prescription of the NRT in dental settings is lacking. This study aimed to assess and compare the attitudes, practices, beliefs, and barriers in prescribing NRT for tobacco cessation among dental interns and post-graduates (PGs). Methods: For a cross-sectional survey among 232 participants from 10 dental colleges in Bangalore, India, a 21-item questionnaire was developed: 11-item based on attitudes and practices toward tobacco cessation and 10-items regarding scope and challenges in prescribing NRT. The data were analyzed using descriptive statistics, chi-square test, and test of proportions. Findings: Majority of participants reported ongoing tobacco cessation activity in their college. Statistically significant difference was found between interns and PGs for items related to tobacco cessation practice (assisting, providing follow-up visit). Regarding NRT, significant differences were seen for items related to practice (assisting, assessing motivation for NRT), belief (not an appropriate activity for dentist, a valuable resource, increase in quit attempts) and barriers (bitter taste, cost, and fear of addiction) (P < 0.050). The majority of the PGs suggested cessation center followed by health care workers and pharmacists for the provision of subsidized nicotine gums. Conclusion: Favorable practices and beliefs are seen regarding the prescription of NRT among interns and PGs although differences exist. Time, cost, and taste emerged as major barriers

HOMS: Hindi Opinion Mining System

With the increasing popularity of the Web 2.0, we are provided with more documents which express opinions on different issues. Online posting reviews has become an increasingly preferred way for people to express opinions and sentiments towards the products bought/used or services received. Analysing the large volume of online review data available, would produce useful knowledge, which could be of economic values to vendors and other interested parties. A lot of work in Opinion Mining exists for English language. In the last few years, web contents are increasing in other languages also at a faster rate and hence there is a requirement to execute opinion mining in other languages. In this paper, a Hindi Opinion Mining System (HOMS) is proposed for movie review data. It performs the task of opinion mining at the document level and classifies the documents as positive, negative and neutral using two different methods: Machine learning technique and Part-Of-Speech (POS) tagging. We have used Naive Bayes Classifier for Machine learning and in POS tagging, we have considered adjectives as opinion words. Extensive simulations conducted on a large movie data set confirms the effectiveness of the proposed approach

A Clinical Study Of Physical, Behavioural And Cognitive Phenotype In Fragile X Syndrome

Fragile X Syndrome is a common cause of mental retardation and the first disorder detected to have the novel trinucleotide repeat mutation. In this study, a detailed evaluation of physical, cognitive, and behavioral phenotype was carried out. The sample consisted of 16 subjects (13 male and 3 female) who were cytogenetically proven to have fragile X syndrome. A battery of standard tests was used to elicit the different facts of the phenotype. Results indicate that by and large, the pattern and prevalence of phenotypic characteristics are similar to what is reported in the literature. However, two unusual findings: one subject having severe microcephaly, and two sibs with rapidly progressive neurological disorder and described. Possibility of other etiologic factors in addition to fragile X syndrome could not be ruled out in these three subjects

Semantics in Content-based Multimedia Retrieval

This contribution investigates the content-based feature extraction methods used in visual information retrieval, focusing on concepts that are employed for the semantic representation of media content. The background part describes the building blocks of feature extraction functions. Since numerous methods have been proposed we concentrate on the metaconcepts. The building blocks lead to a discussion of starting points for semantic enrichment of low-level features. The second part reviews features from the perspective of data quality. A case study on content-based MPEG-7 features illustrates the relativity of terms like “low-level, ” “highlevel” and “semantics”. For example, often more semantics mean just more redundancy. The final part sketches the application of features in retrieval scenarios. The results of a case study suggest that – from the retrieval perspective, too – “semantic enrichment of low-level features ” is a partially questionable concept. The performance of classification-based retrieval, it seems, does hardly depend on the context of features.