Challenges for dental professionals during COVID-19 pandemic: Are we prepared?

BACKGROUND: With the emerging knowledge and understanding of novel coronavirus infection, dentists must be capable of resuming their practice with necessary precautions in near future; hence, the aim of the present study was to evaluate the knowledge, attitude, and practices along with felt challenges by the dentists concerning practicing dentistry during pandemic in India. MATERIALS AND METHODS: An online cross-sectional questionnaire study was conducted on the dental practitioners of Uttarakhand, India. The dentists were approached by obtaining their E-mail address from the heads of dental institutions or dental association branches in the state. The knowledge, attitude, and practices along with apparent challenges of practicing during pandemic were assessed using closed-ended questionnaire. The relationship between the mean scores and demographic variables was determined using Student's unpaired t-test by keeping the significance level below 0.05. RESULTS: Out of 759 respondents, a total of 458 respondents were male (60%), whereas 301 (40%) were female. The mean knowledge scores were higher in females (11.06 ± 2.12) compared to males (9.72 ± 4.53), which was statistically significant (P < 0.05). The mean practice score was lower in males (17.49 ± 6.47) compared to females (19.26 ± 6.69) and likewise lower scores were seen in graduates compared to specialists and these differences were again statistically significant (P < 0.05). Majority of the study participants felt that following various safety guidelines were not feasible (96.97%) and too expensive (96.44%) and considered them as a challenge. CONCLUSION: It is imperative that dentists should be fully prepared before resuming their services and reach the right kind of awareness to limit the spread of the disease.

Preparedness among dental professionals towards COVID-19 in India.

INTRODUCTION: novel corona virus infection has become a public health crisis leading the world to a standstill including dentistry. However, since the dental services cannot be stopped for a long period it is important that dentist be fully prepared before resuming their services. Therefore, the current study was carried out for evaluating knowledge, attitude and practices (KAP) along with perceived barriers to practice dentistry during pandemic. METHODS: this cross-sectional study was conducted through an online survey questionnaire on dentists of India. Dentists were enquired for demographics, KAP and perceived barriers regarding practicing dentistry during pandemic. The knowledge was assessed based on 16 items in true or false or multiple choice questions format getting score of 1 or 0. The attitude and practices by 8 items each, on 5-point Likert scale and 4 items perceived barriers were enquired. The differences between the median scores among demographic variables were determined by applying student's t-test and keeping level of significance at below 0.05. RESULTS: out of 500 dentists who were approached through email, a total of 296 dentists returned the questionnaire (response rate, 59.2%) among which 22 questionnaires were incomplete and thus excluded making 274 as final study participants. Overall poor median scores of knowledge and practices were obtained whereas for attitude total median score was good. Median practice scores were significantly higher among female respondents (20(6)). Median knowledge and practice scores were significantly better in study participants with age <40 years (6(4) and 19(5), respectively). CONCLUSION: with the recent claims of authorities that virus is going to stay in world for quite some time it is essential that dentists must be fully prepared before resuming their services and must attain proper awareness to limit the disease spread.

Detecting biomarkers from microarray data using distributed correlation based gene selection.

BACKGROUND: Over the past few decades, DNA microarray technology has emerged as a prevailing process for early identification of cancer subtypes. Several feature selection (FS) techniques have been widely applied for identifying cancer from microarray gene data but only very few studies have been conducted on distributing the feature selection process for detecting cancer subtypes. OBJECTIVE: Not all the gene expressions are needed in prediction, this research article objective is to select discriminative biomarkers by using distributed FS method which helps in accurately diagnosis of cancer subtype. Traditional feature selection techniques have several drawbacks like unrelated features that could perform well in terms of classification accuracy with a suitable subset of genes will be left out of the selection. METHOD: To overcome the issue, in this paper a new filter-based method for gene selection is introduced which can select the highly relevant genes for distinguishing tissues from the gene expression dataset. In addition, it is used to compute the relation between gene-gene and gene-class and simultaneously identify subset of essential genes. Our method is tested on Diffuse Large B cell Lymphoma (DLBCL) dataset by using well-known classification techniques such as support vector machine, naïve Bayes, k-nearest neighbor, and decision tree. RESULTS: Results on biological DLBCL dataset demonstrate that the proposed method provides promising tools for the prediction of cancer type, with the prediction accuracy of 97.62%, precision of 94.23%, sensitivity of 94.12%, F-measure of 90.12%, and ROC value of 99.75%. CONCLUSION: The experimental results reveal the fact that the proposed method is significantly improved classification accuracy and execution time, compared to existing standard algorithms when applied to the non-partitioned dataset. Furthermore, the extracted genes are biologically sound and agree with the outcome of relevant biomedical studies.

Identification of potential biomarkers on microarray data using distributed gene selection approach.

In recent times, several feature selection (FS) methods have introduced to identify the biomarkers from gene expression datasets. It has gained extensive attention to solve cancer classification problem, but they have some limitations. First, the majority of FS approaches increases the computational cost due to the centralized data structure. Second, an irrelevant ranked gene that could perform well regarding classification accuracy with suitable subset of genes will be left out of the selection. To resolve these problems, we introduce a novel two-stage FS approach by combining Spearman's Correlation (SC) and distributed filter FS methods which can select the highly discriminative genes for distinguishing samples from high dimensional datasets. Concerning distributed FS, data is distributed by features according to vertical distribution and then performs a merging procedure which updates the feature subset along with improved classification accuracy. Moreover, it is used to quantify the relation between gene-gene and the gene-class and simultaneously detect subsets of essential genes. The proposed method is verified on six gene datasets with the help of four well-known classifiers namely, support vector machine, naïve Bayes, k-nearest neighbor, and decision tree. The performance of the proposed method is compared with traditional filter techniques such as Relief-F, Information gain, minimum redundancy maximum relevance, joint mutual information, Chi-square, and t-test. The experimental results demonstrate that the proposed method has significantly improved the performance regarding computational time and classification accuracy in comparison to standard algorithms when applied to the non-partitioned dataset.