Salivary Proteinase 3 as a Biomarker for Caries Severity in Children: A Cross-sectional Study.

AIM: This study aims to evaluate the relation between salivary proteinase 3 (PR3) concentration and caries severity in children. MATERIALS AND METHODS: Six-to-eight-year age group children, from the Outpatient Department of Pediatric and Preventive dentistry at PMS Dental College were selected for the study. From these children, three groups each consisting of 28 children were selected according to the dental caries severity. Three groups were: (1) No Dental Caries group, (2) Low Dental Caries group with DMFT/DEFT score of 1-4, and (3) High Dental Caries group with DMFT/DEFT score of 5-15. Thus, a total of 84 children who satisfied the inclusion criteria were selected. The concentration of PR3 in saliva of the donors were analyzed using an ELISA kit. One way ANOVA was used for finding the relation of salivary PR3 concentration with caries severity. Pairwise comparison of PR3 concentration and caries severity were analyzed using post hoc Tukey test. RESULTS: Severity of caries and concentration of salivary PR3 showed an inverse relation. As the caries severity increases there was a decrease in PR3 concentration and vice versa. CONCLUSION: The children with high caries severity showed lower concentration of PR3 in their saliva compared with those with lower caries severity which indicates that PR3 can be used as a biomarker for assessing caries severity and also paves way to use PR3 as a caries vaccine in future. Nowadays, interest toward noninvasive and personalized dentistry has been increased. Molecular assays using salivary biomarkers can be an effective tool in detecting the caries in earlier stages and assessing a patient's caries risk. CLINICAL SIGNIFICANCE: Salivary PR3 can be used as prognostic biomarker for assessing caries severity and after treatment the value of PR3 can be used as a assessment tool to confirm its relation with caries. How to cite this article: Karthika S, George S, Soman A, et al. Salivary Proteinase 3 as a Biomarker for Caries Severity in Children: A Cross-sectional Study. J Contemp Dent Pract 2024;25(3):236-240.

Enhancement of Classifier Performance with Adam and RanAdam Hyper-Parameter Tuning for Lung Cancer Detection from Microarray Data-In Pursuit of Precision.

Microarray gene expression analysis is a powerful technique used in cancer classification and research to identify and understand gene expression patterns that can differentiate between different cancer types, subtypes, and stages. However, microarray databases are highly redundant, inherently nonlinear, and noisy. Therefore, extracting meaningful information from such a huge database is a challenging one. The paper adopts the Fast Fourier Transform (FFT) and Mixture Model (MM) for dimensionality reduction and utilises the Dragonfly optimisation algorithm as the feature selection technique. The classifiers employed in this research are Nonlinear Regression, Naïve Bayes, Decision Tree, Random Forest and SVM (RBF). The classifiers' performances are analysed with and without feature selection methods. Finally, Adaptive Moment Estimation (Adam) and Random Adaptive Moment Estimation (RanAdam) hyper-parameter tuning techniques are used as improvisation techniques for classifiers. The SVM (RBF) classifier with the Fast Fourier Transform Dimensionality Reduction method and Dragonfly feature selection achieved the highest accuracy of 98.343% with RanAdam hyper-parameter tuning compared to other classifiers.

Factors related to parental COVID-19 vaccine hesitancy for children: An exploratory factor analysis model.

Introduction: This pandemic has been accompanied by an overabundance of information and misinformation, an 'infodemic' on a global scale which have made acceptability of COVID-19 vaccine a major challenge. The objective of the study was to find out the Beliefs of mothers towards COVID-19 vaccine for children and its impact on vaccine hesitancy. Materials and Methods: After obtaining Institutional ethical clearance and informed consent, a total of 360 mothers who were having at least one child from 5 to 18 years were recruited based on simple random sampling. Face-to-face interview was conducted. The questionnaire was based on the WHO-SAGE working group on vaccine hesitancy. Based on Bartlett's test of sphericity, the model was found to be fit (there is adequate number of correlations for factor analysis) and KMO test results imply the samples are adequate for factor analysis. K means cluster analysis was used to divide the samples into three clusters and individual respondent's cluster membership was also identified. Results: It was found 49.4% of mothers were hesitant to vaccine their children against COVID-19. The seven items (reasons for vaccine hesitancy) were grouped into three dimensions (believes in vaccine effectiveness but concerned about side effects, believes in the importance of taking COVID vaccine, trusts information about COVID vaccine), based on how each item loads on to each of three factors. Cluster 1 was that they believe in effectiveness of vaccine but are concerned about side effects (.92094). Conclusion: Mothers' concerns were mainly about the safety and adverse effects of vaccine.

Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene-A Paradigm Shift.

Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant challenges during data classification and disease detection. The dataset used for the research is the Lung Harvard 2 Dataset (LH2) which consists of 150 Adenocarcinoma subjects and 31 Mesothelioma subjects. The paper proposes a two-level strategy involving feature extraction and selection methods before the classification step. The feature extraction step utilizes Short Term Fourier Transform (STFT), and the feature selection step employs Particle Swarm Optimization (PSO) and Harmonic Search (HS) metaheuristic methods. The classifiers employed are Nonlinear Regression, Gaussian Mixture Model, Softmax Discriminant, Naive Bayes, SVM (Linear), SVM (Polynomial), and SVM (RBF). The two-level extracted relevant features are compared with raw data classification results, including Convolutional Neural Network (CNN) methodology. Among the methods, STFT with PSO feature selection and SVM (RBF) classifier produced the highest accuracy of 94.47%.

Bioconjugation of Meldrum's acid activated furan: A detergent compatible assay for protein quantitation.

A simple yet efficient assay for the quantitation of proteins ranging from plasma proteins to purified proteins from whole cell lysate, based on the bioconjugation reaction between protein and Meldrum's acid Activated Furan (MAF) is described. This easy to use, sensitive method is based on the conjugation of amine functionalities present on the protein with MAF to form the corresponding Donor Acceptor Stenhouse Adducts (DASAs) with characteristic absorption in the visible region. The reaction is rapid as well as reproducible and shows a proportionate increase in color change over a broad range of protein concentration. The assay was found to be sensitive up to 0.125 mg/mL concentration of the protein and was compatible with most of the commonly employed detergents and isolation protocols which makes it ideal for the estimation of protein samples containing detergents. Another striking feature of this protocol is its tolerance towards other major interference contributors such as chelating agents, reducing agents, carbohydrates and protease inhibitors.

Analysis of Spin in RCTs of Spine Surgery Using ORG-LOC Grading Tool.

Background: Even in highly credible research models, such as randomised control trials (RCTs), many pitfalls do exist that a practitioner must be aware of, to get the actual sense of the research. The one such pitfall that is much more common but ironically less explored is the Writers' bias or the spin. Particularly in the abstracts, it is a potential source of deception to the readers. Methods: We selected 250 recent RCTs from the top 5 spine journals. Baseline data and CONSORT Adherence Score (CAS) were collected. Abstracts of the RCTs were graded using the level of confidence (LOC) grading tool developed by the Orthopaedic Research Group. All possible associations of spin were studied to assess the significance. Results: The median CAS was 11 (IQR 10-12). Only 47.6% (n = 119) articles had High LOC with no or one non-critical spin in abstract. 12.4% (n = 31) had Moderate LOC and 28% (n = 70) had Low LOC. The rest had Critically Low LOC with more than one critical spin. Of the variables analyzed in multivariate regression analysis, only CAS had a (negative) correlation with the LOC of the abstracts. Conclusions: Spin-based grading of RCTs is the need of the hour to aid readers to interpret the true essence of research papers. 40% of the RCTs in top spine journals had low to critically low LOC. Objective structuring of abstracts with adherence to CONSORT guidelines is the way forward to prevent spin.

Craniofacial and oral manifestation of child abuse: A dental surgeon's guide.

Children should be given the privilege to mature in a loving, supportive family environment that promotes the development of an individual to his/her full potential. The abuse and neglect of children is a problem that pervades all segments of society. Dentists/forensic odontologists are in a strategic position to recognize mistreated children. While the detection of dental care neglect is an obvious responsibility for dentists, other types of child abuse and neglect also may present themselves in the dental office. Once this information is known to the dentist, he/she can join physicians in protecting children from injury.