Effect of Child Health Drinks on Color Stability of Various Aesthetic Restorative Materials - An In Vitro Study.

Introduction: In pediatric dentistry, the esthetically pleasing materials have tremendously changed the concept of restorative practice. Aim and Objectives: 1. The aim of the study is to evaluate the effect of child health drinks on color stability of various esthetic restorative materials via spectrophotometric analysis and to identify the esthetic restorative material that is color-stable against child health drinks. Materials and Methods: A total of 120 disk-shaped specimens with a diameter of 12 mm and a thickness of 2 mm were made from a composite (Dyract), giomer (Shofu), and composite (GC). Each group of 40 specimens of each material was divided into four sub-groups. Each sub-group was stored in different solutions, distilled water, Bournvita, Horlicks, and Pediasure, for 60 days. The color change measurement was done using a spectrophotometer both before and after staining. Result: One-way analysis of variance depicted that when the ΔE values of the restorative material were tested against each of the child health drink, there was a statistically significant difference between all child health drinks (P < 0.00001). When restorative materials were compared with the staining agent on 60 days duration, except Pediasure and Control, others (i.e. Horlicks and Bournvita) showed a statistically high significance (P < 0.05). Conclusion: Bournvita caused the highest color changes in 60 days time interval, followed by Horlicks and Pediasure. When the discoloration of several materials is studied, the highest level of discoloration was observed in the compomer, followed by the giomer, and the lowest level in the composite.

Microbiological Profile of Primary Teeth with Irreversible Pulpitis and Pulp Necrosis with/without Abscess and their Susceptibility to Three Antibiotics as Intracanal Medication.

Context: The ineffective disinfection potential of conventional intracanal medicaments to eliminate enteropathogens from root canal systems leads to their persistence contributing to endodontic treatment failures. Hence, the use of appropriate intracanal medicament becomes the essential phase to accomplishing comprehensive decontamination of the root canal system. When applied topically as an intracanal medicament, antibiotics eradicate residual microorganisms from tortuous endodontic spaces, minimizing the risk of systemic toxicity. Aims and objectives: To evaluate the prevalence of various bacterial species associated with signs of irreversible pulpitis and pulp necrosis with/without abscess in primary teeth root canals and their susceptibility against three antimicrobial agents. Materials and methods: The pulp tissue and organic debris were retrieved from deciduous teeth (n = 50) from children between the age of 3-10 years and cultured. The bacterial identification and antibacterial profiling of isolated bacteria were done against clindamycin, metronidazole, and doxycycline through minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) assay. The MIC and MBC of each antibiotic were expressed as mean ± standard deviation (SD), range, and standard error (SE of the mean). The intergroup comparisons were done by the Kruskal-Wallis test, while intragroup pair-wise comparisons were done using the Wilcoxon signed-rank test. The confidence level will be set at 95%. Results: Aerobic bacteria were found in 54%, microaerophilic bacteria in 76%, facultative anaerobes in 26%, and obligatory anaerobes were isolated from 30% of teeth. The intragroup and intergroup comparisons of test agent MIC revealed a nonsignificant difference (p > 0.05). The intragroup MBC comparisons of all the test agents revealed statistically nonsignificant (p > 0.05), while intergroup comparisons demonstrated nonsignificant (p > 0.05) to highly significant difference (p < 0.001). Conclusion: Clindamycin demonstrated promising antibacterial activity against most of the isolated bacteria, while against metronidazole and doxycycline, most of the bacteria were moderate to highly resistant. Clinical significance: Determining the antibacterial agents' efficacy along with modifications can help to target maximum pathogenic microbes and reduce catastrophic endodontic therapy failures. How to cite this article: Dahake PT, Kothari S. Microbiological Profile of Primary Teeth with Irreversible Pulpitis and Pulp Necrosis with/without Abscess and their Susceptibility to Three Antibiotics as Intracanal Medication. Int J Clin Pediatr Dent 2023;16(2):312-320.

A Comparative Evaluation between the Use of Buccal Pad of Fat and Bovine Collagen Membrane in the Management of Oral Submucous Fibrosis.

PURPOSE: This study is intended to assess and compare the effectiveness of BFP and BCM as reconstruction materials in treating oral submucous fibrosis (OSMF). MATERIALS AND METHODS: This study comprised twenty patients of 20 and 60 years who were clinically diagnosed with OSMF. All patients were subjected to fibrotomy with reconstruction under general anesthesia. In all the patients, following fibrotomy reconstruction was done using the buccal pad of fat on the left and with the collagen membrane on the right. The temporal muscle insertions were released, and coronoidectomy was performed as and when required. Any third molars if present were removed. All patients were feeded for 7 days by Ryle's tube and were on intravenous antibiotics for 5 days. Clinical evaluation was done at periodic intervals of 7, 30, 90, and 180 days postoperatively for mouth opening, burning, pain on mouth opening, and recurrence. RESULTS: The mean age of patients was 27.3 years. A 12 mm was mean preoperative mouth opening. Intraoperative mouth opening was 37 mm in all the patients and maintained at 36 mm at the 6th-month postoperative period. No significant difference was observed between both sides pertaining to pain on maximal mouth opening, burning sensation, or postoperative infection. However, there was a significant difference in the time taken for epithelization on both sides. CONCLUSION: The results of this study reveal that both Buccal Pad of Fat (BPF) and BCM are viable reconstruction options, but BFP as a reconstruction material exhibited prompt epithelization with the lowest wound contracture.

Massive radicular cyst involving multiple teeth in pediatric mandible- A case report.

Radicular or periapical cysts are one of the most commonly occurring odontogenic cysts of the jaws. The present article reported a case of a 12-year-old female with the chief complaint of swelling and pain on the right lower back tooth region. The radiographic examination revealed the presence of a well-defined radiolucency surrounded by a corticated border with respect to the right mandibular 1st, 2nd and 3rd molar. The case was managed by complete enucleation under general extraction with the extraction of right mandibular 1st, 2nd and 3rd molar. The success of the surgery was apparent by the uneventful healing during the follow-up period and evidence of complete healing after 1-month follow-up. Early diagnosis of the lesion would have lead to a less aggressive treatment plan.

Integration of Multi-Modal Biomedical Data to Predict Cancer Grade and Patient Survival.

The Big Data era in Biomedical research has resulted in large-cohort data repositories such as The Cancer Genome Atlas (TCGA). These repositories routinely contain hundreds of matched patient samples for genomic, proteomic, imaging, and clinical data modalities, enabling holistic and multi-modal integrative analysis of human disease. Using TCGA renal and ovarian cancer data, we conducted a novel investigation of multi-modal data integration by combining histopathological image and RNA-seq data. We compared the performances of two integrative prediction methods: majority vote and stacked generalization. Results indicate that integration of multiple data modalities improves prediction of cancer grade and outcome. Specifically, stacked generalization, a method that integrates multiple data modalities to produce a single prediction result, outperforms both single-data-modality prediction and majority vote. Moreover, stacked generalization reveals the contribution of each data modality (and specific features within each data modality) to the final prediction result and may provide biological insights to explain prediction performance.

Automated Risk Prediction for Esophageal Optical Endomicroscopic Images.

Biomedical in vivo imaging has been playing an essential role in diagnoses and treatment in modern medicine. However, compared with the fast development of medical imaging systems, the medical imaging informatics, especially automated prediction, has not been fully explored. In our paper, we compared different feature extraction and classification methods for prediction pipeline to analyze in vivo endomicroscopic images, obtained from patients who are at risks for the development of gastric disease, esophageal adenocarcionoma. Extensive experiment results show that the selected feature representation and prediction algorithms achieved high accuracy in both binary and multi-class prediction tasks.

Self-medication Practice Amongst Patients Visiting a Tertiary-care Dental Hospital in Central India.

PURPOSE: To assess self-medication practice-related awareness for correct usage and its association with demographic factors among patients reporting to a dental college. MATERIALS AND METHODS: This descriptive cross-sectional questionnaire-based study was conducted among 300 patients reporting to the People's College of Dental Sciences and Research Centre, Bhopal, India. Only patients ≥ 18 years of age were included and consenting participants anonymously completed the questionnaire, with incomplete questionnaires being excluded from the study. The semi-structured questionnaire containing both open- and closedended questions was prepared in the local language and included demographic data, name of self-medication, frequency of self-medication, periods of illness, duration, dose, frequency of drug administration, symptoms for which drugs were used, satisfaction with healthcare facilities, source of information for self-medication, presence of chronic illness, adverse effects to self-medication seen in patients and drug interactions. The unpaired t-test and chi-square test were used for statistical analysis. p-values < 0.05 were considered statistically significant. RESULTS: A significant association was seen between education and self-medication. It was observed that the subjects who fell ill more frequently consumed medications on their own more often. Medications were most commonly taken for cough, cold and fever. The most preferred medicine was paracetamol. Most of the subjects found the medicines effective in helping them relieve their symptoms. However, not even half of the subjects were aware of the dose, duration, side-effects or interactions of medicines. There was a significant association between knowledge about side-effects and side-effects experienced from medication. A significant association was also seen between knowledge about side-effects and frequency of self-medication. CONCLUSION: Self-medication and non-doctor prescribing are relatively common in Bhopal. Knowledge regarding the appropriate usage of medication is inadequate. Education to help patients decide on the appropriateness of selfmedication is required.

Removing batch effects from histopathological images for enhanced cancer diagnosis.

Researchers have developed computer-aided decision support systems for translational medicine that aim to objectively and efficiently diagnose cancer using histopathological images. However, the performance of such systems is confounded by nonbiological experimental variations or "batch effects" that can commonly occur in histopathological data, especially when images are acquired using different imaging devices and patient samples. This is even more problematic in large-scale studies in which cross-laboratory sharing of large volumes of data is necessary. Batch effects can change quantitative morphological image features and decrease the prediction performance. Using four batches of renal tumor images, we compare one image-level and five feature-level batch effect removal methods. Principal component variation analysis shows that batch is a large source of variance in image features. Results show that feature-level normalization methods reduce batch-contributed variance to almost zero. Moreover, feature-level normalization, especially ComBatN, improves cross-batch and combined-batch prediction performance. Compared to no normalization, ComBatN improves performance in 83% and 90% of cross-batch and combined-batch prediction models, respectively.

Comparison of normalization algorithms for cross-batch color segmentation of histopathological images.

Automated processing of digital histopathology slides has the potential to streamline patient care and provide new tools for cancer classification and grading. Before automatic analysis is possible, quality control procedures are applied to ensure that each image can be read consistently. One important quality control step is color normalization of the slide image, which adjusts for color variances (batch-effects) caused by differences in stain preparation and image acquisition equipment. Color batch-effects affect color-based features and reduce the performance of supervised color segmentation algorithms on images acquired separately. To identify an optimal normalization technique for histopathological color segmentation applications, five color normalization algorithms were compared in this study using 204 images from four image batches. Among the normalization methods, two global color normalization methods normalized colors from all stain simultaneously and three stain color normalization methods normalized colors from individual stains extracted using color deconvolution. Stain color normalization methods performed significantly better than global color normalization methods in 11 of 12 cross-batch experiments (p<;0.05). Specifically, the stain color normalization method using k-means clustering was found to be the best choice because of high stain segmentation accuracy and low computational complexity.

Evaluation of performance metrics for histopathological image classifier optimization.

Clinical decision support systems use image processing and machine learning methods to objectively predict cancer in histopathological images. Integral to the development of machine learning classifiers is the ability to generalize from training data to unseen future data. A classification model's ability to accurately predict class label for new unseen data is measured by performance metrics, which also informs the classifier model selection process. Based on our research, commonly used metrics in literature (such as accuracy, ROC curve) do not accurately reflect the trained model's robustness. To the best of our knowledge, no research has been conducted to quantitatively compare performance metrics in the context of cancer prediction in histopathological images. In this paper, we evaluate various performance metrics and show that the Lift metric has the highest correlation between internal and external validation sets of a nested cross validation pipeline (R(2) = 0.57). Thus, we demonstrate that the Lift metric best generalizes classifier performance among the 23 metrics that were evaluated. Using the lift metric, we develop a classifier with a misclassification rate of 0.25 (4-class classifier) for data that the model was not trained on (external validation).

Semantic interpretation of robust imaging features for Fuhrman grading of renal carcinoma.

Pattern recognition in tissue biopsy images can assist in clinical diagnosis and identify relevant image characteristics linked with various biological characteristics. Although previous work suggests several informative imaging features for pattern recognition, there exists a semantic gap between characteristics of these features and pathologists' interpretation of histopathological images. To address this challenge, we develop a clinical decision support system for automated Fuhrman grading of renal carcinoma biopsy images. We extract 1316 color, shape, texture and topology features and develop one vs. all models for four Fuhrman grades. Our models are highly accurate with 90.4% accuracy in a four-class prediction. Predictivity analysis suggests good generalization of the model development methodology through robustness to dataset sampling in cross-validation. We provide a semantic interpretation for the imaging features used in these models by linking features to pathologists' grading criteria. Our study identifies novel imaging features that are semantically linked to Fuhrman grading criteria.

omniClassifier: a Desktop Grid Computing System for Big Data Prediction Modeling.

Robust prediction models are important for numerous science, engineering, and biomedical applications. However, best-practice procedures for optimizing prediction models can be computationally complex, especially when choosing models from among hundreds or thousands of parameter choices. Computational complexity has further increased with the growth of data in these fields, concurrent with the era of "Big Data". Grid computing is a potential solution to the computational challenges of Big Data. Desktop grid computing, which uses idle CPU cycles of commodity desktop machines, coupled with commercial cloud computing resources can enable research labs to gain easier and more cost effective access to vast computing resources. We have developed omniClassifier, a multi-purpose prediction modeling application that provides researchers with a tool for conducting machine learning research within the guidelines of recommended best-practices. omniClassifier is implemented as a desktop grid computing system using the Berkeley Open Infrastructure for Network Computing (BOINC) middleware. In addition to describing implementation details, we use various gene expression datasets to demonstrate the potential scalability of omniClassifier for efficient and robust Big Data prediction modeling. A prototype of omniClassifier can be accessed at http://omniclassifier.bme.gatech.edu/.

Eliminating tissue-fold artifacts in histopathological whole-slide images for improved image-based prediction of cancer grade.

BACKGROUND: Analysis of tissue biopsy whole-slide images (WSIs) depends on effective detection and elimination of image artifacts. We present a novel method to detect tissue-fold artifacts in histopathological WSIs. We also study the effect of tissue folds on image features and prediction models. MATERIALS AND METHODS: We use WSIs of samples from two cancer endpoints - kidney clear cell carcinoma (KiCa) and ovarian serous adenocarcinoma (OvCa) - publicly available from The Cancer Genome Atlas. We detect tissue folds in low-resolution WSIs using color properties and two adaptive connectivity-based thresholds. We optimize and validate our tissue-fold detection method using 105 manually annotated WSIs from both cancer endpoints. In addition to detecting tissue folds, we extract 461 image features from the high-resolution WSIs for all samples. We use the rank-sum test to find image features that are statistically different among features extracted from the same set of WSIs with and without folds. We then use features that are affected by tissue folds to develop models for predicting cancer grades. RESULTS: When compared to the ground truth, our method detects tissue folds in KiCa with 0.50 adjusted Rand index (ARI), 0.77 average true rate (ATR), 0.55 true positive rate (TPR), and 0.98 true negative rate (TNR); and in OvCa with 0.40 ARI, 0.73 ATR, 0.47 TPR, and 0.98 TNR. Compared to two other methods, our method is more accurate in terms of ARI and ATR. We found that 53 and 30 image features were significantly affected by the presence of tissue-fold artifacts (detected using our method) in OvCa and KiCa, respectively. After eliminating tissue folds, the performance of cancer-grade prediction models improved by 5% and 1% in OvCa and KiCa, respectively. CONCLUSION: The proposed connectivity-based method is more effective in detecting tissue folds compared to other methods. Reducing tissue-fold artifacts will increase the performance of cancer-grade prediction models.

Pathology imaging informatics for quantitative analysis of whole-slide images.

OBJECTIVES: With the objective of bringing clinical decision support systems to reality, this article reviews histopathological whole-slide imaging informatics methods, associated challenges, and future research opportunities. TARGET AUDIENCE: This review targets pathologists and informaticians who have a limited understanding of the key aspects of whole-slide image (WSI) analysis and/or a limited knowledge of state-of-the-art technologies and analysis methods. SCOPE: First, we discuss the importance of imaging informatics in pathology and highlight the challenges posed by histopathological WSI. Next, we provide a thorough review of current methods for: quality control of histopathological images; feature extraction that captures image properties at the pixel, object, and semantic levels; predictive modeling that utilizes image features for diagnostic or prognostic applications; and data and information visualization that explores WSI for de novo discovery. In addition, we highlight future research directions and discuss the impact of large public repositories of histopathological data, such as the Cancer Genome Atlas, on the field of pathology informatics. Following the review, we present a case study to illustrate a clinical decision support system that begins with quality control and ends with predictive modeling for several cancer endpoints. Currently, state-of-the-art software tools only provide limited image processing capabilities instead of complete data analysis for clinical decision-making. We aim to inspire researchers to conduct more research in pathology imaging informatics so that clinical decision support can become a reality.

Histological image classification using biologically interpretable shape-based features.

BACKGROUND: Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis. METHODS: We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features. RESULTS: The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors. CONCLUSIONS: Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions.

Scale normalization of histopathological images for batch invariant cancer diagnostic models.

Histopathological images acquired from different experimental set-ups often suffer from batch-effects due to color variations and scale variations. In this paper, we develop a novel scale normalization model for histopathological images based on nuclear area distributions. Results indicate that the normalization model closely fits empirical values for two renal tumor datasets. We study the effect of scale normalization on classification of renal tumor images. Scale normalization improves classification performance in most cases. However, performance decreases in a few cases. In order to understand this, we propose two methods to filter extracted image features that are sensitive to image scaling and features that are uncorrelated with scaling factor. Feature filtering improves the classification performance of cases that were initially negatively affected by scale normalization.

Biological Interpretation of Morphological Patterns in Histopathological Whole-Slide Images.

We propose a framework for studying visual morphological patterns across histopathological whole-slide images (WSIs). Image representation is an important component of computer-aided decision support systems for histopathological cancer diagnosis. Such systems extract hundreds of quantitative image features from digitized tissue biopsy slides and produce models for prediction. The performance of these models depends on the identification of informative features for selection of appropriate regions-of-interest (ROIs) from heterogeneous WSIs and for development of models. However, identification of informative features is hindered by the semantic gap between human interpretation of visual morphological patterns and quantitative image features. We address this challenge by using data mining and information visualization tools to study spatial patterns formed by features extracted from sub-sections of WSIs. Using ovarian serous cystadenocarcinoma (OvCa) WSIs provided by the cancer genome atlas (TCGA), we show that (1) individual and (2) multivariate image features correspond to biologically relevant ROIs, and (3) supervised image feature selection can map histopathology domain knowledge to quantitative image features.

Histological Image Feature Mining Reveals Emergent Diagnostic Properties for Renal Cancer.

Computer-aided histological image classification systems are important for making objective and timely cancer diagnostic decisions. These systems use combinations of image features that quantify a variety of image properties. Because researchers tend to validate their diagnostic systems on specific cancer endpoints, it is difficult to predict which image features will perform well given a new cancer endpoint. In this paper, we define a comprehensive set of common image features (consisting of 12 distinct feature subsets) that quantify a variety of image properties. We use a data-mining approach to determine which feature subsets and image properties emerge as part of an "optimal" diagnostic model when applied to specific cancer endpoints. Our goal is to assess the performance of such comprehensive image feature sets for application to a wide variety of diagnostic problems. We perform this study on 12 endpoints including 6 renal tumor subtype endpoints and 6 renal cancer grade endpoints. Keywords-histology, image mining, computer-aided diagnosis.