Prenatal dental care and periodontal health: a concise systematic review

Introduction: Periodontal disease is a chronic infection caused by a bacterium that stimulates the immunoinflammatory response, leading to inflammation of the gingival and tooth support tissue, resulting from the pathogenesis of the microorganism and the host's response. Studies have investigated the association between periodontal conditions and possible complications for pregnant women and newborns. Objective: Analyzed the main consequences of periodontal disease during pregnancy and childbirth, as well as changes involving newborns, in order to elucidate the importance of prenatal dental care. Methods: The present study was followed by a systematic literature review model, according to the PRISMA rules. Clinical studies included case reports, retrospective, prospective and randomized trials. The quality of the studies was based on the GRADE instrument. The risk of bias was analyzed according to the Cochrane instrument. Results: A total of 104 articles were found. A total of 48 articles were evaluated in full, and 20 were included and discussed in this study. The overall assessment did not result in significant risks that could compromise the science of the present study. According to the GRADE classification, the studies were of moderate quality. Gingivitis, periodontitis, and tooth loss were found to be associated with premature birth. Periodontal disease can increase the chance of negative neonatal and maternal outcomes, with fetal growth restriction, vulvovaginitis, and premature rupture of the membrane being the main effects. Conclusion: Oral health should be an important focus in the monitoring of pregnant women in all maternal and child health services, making it necessary to incorporate the diagnosis of maternal oral health and periodontal disease for the health of the mother and newborn.

Digital dentistry and guided treatments: a concise systematic review

Introduction: In the landscape of new digital technologies, many dental treatments have benefited from this digital advance. The development of computed tomography (CT) dental scanners has enabled powerful imaging capabilities and software applications. The prosthetic plane and implanted drill guides with the placement of trajectories based on a drill according to the position of the CT 3-D Space markers. Objective: To present, through a systematic review, the main considerations of guided surgery in implant dentistry and its respective advantages, disadvantages, and limitations. Methods: Clinical studies with qualitative and/or quantitative analysis were included, following the rules of the systematic review-PRISMA. Results: Out of a total of 102 articles found, 82 articles were evaluated and 57 were rejected for not meeting the GRADE classification, and only 25 articles were used in this study to compose the textual part. Advances in technology have contributed to the improvement of implant models. 3D reconstructions make it possible to determine the quantity and quality of available bone and also enable the simulation of implant installation in a virtual environment, reducing time and the possibility of errors, allowing for an overall reduction in the costs of oral rehabilitation. Conclusion: Guided preoperative planning or project-guided dental surgery provides high implant and dental rehabilitation success rates, also benefiting prosthetic restorations supported by fixed implants. Furthermore, the concept of using personalized implants with the help of 3D virtual treatment planning improves mandibular restoration with a good facial profile, esthetics, and dental rehabilitation.

Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratio in Univentricular Patients From Birth to Follow-Up After Fontan—Predicting Lymphatic Abnormalities

Background: Reliable laboratory parameters identifying complications after Fontan surgery including the lymphatic abnormalities and the development of protein-losing enteropathy (PLE) are rare. Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocte ratio (PLR) are inflammatory markers and have been studied to predict outcome and prognosis in various diseases. The aim of this study was to investigate NLR and PLR from birth to follow-up after Fontan and evaluate their use as prognostic parameters for single ventricle patients regarding the development of lymphatic malformations during follow-up.Materials and Methods: Sixty-six univentricular patients who underwent Fontan surgery and had 6-month follow-up magnetic resonance imaging (MRI) with T2 weighted lymphatic imaging after total cavopulmonary connection (TCPC) surgery were included in the study. NLR and PLR were determined at specific time points, from neonatal age to follow-up after Fontan operation and correlated to data from the MRI 6 months after Fontan.Results: NLR and PLR increase significantly over time from the first surgery during infancy to the follow-up after Fontan (both p < 0.0001), with a significant increase after the Glenn surgery for both ratios (each p < 0.0001). Higher NLR (p = 0.002) and higher PLR (p = 0.004) correlated with higher-grade classification of lymphatic abnormalities in T2-weighted imaging 6 months after Fontan surgery and higher NLR correlated with higher transpulmonary gradient prior to Fontan surgery (p = 0.035) Both ratios showed a significant correlation to total protein at follow-up (NLR p = 0.0038; PLR<0.0001).Conclusion: Increased NLR and PLR correlate with higher degree lymphatic malformations after TCPC and therefore might contribute as valuable additional biomarker during follow-up after TCPC. NLR and PLR are simple, inexpensive and easily available parameters to complement diagnostics after TCPC.

O-BN06 A Proposed Difficulty Grading System for Laparoscopic Bile Duct Exploration. Benefits to Practice and Research

Background A gap remains between the mounting evidence for single session management of bile duct stones and the obstacles to wider adoption of this approach. The practice of laparoscopic bile duct exploration (LCBDE) is limited not only by the availability of training opportunities and adequate equipment but also by the perception that the technique is difficult and requires a high skill-set. The aim of this analysis is to compare the preoperative and operative characteristics and the postoperative outcomes in easy vs. difficult LCBDE in a large consecutive series, according to a proposed 5 grade classification. Methods 1326 LCBDEs were graded according to the location, number and size of ductal stones, retrieval techniques used, utilisation of choledochoscopy and specific biliary pathologies encountered. The cohort was divided into two groups: easy (Grades I A&B, and Grade II A&B, requiring transcystic or transductal exploration for up to 15 stones the largest being 15mm) and difficult (Grades III A&B, for over 15 stones or intrahepatic stones of any size needing transcystic choledochoscopy, IV and V with Mirizzi Syndrome, impacted stones, and ducts needing stenting, conversion or bilioenteric anastomosis). Various outcome parameters were compared. Results Age, sex, obesity and previous biliary admissions had no effect on operative difficulty. Emergency admission, obstructive jaundice, previous ERCP and dilated CBD were predictive of difficult explorations. 78.3% of patients with acute cholecystitis or pancreatitis, 37 % of jaundice and 46% of cholangitis had easy explorations. Transcystic stone retrieval was possible in 77.7% of easy explorations and choledochotomy required in 62.3% of difficult explorations (vs. 33.6% in the whole series). Choledochoscopy was utilised in 23.4% of Grades I&II vs. 98% in difficult explorations. As expected more biliary drains, stenting, bilio-enteric anastomosis, conversions, operative time, biliary-related complications, hospital stay, readmissions and retained stones increased with difficulty. Grades I&II patients had 2 or more hospital episodes in 26.5% vs. 41.2% for grades III to V, the median presentation to resolution interval increasing from 1 to 3 weeks. There were 2 deaths in difficulty Grade V and one in Grade IIB. Conclusions Difficulty grading of LCBDE is a useful tool of predicting outcomes. It facilitates comparison between studies and fair assessment of training. LCBDEs are easy in 72% and of these 77% can be completed transcystically. It is hoped this will encourage more units to adopt single session management of bile duct stones through establishing referral protocols, developing and refining the skills through training and acquiring the necessary equipment.

Diabetic Retinopathy Diagnosis Based on RA-EfficientNet

The early detection and grade diagnosis of diabetic retinopathy (DR) are very important for the avoidance of blindness, and using deep learning methods to automatically diagnose DR has attracted great attention. However, the small amount of DR data limits its application. To automatically learn the disease’s features and detect DR more accurately, we constructed a DR grade diagnostic model. To realize the model, the authors performed the following steps: firstly, we preprocess the DR images to solve the existing problems in an APTOS 2019 dataset, such as size difference, information redundancy and the data imbalance. Secondly, to extract more valid image features, a new network named RA-EfficientNet is proposed, in which a residual attention (RA) block is added to EfficientNet to extract more features and to solve the problem of small differences between lesions. EfficientNet has been previously trained on the ImageNet dataset, based on transfer learning technology, to overcome the small sample size problem of DR. Lastly, based on the extracted features, two classifiers are designed, one is a 2-grade classifier and the other a 5-grade classifier. The 2-grade classifier can diagnose DR, and the 5-grade classifier provides 5 grades of diagnosis for DR, as follows: 0 for No DR, 1 for mild DR, 2 for moderate, 3 for severe and 4 for proliferative DR. Experiments show that our proposed RA-EfficientNet can achieve better performance, with an accuracy value of 98.36% and a kappa score of 96.72% in a 2-grade classification and an accuracy value of 93.55% and a kappa score of 91.93% in a 5-grade classification. The results indicate that the proposed model effectively improves DR detection efficiency and resolves the existing limitation of manual feature extraction.

Assessment of artificial and natural sweeteners present in packaged non-alcoholic beverages (NABs) sold on the Singapore market

Background New Nutri-Grade labelling, aimed at reducing Singaporeans’ sugar consumption will be implemented for all pre-packaged non-alcoholic beverages (NABs) sold in retail outlets from end 2021 onwards. It is expected such labelling will have a major impact on sugar content of beverages, as well as the replacement of sugar with non-caloric alternatives. Methods This study used product label data obtained from in-store surveys to investigate sugar and sweetener composition of NABs present on the Singapore market. Using this data we calculated products prospective Nutri-Grade classification in order to compare the current market composition with relation to sugar and/or sweetener use. Results Over half of the NABs on market were sweetened with sugar (59%) and were associated with less healthy Nutri-Grades of ‘C’ and ‘D’. The use of natural sweeteners; Stevia and Monk fruit, remains low (6%). Conclusion With continuous efforts by the government in promoting public health nutrition, it is expected that there will be a greater usage of sugar substitutes among NABs in response to the upcoming implementation of Nutri-Grade and ever-fluctuating consumers’ demands. The data collected in this study provide a point estimate (July–September 2020) on market composition and use of both sugar and artificial sweeteners in beverages prior to integration of the mandatory labelling requirements.

Optimal deep recurrent neural network for sentiment grade classification

Purpose Internet has endorsed a tremendous change with the advancement of the new technologies. The change has made the users of the internet to make comments regarding the service or product. The Sentiment classification is the process of analyzing the reviews for helping the user to decide whether to purchase the product or not. Design/methodology/approach A rider feedback artificial tree optimization-enabled deep recurrent neural networks (RFATO-enabled deep RNN) is developed for the effective classification of sentiments into various grades. The proposed RFATO algorithm is modeled by integrating the feedback artificial tree (FAT) algorithm in the rider optimization algorithm (ROA), which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of term frequency-inverse document frequency (TF-IDF) features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted. The metrics employed for the evaluation in the proposed RFATO algorithm are accuracy, sensitivity, and specificity. Findings By using the proposed RFATO algorithm, the evaluation metrics such as accuracy, sensitivity and specificity are maximized when compared to the existing algorithms. Originality/value The proposed RFATO algorithm is modeled by integrating the FAT algorithm in the ROA, which is used for training the deep RNN classifier for the classification of sentiments in the review data. The pre-processing is performed by the stemming and the stop word removal process for removing the redundancy for smoother processing of the data. The features including the sentiwordnet-based features, a variant of TF-IDF features and spam words-based features are extracted from the review data to form the feature vector. Feature fusion is performed based on the entropy of the features that are extracted.