Primary Molar Pulpotomies with Different Hemorrhage Control Agents and Base Materials: A Randomized Clinical Trial

Objective: To evaluate the clinical and radiographical success of primary molar pulpotomies which used 15.5% ferric sulfate (FS) or 1.25% sodium hypochlorite (NaOCl) for hemostasis and zinc oxide-eugenol (ZOE) and calcium hydroxide (CH) pastes as base materials. Methods: In 29 healthy children, 80 primary molars were randomly allocated to one of the study groups: Group 1: FS-ZOE, Group 2: FS-CH, Group 3: NaOCl-ZOE, and Group 4: NaOCl-CH. After hemostasis with the respective solutions, pulp stumps and floor of the pulp chambers were covered with either ZOE or CH pastes. All teeth were restored with stainless steel crowns. Follow-up examinations were carried out at 1, 3, 6, and 12 months. Results: One tooth in Group 1 and two teeth in Group 4 were extracted because of pain and periapial pathosis at sixth month. After 12 months, clinical success rates of pulpotomies in Groups 1-4 were 95%, 100%, 100%, and 89.5%, respectively. The differences were not significant (P = 0.548). Radiographic success rates for Groups 1-4 were 80%, 88.9%, 78.9%, and 84.2%, respectively. No statistically significant difference was found (P = 0.968). Pain on percussion was the most observed clinical finding. However, internal root resorption was the most common radiological finding and it was observed significantly more in mandibular primary molars (P \u3c 0.05). Conclusion: Both ZOE and CH can be preferred as base materials after hemostasis achieved by the use of 15.5% FS or 1.25% NaOCl in primary tooth pulpotomy

Modelling change with an integrated approach to manufacturing system design

This paper proposes a model that integrates information from product, process and organisation domains with a view to help manage these complex interrelationships with multiple layers of interaction. This model incorporates an integrated mechanism that simulates change effects during the design of complex manufacturing system by populating a Multi-layered Domain Matrix (MDM) and applying a Change Prediction Model (CPM) propagation mechanism to interconnected elements

Deep Learning Based Classification of Unsegmented Phonocardiogram Spectrograms Leveraging Transfer Learning

Cardiovascular diseases (CVDs) are the main cause of deaths all over the world. Heart murmurs are the most common abnormalities detected during the auscultation process. The two widely used publicly available phonocardiogram (PCG) datasets are from the PhysioNet/CinC (2016) and PASCAL (2011) challenges. The datasets are significantly different in terms of the tools used for data acquisition, clinical protocols, digital storages and signal qualities, making it challenging to process and analyze. In this work, we have used short-time Fourier transform (STFT) based spectrograms to learn the representative patterns of the normal and abnormal PCG signals. Spectrograms generated from both the datasets are utilized to perform three different studies: (i) train, validate and test different variants of convolutional neural network (CNN) models with PhysioNet dataset, (ii) train, validate and test the best performing CNN structure on combined PhysioNet-PASCAL dataset and (iii) finally, transfer learning technique is employed to train the best performing pre-trained network from the first study with PASCAL dataset. We propose a novel, less complex and relatively light custom CNN model for the classification of PhysioNet, combined and PASCAL datasets. The first study achieves an accuracy, sensitivity, specificity, precision and F1 score of 95.4%, 96.3%, 92.4%, 97.6% and 96.98% respectively while the second study shows accuracy, sensitivity, specificity, precision and F1 score of 94.2%, 95.5%, 90.3%, 96.8% and 96.1% respectively. Finally, the third study shows a precision of 98.29% on the noisy PASCAL dataset with transfer learning approach. All the three proposed approaches outperform most of the recent competing studies by achieving comparatively high classification accuracy and precision, which make them suitable for screening CVDs using PCG signals

International Veterinary Epilepsy Task Force Consensus Proposal: Outcome of therapeutic interventions in canine and feline epilepsy

Common criteria for the diagnosis of drug resistance and the assessment of outcome are needed urgently as a prerequisite for standardized evaluation and reporting of individual therapeutic responses in canine epilepsy. Thus, we provide a proposal for the definition of drug resistance and partial therapeutic success in canine patients with epilepsy. This consensus statement also suggests a list of factors and aspects of outcome, which should be considered in addition to the impact on seizures. Moreover, these expert recommendations discuss criteria which determine the validity and informative value of a therapeutic trial in an individual patient and also suggest the application of individual outcome criteria. Agreement on common guidelines does not only render a basis for future optimization of individual patient management, but is also a presupposition for the design and implementation of clinical studies with highly standardized inclusion and exclusion criteria. Respective standardization will improve the comparability of findings from different studies and renders an improved basis for multicenter studies. Therefore, this proposal provides an in-depth discussion of the implications of outcome criteria for clinical studies. In particular ethical aspects and the different options for study design and application of individual patient-centered outcome criteria are considered

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species

Learning environment, attitudes and anxiety across the transition from primary to secondary school mathematics

Past research has revealed that, relative to primary-school students, high-school students have less-positive attitudes to mathematics and perceive their classroom environments and teacher–student relationships less favourably. This study involved the transition experience of 541 students in 47 classes in 15 primary (year 7) and secondary (year 8) government and Catholic schools in metropolitan and regional South Australia. Scales were adapted from three established instruments, namely, the What Is Happening In this Class?, Test of Mathematics Related Attitudes and Revised Mathematics Anxiety Ratings Scale, to identify changes across the transition from primary to secondary school in terms of the classroom learning environment and students’ attitude/anxiety towards mathematics. Relative to year 7 students, year 8 students reported less Involvement, less positive Attitude to Mathematical Inquiry, less Enjoyment of Mathematics and greater Mathematics Anxiety. Differences between students in Years 7 and 8 were very similar for male and female students, although the magnitude of sex differences in attitudes was slightly different in Years 7 and 8

Prevalence of vitamin D deficiency among Turkish, Moroccan, Indian and sub-Sahara African populations in Europe and their countries of origin: an overview

Public Health and primary carePrevention and community carePrevention, Population and Disease management (PrePoD