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Background: Echocardiographic quantification of left ventricular (LV) volume and ejection fraction (EF) is widely used in the pediatric population. However, there is no consensus on the most accurate method of quantifying ventricular volumes and systolic function. Purpose: The purpose of this study is to compare two commonly used echocardiographic methods for the evaluation of LV volume and quantification of EF, the five-sixth area-length (5/6 AL) and the modified biplane Simpson (BS), to cardiac magnetic resonance (CMR) imaging in children. Methods: CMR studies were paired with echocardiograms and retrospectively analyzed in children 18 years of age and younger. Studies performed more than 3 months between modalities, patients with congenital heart disease, and patients who had changes in medication regimen between corresponding CMR and echocardiograms were excluded. LV volumes and EF were calculated using the 5/6 AL and BS methods and compared to volumes and EF measured on corresponding CMR studies. Subgroup analyses were conducted based on LV function, pathology, and weight. Results: We retrospectively analyzed 53 CMR and corresponding echocardiogram studies (23 studies for myocarditis and 30 studies for cardiomyopathy) in 46 patients. LVEF derived by both echocardiographic methods showed a good correlation to CMR (5/6 AL r = 0.85 and BS r = 0.82). However, both echocardiographic methods overestimated LVEF and underestimated LV volumes when compared to CMR. Conclusion: Left ventricular volumes and EF, as measured by echocardiography, correlate well with CMR measurements. Echocardiography underestimates LV systolic and diastolic volumes and overestimates LVEF. While echocardiography is a good surrogate for estimating LVEF, CMR should be considered in patients for whom accurate measurements are needed for critical clinical decision-making.

Effective Global Citizenship Education (GCED) and Education for Sustainable Development (ESD) benefit from three interrelated dimensions of learning identified by UNESCO: cognitive, social-emotional and behavioural. The latter two of these are especially relevant to mindful compassion pedagogies, and the purpose of the literature review presented here was to ascertain how well such pedagogies could cultivate GCED and ESD social and emotional behavioural competencies in preschool to secondary-level education, tertiary-level education and professional development programmes for educators. The research team analysed 37 relevant peer-reviewed research articles and reports published between 1956 and 2021. Their findings point to many possibilities for how mindful compassion practices could serve as building blocks for desired GCED and ESD competencies. However, this requires a great deal of intentionality on the part of those responsible for the design, implementation and evaluation of mindful compassion-based programmes seeking to cultivate GCED- and ESD-related behavioural competencies. In essence, curriculum designers need to provide opportunities for students to practise mindful compassion as a microskill that leads to the attainment of behavioural competencies conducive to global citizenship and sustainable development. This article summarises the researchers' findings and posits guiding questions for educators and researchers to consider as they design and evaluate social-emotional competencies of the kind needed for GCED and ESD to be effective.

During the COVID-19 pandemic, several forecasting models were released to predict the spread of the virus along variables vital for public health policymaking. Of these, the susceptible–infected–recovered (SIR) compartmental model was the most common. In this paper, we investigated the forecasting performance of The University of Texas COVID-19 Modeling Consortium SIR model. We considered the following daily outcomes: hospitalizations, ICU patients, and deaths. We evaluated the overall forecasting performance, highlighted some stark forecast biases, and considered forecast errors conditional on different pandemic regimes. We found that this model tends to overforecast over the longer horizons and when there is a surge in viral spread. We bolstered these findings by linking them to faults with the SIR framework itself.

Abstract Background Cancer progression can be promoted by chronic inflammation. Local immune response may be associated with favourable or unfavourable prognosis of Papillary Thyroid Carcinoma (PTC). Regulatory T (Treg) cells and T helper 17 (Th17) cells exert opposing function and their balance may have a vital role in promotion of tumor growth. Treg cells in tumor microenvironment (TME) may promote tumor progression and reduced survival of patients. Whereas, Th17 cells can promote or inhibit tumor progression depending on phenotypic characteristics of tumor. In this study, we aimed to analyse the kind of immune response developed and its prognostic impact in future therapeutics. Methods Cytometric Bead Array (CBA) analysis of pro and anti-inflammatory cytokines (IFN-gamma, IL-2, IL-6, IL-17 A, TNF-alpha and IL-4, IL-10) was done in 15 PTC irrespective of Lymphocytic Thyroiditis (LT) and 16 Hashimoto’s Thyroiditis (HT) cases. Immunohistochemical expression of FoxP3 and IL-17 A was studied in 27 cases of PTC with LT. Whereas, quantitative gene expression of both was analysed in 10 cases. Results All the pro-inflammatory cytokines showed mild elevation in PTC with LT. On IHC, IL-17 A expression was observed in 74% PTC with LT. Whereas, FoxP3 was present in only 40% cases. Also, IL-17 A expression was significantly associated with age group (> 45 years), tumor size ≤ 1 cm and disease progression. Conclusions Increased expression of cytokines suggested correlation between inflammatory factors and progression of thyroid tumors. Along with this, the balance between IL-17 A and FoxP3 may play an important role in PTC development, prognosis and future management.

Diabetic Retinopathy (DR) is a major type of eye defect that is caused by abnormalities in the blood vessels within the retinal tissue. Early detection by automatic approach using modern methodologies helps prevent consequences like vision loss. So, this research has developed an effective segmentation approach known as Level-set Based Adaptive-active Contour Segmentation (LBACS) to segment the images by improving the boundary conditions and detecting the edges using Level Set Method with Improved Boundary Indicator Function (LSMIBIF) and Adaptive-Active Counter Model (AACM). For evaluating the DR system, the information is collected from the publically available datasets named as Indian Diabetic Retinopathy Image Dataset (IDRiD) and Diabetic Retinopathy Database 1 (DIARETDB 1). Then the collected images are pre-processed using a Gaussian filter, edge detection sharpening, Contrast enhancement, and Luminosity enhancement to eliminate the noises/interferences, and data imbalance that exists in the available dataset. After that, the noise-free data are processed for segmentation by using the Level set-based active contour segmentation technique. Then, the segmented images are given to the feature extraction stage where Gray Level Co-occurrence Matrix (GLCM), Local ternary, and binary patterns are employed to extract the features from the segmented image. Finally, extracted features are given as input to the classification stage where Long Short-Term Memory (LSTM) is utilized to categorize various classes of DR. The result analysis evidently shows that the proposed LBACS-LSTM achieved better results in overall metrics. The accuracy of the proposed LBACS-LSTM for IDRiD and DIARETDB 1 datasets is 99.43% and 97.39%, respectively which is comparably higher than the existing approaches such as Three-dimensional semantic model, Delimiting Segmentation Approach Using Knowledge Learning (DSA-KL), K-Nearest Neighbor (KNN), Computer aided method and Chronological Tunicate Swarm Algorithm with Stacked Auto Encoder (CTSA-SAE).

ABSTRACTBackground Critical thinking is essential for the accurate diagnosis and management of patients. It is correlated with academic success.Objective Our objective was to design a novel tool for interactive online learning to improve knowledge and to assess trainees’ critical thinking skills using the framework of the American Philosophical Association (APA).Methods Residents, fellows and students participated in an online, self-directed case-based vignette activity to learn malaria diagnosis and management. Pre and post-tests with multiple choice and open-ended case-based questions assessed knowledge and critical thinking. Comparison between pre and post-test scores across subgroups were performed using paired t-tests or one-way ANOVA.Results Between 4 April 2017 to 14 July 2019, 62 of 75 (82%) eligible subjects completed both the pre and the post-test. Improved post-test scores occurred in 90% of medical students, p=0.001, 77% of residents, p

Osteosarcoma is a cancerous tumor that occurs in bones. Although it can occur in any bone, it often occurs in long bones such as arms and legs. The exact cause of this cancerous tumor is still unknown, but according to experts, it occurs due to the deoxyribonucleic acid (DNA) mutations inside the bones. This creates immature, irregular, diseased bone and can destroy healthy body tissue. About 75 out of 100 people who have osteosarcoma can be cured if the cancer is not dispersed to the additional body parts. The bone X-ray is the initial test when a bone tumor is suspected. X-ray and imaging tests are the best way to identify osteosarcoma from the bones. A biopsy is the suggested method that can make a definitive diagnosis. This is a time-consuming and difficult procedure that can be automated. We propose several supervised deep-learning methods and select the most suitable model. The selection is made through the weightage from the users’ data to detect bone cancer. We show the model selected meets the expectations with the highest accuracy 90.36% using the residual neural network(ResNet101) algorithm and 89.51% precision in the prediction tasks.