A Cross-Sectional Study to Assess Thyroid Function in Infants and Children in a City from South-Western Romania.

Thyroid hormones are critical regulators of growth, myelination of the nervous system, metabolism, and organ function. The most prevalent endocrinopathies in childhood are related to thyroid disorders. Thyroid problems in children and adolescents have a significantly different etiology and clinical presentation than in adults. Thus, pediatric medical care involves an understanding of the unique features of thyroid function and dysfunction during childhood and adolescence. The etiology and clinical manifestations of thyroid disorders in children and adolescents are vastly different from those in adults. The particular aspects of thyroid function and malfunction in childhood and adolescence are hence part of pediatric medical therapy. To prevent persistent nervous system damage and developmental problems, it is vital to recognize and treat thyroid dysfunction in neonates as early as possible. The purpose of the research was to understand more how children's thyroid problems function, structure, and prevalence. The research examined 30 children under the age of 16 years who had symptoms that were linked to thyroid problems. In addition to demographic and family information, thyroid ultrasounds and blood samples for the detection of T3, T4, and TSH were obtained. Females surpassed males by a small majority (2.33:1 ratio).Out of the total children included in the study, 14(46.7%) cases for autoimmune thyroiditis, 2(6.67%) cases for congenital hypothyroidism, 1(3.33%) case for hyperthyroidism, 1(3.33%) case for hyperthyroidism-Graves disease, 8(26.7%) cases for hypothyroidism and 4(13.3%) cases for subclinical hypothyroidism.

Deep Learning Algorithm for the Confirmation of Mucosal Healing in Crohn's Disease, Based on Confocal Laser Endomicroscopy Images.

BACKGROUND AND AIMS: Mucosal healing (MH) is associated with a stable course of Crohn's disease (CD) which can be assessed by confocal laser endomicroscopy (CLE). To minimize the operator's errors and automate assessment of CLE images, we used a deep learning (DL) model for image analysis. We hypothesized that DL combined with convolutional neural networks (CNNs) and long short-term memory (LSTM) can distinguish between normal and inflamed colonic mucosa from CLE images. METHODS: The study included 54 patients, 32 with known active CD, and 22 control patients (18 CD patients with MH and four normal mucosa patients with no history of inflammatory bowel diseases). We designed and trained a deep convolutional neural network to detect active CD using 6,205 endomicroscopy images classified as active CD inflammation (3,672 images) and control mucosal healing or no inflammation (2,533 images). CLE imaging was performed on four colorectal areas and the terminal ileum. Gold standard was represented by the histopathological evaluation. The dataset was randomly split in two distinct training and testing datasets: 80% data from each patient were used for training and the remaining 20% for testing. The training dataset consists of 2,892 images with inflammation and 2,189 control images. The testing dataset consists of 780 images with inflammation and 344 control images of the colon. We used a CNN-LSTM model with four convolution layers and one LSTM layer for automatic detection of MH and CD diagnosis from CLE images. RESULTS: CLE investigation reveals normal colonic mucosa with round crypts and inflamed mucosa with irregular crypts and tortuous and dilated blood vessels. Our method obtained a 95.3% test accuracy with a specificity of 92.78% and a sensitivity of 94.6%, with an area under each receiver operating characteristic curves of 0.98. CONCLUSIONS: Using machine learning algorithms on CLE images can successfully differentiate between inflammation and normal ileocolonic mucosa and can be used as a computer aided diagnosis for CD. Future clinical studies with a larger patient spectrum will validate our results and improve the CNN-SSTM model.

Assessment of Deep Learning Methods for Differentiating Autoimmune Disorders in Ultrasound Images.

At present, deep learning becomes an important tool in medical image analysis, with good performance in diagnosing, pattern detection, and segmentation. Ultrasound imaging offers an easy and rapid method to detect and diagnose thyroid disorders. With the help of a computer-aided diagnosis (CAD) system based on deep learning, we have the possibility of real-time and non-invasive diagnosing of thyroidal US images. This paper proposed a study based on deep learning with transfer learning for differentiating the thyroidal ultrasound images using image pixels and diagnosis labels as inputs. We trained, assessed, and compared two pre-trained models (VGG-19 and Inception v3) using a dataset of ultrasound images consisting of 2 types of thyroid ultrasound images: autoimmune and normal. The training dataset consisted of 615 thyroid ultrasound images, from which 415 images were diagnosed as autoimmune, and 200 images as normal. The models were assessed using a dataset of 120 images, from which 80 images were diagnosed as autoimmune, and 40 images diagnosed as normal. The two deep learning models obtained very good results, as follows: the pre-trained VGG-19 model obtained 98.60% for the overall test accuracy with an overall specificity of 98.94% and overall sensitivity of 97.97%, while the Inception v3 model obtained 96.4% for the overall test accuracy with an overall specificity of 95.58% and overall sensitivity of 95.58.

Finite Element Analysis of a Novel Aortic Valve Stent.

Worldwide, one of the leading causes of death for patients with cardiovascular disease is aortic valve failure or insufficiency as a result of calcification and cardiovascular disease. The surgical treatment consists of repair or total replacement of the aortic valve. Artificial aortic valve implantation via a percutaneous or endovascular procedure is the minimally invasive alternative to open chest surgery, and the only option for high-risk or older patients. Due to the complex anatomical location between the left ventricle and the aorta, there are still engineering design optimization challenges which influence the long-term durability of the valve. In this study we developed a computer model and performed a numerical analysis of an original self-expanding stent for transcatheter aortic valve in order to optimize its design and materials. The study demonstrates the current valve design could be a good alternative to the existing commercially available valve devices.

Predictors of early and sustained virological response of viral hepatitis C.

Due to complex interplay between host and viral factors, pathogenesis of chronic hepatitis C (CHC) is considered a challenging issue. Infection with hepatitis C virus (HCV) is not confined only to liver but can induce disturbances in many other organs and systems. Our primary aim for this study was to evaluate biological response rates and sustained virological response (SVR) in patients diagnosed with CHC, treated with Interferon-alpha (IFN-α), Pegylated (PEG)-IFN-α2a or -α2b plus Ribavirin. The second aim of the study was the identification of predictive factors for a favorable response to antiviral therapy in patients diagnosed with CHC. We enrolled in this study 210 patients diagnosed with CHC who have accomplished all inclusion and exclusion criteria, treated with PEG-IFN plus Ribavirin. Patients' recovery progress has been evaluated by determining: age, gender; biochemical tests: alanine aminotransferase (ALT), aspartate aminotransferase (AST); serological assays - detect anti-HCV antibody and molecular assays - detect, quantify and/or characterize hepatitis C viral load (ribonucleic acid) (HCV-RNA); liver histopathological (HP) examination. According to their response to treatment, they were classified into responders (n=145) and non-responders (n=65). Liver biopsies were histopathologically evaluated for necroinflammatory grade and fibrosis stage according to the modified Ishak and Metavir scoring systems for chronic hepatitis. Demographic, laboratory, and HP results were introduced in statistical analysis. These parameters were included in area under curve (AUC) analysis in order to estimate their degree of influence on getting early virological response (EVR) and SVR. Our study demonstrates that factors connected to treatment failure in CHC are linked to older age, high hepatitis C viral load, and impaired glucose tolerance at beginning of treatment [high fasting glucose and insulin, high homeostatic model assessment of insulin resistance (HOMA-IR) index] and also to liver histology features (high fibrosis score, liver steatosis, iron infiltration, and more or less high necroinflammatory activity). Analyzing results of our study shows that HOMA-IR index, serum insulin levels, baseline HCV-RNA, baseline mean blood glucose and HP score like Ishak fibrosis score, steatosis score and liver iron score may have a predictive value for obtaining an EVR in patients diagnosed with CHC.