Navigating a Giant Thyroglossal cyst: A Compelling Case Study.

Thyroglossal duct cysts are congenital neck cysts, formed as a result of the failure of the thyroglossal duct to involute during embryogenesis. We present a case of a 55- year-old male who presented with a history of a large anterior neck mass measuring 11 x 8.5 cm.

Forgiveness and well-being in mothers of children with disabilities.

Objective: To examine the correlations between forgiveness and well-being in mothers of children with disabilities, considering the moderating role of contextual factors.

Background: Well-being in mothers of children with disabilities is associated with coping with the numerous and unique challenges of motherhood. These are often accompanied by a sense of guilt, harm, and shame.

A new family of instance-level loss functions for improving instance-level segmentation and detection of white matter hyperintensities in routine clinical brain MRI.

In this study, we introduce "instance loss functions", a new family of loss functions designed to enhance the training of neural networks in the instance-level segmentation and detection of objects in biomedical image data, particularly those of varied numbers and sizes. Intended to be utilized conjointly with traditional loss functions, these proposed functions, prioritize object instances over pixel-by-pixel comparisons. The specific functions, the instance segmentation loss (L), the instance center loss (L), the false instance rate loss (L), and the instance proximity loss (L), serve distinct purposes.

Diabetic foot ulcers segmentation challenge report: Benchmark and analysis.

Monitoring the healing progress of diabetic foot ulcers is a challenging process. Accurate segmentation of foot ulcers can help podiatrists to quantitatively measure the size of wound regions to assist prediction of healing status. The main challenge in this field is the lack of publicly available manual delineation, which can be time consuming and laborious.

Exploring the performance of implicit neural representations for brain image registration.

Pairwise image registration is a necessary prerequisite for brain image comparison and data integration in neuroscience and radiology. In this work, we explore the efficacy of implicit neural representations (INRs) in improving the performance of brain image registration in magnetic resonance imaging. In this setting, INRs serve as a continuous and coordinate based approximation of the deformation field obtained through a multi-layer perceptron.

BUS-Set: A benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets.

Purpose: BUS-Set is a reproducible benchmark for breast ultrasound (BUS) lesion segmentation, comprising of publicly available images with the aim of improving future comparisons between machine learning models within the field of BUS.

Method: Four publicly available datasets were compiled creating an overall set of 1154 BUS images, from five different scanner types. Full dataset details have been provided, which include clinical labels and detailed annotations.

Neighborhood and Family Characteristics Associated with Adiposity and Physical Activity Engagement among Preschoolers in a Small Rural Community.

Unlabelled: The purpose of this study was to evaluate family and home/neighborhood characteristics associated with physical activity (PA) and adiposity among young children living in a small rural community.

Methods: Participants were 30 parents and their youngest child aged 2-5 years. Children wore accelerometers for 7 days.

The lived experience of fathers with intellectual disabilities: An interpretative phenomenological analysis.

Fathers with mild to moderate intellectual disabilities (ID) are under-represented in research. This article explores the lived childhood experiences of fathers with ID using interpretive phenomenology to understand the meanings ascribed to these experiences and how this has shaped their understanding of self. Rich data was collected through in-depth interviewing of fathers in Poland.

Ultrasound- and biomarker-based assessment of hepatic steatosis in patients with severe obesity.

Introduction: Nonalcoholic fatty liver disease (NAFLD) is a common liver abnormality, but its noninvasive diagnosis in patients with severe obesity remains difficult.

Objectives: Our aim was to investigate the usefulness of the ultrasound‑based hepatorenal index (HRI) technique and 2 biomarker‑based methods, including the hepatic steatosis index (HSI) and NAFLD logit score for the diagnosis of NAFLD in patients referred for bariatric surgery.

Patients And Methods: A total of 162 patients, including 106 with NAFLD, admitted for bariatric surgery participated in the study.

Prediction of response to neoadjuvant chemotherapy in breast cancer with recurrent neural networks and raw ultrasound signals.

. Prediction of the response to neoadjuvant chemotherapy (NAC) in breast cancer is important for patient outcomes. In this work, we propose a deep learning based approach to NAC response prediction in ultrasound (US) imaging.

Posttraumatic growth and interpersonal forgiveness in persons with physical disabilities.

Purpose/objective: To investigate whether there is a relationship between posttraumatic growth (PTG) and forgiveness in people with physical disabilities, and whether contextual factors moderate the association.

Research Method/design: Two hundred and sixty seven persons with physical disabilities completed Polish versions of three inventories: Posttraumatic Growth Inventory (PTGI), Decision to Forgive Scale (DTFS), and Emotional Forgiveness Scale (EFS). The participants also assessed contextual factors of offenses, such as transgression severity, apology being offered, and quality of the relationship with the offender.

Explaining a Deep Learning Based Breast Ultrasound Image Classifier with Saliency Maps.

Aim Of The Study: Deep neural networks have achieved good performance in breast mass classification in ultrasound imaging. However, their usage in clinical practice is still limited due to the lack of explainability of decisions conducted by the networks. In this study, to address the explainability problem, we generated saliency maps indicating ultrasound image regions important for the network's classification decisions.

Inflammation and remodeling pathways and risk of cardiovascular events in patients with ischemic heart failure and reduced ejection fraction.

Patients with heart failure (HF) and coronary artery disease (CAD) have a high risk for cardiovascular (CV) events including HF hospitalization, stroke, myocardial infarction (MI) and sudden cardiac death (SCD). The present study evaluated associations of proteomic biomarkers with CV outcome in patients with CAD and HF with reduced ejection fraction (HFrEF), shortly after a worsening HF episode. We performed a case-control study within the COMMANDER HF international, double-blind, randomized placebo-controlled trial investigating the effects of the factor-Xa inhibitor rivaroxaban.

Composites Containing Nanohydroxyapatites and a Stable TEMPO Radical: Preparation and Characterization Using Spectrophotometry, EPR and H MAS NMR.

Hydroxyapatite is the main constituent of mammalian hard tissues. Basic applications of synthetic hydroxyapatites include bone and dental implantology and drug delivery systems. The study of hydroxyapatite surface properties could give greater insight into the processes of bone mineralization and degradation.

Unsupervised deep learning based approach to temperature monitoring in focused ultrasound treatment.

Temperature monitoring in ultrasound (US) imaging is important for various medical treatments, such as high-intensity focused US (HIFU) therapy or hyperthermia. In this work, we present a deep learning based approach to temperature monitoring based on radio-frequency (RF) US data. We used Siamese neural networks in an unsupervised way to spatially compare RF data collected at different time points of the heating process.

Joint segmentation and classification of breast masses based on ultrasound radio-frequency data and convolutional neural networks.

In this paper, we propose a novel deep learning method for joint classification and segmentation of breast masses based on radio-frequency (RF) ultrasound (US) data. In comparison to commonly used classification and segmentation techniques, utilizing B-mode US images, we train the network with RF data (data before envelope detection and dynamic compression), which are considered to include more information on tissue's physical properties than standard B-mode US images. Our multi-task network, based on the Y-Net architecture, can effectively process large matrices of RF data by mixing 1D and 2D convolutional filters.

The Influence of Surgical Weight Reduction on Left Atrial Strain.

Background: Obesity increases and surgical weight reduction decreases the risk of atrial fibrillation (AF) and heart failure (HF). We hypothesized that surgically induced weight loss may favorably affect left atrial (LA) mechanical function measured by longitudinal strain, which has recently emerged as an independent imaging biomarker of increased AF and HF risk.

Methods: We retrospectively evaluated echocardiograms performed before and 12.

Sense of Stress and Posttraumatic Growth in Mothers of Children with Cystic Fibrosis-The Moderating Role of Resilience.

Objective: The aim of this study was to examine the moderating role of resilience in the relationship between sense of stress and posttraumatic growth (PTG) in mothers of children with cystic fibrosis (CF).

Methods: This cross-sectional study was conducted in a group of 139 mothers of children with CF. A diagnostic survey with Polish versions of the inventories was used.

Development and evaluation of a new method to combine clinical impression survey data with existing laboratory data for veterinary syndromic surveillance with the Canada West Swine Health Intelligence Network (CWSHIN).

The Canada West Swine Health Intelligence Network (CWSHIN) is a surveillance system imbedded in an intelligence network. It has been conducting syndromic surveillance in the four western provinces of Canada since 2012. The quarterly activities include repeated clinical impression surveys, compilation of laboratory data, discussion of trends with an expert group (practitioners, laboratory diagnosticians) and swine health reports for producers and swine practitioners.

New-onset atrial fibrillation in patients with worsening heart failure and coronary artery disease: an analysis from the COMMANDER-HF trial.

Background: Atrial fibrillation (AF) in the presence of heart failure (HF) is associated with poor outcomes including a high-risk of stroke and other thromboembolic events. Identifying patients without AF who are at high-risk of developing this arrhythmia has important clinical implications.

Aims: To develop a risk score to identify HF patients at high risk of developing AF.

Automated cartilage segmentation and quantification using 3D ultrashort echo time (UTE) cones MR imaging with deep convolutional neural networks.

Objective: To develop a fully automated full-thickness cartilage segmentation and mapping of T1, T1ρ, and T2*, as well as macromolecular fraction (MMF) by combining a series of quantitative 3D ultrashort echo time (UTE) cones MR imaging with a transfer learning-based U-Net convolutional neural networks (CNN) model.

Methods: Sixty-five participants (20 normal, 29 doubtful-minimal osteoarthritis (OA), and 16 moderate-severe OA) were scanned using 3D UTE cones T1 (Cones-T1), adiabatic T1ρ (Cones-AdiabT1ρ), T2* (Cones-T2*), and magnetization transfer (Cones-MT) sequences at 3 T. Manual segmentation was performed by two experienced radiologists, and automatic segmentation was completed using the proposed U-Net CNN model.

Liver Fat Assessment in Multiview Sonography Using Transfer Learning With Convolutional Neural Networks.

Objectives: To develop and evaluate deep learning models devised for liver fat assessment based on ultrasound (US) images acquired from four different liver views: transverse plane (hepatic veins at the confluence with the inferior vena cava, right portal vein, right posterior portal vein) and sagittal plane (liver/kidney).

Methods: US images (four separate views) were acquired from 135 participants with known or suspected nonalcoholic fatty liver disease. Proton density fat fraction (PDFF) values derived from chemical shift-encoded magnetic resonance imaging served as ground truth.

Heart failure re-hospitalizations and subsequent fatal events in coronary artery disease: insights from COMMANDER-HF, EPHESUS, and EXAMINE.

Background: Patients with coronary artery disease (CAD) are at increased risk of developing and being hospitalised for heart failure (HFH). However, the risk of HFH versus ischemic events may vary among patients with CAD, depending on whether acute myocardial infarction (MI), left ventricular dysfunction or decompensated HF is present at baseline.

Aims: We aim to explore the risk of non-fatal events (HFH, MI, stroke) and subsequent death in 3 landmark trials, COMMANDER-HF, EPHESUS and EXAMINE that, together, included patients with CAD with and without reduced ejection fraction and acute MI.

Impact of Geographic Region on the COMMANDER-HF Trial.

Objectives: This study sought to compare patient characteristics, outcomes, and treatment effects among regions in the COMMANDER-HF trial.

Background: Globalization of cardiovascular trials increases generalizability. However, regional differences may also introduce heterogeneity in results.

Associations between post-traumatic growth and wisdom in people with long-term paraplegia - the role of disability appraisals and participation.

Purpose: The number of people with long-term spinal cord injury (SCI) is growing but our knowledge about their successful functioning is still limited. The goal of this study was to investigate the correlations between posttraumatic growth (PTG) and wisdom in people with long-term paraplegia, exploring the mediating effect of disability appraisals, and participation/autonomy (P/A).

Methods: One-hundred and sixty-six persons with paraplegia completed a set of questionnaires, which included: The three-dimensional Wisdom Scale; The Post-traumatic Growth Inventory; Appraisals of Disability Primary and Secondary Scale; Impact on Participation and Autonomy Questionnaire.