Usability Testing Via Simulation: Optimizing the NEAR4PEM Preintubation Checklist With a Human Factors Approach.

Objectives: To inform development of a preintubation checklist for pediatric emergency departments via multicenter usability testing of a prototype checklist.

Methods: This was a prospective, mixed methods study across 7 sites in the National Emergency Airway Registry for Pediatric Emergency Medicine (NEAR4PEM) collaborative. Pediatric emergency medicine attending physicians and senior fellows at each site were first oriented to a checklist prototype, including content previously identified using a modified Delphi approach.

Deposition and Optical Characterization of Sputter Deposited p-Type Delafossite CuGaO Thin Films Using CuO and GaO Targets.

CuGaO thin films were deposited using the RF magnetron sputtering technique using CuO and GaO targets. The films were deposited at room temperature onto a quartz slide. The sputtering power of CuO remained constant at 50 W, while the sputtering power of GaO was systematically varied from 150 W to 200 W.

Quality assurance of late gadolinium enhancement cardiac magnetic resonance images: a deep learning classifier for confidence in the presence or absence of abnormality with potential to prompt real-time image optimization.

Background: Late gadolinium enhancement (LGE) of the myocardium has significant diagnostic and prognostic implications, with even small areas of enhancement being important. Distinguishing between definitely normal and definitely abnormal LGE images is usually straightforward, but diagnostic uncertainty arises when reporters are not sure whether the observed LGE is genuine or not. This uncertainty might be resolved by repetition (to remove artifact) or further acquisition of intersecting images, but this must take place before the scan finishes.

Pediatric Emergency Medicine Didactics and Simulation (PEMDAS): Pediatric Sedation Complications.

Introduction: Ketamine and propofol are commonly used agents for sedation in the pediatric emergency department (PED). While these medications routinely provide safe sedations, there are side effects providers should be able to recognize and manage. Currently, no pediatric sedation simulations exist in the literature.

Left sided Scimitar syndrome.

The images depict a rare case of Scimitar syndrome involving the left lower pulmonary vein.

Aortic stenosis assessment from the 3-chamber cine: Ratio of balanced steady-state-free-precession (bSSFP) blood signal between the aorta and left ventricle predicts severity.

Background: Cardiovascular magnetic resonance (CMR) imaging is an important tool for evaluating the severity of aortic stenosis (AS), co-existing aortic disease, and concurrent myocardial abnormalities. Acquiring this additional information requires protocol adaptations and additional scanner time, but is not necessary for the majority of patients who do not have AS. We observed that the relative signal intensity of blood in the ascending aorta on a balanced steady state free precession (bSSFP) 3-chamber cine was often reduced in those with significant aortic stenosis.

A one-stop shoulder clinic to save time and resources.

There is now a need more than ever to streamline services. A one-stop shoulder clinic was introduced during the COVID-19 pandemic. A total of 861 patients were seen, saving 794 future appointments.

Efficient labelling for efficient deep learning: the benefit of a multiple-image-ranking method to generate high volume training data applied to ventricular slice level classification in cardiac MRI.

Background: Getting the most value from expert clinicians' limited labelling time is a major challenge for artificial intelligence (AI) development in clinical imaging. We present a novel method for ground-truth labelling of cardiac magnetic resonance imaging (CMR) image data by leveraging multiple clinician experts ranking multiple images on a single ordinal axis, rather than manual labelling of one image at a time. We apply this strategy to train a deep learning (DL) model to classify the anatomical position of CMR images.

Evaluation of the geomorphological scenario of Shimsha River Basin, Karnataka, India.

Drainage morphometric analysis is very substantial in determining the characteristics of a river basin. It is performed through spatial analysis, which helps study the various hydrological interactions and responses in the watershed. In this research, the authors have tried to study the geomorphological scenario of the Shimsha River basin using the remote sensed data, toposheets, and geographic information systems tools.

Spatiotemporal Rainfall Variability and Trend Analysis of Shimsha River Basin, India.

Karnataka state has the second highest rainfed agricultural land in India, where agricultural output relies heavily on rainfall. The Shimsha basin, a sub-basin of Cauvery in the state, comes under a semi-arid region and predominantly consists of rainfed agricultural land. Rainfall patterns have changed dramatically with time resulting in frequent floods and droughts.

GPCR Signaling Measurement and Drug Profiling with an Automated Live-Cell Microscopy System.

A major limitation of time-lapse microscopy combined with fluorescent biosensors, a powerful tool for quantifying spatiotemporal dynamics of signaling in single living cells, is low-experimental throughput. To overcome this limitation, we created a highly customizable, MATLAB-based platform: flexible automated liquid-handling combined microscope (FALCOscope) that coordinates an OpenTrons liquid handler and a fluorescence microscope to automate drug treatments, fluorescence imaging, and single-cell analysis. To test the feasibility of the FALCOscope, we quantified G protein-coupled receptor (GPCR)-stimulated Protein Kinase A activity and cAMP responses to GPCR agonists and antagonists.

Congenital portosystemic shunt with multiple splenic artery aneurysms: Reversing pulmonary hypertension and preventing aneurysm rupture.

We report an unusual occurrence of multiple splenic artery aneurysms and splenomegaly in a young woman with severe pulmonary hypertension, secondary to a congenital portosystemic shunt (CPS). The splenic artery was occluded using an Amplatzer Duct Occluder-II device, and closure of the large intrahepatic CPS was achieved using a muscular ventricular septal defect occluder. There was resolution of splenomegaly with normal pulmonary artery pressures, a few months after the procedure.

TMS-Net: A segmentation network coupled with a run-time quality control method for robust cardiac image segmentation.

Recently, deep networks have shown impressive performance for the segmentation of cardiac Magnetic Resonance Imaging (MRI) images. However, their achievement is proving slow to transition to widespread use in medical clinics because of robustness issues leading to low trust of clinicians to their results. Predicting run-time quality of segmentation masks can be useful to warn clinicians against poor results.

Validation of two-dimensional video-based inference of finger kinematics with pose estimation.

Accurate capture finger of movements for biomechanical assessments has typically been achieved within laboratory environments through the use of physical markers attached to a participant's hands. However, such requirements can narrow the broader adoption of movement tracking for kinematic assessment outside these laboratory settings, such as in the home. Thus, there is the need for markerless hand motion capture techniques that are easy to use and accurate enough to evaluate the complex movements of the human hand.

Congenital systemic arteriovenous fistulas: Interventional strategies and embryological perspectives.

Background: Data on congenital systemic arteriovenous fistulas are largely based on individual case reports. A true systemic arteriovenous fistula needs to be differentiated from other vascular malformations like capillary or venous hemangiomas, which are far more common.

Objectives: We sought to identify the varied symptoms, diagnostic challenges, describe interventional treatment options, and postulate an embryological basis for this uncommonly described entity.

Validity of Self-testing at Home With Rapid Severe Acute Respiratory Syndrome Coronavirus 2 Antibody Detection by Lateral Flow Immunoassay.

Background: We explore severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody lateral flow immunoassay (LFIA) performance under field conditions compared to laboratory-based electrochemiluminescence immunoassay (ECLIA) and live virus neutralization.

Methods: In July 2021, 3758 participants performed, at home, a self-administered Fortress LFIA on finger-prick blood, reported and submitted a photograph of the result, and provided a self-collected capillary blood sample for assessment of immunoglobulin G (IgG) antibodies using the Roche Elecsys® Anti-SARS-CoV-2 ECLIA. We compared the self-reported LFIA result to the quantitative ECLIA and checked the reading of the LFIA result with an automated image analysis (ALFA).

Machine learning to support visual auditing of home-based lateral flow immunoassay self-test results for SARS-CoV-2 antibodies.

Background: Lateral flow immunoassays (LFIAs) are being used worldwide for COVID-19 mass testing and antibody prevalence studies. Relatively simple to use and low cost, these tests can be self-administered at home, but rely on subjective interpretation of a test line by eye, risking false positives and false negatives. Here, we report on the development of ALFA (Automated Lateral Flow Analysis) to improve reported sensitivity and specificity.

EP-PINNs: Cardiac Electrophysiology Characterisation Using Physics-Informed Neural Networks.

Accurately inferring underlying electrophysiological (EP) tissue properties from action potential recordings is expected to be clinically useful in the diagnosis and treatment of arrhythmias such as atrial fibrillation. It is, however, notoriously difficult to perform. We present EP-PINNs (Physics Informed Neural Networks), a novel tool for accurate action potential simulation and EP parameter estimation from sparse amounts of EP data.

Automatic Diagnosis Labeling of Cardiovascular MRI by Using Semisupervised Natural Language Processing of Text Reports.

Purpose: To assess whether the semisupervised natural language processing (NLP) of text from clinical radiology reports could provide useful automated diagnosis categorization for ground truth labeling to overcome manual labeling bottlenecks in the machine learning pipeline.

Materials And Methods: In this retrospective study, 1503 text cardiac MRI reports from 2016 to 2019 were manually annotated for five diagnoses by clinicians: normal, dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy, myocardial infarction (MI), and myocarditis. A semisupervised method that uses bidirectional encoder representations from transformers (BERT) pretrained on 1.

An Absorbing Improvement for Space Infection Decompression: A Novel Drainage Device.

Introduction: Infection of the facial spaces and the associated exudate can often necessitate surgical intervention. Whilst traditional decompression methodologies have reduced the mortality rate from complications such as Ludwig's Angina, there has been relatively little innovation in the procedure to minimize treatment times and patient distress. Negative pressure wound therapy, which can yield improvements to treatment time, wound healing and patient experience, has gained traction in abscess treatments in other parts of the body but seen limited adoption in maxillofacial surgeries.

Potential global impacts of alternative dosing regimen and rollout options for the ChAdOx1 nCoV-19 vaccine.

The high efficacy, low cost, and long shelf-life of the ChAdOx1 nCoV-19 vaccine positions it well for use in in diverse socioeconomic settings. Using data from clinical trials, an individual-based model was constructed to predict its 6-month population-level impact. Probabilistic sensitivity analyses evaluated the importance of epidemiological, demographic and logistical factors on vaccine effectiveness.

LA-Net: A Multi-Task Deep Network for the Segmentation of the Left Atrium.

Although atrial fibrillation (AF) is the most common sustained atrial arrhythmia, treatment success for this condition remains suboptimal. Information from magnetic resonance imaging (MRI) has the potential to improve treatment efficacy, but there are currently few automatic tools for the segmentation of the atria in MR images. In the study, we propose a LA-Net, a multi-task network optimised to simultaneously generate left atrial segmentation and edge masks from MRI.

Pediatric Caregiver Behaviors Related to Oral Antibiotic Use.

In a survey of 396 caregivers of children, 119 (30%) reported requesting antibiotics from clinicians and 65 (16%) had stored antibiotics at home. In addition, 47 (12%) reported past or intended nonprescription antibiotic administration; this finding was associated with household income of ≥$75 000 annually (odds ratio 2.042, 95% confidence interval 1.

Automated Identification of Orthopedic Implants on Radiographs Using Deep Learning.

Accurate identification of metallic orthopedic implant design is important for preoperative planning of revision arthroplasty. Surgical records of implant models are frequently unavailable. The aim of this study was to develop and evaluate a convolutional neural network for identifying orthopedic implant models using radiographs.