Improving the Generalizability and Performance of an Ultrasound Deep Learning Model Using Limited Multicenter Data for Lung Sliding Artifact Identification.

Deep learning (DL) models for medical image classification frequently struggle to generalize to data from outside institutions. Additional clinical data are also rarely collected to comprehensively assess and understand model performance amongst subgroups. Following the development of a single-center model to identify the lung sliding artifact on lung ultrasound (LUS), we pursued a validation strategy using external LUS data.

Assessing Squarates as Amine-Reactive Probes.

Probes that covalently label protein targets facilitate the identification of ligand-binding sites. Lysine residues are prevalent in the proteome, making them attractive substrates for covalent probes. However, identifying electrophiles that undergo amine-specific, regioselective reactions with binding site lysine residues is challenging.

Validity of constructed-response situational judgment tests in training programs for the health professions: A systematic review and meta-analysis protocol.

Background: Situational judgments tests have been increasingly used to help training programs for the health professions incorporate professionalism attributes into their admissions process. While such tests have strong psychometric properties for testing professional attributes and are feasible to implement in high-volume, high-stakes selection, little is known about constructed-response situational judgment tests and their validity.

Methods: We will conduct a systematic review of primary published or unpublished studies reporting on the association between scores on constructed-response situational judgment tests and scores on other tests that measure personal, interpersonal, or professional attributes in training programs for the health professions.

Prospective Real-Time Validation of a Lung Ultrasound Deep Learning Model in the ICU.

Objectives: To evaluate the accuracy of a bedside, real-time deployment of a deep learning (DL) model capable of distinguishing between normal (A line pattern) and abnormal (B line pattern) lung parenchyma on lung ultrasound (LUS) in critically ill patients.

Design: Prospective, observational study evaluating the performance of a previously trained LUS DL model. Enrolled patients received a LUS examination with simultaneous DL model predictions using a portable device.

Enhancing Annotation Efficiency with Machine Learning: Automated Partitioning of a Lung Ultrasound Dataset by View.

Background: Annotating large medical imaging datasets is an arduous and expensive task, especially when the datasets in question are not organized according to deep learning goals. Here, we propose a method that exploits the hierarchical organization of annotating tasks to optimize efficiency.

Methods: We trained a machine learning model to accurately distinguish between one of two classes of lung ultrasound (LUS) views using 2908 clips from a larger dataset.

Accurate assessment of the lung sliding artefact on lung ultrasonography using a deep learning approach.

Pneumothorax is a potentially life-threatening condition that can be rapidly and accurately assessed via the lung sliding artefact generated using lung ultrasound (LUS). Access to LUS is challenged by user dependence and shortage of training. Image classification using deep learning methods can automate interpretation in LUS and has not been thoroughly studied for lung sliding.

Automation of Lung Ultrasound Interpretation via Deep Learning for the Classification of Normal versus Abnormal Lung Parenchyma: A Multicenter Study.

Lung ultrasound (LUS) is an accurate thoracic imaging technique distinguished by its handheld size, low-cost, and lack of radiation. User dependence and poor access to training have limited the impact and dissemination of LUS outside of acute care hospital environments. Automated interpretation of LUS using deep learning can overcome these barriers by increasing accuracy while allowing point-of-care use by non-experts.

Subway-related trauma at a level 1 trauma centre in Toronto, Ontario.

Background: Given the rising prevalence of subways in combination with an increasing incidence of subway-related injuries, understanding subway-related trauma is becoming ever more relevant. The aim of this study was to characterize the potential causes, injury characteristics and outcomes of subway-related trauma at a level 1 adult trauma centre in Toronto, Ontario.

Methods: We conducted a retrospective cohort study to identify patients who presented to the emergency department a level 1 adult trauma centre with a subway-related injury between Jan.

Distinguishing Galactoside Isomers with Mass Spectrometry and Gas-Phase Infrared Spectroscopy.

Sequencing glycans is demanding due to their structural diversity. Compared to mammalian glycans, bacterial glycans pose a steeper challenge because they are constructed from a larger pool of monosaccharide building blocks, including pyranose and furanose isomers. Though mammalian glycans incorporate only the pyranose form of galactose (Gal), many pathogens, including and , contain galactofuranose (Gal) residues in their cell envelope.

Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study.

Objectives: Lung ultrasound (LUS) is a portable, low-cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning (DL) techniques to match or exceed human-level, diagnostic specificity among similar appearing, pathological LUS images.

Design: A convolutional neural network (CNN) was trained on LUS images with B lines of different aetiologies.

Autopsy-reported cause of death in a population-based cohort of sudden unexpected death in epilepsy.

Objective: Sudden unexpected death in epilepsy (SUDEP) is a diagnosis of exclusion; the definition includes individuals with epilepsy who die suddenly without an identifiable toxicological or anatomical cause of death. Limited data suggest underidentification of SUDEP as the cause of death on death certificates. Here, we evaluate the autopsy-reported cause of death in a population-based cohort of SUDEP cases.

Comparisons of Four Protein-Binding Models Characterizing the Pharmacokinetics of Unbound Phenytoin in Adult Patients Using Non-Linear Mixed-Effects Modeling.

Background And Objective: Phenytoin is extensively protein bound with a narrow therapeutic range. The unbound phenytoin is pharmacologically active, but total concentrations are routinely measured in clinical practice. The relationship between free and total phenytoin has been described by various binding models with inconsistent findings.

In a Time of Need: A Grassroots Initiative in Response to PPE Shortage in the COVID-19 Pandemic.

Setting: Primary care is the first line of defence in healthcare, particularly during the coronavirus disease 2019 (COVID-19) pandemic. In the London-Middlesex region of Ontario, a critical shortage of personal protective equipment (PPE) was identified among primary care physicians (PCPs).

Intervention: With the help of the London-Middlesex Primary Care Alliance, volunteer administrators, physicians and medical students coordinated the acquisition and redistribution of community-donated PPE to PCPs across London-Middlesex.

Drowning in epilepsy: A population-based case series.

Objectives: The risk of drowning is reported to be 15-19 times greater in people with epilepsy compared to the general population. Despite this disproportionate burden, there is limited data about the circumstances surrounding drowning deaths in people with epilepsy. This population-based case series characterizes drowning deaths in people with epilepsy.

Giardia spp. Are Commonly Found in Mixed Assemblages in Surface Water, as Revealed by Molecular and Whole-Genome Characterization.

Giardia is the most common parasitic cause of gastrointestinal infections worldwide, with transmission through surface water playing an important role in various parts of the world. Giardia duodenalis (synonyms: G. intestinalis and G.