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. Squarates can engage in two sequential conjugate addition-elimination reactions with amines. Nitrogen donation reduces the second reaction rate, making the mono squaramide a mild electrophile. We postulated that this mild electrophilicity would demand a longer residence time near the amine, affording higher selectivity for binding site lysines. Therefore, we compared the kinetics of squarate and monosquaramide amine substitution to alternative amine bioconjugation handles. The data revealed that N-hydroxy succinimidyl esters react 4 orders of magnitude faster, consistent with their labeling promiscuity. Squarate reactivity can be tuned by a substitution pattern. Electron-withdrawing groups on the vinylogous ester or amide increase reaction rates. Dithionosquarates react more rapidly than squarates, while vinylogous thioester analogs, dithiosquarates, react more slowly. We assessed squarate selectively using the UDP-sugar processing enzyme GlfT2 from Mycobacterium tuberculosis, which possesses 21 surface-exposed lysines. The reaction predominately modified one lysine proximal to a binding site to afford covalent inhibition. These findings demonstrate the selectivity of squaric esters and squaramides, which is a critical feature for affinity-based chemoproteomic probes.

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. Clip-level model predictions were analyzed and compared with blinded expert review for A versus B line pattern. Four prediction thresholding approaches were applied to maximize model sensitivity and specificity at bedside. SETTING: Academic ICU. PATIENTS: One-hundred critically ill patients admitted to ICU, receiving oxygen therapy, and eligible for respiratory imaging were included. Patients who were unstable or could not undergo an LUS examination were excluded. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 100 unique ICU patients (400 clips) were enrolled from two tertiary-care sites. Fifty-six patients were mechanically ventilated. When compared with gold standard expert annotation, the real-time inference yielded an accuracy of 95%, sensitivity of 93%, and specificity of 96% for identification of the B line pattern. Varying prediction thresholds showed that real-time modification of sensitivity and specificity according to clinical priorities is possible. CONCLUSIONS: A previously validated DL classification model performs equally well in real-time at the bedside when platformed on a portable device. As the first study to test the feasibility and performance of a DL classification model for LUS in a dedicated ICU environment, our results justify further inquiry into the impact of employing real-time automation of medical imaging into the care of the critically ill.

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 (Galp), many pathogens, including Mycobacterium tuberculosis and Klebsiella pneumoniae, contain galactofuranose (Galf) residues in their cell envelope. Thus, glycan sequencing would benefit from methods to distinguish between pyranose and furanose isomers of different anomeric configurations. We used infrared multiple photon dissociation (IRMPD) spectroscopy with mass spectrometry (MS-IR) to differentiate between pyranose- and furanose-linked galactose residues. These targets pose a challenge for MS-IR because the saccharides lack basic groups, and galactofuranose residues are highly flexible. We postulated cationic groups that could complex through hydrogen bonding would offer a solution. Here, we present the first MS-IR analysis of hexose ammonium adducts. We compared their IR fingerprints with those of lithium adducts. We determined the diagnostic MS-IR signatures of the α- and ß-anomers of galactose in furanose and pyranose forms. We also showed these signatures could be applied to disaccharides to assign galactose ring size. Our findings highlight the utility of MS-IR for analyzing the unique substructures that occur in bacterial glycans.

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. METHODS: Case summaries of forensic autopsies conducted in Ontario, Canada between January 2014 and June 2016 were retrospectively screened using a language processing script for decedents with a history of epilepsy or seizures. After manual review for potential SUDEP cases, two neurologists independently examined the autopsy reports and classified deaths by Nashef criteria. Demographic characteristics and consideration by the forensic pathologist of the role of epilepsy, seizure, and SUDEP in death were summarized. RESULTS: One hundred and eight Definite, 34 Definite Plus, and 22 Possible SUDEP cases were identified. Seventy-five percent of Definite/Definite Plus SUDEP cases identified by the neurologists were attributed to SUDEP, epilepsy, or seizure disorder in the autopsy report. There was a significant association between the proportion of cases listed in the autopsy report as SUDEP, epilepsy, or seizure disorder and neurologists' SUDEP classification (86% of Definite, 38% of Definite Plus, 0% of Possible). Age was significantly associated with SUDEP classification; Definite cases were younger than Definite Plus, which were younger than Possible SUDEP cases. SIGNIFICANCE: Most SUDEP cases identified by neurologists were classified concordantly by forensic pathologists in Ontario, Canada; however, concordance decreased with increased case complexity. Although the role of epilepsy/seizures was considered in most Definite/Definite Plus cases, this study highlights the need for autopsy report review of potential SUDEP cases in research studies and assessments of the public health burden of SUDEP. The relationship between age and SUDEP classification has important public health implications; SUDEP incidence may be underappreciated in older adults.

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. 1, 2010, and Dec. 31, 2018. Patients were identified via International Statistical Classification of Diseases and Related Health Problems, 10th Revision E-codes (X81, Y02, V050, V051 and W17). We then further screened for descriptions of subway-related injuries. Patients whose injuries did not involve a moving subway train were excluded. RESULTS: We identified 51 patients who presented to the emergency department after being hit by a moving subway train. The majority of incidents (39 [76%]) were due to self-harm, 10 (20%) were unintentional injuries, and 2 (4%) were due to assault. The presence of alcohol was detected in 8 patients (80%) with unintentional injuries and 3 (8%) of those with self-inflicted injuries. Thirteen patients (25%) had a systolic blood pressure less than 90 mm Hg. The median Injury Severity Score was 17 (interquartile range 9-29). Seventeen patients (33%) presented with severe injuries (Abbreviated Injury Scale score ≥ 3) in 1 body region, and 19 (37%) had severe injuries in 2 or more body regions. The most common isolated severe injury was in the lower extremity, and the most common combinations of severe injuries were in the head and lower extremity, and head and thorax. Ten patients (20%) were declared dead in the emergency department. Of the 41 patients who survived their initial presentation, 12 (29%) went directly to the operating room, and 17 (41%) were transferred to the intensive care unit. The overall mortality rate was 29%. CONCLUSION: Patients with subway-related injuries experienced high mortality rates and severe injuries. Most incidents were due to self-harm or alcohol-related. Further research into early identification of those at risk and optimal prevention strategies is necessary to curb further incidents.

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. Our scope evolved to include PPE reusability and stewardship and PCP wellness. OUTCOME: Beginning on March 16, 2020, our initial four-week operation provided PPE to over 200 PCPs. We received 60 donations, including over 118,000 gloves, 13,700 masks, 700 wellness kits and reusable cloth masks and gowns. Each delivery included educational pamphlets, and our online PPE stewardship session was attended by over 30 physicians. IMPLICATIONS: In response to the PPE shortage in COVID-19, our efforts evolved into a complex adaptive system, supported by an organizational body with a pre-existing communication infrastructure, to great success. Our scope extended beyond simple PPE provision to PCPs. Furthermore, our initiative established a framework for a centralized response to PPE shortage in Ontario Health West.

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. lamblia), a multispecies complex, has two zoonotic subtypes, assemblages A and B. When British Columbia (BC), a western Canadian province, experienced several waterborne giardiasis outbreaks due to unfiltered surface drinking water in the late 1980s, collection of isolates from surface water, as well as from humans and beavers (Castor canadensis), throughout the province was carried out. To better understand Giardia in surface water, 71 isolates, including 29 from raw surface water samples, 29 from human giardiasis cases, and 13 from beavers in watersheds from this historical library were characterized by PCR. Study isolates also included isolates from waterborne giardiasis outbreaks. Both assemblages A and B were identified in surface water, human, and beavers samples, including a mixture of both assemblages A and B in waterborne outbreaks. PCR results were confirmed by whole-genome sequencing (WGS) for one waterborne outbreak and supported the clustering of human, water, and beaver isolates within both assemblages. We concluded that contamination of surface water by Giardia is complex, that the majority of our surface water isolates were assemblage B, and that both assemblages A and B may cause waterborne outbreaks. The higher-resolution data provided by WGS warrants further study to better understand the spread of Giardia.

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. As annotated LUS data are relatively scarce-compared to other medical imaging data-we adopted a novel technique to optimize the use of limited external data to improve model generalizability. Externally acquired LUS data from three tertiary care centers, totaling 641 clips from 238 patients, were used to assess the baseline generalizability of our lung sliding model. We then employed our novel Threshold-Aware Accumulative Fine-Tuning (TAAFT) method to fine-tune the baseline model and determine the minimum amount of data required to achieve predefined performance goals. A subgroup analysis was also performed and Grad-CAM++ explanations were examined. The final model was fine-tuned on one-third of the external dataset to achieve 0.917 sensitivity, 0.817 specificity, and 0.920 area under the receiver operator characteristic curve (AUC) on the external validation dataset, exceeding our predefined performance goals. Subgroup analyses identified LUS characteristics that most greatly challenged the model's performance. Grad-CAM++ saliency maps highlighted clinically relevant regions on M-mode images. We report a multicenter study that exploits limited available external data to improve the generalizability and performance of our lung sliding model while identifying poorly performing subgroups to inform future iterative improvements. This approach may contribute to efficiencies for DL researchers working with smaller quantities of external validation data.

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. In addition to searching electronic databases, we will contact academics and researchers and undertake backward and forward searching. Two reviewers will independently screen the papers and decide on their inclusion, first based on the titles and abstracts of all citations, and then according to the full texts. Data extraction will be done independently by two reviewers using a data extraction form to chart study details and key findings. Studies will be assessed for the risk of bias and quality by two reviewers using the "Quality In Prognosis Studies" tool. To synthesize evidence, we will test the statistical heterogeneity and conduct a psychometric meta-analysis using a random-effects model. If adequate data are available, we will explore whether the meta-analytic correlation varies across different subgroups (e.g., race, gender). DISCUSSION: The findings of this study will inform best practices for admission and selection of applicants for training programs for the health professions and encourage further research on constructed-response situational judgment tests, in particular their validity. TRIAL REGISTRATION: The protocol for this systematic review has been registered in PROSPERO [CRD42022314561]. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022314561.

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. Using a labelled LUS dataset from 2 academic hospitals, clinical B-mode (also known as brightness or two-dimensional mode) videos featuring both presence and absence of lung sliding were transformed into motion (M) mode images. These images were subsequently used to train a deep neural network binary classifier that was evaluated using a holdout set comprising 15% of the total data. Grad-CAM explanations were examined. Our binary classifier using the EfficientNetB0 architecture was trained using 2535 LUS clips from 614 patients. When evaluated on a test set of data uninvolved in training (540 clips from 124 patients), the model performed with a sensitivity of 93.5%, specificity of 87.3% and an area under the receiver operating characteristic curve (AUC) of 0.973. Grad-CAM explanations confirmed the model's focus on relevant regions on M-mode images. Our solution accurately distinguishes between the presence and absence of lung sliding artefacts on LUS.

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. Partitioning the remaining dataset by view would reduce downstream labelling efforts by enabling annotators to focus on annotating pathological features specific to each view. RESULTS: In a sample view-specific annotation task, we found that automatically partitioning a 780-clip dataset by view saved 42 min of manual annotation time and resulted in 55±6 additional relevant labels per hour. CONCLUSIONS: Automatic partitioning of a LUS dataset by view significantly increases annotator efficiency, resulting in higher throughput relevant to the annotating task at hand. The strategy described in this work can be applied to other hierarchical annotation schemes.

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. CNN diagnostic performance, as validated using a 10% data holdback set, was compared with surveyed LUS-competent physicians. SETTING: Two tertiary Canadian hospitals. PARTICIPANTS: 612 LUS videos (121 381 frames) of B lines from 243 distinct patients with either (1) COVID-19 (COVID), non-COVID acute respiratory distress syndrome (NCOVID) or (3) hydrostatic pulmonary edema (HPE). RESULTS: The trained CNN performance on the independent dataset showed an ability to discriminate between COVID (area under the receiver operating characteristic curve (AUC) 1.0), NCOVID (AUC 0.934) and HPE (AUC 1.0) pathologies. This was significantly better than physician ability (AUCs of 0.697, 0.704, 0.967 for the COVID, NCOVID and HPE classes, respectively), p<0.01. CONCLUSIONS: A DL model can distinguish similar appearing LUS pathology, including COVID-19, that cannot be distinguished by humans. The performance gap between humans and the model suggests that subvisible biomarkers within ultrasound images could exist and multicentre research is merited.

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. In this multicenter study, we seek to automate the clinically vital distinction between A line (normal parenchyma) and B line (abnormal parenchyma) on LUS by training a customized neural network using 272,891 labelled LUS images. After external validation on 23,393 frames, pragmatic clinical application at the clip level was performed on 1162 videos. The trained classifier demonstrated an area under the receiver operating curve (AUC) of 0.96 (±0.02) through 10-fold cross-validation on local frames and an AUC of 0.93 on the external validation dataset. Clip-level inference yielded sensitivities and specificities of 90% and 92% (local) and 83% and 82% (external), respectively, for detecting the B line pattern. This study demonstrates accurate deep-learning-enabled LUS interpretation between normal and abnormal lung parenchyma on ultrasound frames while rendering diagnostically important sensitivity and specificity at the video clip level.

A proteome-wide atlas of lysine-reactive chemistry.

Recent advances in chemical proteomics have begun to characterize the reactivity and ligandability of lysines on a global scale. Yet, only a limited diversity of aminophilic electrophiles have been evaluated for interactions with the lysine proteome. Here, we report an in-depth profiling of >30 uncharted aminophilic chemotypes that greatly expands the content of ligandable lysines in human proteins. Aminophilic electrophiles showed disparate proteomic reactivities that range from selective interactions with a handful of lysines to, for a set of dicarboxaldehyde fragments, remarkably broad engagement of the covalent small-molecule-lysine interactions captured by the entire library. We used these latter 'scout' electrophiles to efficiently map ligandable lysines in primary human immune cells under stimulatory conditions. Finally, we show that aminophilic compounds perturb diverse biochemical functions through site-selective modification of lysines in proteins, including protein-RNA interactions implicated in innate immune responses. These findings support the broad potential of covalent chemistry for targeting functional lysines in the human proteome.

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. Systematic comparison of these binding models in a single experimental setting is warranted to determine the optimal binding behaviors. METHODS: Non-linear mixed-effects modeling was conducted on retrospectively collected data (n = 37 adults receiving oral or intravenous phenytoin) using a stochastic approximation expectation-maximization algorithm in MonolixSuite-2019R2. The optimal base structural model was initially developed and utilized to compare four binding models: Winter-Tozer, linear binding, non-linear single-binding site, and non-linear multiple-binding site. Each binding model was subjected to error and covariate modeling. The final model was evaluated using relative standard errors (RSEs), goodness-of-fit plots, visual predictive check, and bootstrapping. RESULTS: A one-compartment, first-order absorption, Michaelis-Menten elimination, and linear protein-binding model best described the population pharmacokinetics of free phenytoin at typical clinical concentrations. The non-linear single-binding-site model also adequately described phenytoin binding but generated larger RSEs. The non-linear multiple-binding-site model performed the worst, with no identified covariates. The optimal linear binding model suggested a relatively high binding capacity using a single albumin site. Covariate modeling indicated a positive relationship between albumin concentration and the binding proportionality constant. CONCLUSIONS: The linear binding model best described the population pharmacokinetics of unbound phenytoin in adult subjects and may be used to improve the prediction of free phenytoin concentrations.