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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.

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.

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.

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.

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.

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. A deep chemical proteomic investigation of diverse aminophilic electrophiles has identified ligandable lysines across a wide range of human proteins. The proteins cover different functional and structural classes, and the aminophilic electrophiles include compounds that disrupt protein–protein and protein–RNA interactions. This dataset provides a proteome-wide atlas of lysine-reactive chemistry.

We present a case of pervasive refusal syndrome (PRS), in which a 11-year-old female patient refused to eat, drink, interact, or self-care in any way. The patient was kept on IV nutrition and multiple therapeutic options were tried to no effect. After 18 months, the patient spontaneously recovered with no lasting harm, and her symptoms did not recur. We further discuss the current state of clinical knowledge on PRS, as well as the form that has appeared amongst children in asylum-seeking families in Sweden.

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.

International audience; 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 β-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.

ObjectivesLung 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.DesignA 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.SettingTwo tertiary Canadian hospitals.Participants612 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).ResultsThe 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), pConclusionsA 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.

ObjectivesLung 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 techniques to match or exceed human-level, diagnostic specificity among similar appearing, pathological LUS images.DesignA convolutional neural network was trained on LUS images with B lines of different etiologies. CNN diagnostic performance, as validated using a 10% data holdback set was compared to surveyed LUS-competent physicians.SettingTwo tertiary Canadian hospitals.Participants600 LUS videos (121,381 frames) of B lines from 243 distinct patients with either 1) COVID-19, Non-COVID acute respiratory distress syndrome (NCOVID) and 3) Hydrostatic pulmonary edema (HPE).ResultsThe trained CNN performance on the independent dataset showed an ability to discriminate between COVID (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.ConclusionsA deep learning 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 multi-center research is merited.

The article presents a case of a 32-year-old woman who has been experiencing episodes of musical obsessions for the past 12 years. The case highlights the importance of addressing stuck song syndrome due to its effects on the functioning of the patient. The psychopathological expressions of this condition in relation to obsessive-compulsive disorder (OCD) are described. The discussion provides distinction between the differential diagnoses of palinacousis, musical hallucinations, and musical obsessions. Lastly, the recognized treatment options for musical obsessions are explored.

We report clinical details and imaging findings of a case of a giant pseudocyst occurring in the anterior abdominal wall of a 61-year-old female. This was a late complication following the repair of an incisional hernia with mesh. Surgical excision revealed a well-encapsulated pseudocyst with histopathology confirming absence of epithelial cells. We further discuss current treatment methods for incisional hernias, as well as prevalence, etiology, and management of pseudocysts complicating hernioplasty.

We present a case of superior mesenteric artery syndrome in a 14-year-old female. The patient was initially managed using dietary changes and postprandial assumption of the left lateral decubitus position, however, after two months with no improvement, the patient was treated using a robot-assisted Strong’s procedure. The patient fully recovered within one year. We further discuss superior mesenteric artery syndrome and its current treatment practices, and give a general overview of the current state of robot-assisted surgery.

This article presents a previously reported case of a 44-year-old woman receiving opioids for malignancy-related pain who presented with increasing pain symptoms. After a detailed examination of the patient’s medical history, the pain was determined to be unrelated to her cancer and opioid-induced hyperalgesia (OIH) was diagnosed. The patient was eventually treated through a gradual tapering of opioids, in addition to complementary therapy and counseling. Following the case description, a concise review of the pertinent literature on OIH is conducted, emphasizing distinguishing factors between OIH and other similar syndromes as well as potential molecular targets. Finally, a brief summary of current recommendations is provided, although further research into this area is required to better evaluate the significance and treatment of OIH.

Giardiais 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. intestinalisandG. 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 understandGiardiain 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 byGiardiais 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 ofGiardia.