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Background Rhabdomyolysis, the breakdown of skeletal muscles following an insult or injury, has been established as a possible complication of SARS-CoV-2 infection. Despite being highly effective in preventing COVID-19-related morbidity and mortality, several cases of COVID-19 mRNA vaccination-induced rhabdomyolysis have been identified. We provide the second description of a pediatric case of severe rhabdomyolysis presenting after COVID-19 mRNA vaccination. Case: diagnosis/treatment A 16-year-old male reported to the emergency department with a 2-day history of bilateral upper extremity myalgias and dark urine 2 days after his first dose of COVID-19 vaccine (Pfizer-BioNtech). The initial blood work showed an elevated creatinine kinase (CK) of 141,300 units/L and a normal creatinine of 69 umol/L. The urinalysis was suggestive of myoglobinuria, with the microscopy revealing blood but no red blood cells. Rhabdomyolysis was diagnosed, and the patient was admitted for intravenous hydration, alkalinization of urine, and monitoring of kidney function. CK levels declined with supportive care, while his kidney function remained normal, and no electrolyte abnormalities developed. The patient was discharged 5 days after admission as his symptoms resolved. Conclusion While vaccination is the safest and most effective way to prevent morbidity from COVID-19, clinicians should be aware that rhabdomyolysis could be a rare but treatable adverse event of COVID-19 mRNA vaccination. With early recognition and diagnosis and supportive management, rhabdomyolysis has an excellent prognosis.

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Aim Obesity has been associated with changes in autophagy and its increasing prevalence among pregnant women is implicated in higher rates of placental‐mediated complications of pregnancy such as pre‐eclampsia and intrauterine growth restriction. Autophagy is involved in normal placentation, thus changes in autophagy may lead to impaired placental function and development. The aim of this study was to investigate the connection between obesity and autophagy in the placenta in otherwise uncomplicated pregnancies. Methods Immunohistochemistry and western blot analysis were done on placental and omental samples from obese (body mass index [BMI] ≥30 kg/m2) and normal weight (BMI <25 kg/m2) pregnant women with singleton pregnancies undergoing planned Caesarean delivery without labor at term. Samples were analyzed for autophagic markers LC3B and p62 in the peripheral, middle and central regions of the placenta and in omental adipocytes, milky spots and vasculature. Results As pre‐pregnancy BMI increased, there was an increase in both placental and fetal weight as well as decreased levels of LC3B in the central region of the placenta (P = 0.0046). Within the obese patient group, LC3B levels were significantly decreased in the placentas of male fetuses compared to females (P < 0.0001). Adipocytes, compared to milky spots and vasculature, had lower levels of p62 (P = 0.0127) and LC3B (P = 0.003) in obese omenta and lower levels of LC3B in control omenta (P = 0.0071). Conclusion Obesity leads to reduced placental autophagy in uncomplicated pregnancies; thus, changes in autophagy may be involved in the underlying mechanisms of obesity‐related placental diseases of pregnancy.

Background: Cardiovascular disease is a major cause of morbidity and mortality in patients with end-stage kidney disease. Arterio-venous fistulas (AVF), the gold standard for hemodialysis vascular access, are known to alter cardiac morphology and circulatory hemodynamics. We present a prospective case series of patients after creation of an AVF, explore the timeline for changes in their cardiac morphology, and detail considerations for clinicians. Methods: Patients were recruited in 2010 at multiple centers immediately prior to the creation of an upper-arm AVF and the initiation of hemodialysis. Cardiovascular magnetic resonance images were taken at intake before the creation of the AVF, 6-month follow-up, and 12-month follow-up. Image segmentation was used to measure left ventricular volume and mass, left atrial volume, and ejection fraction. Results: Eight patients met eligibility criteria. All eight patients had a net increase in left ventricular mass over enrollment, with a mean increase of 9.16 g (+2.96 to +42.66 g). Five participants had a net decrease in ejection fraction, with a mean change in ejection fraction of −5.4% (−21% to +5%). Upon visual inspection the patients with the largest ejection fraction decrease had noticeably hypertrophic and dilated ventricles. Left atrial volume change was varied, decreasing in five participants, while increasing in three participants. Changes in morphology were present at 6-month follow-up, even in patients who did not maintain AVF patency for the entirety of the 6-month period. Conclusion: All patients included in this prospective case series had increases in left ventricular mass, with variability in the effects on the ejection fraction and left atrial volume. As left ventricular mass is an independent predictor of morbidity and mortality, further research to determine appropriate vascular access management in both end-stage kidney disease and kidney transplant populations is warranted.

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Background Emerging artificial intelligence (AI) technologies have diverse applications in medicine. As AI tools advance towards clinical implementation, skills in how to use and interpret AI in a healthcare setting could become integral for physicians. This study examines undergraduate medical students’ perceptions of AI, educational opportunities about of AI in medicine, and the desired medium for AI curriculum delivery. Methods A 32 question survey for undergraduate medical students was distributed from May–October 2021 to students to all 17 Canadian medical schools. The survey assessed the currently available learning opportunities about AI, the perceived need for learning opportunities about AI, and barriers to educating about AI in medicine. Interviews were conducted with participants to provide narrative context to survey responses. Likert scale survey questions were scored from 1 (disagree) to 5 (agree). Interview transcripts were analyzed using qualitative thematic analysis. Results We received 486 responses from 17 of 17 medical schools (roughly 5% of Canadian undergraduate medical students). The mean age of respondents was 25.34, with 45% being in their first year of medical school, 27% in their 2nd year, 15% in their 3rd year, and 10% in their 4th year. Respondents agreed that AI applications in medicine would become common in the future (94% agree) and would improve medicine (84% agree Further, respondents agreed that they would need to use and understand AI during their medical careers (73% agree; 68% agree), and that AI should be formally taught in medical education (67% agree). In contrast, a significant number of participants indicated that they did not have any formal educational opportunities about AI (85% disagree) and that AI-related learning opportunities were inadequate (74% disagree). Interviews with 18 students were conducted. Emerging themes from the interviews were a lack of formal education opportunities and non-AI content taking priority in the curriculum. Conclusion A lack of educational opportunities about AI in medicine were identified across Canada in the participating students. As AI tools are currently progressing towards clinical implementation and there is currently a lack of educational opportunities about AI in medicine, AI should be considered for inclusion in formal medical curriculum.