Citations

Abstract Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that affects motor neurons, causing progressive muscle weakness and respiratory failure. The presence of an expanded hexanucleotide repeat in chromosome 9 open reading frame 72 (C9ORF72) accounts for most cases of familial ALS and frontotemporal dementia (FTD). To determine if suppressing expression of C9ORF72 gene products can reduce toxicity, we designed a set of artificial microRNAs (amiRNA) targeting the human C9ORF72 gene. Here we report that an AAV9-mediated amiRNA significantly suppresses expression of the C9ORF72 mRNA, protein, and toxic poly-dipeptides generated by the expanded repeat in the brain and spinal cord.

Background Cardiac troponin (cTn) can be elevated in many patients presenting to the emergency department (ED) with chest pain but without a diagnosis of acute coronary syndrome (ACS). We compared the prognostic significance of cTn in these different populations. Methods We retrospectively analyzed the CHOPIN study, which enrolled patients who presented to the ED with chest pain. Patients were grouped as ACS, non-ACS cardiovascular disease, noncardiac chest pain and chest pain not otherwise specified (NOS). We examined the prognostic ability of cTnI for the clinical endpoints of mortality and major adverse cardiovascular event (MACE; a composite of acute myocardial infarction, unstable angina, revascularization, reinfarction, and congestive heart failure and stroke) at 180-day follow-up. Results Among 1982 patients analyzed, 14% had ACS, 21% had non-ACS cardiovascular disease, 31% had a noncardiac diagnosis and 34% had chest pain NOS. cTnI elevation above the 99th percentile was observed in 52, 18, 6 and 7% in these groups, respectively. cTnI elevation was associated with mortality and MACE, and their relationships were more prominent in noncardiac diagnosis and chest pain NOS than in ACS and non-ACS cardiovascular diagnoses for mortality, and in non-ACS patients than in ACS patients for MACE (hazard ratio for doubling of cTnI 1.85, 2.05, 8.26 and 4.14, respectively; P for interaction 0.011 for mortality; 1.04, 1.23, 1.54 and 1.42, respectively; P for interaction <0.001 for MACE). Conclusion In patients presenting to the ED with chest pain, cTnI elevation was associated with a worse prognosis in non-ACS patients than in ACS patients.

Abstract Background The literature regarding clinical olfaction, olfactory loss, and olfactory dysfunction has expanded rapidly over the past two decades, with an exponential rise in the past year. There is substantial variability in the quality of this literature and a need to consolidate and critically review the evidence. It is with that aim that we have gathered experts from around the world to produce this International Consensus on Allergy and Rhinology: Olfaction (ICAR:O). Methods Using previously described methodology, specific topics were developed relating to olfaction. Each topic was assigned a literature review, evidence‐based review, or evidence‐based review with recommendations format as dictated by available evidence and scope within the ICAR:O document. Following iterative reviews of each topic, the ICAR:O document was integrated and reviewed by all authors for final consensus. Results The ICAR:O document reviews nearly 100 separate topics within the realm of olfaction, including diagnosis, epidemiology, disease burden, diagnosis, testing, etiology, treatment, and associated pathologies. Conclusion This critical review of the existing clinical olfaction literature provides much needed insight and clarity into the evaluation, diagnosis, and treatment of patients with olfactory dysfunction, while also clearly delineating gaps in our knowledge and evidence base that we should investigate further.

Introduction: Copeptin has demonstrated a role in early rule out for acute myocardial infarction in combination with a negative troponin. However, the value of copeptin in patients with a positive troponin is not established. Hypothesis: The addition of a baseline copeptin at chest pain presentation will improve risk stratification in patients with mild troponin elevation. Methods: The multi-center CHOPIN trial enrolled 2071 acute chest pain patients. All subjects with ST segment elevations were excluded from this analysis. Of the remaining patients, 124 had a mildly elevated troponin (defined by < 2x URL) and were included in the study cohort. Baseline cTnI and copeptin levels were drawn on presentation, and another cTnI at 2 hours. Copeptin ≤14 pmol/l and troponin deltas ≤10% from baseline were considered positive. Two independent blinded cardiologists adjudicated AMI diagnosis. The value of a baseline copeptin in this cohort was assessed using AMI incidence, odds ratio, sensitivity, and negative predictive value (NPV). Results: Of the 124 patients in the study cohort, 73 (59%) had an elevated copeptin and an associated AMI incidence of 25% (18 AMIs). The remaining 51 patients (41%) were copeptin negative and diagnosed with 3 AMIs, an incidence of 5.9% (p = 0.006, figure 1). A positive copeptin increased the likelihood of AMI diagnosis with an odds ratio of 5.3 (95% CI: 1.5-19.2). The sensitivity and NPV for AMI were 86% and 94%, both of which were higher than the delta troponin (table 1). When an initial copeptin was combined with the delta troponin the sensitivity and NPV for AMI improved to 100%. Conclusion: Use of copeptin in chest pain patients with mild troponin elevation provides further risk stratification. The combination of copeptin with a delta troponin may aid in earlier AMI rule out in this subset of patients. This approach could be especially relevant as the increasing use of high sensitivity troponin assays leads to greater rates of mild troponin elevations.

Abstract The coronavirus disease 2019 (COVID-19) pandemic has increased awareness that severe acute respiratory distress syndrome coronavirus-2 (SARS-CoV-2) may have profound effects on the cardiovascular system. COVID-19 often affects patients with pre-existing cardiac disease, and may trigger acute respiratory distress syndrome (ARDS), venous thromboembolism (VTE), acute myocardial infarction (AMI), and acute heart failure (AHF). However, as COVID-19 is primarily a respiratory infectious disease, there remain substantial uncertainty and controversy whether and how cardiovascular biomarkers should be used in patients with suspected COVID-19. To help clinicians understand the possible value as well as the most appropriate interpretation of cardiovascular biomarkers in COVID-19, it is important to highlight that recent findings regarding the prognostic role of cardiovascular biomarkers in patients hospitalized with COVID-19 are similar to those obtained in studies for pneumonia and ARDS in general. Cardiovascular biomarkers reflecting pathophysiological processes involved in COVID-19/pneumonia and its complications have a role evaluating disease severity, cardiac involvement, and risk of death in COVID-19 as well as in pneumonias caused by other pathogens. First, cardiomyocyte injury, as quantified by cardiac troponin concentrations, and haemodynamic cardiac stress, as quantified by natriuretic peptide concentrations, may occur in COVID-19 as in other pneumonias. The level of those biomarkers correlates with disease severity and mortality. Interpretation of cardiac troponin and natriuretic peptide concentrations as quantitative variables may aid in risk stratification in COVID-19/pneumonia and also will ensure that these biomarkers maintain high diagnostic accuracy for AMI and AHF. Second, activated coagulation as quantified by D-dimers seems more prominent in COVID-19 as in other pneumonias. Due to the central role of endothelitis and VTE in COVID-19, serial measurements of D-dimers may help physicians in the selection of patients for VTE imaging and the intensification of the level of anticoagulation from prophylactic to slightly higher or even therapeutic doses.

Abstract The formation of a thrombus in an epicardial artery may result in an acute myocardial infarction (AMI). Despite major advances in acute treatment using network approaches to allocate patients to timely reperfusion and optimal antithrombotic treatment, patients remain at high risk for thrombotic complications. Ongoing activation of the coagulation system as well as thrombin-mediated platelet activation may both play a crucial role in this context. Whether measurement of circulating biomarkers of coagulation and fibrinolysis could be useful for risk stratification in secondary prevention is currently not fully understood. In addition, measurement of such biomarkers could be helpful to identify thrombus formation as the leading mechanism for AMI. The introduction of biomarkers of myocardial injury such as high-sensitivity cardiac troponins made rule-out of AMI even more precise. However, elevated markers of myocardial injury cannot provide proof of a type 1 AMI, let alone thrombus formation. The combined measurement of markers of myocardial injury with biomarkers reflecting ongoing thrombus formation might be helpful for the fast and correct diagnosis of an atherothrombotic type 1 AMI. This position paper gives an overview of the current knowledge and possible role of biomarkers of coagulation and fibrinolysis for the diagnosis of AMI, risk stratification, and individualized treatment strategies in patients with AMI.

Background: The observed incidence of type 2 myocardial infarction (T2MI) is expected to increase with the implementation of increasingly sensitive cTn assays. However, it remains to be determined how to diagnose, risk-stratify, and treat patients with T2MI. We aimed to discriminate and risk-stratify T2MI using biomarkers. Methods: Patients presenting to the emergency department with chest pain, enrolled in the CHOPIN study (Copeptin Helps in the early detection Of Patients with acute myocardial INfarction), were retrospectively analyzed. Two cardiologists adjudicated type 1 MI (T1MI) and T2MI. The prognostic ability of several biomarkers alone or in combination to discriminate T2MI from T1MI was investigated using receiver operating characteristic curve analysis. The biomarkers analyzed were cTnI, copeptin, MR-proANP (midregional proatrial natriuretic peptide), CT-proET1 (C-terminal proendothelin-1), MR-proADM (midregional proadrenomedullin), and procalcitonin. The prognostic utility of these biomarkers for all-cause mortality and major adverse cardiovascular event (a composite of acute myocardial infarction, unstable angina pectoris, reinfarction, heart failure, and stroke) at 180-day follow-up was also investigated. Results: Among the 2071 patients, T1MI and T2MI were adjudicated in 94 and 176 patients, respectively. Patients with T1MI had higher levels of baseline cTnI, whereas those with T2MI had higher baseline levels of MR-proANP, CT-proET1, MR-proADM, and procalcitonin. The area under the receiver operating characteristic curve for the diagnosis of T2MI was higher for CT-proET1, MR-proADM, and MR-proANP (0.765, 0.750, and 0.733, respectively) than for cTnI (0.631). Combining all biomarkers resulted in a similar accuracy to a model using clinical variables and cTnI (0.854 versus 0.884, P =0.294). Addition of biomarkers to the clinical model yielded the highest area under the receiver operating characteristic curve (0.917). Other biomarkers, but not cTnI, were associated with mortality and major adverse cardiovascular event at 180 days among all patients, with no interaction between the diagnosis of T1MI or T2MI. Conclusions: Assessment of biomarkers reflecting pathophysiologic processes occurring with T2MI might help differentiate it from T1MI. All biomarkers measured, except cTnI, were significant predictors of prognosis, regardless of the type of myocardial infarction.

Background: Variations in cardiac troponin concentrations by age, sex, and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients. Methods: A machine learning algorithm (myocardial-ischemic-injury-index [MI 3 ]) incorporating age, sex, and paired high-sensitivity cardiac troponin I concentrations, was trained on 3013 patients and tested on 7998 patients with suspected myocardial infarction. MI 3 uses gradient boosting to compute a value (0–100) reflecting an individual’s likelihood of a diagnosis of type 1 myocardial infarction and estimates the sensitivity, negative predictive value, specificity and positive predictive value for that individual. Assessment was by calibration and area under the receiver operating characteristic curve. Secondary analysis evaluated example MI 3 thresholds from the training set that identified patients as low risk (99% sensitivity) and high risk (75% positive predictive value), and performance at these thresholds was compared in the test set to the 99th percentile and European Society of Cardiology rule-out pathways. Results: Myocardial infarction occurred in 404 (13.4%) patients in the training set and 849 (10.6%) patients in the test set. MI 3 was well calibrated with a very high area under the receiver operating characteristic curve of 0.963 [0.956–0.971] in the test set and similar performance in early and late presenters. Example MI 3 thresholds identifying low- and high-risk patients in the training set were 1.6 and 49.7, respectively. In the test set, MI 3 values were <1.6 in 69.5% with a negative predictive value of 99.7% (99.5–99.8%) and sensitivity of 97.8% (96.7–98.7%), and were ≥49.7 in 10.6% with a positive predictive value of 71.8% (68.9–75.0%) and specificity of 96.7% (96.3–97.1%). Using these thresholds, MI 3 performed better than the European Society of Cardiology 0/3-hour pathway (sensitivity, 82.5% [74.5–88.8%]; specificity, 92.2% [90.7–93.5%]) and the 99th percentile at any time point (sensitivity, 89.6% [87.4–91.6%]); specificity, 89.3% [88.6–90.0%]). Conclusions: Using machine learning, MI 3 provides an individualized and objective assessment of the likelihood of myocardial infarction, which can be used to identify low- and high-risk patients who may benefit from earlier clinical decisions. Clinical Trial Registration: URL: https://www.anzctr.org.au . Unique identifier: ACTRN12616001441404.