2947 Derangements of the blood-brain barrier (BBB) or blood-retinal barrier (BRB) occur in disorders ranging from stroke, cancer, diabetic retinopathy, and Alzheimer’s disease. The Norrin/FZD4... Show more2947 Derangements of the blood-brain barrier (BBB) or blood-retinal barrier (BRB) occur in disorders ranging from stroke, cancer, diabetic retinopathy, and Alzheimer’s disease. The Norrin/FZD4/TSPAN12 pathway activates WNT/β-catenin signaling, which is essential for BBB and BRB function. However, systemic pharmacologic FZD4 stimulation is hindered by obligate palmitoylation and insolubility of native WNTs and suboptimal properties of the FZD4-selective ligand Norrin. Here, we develop L6-F4-2, a non-lipidated, FZD4-specific surrogate which significantly improves subpicomolar affinity versus native Norrin. In Norrin knockout (NdpKO) mice, L6-F4-2 not only potently reverses neonatal retinal angiogenesis deficits, but also restores BRB and BBB function. In adult C57Bl/6J mice, post-stroke systemic delivery of L6-F4-2 strongly reduces BBB permeability, infarction, and edema, while improving neurologic score and capillary pericyte coverage. Our findings reveal systemic efficacy of a bioengineered FZD4-selective WNT surrogate during ischemic BBB dysfunction, with potential applicability to adult CNS disorders characterized by an aberrant blood-brain barrier. Show less
Laumer, F.; Vece, D. di; Cammann, V.L.; Wurdinger, M.; Petkova, V.; Schonberger, M.; ... ; Templin, C. 2022
IMPORTANCE Machine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in... Show moreIMPORTANCE Machine learning algorithms enable the automatic classification of cardiovascular diseases based on raw cardiac ultrasound imaging data. However, the utility of machine learning in distinguishing between takotsubo syndrome (TTS) and acute myocardial infarction (AMI) has not been studied.Objectives To assess the utility of machine learning systems for automatic discrimination of TTS and AMI.Design, Settings, and Participants This cohort study included clinical data and transthoracic echocardiogram results of patients with AMI from the Zurich Acute Coronary Syndrome Registry and patients with TTS obtained from 7 cardiovascular centers in the International Takotsubo Registry. Data from the validation cohort were obtained from April 2011 to February 2017. Data from the training cohort were obtained from March 2017 to May 2019. Data were analyzed from September 2019 to June 2021.Exposure Transthoracic echocardiograms of 224 patients with TTS and 224 patients with AMI were analyzed.Main Outcomes and Measures Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the machine learning system evaluated on an independent data set and 4 practicing cardiologists for comparison. Echocardiography videos of 228 patients were used in the development and training of a deep learning model. The performance of the automated echocardiogram video analysis method was evaluated on an independent data set consisting of 220 patients. Data were matched according to age, sex, and ST-segment elevation/non-ST-segment elevation (1 patient with AMI for each patient with TTS). Predictions were compared with echocardiographic-based interpretations from 4 practicing cardiologists in terms of sensitivity, specificity, and AUC calculated from confidence scores concerning their binary diagnosis.Results In this cohort study, apical 2-chamber and 4-chamber echocardiographic views of 110 patients with TTS (mean [SD] age, 68.4 [12.1] years; 103 [90.4%] were female) and 110 patients with AMI (mean [SD] age, 69.1 [12.2] years; 103 [90.4%] were female) from an independent data set were evaluated. This approach achieved a mean (SD) AUC of 0.79 (0.01) with an overall accuracy of 74.8 (0.7%). In comparison, cardiologists achieved a mean (SD) AUC of 0.71 (0.03) and accuracy of 64.4 (3.5%) on the same data set. In a subanalysis based on 61 patients with apical TTS and 56 patients with AMI due to occlusion of the left anterior descending coronary artery, the model achieved a mean (SD) AUC score of 0.84 (0.01) and an accuracy of 78.6 (1.6%), outperforming the 4 practicing cardiologists (mean [SD] AUC, 0.72 [0.02]) and accuracy of 66.9 (2.8%).Conclusions and Relevance In this cohort study, a real-time system for fully automated interpretation of echocardiogram videos was established and trained to differentiate TTS from AMI. While this system was more accurate than cardiologists in echocardiography-based disease classification, further studies are warranted for clinical application. Show less
The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to... Show moreThe genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-19(1,2), host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases(3-7). They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease. Show less
Research suggests that job stability and job quality are vital in enhancing the crime suppression effects of employment. Unfortunately, with the erosion of the manufacturing economy and the... Show moreResearch suggests that job stability and job quality are vital in enhancing the crime suppression effects of employment. Unfortunately, with the erosion of the manufacturing economy and the increase in the service-dominated economy, offenders who are typically on the margins of society, are pushed towards the informal economy now more than before. The current study attends to the relationship between informal work and crime by analyzing data from the Pathways to Desistance study. Results from fixed effects linear probability models show that informal work is associated with a higher probability of engaging in expressive crimes, but not instrumental crimes. Neither informal nor formal work arrangements seem to work as crime suppressants, but informal arrangements appear more criminogenic. Show less
