Going down the slippery slope of legitimacy lies in early‑stage ventures: the role of moral disengagement

It would seem, on the surface, logical that entrepreneurs would treat stakeholders with honesty and respect. However, this is not always the case—at times, entrepreneurs lie to stakeholders in order to take a step closer to achieving legitimacy. It is these legitimacy lies that are the focus of the current work. Overall, while we know that legitimacy lies are told, we know very little about the psychological processes at work that may make it more likely for someone to tell a legitimacy lie. Thus, we theorize about the pressure to pursue legitimacy, the situational and individual factors that affect this pursuit, as well as how this context can lead to moral disengagement and the telling of legitimacy lies. Our theorizing advances the existing literature and provides a dynamic framework by which future research can delve more deeply into the nuanced context that breeds the escalation of legitimacy lies

Noninvasive rapid cardiac magnetic resonance for the assessment of cardiomyopathies in low-middle income countries

INTRODUCTION: Cardiac Magnetic Resonance (CMR) is a crucial diagnostic imaging test that redefines diagnosis and enables targeted therapies, but the access to CMR is limited in low-middle Income Countries (LMICs) even though cardiovascular disease is an emergent primary cause of mortality in LMICs. New abbreviated CMR protocols can be less expensive, faster, whilst maintaining accuracy, potentially leading to a higher utilization in LMICs. AREAS COVERED: This article will review cardiovascular disease in LMICs and the current role of CMR in cardiac diagnosis and enable targeted therapy, discussing the main obstacles to prevent the adoption of CMR in LMICs. We will then review the potential utility of abbreviated, cost-effective CMR protocols to improve cardiac diagnosis and care, the clinical indications of the exam, current evidence and future directions. EXPERT OPINION: Rapid CMR protocols, provided that they are utilized in potentially high yield cases, could reduce cost and increase effectiveness. The adoption of these protocols, their integration into care pathways, and prioritizing key treatable diagnoses can potentially improve patient care. Several LMIC countries are now pioneering these approaches and the application of rapid CMR protocols appears to have a bright future if delivered effectively

Alginate hydrogel improves anti-angiogenic bevacizumab activity in cancer therapy

Alginate hydrogel improves anti-angiogenic bevacizumab activity in cancer therapyAnti-vascular endothelial growth factor (anti-VEGF) therapy applied to solid tumors is a promising strategy, yet, the challenge to deliver these agents at high drug concentrations together with the maintenance of therapeutic doses locally, at the tumor site, minimizes its benefits. To overcome these obstacles, we propose the development of a bevacizumab-loaded alginate hydrogel by electrostatic interactions to design a delivery system for controlled and anti-angiogenic therapy under tumor microenvironrnental conditions. The tridimensional hydrogel structure produced provides drug stability and a system able to be introduced as a flowable solution, stablishing a depot after local administration. Biological performance by the chick embryo chorioallantoic membrane (CAM) assay indicated a pH-independent improved anti-angiogenic activity (similar to 50%) compared to commercial available anti-VEGF drug. Moreover, there was a considerable regression in tumor size when treated with this system. Immunohistochemistry highlighted a reduced number and disorganization of microscopic blood vessels resulting from applied therapy. These results suggest that the developed hydrogel is a promising approach to create an innovative delivery system.that offers the possibility to treat different solid tumors by intratumoral administration.Brazilian Fundação de Amparo e Pesquisa do Estado de São Paulo (FAPESP), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). Additionally, this article has been developed under the scope of the project NORTE-01-0145-FEDER-000013, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER) Project PTDC/SAU-TOX/114549/2009 – FCOMP-01-0124-FEDER-016057, through the Competitiveness Factors Operational Programme (COMPETE), and by National funds, through the Foundation for Science and Technology (FCT), under the scope of the project POCI-01-0145-FEDER-007038info:eu-repo/semantics/publishedVersio

Dark blood ischemic LGE segmentation using a deep learning approach

The extent of ischemic scar detected by Cardiac Magnetic Resonance (CMR) with late gadolinium enhancement (LGE) is linked with long-term prognosis, but scar quantification is time-consuming. Deep Learning (DL) approaches appear promising in CMR segmentation. Purpose: To train and apply a deep learning approach to dark blood (DB) CMR-LGE for ischemic scar segmentation, comparing results to 4-Standard Deviation (4-SD) semi-automated method. Methods: We trained and validated a dual neural network infrastructure on a dataset of DB-LGE short-axis stacks, acquired at 1.5T from 33 patients with ischemic scar. The DL architectures were an evolution of the U-Net Convolutional Neural Network (CNN), using data augmentation to increase generalization. The CNNs worked together to identify and segment 1) the myocardium and 2) areas of LGE. The first CNN simultaneously cropped the region of interest (RoI) according to the bounding box of the heart and calculated the area of myocardium. The cropped RoI was then processed by the second CNN, which identified the overall LGE area. The extent of scar was calculated as the ratio of the two areas. For comparison, endo- and epi-cardial borders were manually contoured and scars segmented by a 4-SD technique with a validated software. Results: The two U-Net networks were implemented with two free and open-source software library for machine learning. We performed 5-fold cross-validation over a dataset of 108 and 385 labelled CMR images of the myocardium and scar, respectively. We obtained high performance (> ∼0.85) as measured by the Intersection over Union metric (IoU) on the training sets, in the case of scar segmentation. With regards to heart recognition, the performance was lower (> ∼0.7), although improved (∼ 0.75) by detecting the cardiac area instead of heart boundaries. On the validation set, performances oscillated between 0.8 and 0.85 for scar tissue recognition, and dropped to ∼0.7 for myocardium segmentation. We believe that underrepresented samples and noise might be affecting the overall performances, so that additional data might be beneficial. Figure1: examples of heart segmentation (upper left panel: training; upper right panel: validation) and of scar segmentation (lower left panel: training; lower right panel: validation). Conclusion: Our CNNs show promising results in automatically segmenting LV and quantify ischemic scars on DB-LGE-CMR images. The performances of our method can further improve by expanding the data set used for the training. If implemented in a clinical routine, this process can speed up the CMR analysis process and aid in the clinical decision-making

Children's daily travel to school in Johannesburg-Soweto, South Africa: geography and school choice in the Birth to Twenty cohort study

This paper has two aims: to explore approaches to the measurement of children’s daily travel to school in a context of limited geospatial data availability, and to provide data regarding school choice and distance travelled to school in Soweto-Johannesburg, South Africa. The paper makes use of data from the Birth to Twenty cohort study (n=1428) to explore three different approaches to estimating school choice and travel to school. Firstly, straight-line distance between home and school is calculated. Secondly, census geography is used to determine whether a child's home and school fall in the same area. Thirdly, distance data are used to determine whether a child attends the nearest school. Each of these approaches highlights a different aspect of mobility, and all provide valuable data. Overall, primary school aged children in Soweto-Johannesburg are shown to be travelling substantial distances to school on a daily basis. Over a third travel more than 3km, one-way, to school, 60% attend schools outside of the suburb in which they live, and only 18% attend their nearest school. These data provide evidence for high levels of school choice in Johannesburg-Soweto, and that families and children are making substantial investments in pursuit of high quality educational opportunities. Additionally, these data suggest that two patterns of school choice are evident: one pattern involving travel of substantial distances and requiring a higher level of financial investment, and a second pattern, involving choice between more local schools, requiring less travel and a more limited financial investment

An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar

Background and objectives: Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients' clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent.Methods: DB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) mod-els based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo-and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality.Results: The dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (> 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets.Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker ( <1 min versus 7 +/- 3 min), and required minimal user interaction. Conclusions: Our solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report.(c) 2022 Elsevier B.V. All rights reserved

Identification of Regions Involved in Substrate Binding and Dimer Stabilization within the Central Domains of Yeast Hsp40 Sis1

Protein folding, refolding and degradation are essential for cellular life and are regulated by protein homeostatic processes such those that involve the molecular chaperone DnaK/Hsp70 and its co-chaperone DnaJ. Hsp70 action is initiated when proteins from the DnaJ family bind an unfolded protein for delivery purposes. In eukaryotes, the DnaJ family can be divided into two main groups, Type I and Type II, represented by yeast cytosolic Ydj1 and Sis1, respectively. Although sharing some unique features both members of the DnaJ family, Ydj1 and Sis1 are structurally and functionally distinct as deemed by previous studies, including the observation that their central domains carry the structural and functional information even in switched chimeras. In this study, we combined several biophysical tools for evaluating the stability of Sis1 and mutants that had the central domains (named Gly/Met rich domain and C-terminal Domain I) deleted or switched to those of Ydj1 to gain insight into the role of these regions in the structure and function of Sis1. The mutants retained some functions similar to full length wild-type Sis1, however they were defective in others. We found that: 1) Sis1 unfolds in at least two steps as follows: folded dimer to partially folded monomer and then to an unfolded monomer. 2) The Gly/Met rich domain had intrinsically disordered characteristics and its deletion had no effect on the conformational stability of the protein. 3) The deletion of the C-terminal Domain I perturbed the stability of the dimer. 4) Exchanging the central domains perturbed the conformational stability of the protein. Altogether, our results suggest the existence of two similar subdomains in the C-terminal domain of DnaJ that could be important for stabilizing each other in order to maintain a folded substrate-binding site as well as the dimeric state of the protein.Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)Ministerio da Ciencia e Tecnologia/Conselho Nacional de Pesquisa e Desenvolvimento (MCT/CNPq

Prospective Case-Control Study of Cardiovascular Abnormalities 6 Months Following Mild COVID-19 in Healthcare Workers

OBJECTIVES: The purpose of this study was to detect cardiovascular changes after mild severe acute respiratory syndrome coronavirus 2 infection. BACKGROUND: Concern exists that mild coronavirus disease 2019 may cause myocardial and vascular disease. METHODS: Participants were recruited from COVIDsortium, a 3-hospital prospective study of 731 health care workers who underwent first-wave weekly symptom, polymerase chain reaction, and serology assessment over 4 months, with seroconversion in 21.5% (n = 157). At 6 months post-infection, 74 seropositive and 75 age-, sex-, and ethnicity-matched seronegative control subjects were recruited for cardiovascular phenotyping (comprehensive phantom-calibrated cardiovascular magnetic resonance and blood biomarkers). Analysis was blinded, using objective artificial intelligence analytics where available. RESULTS: A total of 149 subjects (mean age 37 years, range 18 to 63 years, 58% women) were recruited. Seropositive infections had been mild with case definition, noncase definition, and asymptomatic disease in 45 (61%), 18 (24%), and 11 (15%), respectively, with 1 person hospitalized (for 2 days). Between seropositive and seronegative groups, there were no differences in cardiac structure (left ventricular volumes, mass, atrial area), function (ejection fraction, global longitudinal shortening, aortic distensibility), tissue characterization (T1, T2, extracellular volume fraction mapping, late gadolinium enhancement) or biomarkers (troponin, N-terminal pro-B-type natriuretic peptide). With abnormal defined by the 75 seronegatives (2 SDs from mean, e.g., ejection fraction 1,072 ms, septal T2 >52.4 ms), individuals had abnormalities including reduced ejection fraction (n = 2, minimum 50%), T1 elevation (n = 6), T2 elevation (n = 9), late gadolinium enhancement (n = 13, median 1%, max 5% of myocardium), biomarker elevation (borderline troponin elevation in 4; all N-terminal pro-B-type natriuretic peptide normal). These were distributed equally between seropositive and seronegative individuals. CONCLUSIONS: Cardiovascular abnormalities are no more common in seropositive versus seronegative otherwise healthy, workforce representative individuals 6 months post-mild severe acute respiratory syndrome coronavirus 2 infection

Advanced Imaging Modalities to Monitor for Cardiotoxicity

OPINION STATEMENT: Early detection and treatment of cardiotoxicity from cancer therapies is key to preventing a rise in adverse cardiovascular outcomes in cancer patients. Over-diagnosis of cardiotoxicity in this context is however equally hazardous, leading to patients receiving suboptimal cancer treatment, thereby impacting cancer outcomes. Accurate screening therefore depends on the widespread availability of sensitive and reproducible biomarkers of cardiotoxicity, which can clearly discriminate early disease. Blood biomarkers are limited in cardiovascular disease and clinicians generally still use generic screening with ejection fraction, based on historical local expertise and resources. Recently, however, there has been growing recognition that simple measurement of left ventricular ejection fraction using 2D echocardiography may not be optimal for screening: diagnostic accuracy, reproducibility and feasibility are limited. Modern cancer therapies affect many myocardial pathways: inflammatory, fibrotic, metabolic, vascular and myocyte function, meaning that multiple biomarkers may be needed to track myocardial cardiotoxicity. Advanced imaging modalities including cardiovascular magnetic resonance (CMR), computed tomography (CT) and positron emission tomography (PET) add improved sensitivity and insights into the underlying pathophysiology, as well as the ability to screen for other cardiotoxicities including coronary artery, valve and pericardial diseases resulting from cancer treatment. Delivering screening for cardiotoxicity using advanced imaging modalities will however require a significant change in current clinical pathways, with incorporation of machine learning algorithms into imaging analysis fundamental to improving efficiency and precision. In the future, we should aspire to personalized rather than generic screening, based on a patient's individual risk factors and the pathophysiological mechanisms of the cancer treatment they are receiving. We should aspire that progress in cardiooncology is able to track progress in oncology, and to ensure that the current 'one size fits all' approach to screening be obsolete in the very near future