An independent audit oversight system in a non-developed market: the case of Egypt

YesThis study aims to provide a better understanding of oversight the audit profession in Egypt, including its motivations, objectives and its working approach. Further, it reaches a better understanding of the Egyptian Big 4 partners’ perceptions of the new Audit Oversight Board (AOB). Previous studies have frequently examined the audit oversight system in developed countries (US and UK in particular), but little is known on how the system works on developing countries. We believe that facing different problems and challenges demands that audit regulators in developing countries follow different approaches in order to improve the quality of their audit markets. Lack of skilled auditors, lack of transparency and public accountability, and a high level of corruption are the main problems facing the audit profession in Egypt (Awadallah, 2006, Wahdan et. al., 2005: a). Our findings suggest that establishing an audit oversight board in Egypt has been motivated by the need to attract foreign investments and follow the global trend of auditing in developed countries. A number of legal changes are needed in order to improve the AOB’s efficiency

A general framework for hepatic iron overload quantification using MRI

Magnetic resonance imaging (MRI) has been considered for the quantification of iron overload in the liver. Iron overload was found to correlate with T2* measurement using T2* weighted images. In this work, we address the problem of iron overload estimation in the liver using MRI. We propose a general framework for all liver models proposed in the literature. The iron overload estimation task is then formulated as a minimization problem, and suitable regularization functions are assigned to the unknown model parameters. Subsequently, an alternating direction method of multipliers (ADMM) is used to estimate these unknown parameters. Three different models are derived, tested and compared; namely the single exponential (SEXP), the bi-exponential (BiEXP), and the exponential plus constant (CEXP). Simulations conducted using synthetic datasets indicate good correlation between estimated and ground truth T2* for all models. Moreover, the algorithms are evaluated using MRI scans of nine patients of different iron concentrations, using a 3-Tesla MRI scanner. The estimated T2* values of the proposed approaches are found to correlate with those obtained by the MRI scanner console. Moreover, the proposed approaches outperform several existing methods in the literature for iron overload estimation

Influence of the analysis technique on estimating hepatic iron content using MRI

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134998/1/jmri25317.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134998/2/jmri25317_am.pd

Simultaneous cell detection and classification in bone marrow histology images

Recently, deep learning frameworks have been shown to be successful and efficient in processing digital histology images for various detection and classification tasks. Among these tasks, cell detection and classification are key steps in many computer-assisted diagnosis systems. Traditionally, cell detection and classification is performed as a sequence of two consecutive steps by using two separate deep learning networks, one for detection and the other for classification. This strategy inevitably increases the computational complexity of the training stage. In this paper, we propose a synchronized deep autoencoder network for simultaneous detection and classification of cells in bone marrow histology images. The proposed network uses a single architecture to detect the positions of cells and classify the detected cells, in parallel. It uses a curve-support Gaussian model to compute probability maps that allow detecting irregularly-shape cells precisely. Moreover, the network includes a novel neighborhood selection mechanism to boost the classification accuracy. We show that the performance of the proposed network is superior than traditional deep learning detection methods and very competitive compared to traditional deep learning classification networks. Runtime comparison also shows that our network requires less time to be trained

Dual-channel active contour model for megakaryocytic cell segmentation in bone marrow trephine histology images

Assessment of morphological features of megakaryocytes (special kind of cells) in bone marrow trephine biopsies play an important role in the classification of different subtypes of Philadelphia-chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs). In order to aid hematopathologists in the study of megakaryocytes, we propose a novel framework that can efficiently delineate the nuclei and cytoplasm of these cells in digitized images of bone marrow trephine biopsies. The framework first employs a supervised machine learning approach that utilizes color and texture features to delineate megakaryocytic nuclei. It then employs a novel dual-channel active contour model to delineate the boundary of megakaryocytic cytoplasm by using different deconvolved stain channels. Compared to other recent models, the proposed framework achieves accurate results for both megakaryocytic nuclear and cytoplasmic delineation

How to regain public trust in audit firms? The case of the Financial Reporting Council

YesThis study aims to provide a better understanding of the role of the Financial Reporting Council (FRC) in restoring public trust in audit profession in the UK. It analyses the views of partners in the Big 4 audit firms on this role. This study identifies three main strategies to promote trust and enhance the choice of auditors in the UK audit market. These strategies are improving audit quality, increasing the transparency of the big audit firms and reducing the barriers to competition in the audit market. The findings suggest that partners of the Big 4 believe that the FRC's projects effectively participate in improving audit quality as well as providing wider information about the audit firms to the public. However, different actions need to be taken to enhance the choice in the market

Genetic modification of primary human B cells to model high-grade lymphoma

Abstract: Sequencing studies of diffuse large B cell lymphoma (DLBCL) have identified hundreds of recurrently altered genes. However, it remains largely unknown whether and how these mutations may contribute to lymphomagenesis, either individually or in combination. Existing strategies to address this problem predominantly utilize cell lines, which are limited by their initial characteristics and subsequent adaptions to prolonged in vitro culture. Here, we describe a co-culture system that enables the ex vivo expansion and viral transduction of primary human germinal center B cells. Incorporation of CRISPR/Cas9 technology enables high-throughput functional interrogation of genes recurrently mutated in DLBCL. Using a backbone of BCL2 with either BCL6 or MYC, we identify co-operating genetic alterations that promote growth or even full transformation into synthetically engineered DLBCL models. The resulting tumors can be expanded and sequentially transplanted in vivo, providing a scalable platform to test putative cancer genes and to create mutation-directed, bespoke lymphoma models

Genetic modification of primary human B cells to model high-grade lymphoma

Sequencing studies of diffuse large B cell lymphoma (DLBCL) have identified hundreds of recurrently altered genes. However, it remains largely unknown whether and how these mutations may contribute to lymphomagenesis, either individually or in combination. Existing strategies to address this problem predominantly utilize cell lines, which are limited by their initial characteristics and subsequent adaptions to prolonged in vitro culture. Here, we describe a co-culture system that enables the ex vivo expansion and viral transduction of primary human germinal center B cells. Incorporation of CRISPR/Cas9 technology enables high-throughput functional interrogation of genes recurrently mutated in DLBCL. Using a backbone of BCL2 with either BCL6 or MYC, we identify co-operating genetic alterations that promote growth or even full transformation into synthetically engineered DLBCL models. The resulting tumors can be expanded and sequentially transplanted in vivo, providing a scalable platform to test putative cancer genes and to create mutation-directed, bespoke lymphoma models

Clinical and organizational factors associated with mortality during the peak of first COVID-19 wave: the global UNITE-COVID study

Purpose: To accommodate the unprecedented number of critically ill patients with pneumonia caused by coronavirus disease 2019 (COVID-19) expansion of the capacity of intensive care unit (ICU) to clinical areas not previously used for critical care was necessary. We describe the global burden of COVID-19 admissions and the clinical and organizational characteristics associated with outcomes in critically ill COVID-19 patients. Methods: Multicenter, international, point prevalence study, including adult patients with SARS-CoV-2 infection confirmed by polymerase chain reaction (PCR) and a diagnosis of COVID-19 admitted to ICU between February 15th and May 15th, 2020. Results: 4994 patients from 280 ICUs in 46 countries were included. Included ICUs increased their total capacity from 4931 to 7630 beds, deploying personnel from other areas. Overall, 1986 (39.8%) patients were admitted to surge capacity beds. Invasive ventilation at admission was present in 2325 (46.5%) patients and was required during ICU stay in 85.8% of patients. 60-day mortality was 33.9% (IQR across units: 20%–50%) and ICU mortality 32.7%. Older age, invasive mechanical ventilation, and acute kidney injury (AKI) were associated with increased mortality. These associations were also confirmed specifically in mechanically ventilated patients. Admission to surge capacity beds was not associated with mortality, even after controlling for other factors. Conclusions: ICUs responded to the increase in COVID-19 patients by increasing bed availability and staff, admitting up to 40% of patients in surge capacity beds. Although mortality in this population was high, admission to a surge capacity bed was not associated with increased mortality. Older age, invasive mechanical ventilation, and AKI were identified as the strongest predictors of mortality