Frequency and risk factors of non-alcoholic fatty liver disease in Helicobacter pylori-infected dyspeptic patients: A cross-sectional study

Background: In dyspeptic patients with Helicobacter pylori contributes to non-alcoholic fatty liver disease. However, little evidence available from Pakistan.Objective: The study aims to determine the frequency and risk factors of non-alcoholic fatty liver disease in dyspeptic patients with Helicobacter pylori.Methods: This cross-sectional study was conducted between 22 November 2016 and 30 June 2018. Adults of age between 18 and 90 years who attended the out-patient department due to abdominal discomfort, pain, fullness, and bloating who underwent upper gastrointestinal tract endoscopy were enrolled after taking informed consent. Patients with celiac disease, inflammatory bowel disease, taking alcohol, pregnant women and lactating mothers, known cases of hepatitis B and C, and history of recent antibiotic use were excluded. Data on age, gender, smoking, alcohol use, dyslipidemia, hypertension, type 2 diabetes mellitus, and ischemic heart disease were collected. Non-alcoholic fatty liver disease was diagnosed through ultrasonography. Helicobacter pylori infection was detected using a carbon urea breath test.Results: A total of 698 patients were screened for eligibility, and 399 (57.2%) had Helicobacter pylori infection and were enrolled in the study after consent. The median age was 50.1 (interquartile range = 14.5) years and 209 (52.4%) were males. Frequency of non-alcoholic fatty liver disease in patients with Helicobacter pylori dyspeptic patients was 153 (38.3%). Factors associated with non-alcoholic fatty liver disease in the presence of Helicobacter pylori were dyslipidemia 7.38 (95% confidence interval = 2.4-22.71), type 2 diabetes mellitus 5.96 (95% confidence interval = 1.86-19.07), hypertension 3.0 (95% confidence interval = 1.21-7.45), and moderate gastritis 2.81 (95% confidence interval = 1.2-6.59).Conclusion: The frequency of non-alcoholic fatty liver disease in Helicobacter Pylori dyspeptic patients was 38.3%. Male gender, dyslipidemia, hypertension, ischemic heart disease, and moderate gastritis were associated with non-alcoholic fatty liver disease

Deep Learning Assisted Automated Assessment of Thalassaemia from Haemoglobin Electrophoresis Images

Haemoglobin (Hb) electrophoresis is a method of blood testing used to detect thalassaemia. However, the interpretation of the result of the electrophoresis test itself is a complex task. Expert haematologists, specifically in developing countries, are relatively few in number and are usually overburdened. To assist them with their workload, in this paper we present a novel method for the automated assessment of thalassaemia using Hb electrophoresis images. Moreover, in this study we compile a large Hb electrophoresis image dataset, consisting of 103 strips containing 524 electrophoresis images with a clear consensus on the quality of electrophoresis obtained from 824 subjects. The proposed methodology is split into two parts: (1) single-patient electrophoresis image segmentation by means of the lane extraction technique, and (2) binary classification (normal or abnormal) of the electrophoresis images using state-of-the-art deep convolutional neural networks (CNNs) and using the concept of transfer learning. Image processing techniques including filtering and morphological operations are applied for object detection and lane extraction to automatically separate the lanes and classify them using CNN models. Seven different CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, SqueezeNet and MobileNetV2) were investigated in this study. InceptionV3 outperformed the other CNNs in detecting thalassaemia using Hb electrophoresis images. The accuracy, precision, recall, f1-score, and specificity in the detection of thalassaemia obtained with the InceptionV3 model were 95.8%, 95.84%, 95.8%, 95.8% and 95.8%, respectively. MobileNetV2 demonstrated an accuracy, precision, recall, f1-score, and specificity of 95.72%, 95.73%, 95.72%, 95.7% and 95.72% respectively. Its performance was comparable with the best performing model, InceptionV3. Since it is a very shallow network, MobileNetV2 also provides the least latency in processing a single-patient image and it can be suitably used for mobile applications. The proposed approach, which has shown very high classification accuracy, will assist in the rapid and robust detection of thalassaemia using Hb electrophoresis images. 2022 by the authors.A part of the research was funded by the Higher Education Commission of Pakistan through its funded project of Artificial Intelligence in Healthcare, Intelligent Information Processing Lab, National Center of Artificial Intelligence.Scopu

Asymmetric information, credit risk and instrument characteristics in islamic finance

This thesis investigates whether traditional finance theories can explain the different characteristics of Islamic financial instruments. The work is divided into three sections. The first develops new theoretical models based on asymmetric information and risk averse bank customers to explain the dominance of debt in Islamic banks, even though many consider Islamic joint venture (IJV) funding to be the ideal Islamic mode of financing. The second section focuses on Islamic bonds and examines whether conventional structural credit risk models, such as the Finger et al. (2002) CreditGrades model, truly capture Islamic bonds’ underlying risk. The structural models’ various extensions have been adjusted for the Islamic bonds’ unique characteristics and are tested through simulations to identify if they favour some Islamic bond structures over others. Data from 52 Malaysian Islamic bond issuers is also tested with these models. The third section analyses the determinants of Islamic bond credit ratings using data for 458 Malaysian Islamic bonds and the issuer’s choice of Islamic bond type using data for 456 Malaysian Islamic bonds. Here, the impact of firm specific variables, specific events (such as the 2008 AAOIFI recommendations) and specific Islamic instrument characteristics (such as Shariah advisor effect) are analysed using ordered and multinomial probit models. The findings for the first section suggest that asymmetric information models, when augmented with the risk averse bank customer behaviour, can help explain the lack of IJV Islamic banks. For the second section, the simulation results suggest that conventional structural models and their Islamic extensions have a bias against IJV bonds. Conventional models, however, can be used to capture the risk of secured against real asset bonds (SARA). The third section shows that firm specific variables (such as leverage and profitability ratios), specific events (2008 AAOIFI recommendation) and Islamic instrument specific characteristics (such as the Shariah-advisor effect) are all significant determinants of Islamic bond ratings as well as the issuer’s choice of Islamic bond type. This work offers several contributions to the literature. It is the first to augment asymmetric information models with risk averse utility function of bank customers to offer a comprehensive explanation of the negligible use of IJV by Islamic banks. Secondly, it is the first to develop Islamic extensions of conventional structural models and analyse how these models favour some Islamic bond structures over others. Thirdly, it is the first to examine the impact of firm variables, specific events and Islamic instrument specific determinants of Islamic bond ratings and the issuer’s choice of Islamic bonds. These findings have several implications for policy makers/regulators, Islamic banks, credit rating agencies and Islamic bond issuers. For policy makers/regulators and Islamic banks, this study may help implement more IJV mode of financing. For credit rating agencies, it should enable them to refine their Islamic bond credit rating models by identifying those variables that have the most impact on the bonds’ underlying risk. For corporate issuers, it should help achieve higher credit ratings and better decision making when issuing Islamic bonds

Asymmetric information, credit risk and instrument characteristics in islamic finance

This thesis investigates whether traditional finance theories can explain the different characteristics of Islamic financial instruments. The work is divided into three sections. The first develops new theoretical models based on asymmetric information and risk averse bank customers to explain the dominance of debt in Islamic banks, even though many consider Islamic joint venture (IJV) funding to be the ideal Islamic mode of financing. The second section focuses on Islamic bonds and examines whether conventional structural credit risk models, such as the Finger et al. (2002) CreditGrades model, truly capture Islamic bonds’ underlying risk. The structural models’ various extensions have been adjusted for the Islamic bonds’ unique characteristics and are tested through simulations to identify if they favour some Islamic bond structures over others. Data from 52 Malaysian Islamic bond issuers is also tested with these models. The third section analyses the determinants of Islamic bond credit ratings using data for 458 Malaysian Islamic bonds and the issuer’s choice of Islamic bond type using data for 456 Malaysian Islamic bonds. Here, the impact of firm specific variables, specific events (such as the 2008 AAOIFI recommendations) and specific Islamic instrument characteristics (such as Shariah advisor effect) are analysed using ordered and multinomial probit models. The findings for the first section suggest that asymmetric information models, when augmented with the risk averse bank customer behaviour, can help explain the lack of IJV Islamic banks. For the second section, the simulation results suggest that conventional structural models and their Islamic extensions have a bias against IJV bonds. Conventional models, however, can be used to capture the risk of secured against real asset bonds (SARA). The third section shows that firm specific variables (such as leverage and profitability ratios), specific events (2008 AAOIFI recommendation) and Islamic instrument specific characteristics (such as the Shariah-advisor effect) are all significant determinants of Islamic bond ratings as well as the issuer’s choice of Islamic bond type. This work offers several contributions to the literature. It is the first to augment asymmetric information models with risk averse utility function of bank customers to offer a comprehensive explanation of the negligible use of IJV by Islamic banks. Secondly, it is the first to develop Islamic extensions of conventional structural models and analyse how these models favour some Islamic bond structures over others. Thirdly, it is the first to examine the impact of firm variables, specific events and Islamic instrument specific determinants of Islamic bond ratings and the issuer’s choice of Islamic bonds. These findings have several implications for policy makers/regulators, Islamic banks, credit rating agencies and Islamic bond issuers. For policy makers/regulators and Islamic banks, this study may help implement more IJV mode of financing. For credit rating agencies, it should enable them to refine their Islamic bond credit rating models by identifying those variables that have the most impact on the bonds’ underlying risk. For corporate issuers, it should help achieve higher credit ratings and better decision making when issuing Islamic bonds