Public perception about vaccines is imperative for successful vaccination programs. This study ai... more Public perception about vaccines is imperative for successful vaccination programs. This study aims to measure the shift of sentiment towards vaccines after the COVID-19 outbreak in the Arab-speaking population. The study used vaccine-related Arabic Tweets and analyzed the sentiment of users in two different time frames, before 2020 (T1) and after 2020 (T2). The analysis showed that in T1, 48.05% of tweets were positive, and 16.47% of tweets were negative. In T2, 43.03% of tweets were positive, and 20.56% of tweets were negative. Among the Twitter users, the sentiment of 15.92% users shifted towards positive, and the sentiment of 17.90% users shifted towards negative. Public sentiment that have shifted towards positive may be due to the hope of vaccine efficacy, whereas public sentiment that have shifted towards negative may be due to the concerns related to vaccine side effects and misinformation. This study can support policymakers in the Arab world to combat the COVID-19 pandemic...
This study aims to develop models to accurately classify patients with type 2 diabetes using the ... more This study aims to develop models to accurately classify patients with type 2 diabetes using the Practice Fusion dataset. We use Random Forest (RF), Support Vector Classifier (SVC), AdaBoost classifier, an ensemble model, and automated machine learning (AutoML) model. We compare the performance of all models in a five-fold cross-validation scheme using four evaluation measures. Experimental results demonstrate that the AutoML model outperformed individual and ensemble models in all evaluation measures.
We studied the suitability of Artificial Intelligence (AI)-based models to predict vaccine-critic... more We studied the suitability of Artificial Intelligence (AI)-based models to predict vaccine-critical tweets on the social media platform Twitter. We manually labeled a sample of 800 tweets as either “vaccine-critical” (i.e, anti-vaccine tweets, mentioned concerns related to vaccine safety and efficacy, and are against vaccine mandates or vaccine passports) or “other” (i.e., tweets that are neutral, report news, or are ambiguous) and used them to train and test AI-based models for automatically predicting vaccine-critical tweets. We fine-tuned two pre-trained deep learning-based language models, BERT and BERTweet, and implemented four classical AI-based models, Random Forest, Logistics Regression, Linear Support Vector Machines, and Multinomial Naïve Bayes. We evaluated these AI-based models using f1 score, accuracy, precision, and recall in three-fold cross-validation. We found that BERTweet outperformed all other models using these measures.
Background: Vaccination programs are effective only when a significant percentage of people are v... more Background: Vaccination programs are effective only when a significant percentage of people are vaccinated. However, vaccine acceptance varies among communities around the world. Social media usage is arguably one of the factors affecting public attitudes towards vaccines. Objective: This study aims to identify if the social media usages factors can be used to predict attitudes and behavior towards the COVID-19 vaccines among the people in the Arab world. Methods: An online survey was conducted in the Arab countries and 217 Arab people participated in this study. Logistic regression was applied to identify what demographics and social media usage factors predict public attitudes and behavior towards the COVID-19 vaccines. Results: Of the 217 participants, 56.22% of them were willing to accept the vaccine and 41.47% of them were hesitant. This study shows that none of the social media usages factors were significant enough to predict the actual vaccine acceptance behavior. Whereas the analysis showed few of the social media usage factors can predict public attitudes towards the COVID-19 vaccines. For example, frequent social media users were 2.85 times more likely to agree that the risk of COVID-19 is being exaggerated (OR=2.85, 95% CI=0.86-9.45, p=0.046) than infrequent social media users. Whereas participants having more trust in vaccine information shared by their contacts are less likely to agree that decision-makers have verified that vaccines are safe (OR=0.528, 95% CI= 0.276-1.012, p=0.05). Conclusion: The use of social media and information shared on it may affect public attitudes towards COVID-19 vaccines. Therefore, disseminating correct and validated information about COVID-19 and other vaccines on social media is important for increasing public trust and countering the impact of incorrect and misinformation.
The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made... more The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made available. Generative adversarial networks (GANs) showed a lot of potential to generate synthetic MRI data that can capture the distribution of real MRI. Besides, GANs are also popular for segmentation, noise removal, and super-resolution of brain MRI images. This scoping review aims to explore how GANs methods are being used on brain MRI data, as reported in the literature. The review describes the different applications of GANs for brain MRI, presents the most commonly used GANs architectures, and summarizes the publicly available brain MRI datasets for advancing the research and development of GANs-based approaches. This review followed the guidelines of PRISMA-ScR to perform the study search and selection. The search was conducted on five popular scientific databases. The screening and selection of studies were performed by two independent reviewers, followed by validation by a thir...
Studies in health technology and informatics, 2021
Artificial intelligence (AI) techniques can contribute to the early diagnosis of prostate cancer.... more Artificial intelligence (AI) techniques can contribute to the early diagnosis of prostate cancer. Recently, there has been a sharp increase in the literature on AI techniques for prostate cancer diagnosis. This review article presents a summary of the AI methods that detect and diagnose prostate cancer using different medical imaging modalities. Following the PRISMA-ScR principle, this review covers 69 studies selected from 1441 searched papers published in the last three years. The application of AI methods reported in these articles can be divided into three broad categories: diagnosis, grading, and segmentation of tissues that have prostate cancer. Most of the AI methods leveraged convolutional neural networks (CNNs) due to their ability to extract complex features. Some studies also reported traditional machine learning methods, such as support vector machines (SVM), decision trees for classification, LASSO, and Ridge regression methods for features extraction. We believe that t...
Studies in Health Technology and Informatics, 2022
For the past ten years, the healthcare sector and industry has witnessed a surge in Artificial In... more For the past ten years, the healthcare sector and industry has witnessed a surge in Artificial Intelligence (AI) technologies being used in many different medical specialties. Recently, AI-driven technologies have been utilized in medical care for pregnancy. In this work, we present a scoping review that explores the features of AI-driven technologies used in caring for pregnant patients. This review was conducted using the Preferred Reporting Items for Systematic review and Meta-Analyses extension for Scoping Reviews. Our analysis revealed that AI techniques were used in predicting pregnancy disorders such as preeclampsia and gestational diabetes, along with managing and treating ectopic pregnancies. We also found that AI technologies were used to assess risk factors and safety surveillance of pregnant women. We believe that AI-driven technologies have the potential to improve the healthcare provided to pregnant women.
Studies examining how sentiment on social media varies over time and space appear to produce inco... more Studies examining how sentiment on social media varies over time and space appear to produce inconsistent results. Analysing 16.54 million English-language tweets from 100 cities posted between 13 July and 30 November 2017, our aim was to clarify how spatiotemporal and social factors contributed to variation in sentiment on Twitter. We estimated positive and negative sentiment for each of the cities using dictionary-based sentiment analysis and constructed models to explain differences in sentiment using time of day, day of week, weather, interaction type (social or non-social), and city as factors. Tests in a distinct but contiguous period of time showed that all factors were independently associated with sentiment. In the full multivariable model of positive (Pearson's R in test data 0.236; 95% CI 0.231-0.241), and negative (Pearson's R in test data 0.306 95% CI 0.301-0.310) sentiment, city and time of day explained more of the variance than other factors. Extreme differen...
Asia Pacific Journal of Innovation and Entrepreneurship, 2021
Purpose The purpose of the study is to probe the influence of individual social entrepreneurship ... more Purpose The purpose of the study is to probe the influence of individual social entrepreneurship orientations (ISEO) and SE education (SEE) which could affect social entrepreneurial intentions (SEI) among students. Design/methodology/approach The data were gathered from 241 entrepreneurship discipline university students. The data were analyzed with structural equation modeling. Findings Findings suggest that ISEO plays a vital role in stimulating SEI; moreover, SEE further moderates ISEO and SEI's relationship among students. Practical implications Based on the results, academia should focus on SEE and the government should devise policies to encourage social entrepreneurial ventures to aid in social problems solution. Originality/value This study validates the relationship of different factors (orientations and intentions) of the theory of planned behavior in the SE domain and confirms the significance of SEE.
Background The usefulness of right heart catherization (RHC) has long been debated, and thus, we ... more Background The usefulness of right heart catherization (RHC) has long been debated, and thus, we aimed to study the real‐world impact of the use of RHC in cardiogenic shock. Methods and Results In the Nationwide Readmissions Database using International Classification of Diseases, Tenth Revision ( ICD‐1 0 ), we identified 236 156 patient hospitalizations with cardiogenic shock between 2016 and 2017. We sought to evaluate the impact of RHC during index hospitalization on management strategies, complications, and outcomes as well as on 30‐day readmission rate. A total 25 840 patients (9.6%) received RHC on index admission. The RHC group had significantly more comorbidities compared with the non‐RHC group. During the index admission, the RHC group had lower death (25.8% versus 39.5%, P <0.001) and stroke rates (3.1% versus 3.4%, P <0.001). Thirty‐day readmission rates (18.7% versus 19.7%, P =0.04) and death on readmission (7.9% versus 9.3%, P =0.03) were also lower in the RHC gro...
General science textbooks due to integrated contents of Biology, Chemistry, Physics and; Earth an... more General science textbooks due to integrated contents of Biology, Chemistry, Physics and; Earth and Space, need special attention of curriculum developers. The contents in general science textbooks can be evaluated through different methods and curriculum analysis taxonomy (CAT) is one of these. This research paper explores the contents of general science textbook of 8th grade taught in all government schools and in some private schools of Punjab province. The contents of textbook were broken down in term of Piagetian developmental levels by using Curriculum Analysis Taxonomy (CAT). The findings show that majority of contents of General science textbook were at Concrete Operational level while a small number of contents demands Formal Operational level. This uneven distribution of contents at different Piagetian developmental levels were also observed when contents of Biology, Chemistry, Physics and; Earth and Space were separately studied. It is recommended that the distribution of ...
Background Left ventricular thrombus (LVT) is not uncommon and pose a risk of systemic embolism, ... more Background Left ventricular thrombus (LVT) is not uncommon and pose a risk of systemic embolism, which can be mitigated by adequate anticoagulation. Direct oral anticoagulants (DOACs) are increasingly being used as alternatives to warfarin for anticoagulation, but their efficacy and safety profile has been debated. We aim to compare the therapeutic efficacy and safety of DOACs versus warfarin for the treatment of LVT. Methodology We systematically searched PubMed/Medline, Google Scholar, Cochrane library, and LILCAS databases from inception to 14th August 2020 to identify relevant studies comparing warfarin and DOACs for LVT treatment and used the pooled data extracted from retrieved studies to perform a meta-analysis. Results We report pooled data on 1955 patients from 8 studies, with a mean age of 61 years and 59.7 years in warfarin and DOACs group, respectively. The pooled odds ratio for thrombus resolution was 1.11 (95% CI 0.51–2.39) on comparing warfarin to DOAC, but it did not...
Background Shortly after the emergence of COVID-19, researchers rapidly mobilized to study numero... more Background Shortly after the emergence of COVID-19, researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccinations. This led to a rapid increase in the number of COVID-19–related publications. Identifying trends and areas of interest using traditional review methods (eg, scoping and systematic reviews) for such a large domain area is challenging. Objective We aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature. Methods We used the COVID-19 Open Research Dataset (CORD-19) that consists of a large number of research articles related to all coronaviruses. We used a machine learning–based method to analyze the most relevant COVID-19–related articles and extracted the most prominent topics. Specifically, we used a clustering algorithm to group published articles based on the similarity of their abstracts to identify research hotspots...
Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and ana... more Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, it is of paramount importance to determine its mutational status. The haematoxylin and eosin (H&E) staining is a valuable tool in precision oncology as it guides histopathology-based diagnosis and proceeding patient’s treatment. However, H&E staining alone does not determine the IDH mutational status of a tumour. Deep learning methods applied to MRI data have been demonstrated to be a useful tool in IDH status prediction, however the effectiveness of deep learning on H&E slides in the clinical setting has not been investigated so far. Furthermore, the performance of deep learning methods in medical imaging has been practically limited by small sample sizes currently available. Here we propose a data augmentation met...
Unusual or unexpected effect of treatment Background: The CardioMEMS heart failure system is a sm... more Unusual or unexpected effect of treatment Background: The CardioMEMS heart failure system is a small sensor that is placed in a branch pulmonary artery for ambulatory monitoring of pulmonary artery pressures. CardioMEMS has been approved for use in the United States in patients with New York Heart Association (NYHA) class III heart failure and frequent hospitalizations. In this report we describe a patient who had hemoptysis after CardioMEMS implantation. Further, we discuss possible etiologies for the occurrence of hemoptysis and suggest strategies to minimize this risk. Case Report: The patient was a 79-year-old female with NYHA class III heart failure with non-ischemic cardiomyopathy (LVEF 40%) and chronic atrial fibrillation who was referred for CardioMEMS implantation. The procedure was completed uneventfully. The patient was transferred out of the procedure suite to the recovery area where she developed a slight cough approximately 20 minutes after the implantation. Within a few coughs the patient started having hemoptysis. She was transferred to the cardiac intensive care unit for observation. She was kept off warfarin and aspirin and her hemoptysis resolved 3 days later. While the exact etiology of hemoptysis in this patient was unclear, we felt that it may have been precipitated by a minor wire-induced distal branch pulmonary artery injury. Conclusions: Our report discusses hemoptysis as a potential life-threatening complication of CardioMEMS sensor implantation while suggesting possible etiologies and avoidance strategies. As the utilization of this technology expands in the years to come, a more comprehensive national registry for surveillance of device related complications will be crucial.
Background Tools used to appraise the credibility of health information are time-consuming to app... more Background Tools used to appraise the credibility of health information are time-consuming to apply and require context-specific expertise, limiting their use for quickly identifying and mitigating the spread of misinformation as it emerges. Objective The aim of this study was to estimate the proportion of vaccine-related Twitter posts linked to Web pages of low credibility and measure the potential reach of those posts. Methods Sampling from 143,003 unique vaccine-related Web pages shared on Twitter between January 2017 and March 2018, we used a 7-point checklist adapted from validated tools and guidelines to manually appraise the credibility of 474 Web pages. These were used to train several classifiers (random forests, support vector machines, and recurrent neural networks) using the text from a Web page to predict whether the information satisfies each of the 7 criteria. Estimating the credibility of all other Web pages, we used the follower network to estimate potential exposur...
Introduction: Human papillomavirus (HPV) vaccine coverage in Australia is 80% for females and 76%... more Introduction: Human papillomavirus (HPV) vaccine coverage in Australia is 80% for females and 76% for males. Attitudes may influence coverage but surveys measuring attitudes are resource-intensive. The aim of this study was to determine whether Twitter-derived estimates of HPV vaccine information exposure were associated with differences in coverage across regions in Australia. Methods: Regional differences in information exposure were estimated from 1,103,448 Australian Twitter users and 655,690 HPV vaccine related tweets posted between 6 September 2013 and 1 September 2017. Tweets about HPV vaccines were grouped using topic modelling; an algorithm for clustering text-based data. Proportional exposure to topics across 25 regions in Australia were used as factors to model HPV vaccine coverage in females and males, and compared to models using employment and education as factors. Results: Models using topic exposure measures were more closely correlated with HPV vaccine coverage (female: Pearson's R = 0.75 [0.49 to 0.88]; male: R = 0.76 [0.51 to 0.89]) than models using employment and education as factors (female: 0.39 [−0.02 to 0.68]; male: 0.36 [−0.04 to 0.66]). In Australia, positivelyframed news tended to reach more Twitter users overall, but vaccine-critical information made up higher proportions of exposures among Twitter users in low coverage regions, where distorted characterisations of safety research and vaccine-critical blogs were popular. Conclusions: Twitter-derived models of information exposure were correlated with HPV vaccine coverage in Australia. Topic exposure measures may be useful for providing timely and localised reports of the information people access and share to inform the design of targeted vaccine promotion interventions.
Background: Studies examining how sentiment on social media varies depending on timing and locati... more Background: Studies examining how sentiment on social media varies depending on timing and location appear to produce inconsistent results, making it hard to design systems that use sentiment to detect localized events for public health applications. Objective: The aim of this study was to measure how common timing and location confounders explain variation in sentiment on Twitter. Methods: Using a dataset of 16.54 million English-language tweets from 100 cities posted between July 13 and November 30, 2017, we estimated the positive and negative sentiment for each of the cities using a dictionary-based sentiment analysis and constructed models to explain the differences in sentiment using time of day, day of week, weather, city, and interaction type (conversations or broadcasting) as factors and found that all factors were independently associated with sentiment. Results: In the full multivariable model of positive (Pearson r in test data 0.236; 95% CI 0.231-0.241) and negative (Pearson r in test data 0.306; 95% CI 0.301-0.310) sentiment, the city and time of day explained more of the variance than weather and day of week. Models that account for these confounders produce a different distribution and ranking of important events compared with models that do not account for these confounders. Conclusions: In public health applications that aim to detect localized events by aggregating sentiment across populations of Twitter users, it is worthwhile accounting for baseline differences before looking for unexpected changes.
Cardiac allograft vasculopathy (CAV) remains an important source of mortality after heart transpl... more Cardiac allograft vasculopathy (CAV) remains an important source of mortality after heart transplant. The aim of our study was to identify structural and microvasculature changes in severe CAV. The study group included heart transplant recipients with severe CAV who underwent retransplantation (severe CAV, n=20). Control groups included time from transplant matched cardiac transplant recipients without CAV (transplant control, n=20), severe ischemic cardiomyopathy patients requiring left ventricular assist device implantation (ischemic control, n=18), and normal hearts donated for research (donor control, n=10). We collected baseline demographic information, echocardiography data, and performed histopathologic examination of myocardial microvasculature. Echocardiographic features of severe CAV included lack of eccentric remodeling and presence of significant diastolic dysfunction. In contrast, diastolic function was preserved in transplant control subjects. Histopathologic examinati...
Public perception about vaccines is imperative for successful vaccination programs. This study ai... more Public perception about vaccines is imperative for successful vaccination programs. This study aims to measure the shift of sentiment towards vaccines after the COVID-19 outbreak in the Arab-speaking population. The study used vaccine-related Arabic Tweets and analyzed the sentiment of users in two different time frames, before 2020 (T1) and after 2020 (T2). The analysis showed that in T1, 48.05% of tweets were positive, and 16.47% of tweets were negative. In T2, 43.03% of tweets were positive, and 20.56% of tweets were negative. Among the Twitter users, the sentiment of 15.92% users shifted towards positive, and the sentiment of 17.90% users shifted towards negative. Public sentiment that have shifted towards positive may be due to the hope of vaccine efficacy, whereas public sentiment that have shifted towards negative may be due to the concerns related to vaccine side effects and misinformation. This study can support policymakers in the Arab world to combat the COVID-19 pandemic...
This study aims to develop models to accurately classify patients with type 2 diabetes using the ... more This study aims to develop models to accurately classify patients with type 2 diabetes using the Practice Fusion dataset. We use Random Forest (RF), Support Vector Classifier (SVC), AdaBoost classifier, an ensemble model, and automated machine learning (AutoML) model. We compare the performance of all models in a five-fold cross-validation scheme using four evaluation measures. Experimental results demonstrate that the AutoML model outperformed individual and ensemble models in all evaluation measures.
We studied the suitability of Artificial Intelligence (AI)-based models to predict vaccine-critic... more We studied the suitability of Artificial Intelligence (AI)-based models to predict vaccine-critical tweets on the social media platform Twitter. We manually labeled a sample of 800 tweets as either “vaccine-critical” (i.e, anti-vaccine tweets, mentioned concerns related to vaccine safety and efficacy, and are against vaccine mandates or vaccine passports) or “other” (i.e., tweets that are neutral, report news, or are ambiguous) and used them to train and test AI-based models for automatically predicting vaccine-critical tweets. We fine-tuned two pre-trained deep learning-based language models, BERT and BERTweet, and implemented four classical AI-based models, Random Forest, Logistics Regression, Linear Support Vector Machines, and Multinomial Naïve Bayes. We evaluated these AI-based models using f1 score, accuracy, precision, and recall in three-fold cross-validation. We found that BERTweet outperformed all other models using these measures.
Background: Vaccination programs are effective only when a significant percentage of people are v... more Background: Vaccination programs are effective only when a significant percentage of people are vaccinated. However, vaccine acceptance varies among communities around the world. Social media usage is arguably one of the factors affecting public attitudes towards vaccines. Objective: This study aims to identify if the social media usages factors can be used to predict attitudes and behavior towards the COVID-19 vaccines among the people in the Arab world. Methods: An online survey was conducted in the Arab countries and 217 Arab people participated in this study. Logistic regression was applied to identify what demographics and social media usage factors predict public attitudes and behavior towards the COVID-19 vaccines. Results: Of the 217 participants, 56.22% of them were willing to accept the vaccine and 41.47% of them were hesitant. This study shows that none of the social media usages factors were significant enough to predict the actual vaccine acceptance behavior. Whereas the analysis showed few of the social media usage factors can predict public attitudes towards the COVID-19 vaccines. For example, frequent social media users were 2.85 times more likely to agree that the risk of COVID-19 is being exaggerated (OR=2.85, 95% CI=0.86-9.45, p=0.046) than infrequent social media users. Whereas participants having more trust in vaccine information shared by their contacts are less likely to agree that decision-makers have verified that vaccines are safe (OR=0.528, 95% CI= 0.276-1.012, p=0.05). Conclusion: The use of social media and information shared on it may affect public attitudes towards COVID-19 vaccines. Therefore, disseminating correct and validated information about COVID-19 and other vaccines on social media is important for increasing public trust and countering the impact of incorrect and misinformation.
The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made... more The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made available. Generative adversarial networks (GANs) showed a lot of potential to generate synthetic MRI data that can capture the distribution of real MRI. Besides, GANs are also popular for segmentation, noise removal, and super-resolution of brain MRI images. This scoping review aims to explore how GANs methods are being used on brain MRI data, as reported in the literature. The review describes the different applications of GANs for brain MRI, presents the most commonly used GANs architectures, and summarizes the publicly available brain MRI datasets for advancing the research and development of GANs-based approaches. This review followed the guidelines of PRISMA-ScR to perform the study search and selection. The search was conducted on five popular scientific databases. The screening and selection of studies were performed by two independent reviewers, followed by validation by a thir...
Studies in health technology and informatics, 2021
Artificial intelligence (AI) techniques can contribute to the early diagnosis of prostate cancer.... more Artificial intelligence (AI) techniques can contribute to the early diagnosis of prostate cancer. Recently, there has been a sharp increase in the literature on AI techniques for prostate cancer diagnosis. This review article presents a summary of the AI methods that detect and diagnose prostate cancer using different medical imaging modalities. Following the PRISMA-ScR principle, this review covers 69 studies selected from 1441 searched papers published in the last three years. The application of AI methods reported in these articles can be divided into three broad categories: diagnosis, grading, and segmentation of tissues that have prostate cancer. Most of the AI methods leveraged convolutional neural networks (CNNs) due to their ability to extract complex features. Some studies also reported traditional machine learning methods, such as support vector machines (SVM), decision trees for classification, LASSO, and Ridge regression methods for features extraction. We believe that t...
Studies in Health Technology and Informatics, 2022
For the past ten years, the healthcare sector and industry has witnessed a surge in Artificial In... more For the past ten years, the healthcare sector and industry has witnessed a surge in Artificial Intelligence (AI) technologies being used in many different medical specialties. Recently, AI-driven technologies have been utilized in medical care for pregnancy. In this work, we present a scoping review that explores the features of AI-driven technologies used in caring for pregnant patients. This review was conducted using the Preferred Reporting Items for Systematic review and Meta-Analyses extension for Scoping Reviews. Our analysis revealed that AI techniques were used in predicting pregnancy disorders such as preeclampsia and gestational diabetes, along with managing and treating ectopic pregnancies. We also found that AI technologies were used to assess risk factors and safety surveillance of pregnant women. We believe that AI-driven technologies have the potential to improve the healthcare provided to pregnant women.
Studies examining how sentiment on social media varies over time and space appear to produce inco... more Studies examining how sentiment on social media varies over time and space appear to produce inconsistent results. Analysing 16.54 million English-language tweets from 100 cities posted between 13 July and 30 November 2017, our aim was to clarify how spatiotemporal and social factors contributed to variation in sentiment on Twitter. We estimated positive and negative sentiment for each of the cities using dictionary-based sentiment analysis and constructed models to explain differences in sentiment using time of day, day of week, weather, interaction type (social or non-social), and city as factors. Tests in a distinct but contiguous period of time showed that all factors were independently associated with sentiment. In the full multivariable model of positive (Pearson's R in test data 0.236; 95% CI 0.231-0.241), and negative (Pearson's R in test data 0.306 95% CI 0.301-0.310) sentiment, city and time of day explained more of the variance than other factors. Extreme differen...
Asia Pacific Journal of Innovation and Entrepreneurship, 2021
Purpose The purpose of the study is to probe the influence of individual social entrepreneurship ... more Purpose The purpose of the study is to probe the influence of individual social entrepreneurship orientations (ISEO) and SE education (SEE) which could affect social entrepreneurial intentions (SEI) among students. Design/methodology/approach The data were gathered from 241 entrepreneurship discipline university students. The data were analyzed with structural equation modeling. Findings Findings suggest that ISEO plays a vital role in stimulating SEI; moreover, SEE further moderates ISEO and SEI's relationship among students. Practical implications Based on the results, academia should focus on SEE and the government should devise policies to encourage social entrepreneurial ventures to aid in social problems solution. Originality/value This study validates the relationship of different factors (orientations and intentions) of the theory of planned behavior in the SE domain and confirms the significance of SEE.
Background The usefulness of right heart catherization (RHC) has long been debated, and thus, we ... more Background The usefulness of right heart catherization (RHC) has long been debated, and thus, we aimed to study the real‐world impact of the use of RHC in cardiogenic shock. Methods and Results In the Nationwide Readmissions Database using International Classification of Diseases, Tenth Revision ( ICD‐1 0 ), we identified 236 156 patient hospitalizations with cardiogenic shock between 2016 and 2017. We sought to evaluate the impact of RHC during index hospitalization on management strategies, complications, and outcomes as well as on 30‐day readmission rate. A total 25 840 patients (9.6%) received RHC on index admission. The RHC group had significantly more comorbidities compared with the non‐RHC group. During the index admission, the RHC group had lower death (25.8% versus 39.5%, P <0.001) and stroke rates (3.1% versus 3.4%, P <0.001). Thirty‐day readmission rates (18.7% versus 19.7%, P =0.04) and death on readmission (7.9% versus 9.3%, P =0.03) were also lower in the RHC gro...
General science textbooks due to integrated contents of Biology, Chemistry, Physics and; Earth an... more General science textbooks due to integrated contents of Biology, Chemistry, Physics and; Earth and Space, need special attention of curriculum developers. The contents in general science textbooks can be evaluated through different methods and curriculum analysis taxonomy (CAT) is one of these. This research paper explores the contents of general science textbook of 8th grade taught in all government schools and in some private schools of Punjab province. The contents of textbook were broken down in term of Piagetian developmental levels by using Curriculum Analysis Taxonomy (CAT). The findings show that majority of contents of General science textbook were at Concrete Operational level while a small number of contents demands Formal Operational level. This uneven distribution of contents at different Piagetian developmental levels were also observed when contents of Biology, Chemistry, Physics and; Earth and Space were separately studied. It is recommended that the distribution of ...
Background Left ventricular thrombus (LVT) is not uncommon and pose a risk of systemic embolism, ... more Background Left ventricular thrombus (LVT) is not uncommon and pose a risk of systemic embolism, which can be mitigated by adequate anticoagulation. Direct oral anticoagulants (DOACs) are increasingly being used as alternatives to warfarin for anticoagulation, but their efficacy and safety profile has been debated. We aim to compare the therapeutic efficacy and safety of DOACs versus warfarin for the treatment of LVT. Methodology We systematically searched PubMed/Medline, Google Scholar, Cochrane library, and LILCAS databases from inception to 14th August 2020 to identify relevant studies comparing warfarin and DOACs for LVT treatment and used the pooled data extracted from retrieved studies to perform a meta-analysis. Results We report pooled data on 1955 patients from 8 studies, with a mean age of 61 years and 59.7 years in warfarin and DOACs group, respectively. The pooled odds ratio for thrombus resolution was 1.11 (95% CI 0.51–2.39) on comparing warfarin to DOAC, but it did not...
Background Shortly after the emergence of COVID-19, researchers rapidly mobilized to study numero... more Background Shortly after the emergence of COVID-19, researchers rapidly mobilized to study numerous aspects of the disease such as its evolution, clinical manifestations, effects, treatments, and vaccinations. This led to a rapid increase in the number of COVID-19–related publications. Identifying trends and areas of interest using traditional review methods (eg, scoping and systematic reviews) for such a large domain area is challenging. Objective We aimed to conduct an extensive bibliometric analysis to provide a comprehensive overview of the COVID-19 literature. Methods We used the COVID-19 Open Research Dataset (CORD-19) that consists of a large number of research articles related to all coronaviruses. We used a machine learning–based method to analyze the most relevant COVID-19–related articles and extracted the most prominent topics. Specifically, we used a clustering algorithm to group published articles based on the similarity of their abstracts to identify research hotspots...
Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and ana... more Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, it is of paramount importance to determine its mutational status. The haematoxylin and eosin (H&E) staining is a valuable tool in precision oncology as it guides histopathology-based diagnosis and proceeding patient’s treatment. However, H&E staining alone does not determine the IDH mutational status of a tumour. Deep learning methods applied to MRI data have been demonstrated to be a useful tool in IDH status prediction, however the effectiveness of deep learning on H&E slides in the clinical setting has not been investigated so far. Furthermore, the performance of deep learning methods in medical imaging has been practically limited by small sample sizes currently available. Here we propose a data augmentation met...
Unusual or unexpected effect of treatment Background: The CardioMEMS heart failure system is a sm... more Unusual or unexpected effect of treatment Background: The CardioMEMS heart failure system is a small sensor that is placed in a branch pulmonary artery for ambulatory monitoring of pulmonary artery pressures. CardioMEMS has been approved for use in the United States in patients with New York Heart Association (NYHA) class III heart failure and frequent hospitalizations. In this report we describe a patient who had hemoptysis after CardioMEMS implantation. Further, we discuss possible etiologies for the occurrence of hemoptysis and suggest strategies to minimize this risk. Case Report: The patient was a 79-year-old female with NYHA class III heart failure with non-ischemic cardiomyopathy (LVEF 40%) and chronic atrial fibrillation who was referred for CardioMEMS implantation. The procedure was completed uneventfully. The patient was transferred out of the procedure suite to the recovery area where she developed a slight cough approximately 20 minutes after the implantation. Within a few coughs the patient started having hemoptysis. She was transferred to the cardiac intensive care unit for observation. She was kept off warfarin and aspirin and her hemoptysis resolved 3 days later. While the exact etiology of hemoptysis in this patient was unclear, we felt that it may have been precipitated by a minor wire-induced distal branch pulmonary artery injury. Conclusions: Our report discusses hemoptysis as a potential life-threatening complication of CardioMEMS sensor implantation while suggesting possible etiologies and avoidance strategies. As the utilization of this technology expands in the years to come, a more comprehensive national registry for surveillance of device related complications will be crucial.
Background Tools used to appraise the credibility of health information are time-consuming to app... more Background Tools used to appraise the credibility of health information are time-consuming to apply and require context-specific expertise, limiting their use for quickly identifying and mitigating the spread of misinformation as it emerges. Objective The aim of this study was to estimate the proportion of vaccine-related Twitter posts linked to Web pages of low credibility and measure the potential reach of those posts. Methods Sampling from 143,003 unique vaccine-related Web pages shared on Twitter between January 2017 and March 2018, we used a 7-point checklist adapted from validated tools and guidelines to manually appraise the credibility of 474 Web pages. These were used to train several classifiers (random forests, support vector machines, and recurrent neural networks) using the text from a Web page to predict whether the information satisfies each of the 7 criteria. Estimating the credibility of all other Web pages, we used the follower network to estimate potential exposur...
Introduction: Human papillomavirus (HPV) vaccine coverage in Australia is 80% for females and 76%... more Introduction: Human papillomavirus (HPV) vaccine coverage in Australia is 80% for females and 76% for males. Attitudes may influence coverage but surveys measuring attitudes are resource-intensive. The aim of this study was to determine whether Twitter-derived estimates of HPV vaccine information exposure were associated with differences in coverage across regions in Australia. Methods: Regional differences in information exposure were estimated from 1,103,448 Australian Twitter users and 655,690 HPV vaccine related tweets posted between 6 September 2013 and 1 September 2017. Tweets about HPV vaccines were grouped using topic modelling; an algorithm for clustering text-based data. Proportional exposure to topics across 25 regions in Australia were used as factors to model HPV vaccine coverage in females and males, and compared to models using employment and education as factors. Results: Models using topic exposure measures were more closely correlated with HPV vaccine coverage (female: Pearson's R = 0.75 [0.49 to 0.88]; male: R = 0.76 [0.51 to 0.89]) than models using employment and education as factors (female: 0.39 [−0.02 to 0.68]; male: 0.36 [−0.04 to 0.66]). In Australia, positivelyframed news tended to reach more Twitter users overall, but vaccine-critical information made up higher proportions of exposures among Twitter users in low coverage regions, where distorted characterisations of safety research and vaccine-critical blogs were popular. Conclusions: Twitter-derived models of information exposure were correlated with HPV vaccine coverage in Australia. Topic exposure measures may be useful for providing timely and localised reports of the information people access and share to inform the design of targeted vaccine promotion interventions.
Background: Studies examining how sentiment on social media varies depending on timing and locati... more Background: Studies examining how sentiment on social media varies depending on timing and location appear to produce inconsistent results, making it hard to design systems that use sentiment to detect localized events for public health applications. Objective: The aim of this study was to measure how common timing and location confounders explain variation in sentiment on Twitter. Methods: Using a dataset of 16.54 million English-language tweets from 100 cities posted between July 13 and November 30, 2017, we estimated the positive and negative sentiment for each of the cities using a dictionary-based sentiment analysis and constructed models to explain the differences in sentiment using time of day, day of week, weather, city, and interaction type (conversations or broadcasting) as factors and found that all factors were independently associated with sentiment. Results: In the full multivariable model of positive (Pearson r in test data 0.236; 95% CI 0.231-0.241) and negative (Pearson r in test data 0.306; 95% CI 0.301-0.310) sentiment, the city and time of day explained more of the variance than weather and day of week. Models that account for these confounders produce a different distribution and ranking of important events compared with models that do not account for these confounders. Conclusions: In public health applications that aim to detect localized events by aggregating sentiment across populations of Twitter users, it is worthwhile accounting for baseline differences before looking for unexpected changes.
Cardiac allograft vasculopathy (CAV) remains an important source of mortality after heart transpl... more Cardiac allograft vasculopathy (CAV) remains an important source of mortality after heart transplant. The aim of our study was to identify structural and microvasculature changes in severe CAV. The study group included heart transplant recipients with severe CAV who underwent retransplantation (severe CAV, n=20). Control groups included time from transplant matched cardiac transplant recipients without CAV (transplant control, n=20), severe ischemic cardiomyopathy patients requiring left ventricular assist device implantation (ischemic control, n=18), and normal hearts donated for research (donor control, n=10). We collected baseline demographic information, echocardiography data, and performed histopathologic examination of myocardial microvasculature. Echocardiographic features of severe CAV included lack of eccentric remodeling and presence of significant diastolic dysfunction. In contrast, diastolic function was preserved in transplant control subjects. Histopathologic examinati...
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Papers by Zubair Shah