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The region of the Middle East and North Africa (MENA) is diverse and retains a superior growth potential. It benefits from a privileged geographical location with big markets, a young and growing educated population, and competitive advantages in several industries. Regardless of their differences, countries face shared concerns, most notably in health. In response to the COVID-19 pandemic, MENA countries enact reforms to create a more robust and inclusive digital health systems to increase growth, development, and integrity. Throughout the coordinated containment and mitigation efforts, most of the countries have integrated digital technologies into the health systems. These procedures include digital government initiatives, the introduction of digital health training courses, live video surgeries and virtual patient monitoring, rural and remote telemedicine programs, and the development of a national electronic health records (EHR) system. Each country took necessary actions to address equity, literacy, and development of resilient health systems. The nine featured countries in this report illustrate the diversity among the MENA region and account for major opportunities and achievements as well as promises and challenges that digital health presents for its populations. © 2022 IMIA and Georg Thieme Verlag KG.
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Background: A 54-year- old male presented to our centre with a chronic non-productive cough and breathlessness. Recent history of COVID treated and resolved few months back. He had a history of brain surgery performed five years back but details not known. Physical examination revealed no oedema and bilateral coarse creps with bronchiolar breathing. Laboratory findings indicated neutrophilic leucocytosis, elevated inflammatory markers, with elevated troponin I and D dimers. Urine analysis suggested microscopic haematuria with sediments. While 24 hour quantification revealed sub nephrotic proteinuria. As auto immune workup and vasculitis profile was negative and patient has not improved in spite of standard of therapy hence we went ahead with CT-Chest indicating ground-glass opacities in bilateral lung parenchyma and prominent interlobular/intralobular septal thickening. Then Bronchoscopy done which revealed the blood-stained secretions in the main stem bronchi and diffuse alveolar haemorrhage in bilateral bronchial segments indicating an inflammatory study, while tuberculosis diagnostic panel and infective bio fire panel in BAL was negative. Meanwhile, his repeat BAL culture suggested Carbapenem resistant Acinetobacter baumannii complex infection. As the patient did not respond to the standard of care for vasculitis. Probability considered was a small vessel vasculitis (namely Granulomatous polyangiitis) was considered due to lung manifestation involving upper respiratory tract with epistaxis, neutrophilic leucocytosis, elevated acute reactive protein, and renal manifestation including microscopic haematuria and proteinuria. However he responded poorly to conventional standard of treatment including pulse steroids and IVIG. Hence after MDT discussion we proceeded with lung biopsy which showed linear cores of lung tissue infiltrated by a malignant neoplasm and acinar pattern suggesting Invasive mucinous adenocarcinoma. Hence we went ahead with the biopsy diagnosis for the treatment plan. As he was to be started on chemotherapy, but he suddenly collapsed and went into hypotension, bradycardia, and cardiac arrest. In spite of high supports and post 4 cycles of CPR, was unable to revive and sadly succumbed to his illness. Discussion(s): In this rare case, the original diagnosis pointed to the pulmonary-renal syndrome, an autoimmune disease characterized by diffuse pulmonary haemorrhage and glomerulonephritis. However, negative autoimmune antibodies and vasculitis profile along with lung biopsy results indicated an unusual case of malignant lung adenocarcinoma presented with pulmonary renal syndrome. Conclusion(s): In cases suggesting pulmonary-renal syndromes, if autoimmune work up is negative and response is suboptimal relook the diagnosis.
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COVID-19 caused by the transmission of SARS-CoV-2 virus taking a huge toll on global health and caused life-threatening medical complications and elevated mortality rates, especially among older adults and people with existing morbidity. Current evidence suggests that the virus spreads primarily through respiratory droplets emitted by infected persons when breathing, coughing, sneezing, or speaking. These droplets can reach another person through their mouth, nose, or eyes, resulting in infection. The "gold standard" for clinical diagnosis of SARS-CoV-2 is the laboratory-based nucleic acid amplification test, which includes the reverse transcription-polymerase chain reaction (RT-PCR) test on nasopharyngeal swab samples. The main concerns with this type of test are the relatively high cost, long processing time, and considerable false-positive or false-negative results. Alternative approaches have been suggested to detect the SARS-CoV-2 virus so that those infected and the people they have been in contact with can be quickly isolated to break the transmission chains and hopefully, control the pandemic. These alternative approaches include electrochemical biosensing and deep learning. In this review, we discuss the current state-of-the-art technology used in both fields for public health surveillance of SARS-CoV-2 and present a comparison of both methods in terms of cost, sampling, timing, accuracy, instrument complexity, global accessibility, feasibility, and adaptability to mutations. Finally, we discuss the issues and potential future research approaches for detecting the SARS-CoV-2 virus utilizing electrochemical biosensing and deep learning.
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In 2018, the Smart City Research Experience for Undergraduates (REU) and Research Experience for Teachers (RET) (SCR2) Mega-Site program was launched, aiming to improve the participation and graduation rates of post-secondary students of underrepresented and minority groups in the field of Engineering. Funded by the National Science Foundation (NSF), the SCR2 program has been successfully conducted for the last three years, engaging a consortium of 14 Historically Black Colleges and Universities (HBCUs) and 1 Hispanic Serving Institution (HSI). Morgan State University in Baltimore, Maryland, is the lead institution for this program. The SCR2 program is designed to engage underperforming REU students in research opportunities demonstrated to improve students' retention and graduation rates. In addition, teachers from local community colleges and high schools are recruited in this program as RET participants. The experience of RET participants in hands-on engineering research projects helps them encourage their students to pursue engineering as a career. The SCR2 program offers summer research experience (eight weeks for students and six weeks for teachers) focusing on smart and connected cities. In this paper, we present our learnings from the last three years of the SCR2 program, which will inform the progress of engineering education and training in the United States. While the 2019 SCR2 program was able to offer on-campus research experience and mentorship for the REU/RETs, the 2020 program had to go virtual to accommodate the extraneous circumstances posed by the COVID-19 pandemic. Despite this transition, the 2020 program engaged 32 undergraduates and 12 teachers, who successfully participated in 12 research projects across three host sites. Learning from the experience of the summer 2020 virtual program, the 2021 SCR2 program was redesigned as a hybrid program and was able to bring six host sites together, offering 18 projects in which 47 undergraduates and 23 teachers participated. One major success of the program was the positive impact of remote learning on both students and teachers. Despite the hybrid nature of the program, students excelled in their technical skills due to the effective collaboration using video conferencing tools. However, during the post-program survey, one primary concern was reported regarding the reduced participation of women students in the program. Simultaneously, the women participants reported less satisfaction and reduced confidence and knowledge gain than men. The transition of the SCR2 program from on-site to online and finally hybrid model exemplifies how innovation in engineering education can overcome the challenges posed by the health crisis. However, it is evident from the assessment results that more attention is needed concerning the experience of women in the program to improve their sense of belongingness in the field of engineering. © American Society for Engineering Education, 2022.
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Background: Unequal housing access resulted in more than 150 million homeless people worldwide, with mil-lions more expected to be added every year due to the ongoing climate-related crises. Homeless population has a counterproductive effect on the social, psychological integration efforts by the community and exposure to other severe health-related issues. Geographic Information Systems (GIS) have long been applied in urban planning and policy, housing and homelessness, and health-related research. Methods: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to systematically review 24 articles collected from multiple databases (n = 10) that focused on health-related issues among homeless people and used geospatial analysis techniques in their research. Results: Our findings indicated a geographic clustering of case study locations- 26 out of the 31 case study sites are from the USA and Canada. Studies used spatial analysis techniques to identify hotspots, clusters and patterns of patient location and population distribution. Studies also reported relationships among the location of homeless shelters and substance use, discarded needles, different infectious and non-infectious disease clusters. Conclusion: Most studies were restricted in analyzing and visualizing the patterns and disease clusters;however, geospatial analyses techniques are useful and offer diverse techniques for a more sophisticated understanding of the spatial characteristics of the health issues among homeless people. Better integration of GIS in health research among the homeless would help formulate sensible policies to counter health inequities among this vulnerable population group.
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Disease diagnosis is an exciting task due to many associated factors. Inaccuracy in the measurement of a patient's symptoms and the medical expert's expertise has some limitations capacity to articulate cause affects the diagnosis process when several connected variables contribute to uncertainty in the diagnosis process. In this case, a decision support system that can assist clinicians in developing a more accurate diagnosis has a lot of potentials. This work aims to deploy a fuzzy inference-based decision support system to diagnose various diseases. Our suggested method distinguishes new cases based on illness symptoms. Distinguishing symptomatic disorders becomes a time-consuming task in most cases. It is critical to design a system that can accurately track symptoms to identify diseases using a fuzzy inference system (FIS). Different coefficients were used to predict and compute the severity of the predicted diseases for each sign of disease. This study aims to differentiate and diagnose COVID-19, typhoid, malaria and pneumonia. The FIS approach was utilized in this study to determine the condition correlating with input symptoms. The FIS method demonstrates that afflictive illness can be diagnosed based on the symptoms. Our decision support system's findings showed that FIS might be used to identify a variety of ailments. Doctors, patients, medical practitioners and other healthcare professionals could benefit from our suggested decision support system for better diagnosis and treatment.
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The COVID-19 is a highly contagious infection that has already had a catastrophic effect on the most severe diseases, including death, worldwide. Blockchain-based healthcare systems are being introduced to help simplify mobile and telehealth services, reducing patient stress and the cost of critical clinical services. Shared the advantages of blockchain for building a cutting-edge authentication infrastructure and detecting COVID-19 suspicious cases. The authors presented a blockchain-based design for developing a real-time cellular health monitoring system for COVID-19 patients in this paper. This study identifies clinical problems and electronic diagnoses for people with COVID-19 infectious diseases and provides a framework for them. Any mobile application can be configured on digital devices such as smartphones. COVID-19 patients may benefit from such applications. Smartphone apps are designed to save time and money while increasing the efficiency of infectious patients. IoT and Blockchain strategies are presented in the four-layer structure.
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Before COVID-19, online learning was almost non-existent in the educational institutions of Bangladesh. Unavailability of Internet and proper devices among the students, lack of training, and the unwillingness of the institutions in integrating a new way of providing education were the main reasons behind the less prevalence of online education in Bangladesh. Due to their lack of experience, design policies, and infrastructural incapacity, educational institutions struggled enormously to make the transition to online learning from face-to-face teaching during this ongoing pandemic. Primary and secondary educational institutions are the major victims of this hasty transformation. Although universities in Bangladesh are trying to continue their regular academic curriculum, the real scenario is far from perfect. To understand the problems of the online education system of Bangladeshi universities, we conducted a survey among 184 students. The user responses were analyzed in two different ways: unsupervised clustering that revealed socio-economic polarization among the students;and feature specific statistical analysis that identified the emerging marginal student groups. Our analysis shows that the factors behind the polarization and marginalization of students include locality, living conditions, primary device for attending class, Internet connectivity etc. Based on these factors, we lay out an inclusive design policy with three action plans that would reduce the polarization and marginalization of university students in online education.
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The novel Coronavirus Disease 2019 (nCOVID-19) pandemic is a global health challenge, that requires collaborative efforts from multiple research communities. Effective screening of infected patients is a significant step in the fight against COVID-19, as radiological examination being an important screening methods. Early findings reveal that anomalies in chest X-rays of COVID-19 patients exist. As a result, a number of deep learning methods have been developed, and studies have shown that the accuracy of COVID-19 patient recognition using chest X-rays is very high. In this paper, we propose an attention based deep neural network for classifying the COVID-19 images, and extracting useful clinical information. Generative adversarial network is used to generate the synthetic COVID-19 images, as well as a good latent representation of both COVID-19 and normal images. Experiment results on public datasets shows the effectiveness of the proposed approach. © 2021 IEEE.
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While ICT is burgeoning in southeast Asia, online food delivery (OFD) picking upstream due to its concrete influence on the mob’s experience. The COVID-19 pandemic caused an unprecedented impact in most commerce including OFD due to the escalation of safety aspects. On account of the explosion of the pandemic and to prevent the spread of COVID-19, socio- and economic factors arise that likely turn OFD potential user’s attitudes and behavior. This report highlights a six-month-long online survey (n = 158) in Bangladesh that analyzes the fluctuation in OFD consumer tendency, identifying the polarization of potential purchasers during the COVID-19 and considering the safety, e-satisfaction, and e-trust. Besides, we also discussed the associations between these determinants that are responsible for polarizing the consumers into marginal groups during the pandemic in developing countries like Bangladesh and proposed some suggestions for OFD service providers based on our findings. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Background: The outbreak of COVID-19 has remained a massive challenge for healthcare workers specially physicians. Effective professional training has a crucial role in preparing doctors for responding to pandemics. Objective: e: To assess the effectiveness of existing training modules on enhancing knowledge, ensuring safe practice, and improving behavior on COVID-19 among physicians. Methods: This is a descriptive, cross-sectional, online survey;where a virtual questionnaire was used to collect data through online professional platforms. A pre-tested survey tool was employed to assess the impact of professional training on infection prevention and control. Results: Total 161 physicians participated in this survey from 15 different countries. Most of the respondents (72%) received training from various sources like the workplace (60%) and international agencies (21%), through the in-person or online format. Knowledge assessment revealed advanced (43%) and competent (40%) understanding by the participants. Improving knowledge progression was displayed by the cohort who received professional training (p<0.00). Physicians’ positive behavior and good practices were observed with the training modules. Conclusion: It became evident from this study, that professional training is effective in enhancing knowledge, improving behavior, and ensuring safe practices. Hence, designing such training modules for the physicians is warranted to tackle ongoing and future pandemics. © 2021 Umar BU.
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Disease diagnosis is a challenging task due to a large number of associated factors. Uncertainty in the diagnosis process arises from inaccuracy in patient attributes, missing data, and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated variables. This paper aims to demonstrate an integrated view of deploying smart disease diagnosis using the Internet of Things (IoT) empowered by the fuzzy inference system (FIS) to diagnose various diseases. The Fuzzy System is one of the best systems to diagnose medical conditions because every disease diagnosis involves many uncertainties, and fuzzy logic is the best way to handle uncertainties. Our proposed system differentiates new cases provided symptoms of the disease. Generally, it becomes a time-sensitive task to discriminate symptomatic diseases. The proposed system can track symptoms firmly to diagnose diseases through IoT and FIS smartly and efficiently. Different coefficients have been employed to predict and compute the identified disease’s severity for each sign of disease. This study aims to differentiate and diagnose COVID-19, Typhoid, Malaria, and Pneumonia. This study used the FIS method to figure out the disease over the use of given data related to correlating with input symptoms. MATLAB tool is utilised for the implementation of FIS. Fuzzy procedure on the aforementioned given data presents that affectionate disease can derive from the symptoms. The results of our proposed method proved that FIS could be utilised for the diagnosis of other diseases. This study may assist doctors, patients, medical practitioners, and other healthcare professionals in early diagnosis and better treat diseases. © 2022 Tech Science Press. All rights reserved.