Levels and Trends of Maternal Mortality and Morbidity by Cause in North Africa and Middle East, 1990 to 2019: An Analysis for the Global Burden of Disease Study 2019

Background: Since 1990, the maternal mortality significantly decreased at global scale as well as the North Africa and Middle East. However, estimates for mortality and morbidity by cause and age at national scale in this region are not available. Method(s): This study is part of the Global Burden of Diseases, Injuries, and Risk Factors study (GBD) 2019. Here we report maternal mortality and morbidity by age and cause across 21 countries in the region from 1990 to 2019. Result(s): Between 1990 and 2019, maternal mortality ratio (MMR) dropped from 148.8 (129.6-171.2) to 94.3 (73.4-121.1) per 100 000 live births in North Africa and Middle East. In 1990, MMR ranged from 6.0 (5.3-6.8) in Kuwait to 502.9 (375.2-655.3) per 100 000 live births in Afghanistan. Respective figures for 2019 were 5.1 (4.0-6.4) in Kuwait to 269.9 (195.8-368.6) in Afghanistan. Percentages of deaths under 25 years was 26.0% in 1990 and 23.8% in 2019. Maternal hemorrhage, indirect maternal deaths, and other maternal disorders rank 1st to 3rd in the entire region. Ultimately, there was an evident decrease in MMR along with increase in socio-demographic index from 1990 to 2019 in all countries in the region and an evident convergence across nations. Conclusion(s): MMR has significantly declined in the region since 1990 and only five countries (Afghanistan, Sudan, Yemen, Morocco, and Algeria) out of 21 nations didn't achieve the Sustainable Development Goal (SDG) target of 70 deaths per 100 000 live births in 2019. Despite the convergence in trends, there are still disparities across countries.Copyright © 2022 Academy of Medical Sciences of I.R. Iran. All rights reserved.

Security Vulnerability in Face Mask Monitoring System

The COVID-19 pandemic has increased demand for face mask detection systems that utilize deep learning and machine learning algorithms. However, these systems are susceptible to adversarial attacks, where an attacker can manipulate the system to make incorrect predictions. This study aimed to test the vulnerability of a deep learning-based face mask detection model to a specific type of attack called a black box adversarial attack in which the attacker possesses only partial information about the target model. The study's findings showed that the attack successfully reduced the model's accuracy from 96.48% to 49.25%. This emphasizes the need for more robust defense mechanisms in face mask detection systems to ensure their reliability. © 2023 Bharati Vidyapeeth, New Delhi.

Early infection risk in patients with systemic lupus erythematosus treated with rituximab or belimumab from the British Isles Lupus Assessment Group Biologics Register (BILAG-BR): a prospective longitudinal study

Background: Patients with systemic lupus erythematosus (SLE) are at an increased risk of infection relative to the general population. We aimed to describe the frequency and risk factors for serious infections in patients with moderate-to-severe SLE treated with rituximab, belimumab, and standard of care therapies in a large national observational cohort. Method(s): The British Isles Lupus Assessment Group Biologics Register (BILAG-BR) is a UK-based prospective register of patients with SLE. Patients were recruited by their treating physician as part of their scheduled care from 64 centres across the UK by use of a standardised case report form. Inclusion criteria for the BILAG-BR included age older than 5 years, ability to provide informed consent, a diagnosis of SLE, and starting a new biological therapy within the last 12 months or a new standard of care drug within the last month. The primary outcome for this study was the rate of serious infections within the first 12 months of therapy. Serious infections were defined as those requiring intravenous antibiotic treatment, hospital admission, or resulting in morbidity or death. Infection and mortality data were collected from study centres and further mortality data were collected from the UK Office for National Statistics. The relationship between serious infection and drug type was analysed using a multiple-failure Cox proportional hazards model. Finding(s): Between July 1, 2010, and Feb 23, 2021, 1383 individuals were recruited to the BILAG-BR. 335 patients were excluded from this analysis. The remaining 1048 participants contributed 1002.7 person-years of follow-up and included 746 (71%) participants on rituximab, 119 (11%) participants on belimumab, and 183 (17%) participants on standard of care. The median age of the cohort was 39 years (IQR 30-50), 942 (90%) of 1048 patients were women and 106 (10%) were men. Of the patients with available ethnicity data, 514 (56%) of 911 were White, 169 (19%) were Asian, 161 (18%) were Black, and 67 (7%) were of multiple-mixed or other ethnic backgrounds. 118 serious infections occurred in 76 individuals during the 12-month study period, which included 92 serious infections in 58 individuals on rituximab, eight serious infections in five individuals receiving belimumab, and 18 serious infections in 13 individuals on standard of care. The overall crude incidence rate of serious infection was 117.7 (95% CI 98.3-141.0) per 1000 person-years. Compared with standard of care, the serious infection risk was similar in the rituximab (adjusted hazard ratio [HR] 1.68 [0.60-4.68]) and belimumab groups (1.01 [0.21-4.80]). Across the whole cohort in multivariate analysis, serious infection risk was associated with prednisolone dose (>10 mg;2.38 [95%CI 1.47-3.84]), hypogammaglobulinaemia (<6 g/L;2.16 [1.38-3.37]), and multimorbidity (1.45 [1.17-1.80]). Additional concomitant immunosuppressive use appeared to be associated with a reduced risk (0.60 [0.41-0.90]). We found no significant safety signals regarding atypical infections. Six infection-related deaths occurred at a median of 121 days (IQR 60-151) days from cohort entry. Interpretation(s): In patients with moderate-to-severe SLE, rituximab, belimumab, and standard immunosuppressive therapy have similar serious infection risks. Key risk factors for serious infections included multimorbidity, hypogammaglobulinaemia, and increased glucocorticoid doses. When considering the risk of serious infection, we propose that immunosupppressives, rituximab, and belimumab should be prioritised as mainstay therapies to optimise SLE management and support proactive minimisation of glucocorticoid use. Funding(s): None.Copyright © 2023 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license

The impact of state paid sick leave policies on weekday workplace mobility during the COVID-19 pandemic.

OBJECTIVES: This study aimed to evaluate whether the Families First Coronavirus Response Act (FFCRA) modified the association between pre-existing state paid sick leave (PSL) and weekday workplace mobility between February 15 and July 7, 2020. STUDY DESIGN: This was a longitudinal, observational study. METHODS: The 50 US states and Washington, D.C., were divided into exposure groups based on the presence or absence of pre-existing state PSL policies. Derived from Google COVID-19 Community Mobility Reports, the outcome was measured as the daily percent change in weekday workplace mobility. Mixed-effects, interrupted time series regression was performed to evaluate weekday workplace mobility after the implementation of the FFCRA on April 1, 2020. RESULTS: States with pre-existing PSL policies exhibited a greater drop in mobility following the passage of the FFCRA (ß = -8.86, 95% confidence interval: -11.6, -6.10, P < 001). This remained significant after adjusting for state-level health, economic, and sociodemographic indicators (ß = -3.13, 95% confidence interval: -5.92, -0.34; P = .039). CONCLUSIONS: Pre-existing PSL policies were associated with a significant decline in weekday workplace mobility after the FFCRA, which may have influenced local health outcomes. The presence of pre-existing state policies may differentially influence the impact of federal legislation enacted during emergencies.

Sustainable and efficient E-learning internet of things system through blockchain technology

E-Learning Education systems are gaining attention day-to-day because of their inclusive pertinence in the distance education system. Due to COVID-19, the online learning education system has become very popular. Most probably, all education systems have been using the IoT-based E-Learning system to continue the students' education without hindrance during the COVID lockdown. Several E-Learning IoT schemes are explored that reflect privacy and security, but still, there is no detailed scheme;hence, it needs a sustainable, secure E-Learning IoT system. The characteristics and prospects of the Internet of Things are discussed in this article. By analyzing the various functions and capabilities of the Internet of Things, this article aims to provide an overview of the various advantages and challenges of using the platform for e-learning. This paper proposed the E-Learning IoT architecture with Blockchain technology, with layers of different IoT and Blockchain concepts to secure the online education system. Also, the block diagram of the proposed architecture demonstrates how students can securely access or interact with the online learning system through Blockchain technology. By implementing the proposed e-learning IoT architecture, universities and colleges can improve their distance learning programs and increase efficiency without affecting their academic activities. Finally, the study found that e-learning positively impacts students' learning experience and overall quality of education. It also exhibited a significant positive impact on their flexibility and academic productivity. © The Author(s) 2023.

A Recent Update on Therapeutics to Treat Emerging n-COVID 19: A Review

A coronavirus is a group of nonsegmented, single-stranded, enveloped viruses having positive RNA genomes. This virus was first described in 1931, and the first coronavirus was isolated (HCoV-229E) from humans in 1965. People be-come infected with four human coronavirus strains: 229E, NL63, OC43, and HKU1, which cause respiratory associated problems such as SARS and MERS. Lately, a new version of a strain called SARD-CoV-2 has been found. WHO called it novel coronavirus-infected pneumonia (NCIP) and later officially renamed as COVID-19 on 11th Feb 2020. The outbreak began in Wuhan, Hubei, China, in Dec 2019 and from now the outbreak becomes pandemic. Here, we have reviewed various categories of therapeutics, vaccines, and clinically investigated drugs to treat and prevent n-COVID-19. Till now, no specific FDA approved drugs or vaccines are available against n-COVID-19. Several options can be visualized to control or prevent emerging infections, including antivirals, immunomodulators, interferons, vaccines, monoclonal antibodies, and bio-molecules. Given the urgency of the outbreak, we have discussed some potential existing therapeutics for treating n-COVID-19.Copyright © 2021 Bentham Science Publishers.

Mechanism of Cardiac Pathogenesis and Cardiotoxicity of Anti-COVID-19 Drugs

Novel coronavirus (nCoV-19) infection has been declared a pandemic by WHO. More than 223 countries are under the attack of this emergency situation. Primarily, pneumocytes encountered by the nCoV-19 via ACE-2 receptor cause pulmonary edema, damage to alveolar cells, production of inflammatory cells, and hypoxia. It has been found that patients with co-existing cardiovascular diseases are more prone to the infection, and severe cardiovascular dysfunction was further observed when infected with nCoV-19. There is no substantial mechanism available for the pathogenesis of this cardiovascular dysfunction;therefore, we herein present a possible mechanistic approach of cardiotoxicity by nCov-19 infection. The hypothesis of this study is based on immunopathology of nCoV-19 in pneumocytes, presence of ACE-2 on cardiomyocytes membrane, cytokine storm, genomic analysis of virus in cardiac tissue, and several reports published on the cardiovascular complications in nCoV-19 across the globe. We have also analyzed the cardiotoxic profile of recently used repurposed and investigational drugs and highlighted their possible cardiotoxic consequences and drug interactions with cardiovascular medicines, such as statins and anti-coagulants.Copyright © 2021 Bentham Science Publishers.

Emergence of a New Zoonotic COVID-19 that Creates a Pandemic: Update on Current Pharmacotherapeutics and Future Prospective of Plasma Therapy

The current decade started on an unexpected note, with almost the entire world grappling with a newly arisen pandemic. A novel coronavirus, tracing its first human host to a Chinese province, has spread to all geographical areas with human populations. The virus, named SARS-CoV-2, infects the lower respiratory tract, much like other coronaviruses, that caused the 2002 epidemic, to which it is eponymous. The severity of infection is seen in individuals with comorbidities like diabetes, cardiovascular disorders, chronic respiratory problems, hypertension, cancer, etc. This virus represents another incidence of zoonosis to humans and has infected over eighteen million people since December 2019, of its first human transmission. All the currently employed therapies are either aimed at alleviating the severity of the symptoms or being administered on a trial basis. This review attempts to summarize brief aetiology of the virus, epidemiology of the outbreak, clinical symptoms of the disease with a postulated mechanism of pathogenesis and several existing and approved drugs and therapeutics along with plasma therapy, which are being clinically reviewed for their activity, as well as safety, against the disease;none of which are approved yet. A few promising vaccine candidates, as per in vivo studies, are also underway, but their evaluation might take a year at least. Meanwhile, experts have come up with the concept of "social distancing" to stem the viral spread, as the medical research fraternity of the world strives hard to find a safe, successful and effective cure for it.Copyright © 2021 Bentham Science Publishers.

Systematic structure guided clustering of chemical lead compounds targeting RdRp of SARS-CoV-2

BACKGROUND: To combat the global health issue caused by SARS-CoV2, scientists are attempting various therapeutic approaches towards drug discovery including computational biology and drug-repurposing. Recent studies have highlighted the conserved nature of RNA-dependent RNA polymerase (RdRp) of coronaviruses affecting human, bat and animals. In this study attempts have been made to identify the potential inhibitors of RdRp by utilizing molecular docking and MD simulation studies. METHOD(S): Systematic structure-based screening of chemical compounds from public libraries was performed to identify the potential lead molecules inhibiting RdRp. This structure driven clustering of compounds is based on decision tree model generated by combining two properties: 1) shape descriptors;and 2) critical number of multiple bonds. The enabled screening of potential chemical compounds was subjected to molecular docking followed by molecular dynamics simulation studies. RESULT(S): The results revealed that the stability of protein-drug complex structure was in the order of RdRp-Oxoglaucine >RdRp-Flutroline >RdRp-Brucine complex. CONCLUSION(S): This study identifies Oxoglaucine, Brucine and Flutroline as prospective inhibiting agents of SARS-CoV-2 RdRp and further warrants for experimental validation. Copyright © 2022 EDIZIONI MINERVA MEDICA.

In-silico analysis of multiepitope based vaccine targeting respiratory viruses SARS, MERS and SARS-CoV-2

BACKGROUND: Recurrent outbreaks of respiratory viruses like SARS-CoV (severe acute respiratory syndrome-coronavirus, 2002), MERS (Middle East respiratory syndrome, 2012) including the ongoing SARS-CoV-2 (2019) pandemic warrants for a single-broad-spectrum vaccine against these respiratory viruses. METHOD(S): In the present study, phylogenetic analysis followed by in-silico identification of vaccine candidates for SARS, MERS and SARS-CoV-2 was performed by exploiting T-cell and B-cell mapping to ascertain the best possible epitopes for effector humoral- and cell-mediated immune response. Further, population-coverage analysis of the identified epitopes followed by the designing of chimera of epitope-based vaccine was done using linkers and adjuvants. Docking study was done to appraise the interaction of the proposed vaccine with ACE2 (angiotensin converting enzyme-2) receptor (SARS and SARS-CoV-2) and HLA-B7 (human leukocyte antigen) receptor (MERS). The stability of the vaccine chimera was confirmed by molecular dynamics performed for 20 ns;this was followed by codon optimization and in-silico cloning. RESULT(S): Phylogenetic analysis revealed similarity among SARS-CoV-2, SARS-CoV and bat SARS-like coronavirus. Both, SARS-CoV and SARS-CoV-2 were from different class than MERS, whereas SARS-CoV-2 showed more relatedness with Bat SARS-like coronaviruses. The most suitable epitopes found were LSFELLNAPATVCGP (SARS), LVTLAILTALRLCAY (SARS-CoV-2) and YTSAFNWLL (MERS) with nearly 98% population coverage. Molecular docking followed by simulation studies revealed high number of hydrogen bonds, stable RMSD values and acceptable RMSF flexibility scores, indicating stable interactions of the vaccine with ACE2 and MHC receptors (Major histocompat-ibility complex). Expression of the designed multiepitope vaccine in E. coli (Escherichia coli) expression system was confirmed by in-silico cloning/codon optimization. CONCLUSION(S): Further, in-vitro and in-vivo experimental validation studies are required to endorse our current findings. Copyright © 2022 EDIZIONI MINERVA MEDICA.

A Rumination on Highly Resistant the SARS-CoV-2 (Omicron) of Mutation, Spread, and Vaccinations to New Challenges

When the world is largely focused on battling the Delta spread, On 26 November 2021 WHO announced the new Covid-19 variant, which is highly mutated. That is called Omicron, which is a serious variant of the subject. The current study examines and compares the changes in SARS-CoV-2, Alpha to Omicron. In these mentioned variants, we observed mutations that could have an impact and alter. Its activity with Spike protein, with to negotiated Omicron affected countries situation in review, has focused on the evolution of SARS-CoV2 variants, the vaccine efficacy and rt PCR testing. This review is to focus on special strategies to boom vaccine recognition against Omicron, an important theme of covid-19 situations. These views on overcoming the pandemic's modern-day demanding situations provide strategies to include SARS-CoV-2 globally.

Impact of an interdisciplinary goals of care program on hospital outcomes at a comprehensive cancer center during the COVID-19 pandemic: A propensity score analysis

Background: Many hospitals have established goals-of-care (GOC) programs in response to the COVID- 19 pandemic;however, few have reported their outcomes. MD Anderson Cancer Center launched a multicomponent interdisciplinary GOC (myGOC) program in March 2020 that involved risk stratification, team huddles to discuss care planning, oncologist-initiated GOC discussions, communication training, palliative care involvement, rapid-response GOC team deployment, and daily monitoring with immediate feedback. We examined the impact of this myGOC program among medical inpatients. Methods: This single-center study with a quasi-experimental design included consecutive adult patients with cancer admitted to medical units at MD Anderson Cancer Center, Texas during an 8-month pre-implementation (May 1, 2019 to December 31, 2019) and post-implementation period (May 1, 2020 to December 31, 2020). The primary outcome was intensive care unit (ICU) mortality. Secondary outcomes included ICU length of stay, hospital mortality, and proportion/timing of patients with inhospital do-not-resuscitate (DNR) orders, medical power of attorney (MPOA), living will (LW) and outof- hospital DNR (OOHDNR). Propensity score weighting was used to adjust for differences in potential covariates, including age, sex, cancer diagnosis, race/ethnicity, and Sequential Organ Failure Assessment (SOFA) Score. With a sample size of 600 ICU patients over each time period and a baseline ICU mortality of 28%, we had 80% power to detect a 5% reduction in mortality using a two-tailed test at 5% significance level. Results: This study involved 12,941 hospitalized patients with cancer (Pre n = 6,977;Post n = 5,964) including 1365 ICU admissions (Pre n = 727;Post n = 638). After myGOC initiation, we observed a significant reduction in ICU mortality (28.2% vs. 21.9%;change -6.3%, 95% CI -9.6, -3.1;P = 0.0001). We also observed significant decreases in length of ICU stay (mean change -1.4 days, 95% CI -2.0, -0.7 days;P < 0.0001) and in-hospital mortality (7% vs. 6.1%, mean change -0.9%, 95% CI -1.5%, -0.3%;P = 0.004). The proportion of hospitalized patients with an inhospital DNR order increased significantly from 14.7% to 19.6% after implementation (odds ratio [OR] 1.4, 95% CI 1.3, 1.5;P < 0.0001) and DNR was established earlier (mean difference -3.0 d, 95% CI -3.9 d, -2.1 d;P < 0.0001). OOHDNR (OR 1.3, 95% CI 1.1, 1.6, P < 0.0007) also increased post-implementation but not MPOA and LW. MPOA, LW and OOHDNR were documented significantly earlier relative to the index hospitalization in the post-implementation period (P < 0.005 for all). Conclusions: This study showed improvement in hospital outcomes and care plan documentation after implementation of a system-wide, multicomponent GOC intervention. Our findings may have implications for GOC programs during the pandemic and beyond.

Learning management system empowered by machine learning

Using a Learning Management System to generate, share, monitor, and maintain various kinds of education and creation content has proven a milestone in improving online learning. Since the first Learning Management System (LMS) emerged, significant technical developments made this platform a vital technology for curriculum management, rich content management, appraisal and assessment, and complex cooperation. The future expects several improvements in its development, processes, and execution, with many emerging fields of the study exploring different innovations relevant to the LMS. Online learning has been on the increase globally due to the rapid growth in technology in education. The 2019 Coronavirus disease (COVID-19) pandemic has implemented online classes for students in all colleges and universities. However, students are unknown about their ability to consider online learning. This paper presents how machine learning could support the Learning Management System's controlling to resolve student's feedback and queries associated with technical issues and problems when they have engaged in online classes. In this paper, a web-based online survey is conducted. Two hundred fifteen (215) students who have enrolled in various streams of Ph.D., Master, and Bachelor level programs in Colleges and University of Asian countries participated in this survey. Based on students' feedback, results are analyzed. A detailed analysis of possible solutions or suggestions is stated and proposed a Machine Learning-based LMS Model to handle students' problems and challenges efficiently during the online classes. © 2022 Author(s).

COVID-19 Detection Using Feature Extraction and Semi-Supervised Learning from Chest X-ray Images

The world has experienced the very first pandemic of 21st century, called the COVID-19 which is caused by a deadly virus named Coronavirus. In this regard, one of the very first strategy to minimize the number of affected patients and reduce casualties is to diagnose COVID-19 at an early stage. Currently, PCR test is primarily utilized for the diagnosis of COVID-19. However, PCR test requires a huge number of expensive test kits as well as trained experts. Therefore, chest X-Ray imaging technique (including Machine Learning) has been considered as an alternative for COVID-19 diagnosis among the researchers. This particular method is faster, less expensive and will allow the authorities to manage the COVID-19 diagnosis system in a cost-effective way. Machine learning techniques have been proven to be significantly efficient and accurate for image classification problems. On the other hand, One of the most utilized techniques in machine learning is supervised learning which is highly convenient and helping the experts to diagnose and make informed decisions about COVID-19. Supervised learning in image classification requires vast amount of radiography images with notable accuracy which can be a peculiar issue in medical domain. In order to address the problem, we have investigated a distinct approach for COVID-19 Diagnosis with a nominal dataset. In this work, We have studied the effectiveness of Semi-Supervised Learning (SSL) for COVID-19 diagnosis from chest X-ray images. We have investigated a prepossessing technique by extracting and combining local phase image feature into multi-feature image to train our SSL model in teacher/student archetype. Our study have shown that by using 17.0% of the total dataset for training, the SSL model achieve 93.45% accuracy. We also provide comparative metrics of SSL approach against other fully supervised techniques.

A Cost-Effective Solution for Real Time Remote Monitoring of Vital Signs in Patients

The outbreak of COVID-19 hit the world with an incomparable magnitude and introduced new challenges in the diagnosis and treatment of patients. Personal interactions have suddenly become dangerous which can be reduced by the use of digital technology in healthcare. Towards this, we have developed a low-cost remote vital sign monitoring system (VSM) that can be used at hospitals as well as at home for continuous and long-term monitoring of different clinical status, and provide extended support to the vulnerable patients. The proposed VSM has been designed with four layers: sensing layer, data processing layer, networking layer, and applications layer. It comes with three units: a wrist unit, a bedside monitor and a web-based graphical user interface (GUI) accessible by the nurse, physician or attendants remotely from anywhere. The effectiveness of measurement, transmission, and remote monitoring has been demonstrated by experiments. The system is designed with open source and low-cost hardware devices to ensure that it can be afforded and implemented in low resource settings of the developing countries. The proposed system can provide an effective way of delivering care to more patients while protecting everyone involved from infection.

Modelling Tourist's Intention to Adopt Travel Applications during the Pandemic. A Bangladesh Perspective

The study's primary purpose is to determine the factors motivating the adoption of online travel applications for booking vacations during the pandemic, specifically amongst the consumers in Bangladesh. The study aims to measure the mediating role of COVID-19 restrictions and information published from time to time on the entire adoption process. The study takes a quantitative approach. A structured questionnaire is used to gather data from 282 respondents nationwide through online mode. A total of 240 respondents showing an interest in online booking of travel are used to analyze the data using Structural Equation Modelling. The EFA and CFA conducted on the 36 items in the questionnaire extracted four factors-two independent variables: Rewarding Motivations and Health Motivations;the dependent variable of Intention to Use and the mediating variable of COVID-19 information. While generating a fit model, there is a significant influence of Rewarding Motivations found on Intention to Use online travel applications with a partially mediating role of COVID-19 information on both the relationships. Empirical evidence from this study will assist tourism marketers in taking sound strategic marketing decisions, which would help revive the tourism sector. © 2022, ASERS Publishing House. All rights reserved.

Investigating the Robustness of Deep Neural Network Based COVID-19 Detection Models Against Universal Adversarial Attacks

Corona viruses are a type of virus with a large family which can cause several terrible and devastating infectious diseases like middle east respiratory syndrome and severe acute respiratory syndrome. The first task of the authority is to screen as many people as possible to detect COVID-19 patients which arises the challenge of rapid screening. Although polymerase chain reaction(PCR) tests are primarily used for the COVID-19 test but because of it's high false negative results and need of experts leading to an alternative diagnostic system based on radiological images like chest X-ray. Moreover, computer aided diagnosis systems from radiography images has significantly been advanced during the last decade with promising efficiency which can overcome the need of both time and experts. In this case, machine learning(ML) and deep learning(DL) based screening techniques can provide automated, fast and reliable results. Therefore, many researchers have proposed several deep neural network(DNN) models for rapid screening of COVID-19 using chest X-ray images. Nevertheless, the vulnerability issue DNN models are overlooked or poorly evaluated in the COVID-19 screening. DNN models are remarkably vulnerable to perturbation which is addressed universal adversarial perturbation (UAP). UAP can falsely influence a DNN model and can eventually lead to going wrong in most of the classification problems. Here, we experimented and evaluated the performance of several DNN based automated COVID-19 diagnostic models, and investigated the robustness of these models against two types of adversarial attack:non targeted and targeted. We showed that DNN based COVID-19 detection models are highly vulnerable to adversarial attack and it is substantially important to be aware of the risk factors of DNN models before deploying for real life applications. © 2021 IEEE.

Rapidly developable low cost and power-efficient portable turbine-based emergency ventilator

The use of ventilators has always been common in medical scenarios but very expensive to procure or develop. One of the main reasons for these is the components that are being used are expensive and require precise instrumentation, research and development. This paper attempts to mitigate that problem by proposing a novel way to rapidly develop a portable ventilator that uses common 3D printing technology and off-the-shelf components. This turbine and valve-based ventilator feature most of the modes that are commonly used by healthcare professionals. A unique servo-based pressure release mechanism has been designed that makes the system around 36 times more efficient than solenoid-based systems. Reliability and efficiency have been increased further through the use of a novel positive end-expiratory pressure (PEEP) valve that does not contain any electromechanical component. Effective algorithms such as feed-forward and proportional-integral-derivative (PID) controllers were used alongside the unique ĝ€Sensor data filtration methodology'. The system also provides an interactive graphical user interface (GUI) via an android application that can be installed on any readily found tabs while the firmware manages the breathing detection algorithm using a flow meter. This modular and portable ventilator also features a swappable battery and holds the ability to run on solar power. This energy-efficient and low noise system can run for 5 to 6 hours at a stretch without needing to be connected with the main's supply. This ventilator's design and development files have been certified by open-source hardware association (OSHWA): https://certification.oshwa.org/bd000001.html © 2021 ACM.

Clinical Consequences and Economic Burden Associated with Febrile Neutropenia in Hospitalized Cancer Patients: A Prospective Study

Objective: Febrile neutropenia (FN) is viewed as the most decimating oncological crisis particularly in chemotherapy-incited patients. The primary objective of the study was to identify the total direct expenditure of patients during febrile neutropenia with clinical consequences and the secondary aim was to find out the factors associated with higher cost. Materials and Method: This was a single-centered hospital-based study in the largest and only specialized cancer care centre in Bangladesh in the government sector. This prospective study was done in the inpatients’ department of the National Institute of Cancer Research and Hospital from April 2020 to January 2021. The primary outcome was the out-of-pocket patient payments (adjusted by government subsidy) per FN episode. Univariate analysis and multiple linear regression were conducted to identify the factors associated with higher costs. Results and Discussions: A total of 101 patients were enrolled in the study. The mean (SD) age was 33.49 (±15.79) years. Of the 101 participants, 63.4% were male.Among the patients, 13.9% died during the episode and 86.1% recovered. Having co-morbidities and COVID-19 were associated with an increased risk of death. The mean cost was US$ 999.44 (±499.05) and the mean length of hospital stay was 21.98 (±9.3) days. The longer hospital stay was significantly associated with higher costs. Conclusion: This study will help to ascertain the hospital cost and clinical outcome of FN which ultimately can help in policymaking strategy.

The Role of Internet of Things (IoT) to Fight against Covid-19

The Internet of Things (IoT) has become a new topic of research in various scientific and corporate fields, particularly in healthcare, in recent decades. The IoT revolution transforms new healthcare services by the inclusion of scientific, political and environmental prospects. It progresses from traditional to specialized healthcare systems, making it easier to identify, treat and manage patients. COVID - 19 has developed into a pandemic that spread around the globe. Day and night, scientists and engineers are working to produce a vaccine, develop additional research equipment and improve surveillance. Using contactless equipment is one of the critical factors to reduce the spread. To prevent the transmission of the coronavirus IoT can be used. That is an interconnection between the Internet and electronic devices. Devices are sensuous and recordable as well as can watch and respond. In this article, we discussed Covid-19 literature, monitoring techniques and proposed an IoT model to help mitigate the transmission of Covid-19. © 2021 ACM.