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Technology has played a vital part in improving quality of life, especially in healthcare. Artificial intelligence (AI) and the Internet of Things (IoT) are extensively employed to link accessible medical resources and deliver dependable and effective intelligent healthcare. Body wearable devices have garnered attention as powerful devices for healthcare applications, leading to various commercially available devices for multiple purposes, including individual healthcare, activity alerts, and fitness. The paper aims to cover all the advancements made in the wearable Medical Internet of Things (IoMT) for healthcare systems, which have been scrutinized from the perceptions of their efficacy in detecting, preventing, and monitoring diseases in healthcare. The latest healthcare issues are also included, such as COVID-19 and monkeypox. This paper thoroughly discusses all the directions proposed by the researchers to improve healthcare through wearable devices and artificial intelligence. The approaches adopted by the researchers to improve the overall accuracy, efficiency, and security of the healthcare system are discussed in detail. This paper also highlights all the constraints and opportunities of developing AI enabled IoT-based healthcare systems. © 2023 by the authors.
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Case Report: Prolonged fever in children is a symptom that is seen in many different diseases, infections, malignancies, and autoimmune conditions. This can, at times, make the correct diagnosis challenging. A previously healthy 10-year-old male was transferred to our institution with one week history of fever, fatigue, abdominal pain, and vomiting. Laboratory studies demonstrated pancytopenia, transaminitis, electrolyte abnormalities, elevated pro-inflammatory markers & D-Dimer, and hypoalbuminemia. COVID-19 IgG was reactive. Due to the severity in presentation the patient was transferred to the ICU with a presumptive diagnosis of MIS-C. Hewas started on IVIG as well as a five-day course of high-dose methylprednisolone per protocol. Aspirin was added, but later discontinued, due to worsening thrombocytopenia. CT imaging with contrast showed small bilateral pleural effusions & periportal edema, mild splenomegaly, and echocardiogram showed diffuse dilation of the left main and left anterior descending arteries. Given the laboratory findings the differential diagnosis was expanded, Ehrlichia caffeensis serology was sent and empiric Doxycycline started. EBV Nuclear Antigen IgG antibody and EBV Viral Capsid Antigen IgM Antibody resulted as positive suggesting recent or reactivated infection. Respiratory viral PCR with COVID-19, Cytomegalovirus and Parvovirus PCR were negative. Despite initial treatment, the patient continued to have persistent fever, severe pancytopenia, and high ferritin up to 24 426 ng/mL, raising suspicion for Haemophagocytic Lymphohistiocytosis (HLH). Soluble interleukin-2 level was elevated & his presentation was then considered to be more consistent with HLH given that he met 6/8 criteria. Screening for primary HLH including CD107a, perforin and granzyme B, SAP, and XIAP resulted in the latter three being normal but CD107a was abnormal. Next generation sequencing for primary criteria was negative. E. Chaffeensis resulted positive: IgM 1:80, IgG 1:256. MIS-C and HLH have overlapping features but differ in some clinical manifestations. Timely recognition and management is paramount as the management differs. This case illustrates the importance of performing a broad search for potential causes, allowing for appropriate and timely treatment. COVID-19 serology alone should not be the basis for diagnosis of MIS-C in a patient with fever and inflammation. This is important as SARS-CoV2 becomes endemic. Infections such as EBV and Ehrlichiosis should be on the differential particularly in endemic areas and during seasons of higher prevalence for the latter, as these have been well documented to cause HLH. Copyright © 2023 Southern Society for Clinical Investigation.
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Purpose: COVID-19 had been the most talked-about issue since the outbreak of the pandemic. There is abundant information available on the internet and all the other media about the virus and related risks and challenges. If we go out and observe people on the streets as well as in public places, we will encounter people with different kinds of mindsets and with varying degrees of precautions against the virus. It is believed that the varied perceptions, attitudes, and behaviours of people towards the virus are majorly shaped by the kind and level of information that they receive and consume from different media channels. The current study aimed to assess the level of knowledge people have towards COVID-19 and determine where they sourced their information from and to also understand how effective the media has been in disseminating the information. The purpose of this study is also to assess people’s perception regarding State’s/Government role in containing the spread of the virus. Design: Keeping the objectives of the current study in mind, a cross-sectional study was conducted among 606 university-going students using a google form, and at the same time mail interviews were conducted with 20 respondents. The survey was structured to assess their knowledge about viral sources, transmission, symptoms, and complications, sources of information about COVID-19 as well as the role and effectiveness of the Government in managing the cases of COVID-19 and in containing and retarding the spread of the virus. Findings: It was found that the majority of the students relied on the Internet and social media for the consumption of their information on COVID 19. The newspaper is the most credible source of information and most of the students believed in the effectiveness of Government initiatives to control the virus. Deployment of forces for containing the virus was criticized and the credibility of the Government data was questioned. Research limitations: The researchers could not cover geographical locations other than Uttar Pradesh (North India) and Karnataka (South India). Non-probability sampling technique was used for the selection of the two best private universities from both the states as it was not possible to reach out to the students at other colleges and universities due to the pandemic and lockdown. The above-mentioned universities were purposely selected as almost a hundred percent of students at these universities had access to the internet and social media, owing to their demographic profile. The findings of the study could not be generalized to the entire population. Social implications: This paper offers a foundation for future research with a broader geographical area, covering all parts of the country and people of all demography’s to understand how people perceived the pandemic and what are the major and most reliable sources of information for them in times of crisis. Originality/value: This paper highlights the new and problematic reality for Indian young people and can help for a better understanding of the issues they may face in the future. © 2022 RESTORATIVE JUSTICE FOR ALL.
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Background: In the wake of rise in COVID patients in the country, world is experiencing an acute shortage of mechanical ventilators and medical oxygen to an extent that many hypoxic patients are not able to get oxygen support. The need of the hour is a more efficient Oxygen Delivery device which can be easily accessible to most of remote health setups that are devoid of ICU beds or Ventilators. Moreover with the growing Oxygen Crisis, we also need devices that can help in Oxygen conservation. Objective: To assess the efficiency of bains circuit compared to NRBM in covid -19 patients awaiting NIV support based on SpO2 and PaO2. Methodology: Prospective study conducted on patients presenting with moderate to severe COVID 19 Disease. The study subjects will be randomly assigned to the experimental group. Baseline data (spO2 levels, PaO2 levels) will be collected, the experimental group will be Oxygenated via NRBM then shifted to Bains Circuit on same oxygen flow rates. SpO2 and PaO2 levels will be compared in the same group. Also, the total Oxygen consumption by each patient of same group will be compared.Assuming acute shortage of Oxygen, ventilator beds and ICU beds in most parts of India, the use of Bains Circuit, if proven efficient over NRBM can be a major help to most of the rural and low resource setups. It can be a useful device for transportation of severely hypoxic patients to higher DCHCs. Results: A common trend was seen in patients maintaining sufficient respiratory efforts but reduced SpO2 on NRBM, as soon as shifted on Bain's circuit (connected via a BiPaP mask), a sudden jump in SpO2 and PaO2 (approx. 15-20%) was seen at same oxygen flow rates. Conclusion: As we anticipate 3rd wave of Covid 19, keeping Bain's circuit as choice for oxygen therapy can be a lifesaving alternative for patients awaiting non invasive ventilator support.
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According to a recent Deloitte study, the COVID-19 pandemic continues to place a huge strain on the global health care sector. Covid-19 has also catalysed digital transformation across the sector for improving operational efficiencies. As a result, the amount of digitally stored patient data such as discharge letters, scan images, test results or free text entries by doctors has grown significantly. In 2020, 2314 exabytes of medical data was generated globally. This medical data does not conform to a generic structure and is mostly in the form of unstructured digitally generated or scanned paper documents stored as part of a patient’s medical reports. This unstructured data is digitised using Optical Character Recognition (OCR) process. A key challenge here is that the accuracy of the OCR process varies due to the inability of current OCR engines to correctly transcribe scanned or handwritten documents in which text may be skewed, obscured or illegible. This is compounded by the fact that processed text is comprised of specific medical terminologies that do not necessarily form part of general language lexicons. The proposed work uses a deep neural network based self-supervised pre-training technique: Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa) that can learn to predict hidden (masked) sections of text to fill in the gaps of non-transcribable parts of the documents being processed. Evaluating the proposed method on domain-specific datasets which include real medical documents, shows a significantly reduced word error rate demonstrating the effectiveness of the approach. IEEE
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RATIONALE: An age-related mortality risk has been discovered during the COVID-19 pandemic;the elderly being at greater risk. This could be explained by age-related impairment of immunity. Social factors such as congregate housing could also play a role[1]. Similarly, cancer patients have been identified as being high-risk for mortality. Thromboembolic events arising as a result of a cytokine storm has been theorised as a potential cause of death as illustrated in figure 1[2]. Previous studies have highlighted male sex as being another high-risk group for morbidity and mortality[2] . We aim to investigate the effects of age, gender and active cancer on the mortality rate and the length of in-patient stay in patients with COVID-19. Methods: A retrospective study of all in-patients aged ≥ 18 years with a confirmed diagnosis of COVID-19 during the first wave of the pandemic. Statistical analysis was performed using the chi squared and Mann-Whitney U test. Results: 445 COVID-19 positive patients were included in the study, of which 69 had active cancer, 329 were aged <65 years and 116 were aged > 65 years. The study contained 261 males and 263 females. Mortality in patients with active cancer was higher (70%) compared to those without active cancer (48%) (P=0.001). There was no significant difference in the number of patients who had an inpatient stay of >7 days between both groups. We also found that there was a higher mortality rate in patients aged > 65 years (61%) compared to those aged < 65 years (25%) (P<0.05), with a greater number of patients aged > 65 years staying >7 days in-hospital (63%) compared to those aged < 65 years (49%) (P=0.03). There were no significant differences in the mortality rates and the length of in-patient stays of >7 days between male and female patients. However, interestingly males had a greater intubation rate (14%) compared to females (6%) (P=0.025). Conclusion: Our study demonstrated increasing age and active cancer status to be linked to greater risk of mortality. Furthermore, males showed a more severe disease course as compared to females. This data should be considered when highlighting at risk groups and prioritising them for treatment and isolation. Figure 1-A potential mechanism of death in COVID-19 patients References: 1. Kang SJ et al Age-related Morbidity and Mortality among patients with COVID-19. Infect Chemother 2020;52(2):154-164 2. Curigliano G et al Cancer Patients and Risk of Mortality for COVID-19. Cancel Cell 2020;38(2):161-163.
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The error has not been corrected in the PDF or HTML versions of the article. When revisiting the values of Beta, Gamma and R0 in our article, we the listed author(s) have found some errors and here we are pointing out some unintentionally leftover irregularities, typos and mistakes which should be corrected to justify and validate the values of Beta, Gamma and R0, as per the differential equations. Hereby, we bring to your attention the following irregularities, typos, and mistakes in Table 2 Page 14261. In the original paper, the equations (given below 1 to 4) and Table 2 must read as: The rate of susceptible people is calculated for a population using Eq. (1) and the description of the parameters is provided in Table 11: 'Equation Presented' 'Table Presented'. © 2021 The Author(s).
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Given the socio-economic impact of coronavirus disease 2019 (COVID-19), it is essential to gauge the spread of this disease. Pakistan is one of the countries, which initially did not suffer from this disease. To observe prediction of COVID-19 cases in Pakistan, the study would utilize a linear regression model. By this model, we can predict the number of infected cases in Pakistan in an efficient way. Linear regression and correlation are two parameters used in the estimation of the linear relationship between various parameters. Correlation tells about the direction and strength of an intervariable linear relationship without discrimination between dependent and independent variables (daily COVID-19 infections are the independent variable, and prediction value is the dependent variable). While linear regression explains the estimations that can predict the values based on given information (number of infected and number of prediction) and consider dependent and independent variables. A scatter plot is deemed to be a useful tool in the determination of relationship strength between relative variables. A correlation coefficient is the measure of association with numerical configuration between two comparable variables that can stand between [-1, 1]. By using this linear regression model, we can predict the number of cases in Pakistan. © 2020, Editorial board of Journal of Experimental Biology and Agricultural Sciences. All rights reserved.
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The global pandemic of COVID-19 has raised several questions and attracted researchers from all of the disciplines of scientific research. Regardless of advances in science and technology, equipped laboratories of virology, high literacy rates, and medical resources in developed countries, several nations and their health care systems completely failed to overcome the disaster. The fast spread is caused by frequent air travel for business, tourism, education, etc. COVID-19 can infect third world countries severely. United States of America has the highest per capita spending of health still 1/3rd of the global burden of COVID-19 has consumed existing resources. The WHO has declared COVID-19 as a pandemic. More than 200 countries and territories have reported infected cases. The quarantine is the most effective way to slow the spread of disease and “Flatting of Curve” is a phenomenon to tackle the surge by health systems. To achieve good results from existing Medical Health Care Systems (MHCS), an accurate prediction for the spread of disease is crucial. This study utilizes the generalized method of SIR to accurately predict the spread of COVID-19 associated infection, recoveries, and deaths in Pakistan. The data from the National Command and Control of Pakistan (NCCP) is utilized. Through multiple cases applied on currently available data, the proposed mathematical models predict that by the end of April about more than 14553 infected and about 310 deaths are in Pakistan. The recovery rate is highest in the region up to 99.87 %.