ABSTRACT
Due to improvements in information and communication technology and growth of sensor technologies, Internet of Things is now widely used in medical field for optimal resource management and ubiquitous sensing. In hospitals, many IoT devices are linked together via gateways. Importance of gateways in modernization of hospitals cannot be overstated, but their centralized nature exposes them to a variety of security threats, including integrity, certification, and availability. Block chain technology for level monitoring in oxygen cylinders is a scattered record containing the data related to oxygen levels in the cylinder, patient's name, patient's ID number, patient's medical history, and all connected information carried out and distributed among the hospitals (nodes) present in the locality (network). Designing an oxygen level monitoring technique in an oxygen cylinder used as the support system for COVID-19-affected patients is a challenging task. Monitoring the level of oxygen in the cylinders is very important because they are used for saving the lives of the patients suffering from COVID-19. Not only the COVID-19 patients are dependent on this system, but this system will also be helpful for other patients who require oxygen support. The present scenario many COVID-19 hospitalized patients rely upon oxygen supply through oxygen cylinders and manual monitoring of oxygen levels in these cylinders has become a challenging task for the healthcare professionals due to overcrowding. If this level monitoring of oxygen cylinders are automated and developed as a mobile App, it would be of great use to the medical field, saving the lives of the patients who are left unmonitored during this pandemic. This proposal is entitled to develop a system to measure oxygen level using a smartphone App which will send instantaneous values about the level of the oxygen inside the cylinder. Pressure sensors and load cell are fitted to the oxygen cylinders, which will measure the oxygen content inside the cylinder in terms of the pressure and weight. The pressure sensors and load cells are connected to the Arduino board and are programmed to display the actual level of oxygen inside the cylinder in terms of numerical values. A beep sound is generated as an indicator to caution the nurses and attendants of the patients regarding the level of the oxygen inside the cylinder when it is only 15% of the total oxygen level in the cylinder in correlation to the pressure and weight. The signal with respect to the level corresponding to the measured pressure and weight of the cylinder is further transmitted to the monitoring station through Global System for Mobile communication (GSM). Graphical display is used at monitoring end to indicate the level of oxygen inside all oxygen cylinders to facilitate actions like 100% full, 80% full, 60% full, 40% full, 20% full which states that either the oxygen cylinder is in good condition, or requires a replacement of empty cylinders with filled ones in correlation to the pressure and weight being sensed by the sensors. The levels of the oxygen monitored inside the cylinder and other related data can also be stored on a cloud storage which will facilitate the retrieval of the status at any point of time, as when required by the physicians and nurses. These results reported, are valued in monitoring the level of the oxygen cylinder remotely connected to the patients, affected by COVID-19, using a smartphone App. This mobile phone App is an effective tool for investigating the oxygen cylinder level used as a life-support system for COVID-19-affected patients. A virtual model of the partial system is developed using TINKER CAD simulation package. In real time, the sensor data analysis with cloud computing will be deployed to detect and track the level of the oxygen cylinders. © 2023 Elsevier Inc. All rights reserved.
ABSTRACT
Introduction. COVID-19 is an infection caused by severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) which may be associated with a wide range of bacterial and fungal co-infections. Mucormycosis is an opportunistic fungal infection occurring in post COVID-19 patients. Objectives. To study the role of histopathology in mucormycosis and the predisposing factors associated in development of mucormycosis in post COVID-19 patients. Materials and methods. A prospective observational study was conducted in our hospital in the pathology department over a period of 3 months on 200 patients with mucormycosis who were infected with SARS-CoV-2 virus. Results. Out of the 200 patients with mucormycosis studied in post COVID-19 patients, age ranged from 21-80 years, of which 132 were men and 68 were women. Sites involved by mucormycosis were sinuses, orbit, cranium, and cutaneous. Ethmoid sinus was most involved, followed by maxillary sinus. Diabetes was present in 162 patients and hypertension in 92 patients. On histopathological examination, fungal load was severe in 49 patients, angioinvasion was present in 48 patients, perineural invasion was present in 32 patients, and necrosis was present in 121 patients. The number of patients discharged after surgery was 169, whereas 31 died. Conclusion. Histopathological features of mucormycosis like angioinvasion, perineural invasion, severe fungal load, and large areas of necrosis were directly proportional to the mortality rate. Thus, histopathologists can help in assessing prognosis at the time of tissue diagnosis, so that clinicians can optimize treatment accordingly. Diabetes and history of corticosteroid intake for treatment of COVID-19 were the two commonest predisposing factors for development of mucormycosis.
ABSTRACT
Corona Virus Disease (Covid-19) is a label species of the Corona virus family. It can cause a variety of illnesses, from the ordinary cold to advanced respiratory syndromes like Middle-East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). This virus is highly contagious and spreads due to the droplets produced by coughing and sneezing. Though there are several ways to prevent the transmission of Covid-19, one of the most important and effective way is using a face mask or a face shield. In this paper, we constructed face mask detection framework using Viola-Jones algorithm in order to recognize whether an individual is wearing a mask or not. This algorithm includes the selection of Haar features of a face, integral image creation, adaptive boost training and cascading. An extensive study is carried out in order to analyze the performance of the proposed approach;we use a large facial image dataset from the publicly available MAFA dataset. The results indicate the proposed method can accurately identify face mask wearing images with a classifier accuracy of 98.26%, suggesting it might be useful in Covid-19 prevention. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
ABSTRACT
A 43-year-old-married male diagnosed with coronavirus disease 2019 (COVID-19) in July 2020. His symptoms as described by him began with cough and sore throat, breathlessness, generalized body weakness, irritability, stress, and mood swing for a period of one week. He was admitted in our District COVID Care Center (DCCC) located in Tamil Nadu, India. He underwent Integrated Yoga and Naturopathy (IYN) [i.e., Conventional medicine + yoga and naturopathy] for two weeks. The results showed negative Reverse Transcription Polymerase Chain Reaction (RT-PCR) test for COVID-19, improvement in cardiovascular functions (i.e., a reduction in systolic and diastolic blood pressures, pulse rate, mean arterial pressure, rate pressure product, and double product) and mental health (i.e., a reduction in depression, anxiety, and stress levels). The results suggest that IYN might improve cardiovascular and mental health of patients with COVID-19 in addition to positive to negative conversion of RT-PCR. However, further studies are required to warrant these results.
ABSTRACT
The proposed online-based malnutrition-induced anemia detection smart phone app is built, to remotely measure and monitor the anemia and malnutrition in humans by using a non-invasive method. This painless method enables user-friendly measurements of human blood stream parameters like hemoglobin (Hb), iron, folic acid, and vitamin B12 by embedding intelligent image processing algorithms which will process the photos of the fingernails captured by the camera in the smart phone. This smart phone app extracts the color and shape of the fingernails, will classify the anemic and vitamin B12 deficiencies as onset, medieval, and chronic stage with specific and accurate measurements instantly. On the other dimension, this novel technology will place an end to the challenge involved in the disposal of biomedical waste, thereby offering a contactless measurement system during this pandemic Covid-19 situation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Background: Presently, the diagnosis of coronavirus-2019 (COVID-19) is a challenging task worldwide as the disease is spreading at a very faster rate when one person with the disease comes into contact with the other. Current information denotes that several people are detected with COVID-19 and the data analyst say that the rate of spread of the disease is increasing exponentially, across many countries in the world. Novelty: This investigation has facilitated the need for diagnosing the disease within a short duration of time by using the X-ray images of the lungs. This scheme deploys artificial intelligence like deep learning algorithms to diagnose COVID-19 among the affected people by maintaining social distancing. Real-time datasets are gathered from the government hospitals for those who are affected by COVID-19 and healthy people. Further investigation can direct the patients themselves to open the smart phone app which will record the respiratory sounds. Followed by this, the features are extracted using Discrete Wavelet Transform (DWT), where a threshold is applied to extract useful coefficients that can be used to train the deep learning neural networks using Fast Recurrent Convolutional Neural Networks (F-RCNN). The respiratory audio signals are captured to detect patients affected by coronavirus by a way of noncontact, nonintrusive approach. The results reported are valued in detection of COVID-19 by using a smart phone app which is available instantly. Objectives: This approach seems to be an indigenous, noninvasive, and cost-effective approach that will relive the patients from trauma of undergoing the swab test and awaiting the laboratory reports, which incurs time delay. Experimental results are obtained from 20,000 samples of patients suffering from COVID-19 and also persons who are normal. This mobile phone app is effective in diagnosing the COVID-19 from the X-ray images of the lungs. Even low-income people can also use this technology. Methods: The effectiveness of the proposed system which uses DWT, thresholding, and deep learning algorithms resulted with a performance whose F-measure is 96–98%. The classification is carried out to classify the COVID-19-positive and COVID-19-negative cases using Fast Recurrent Convolutional Neural Networks (F-RCNN). Expected Outcome: A smart phone app will be developed to detect the COVID-19 by using a noninvasive and easily affordable technique. The forecasted results were in the range of 89–95% for the above said algorithms. It is significant from the above results that the severe impact of COVID-19 can be diagnosed using a noninvasive mobile phone app using X-ray images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The Covid-19 pandemic situation transformed the education system across the world to the virtual mode. Due to this, the process of teaching learning becomes virtual which made various domains in the education problematical to carry out. Consequently, there is an increase in the rate of purchasing the additional hardware like writing pads and pens for teaching, which is not practicable in the case of deprived. Also, there is an ever-increasing inquisitiveness in crafting systems to automatically recognize free hand drawn sketches as it includes the challenge of recognizing various diverse patterns of sketch and the diagrams in different directions. In this work Deep learning (DL)-based Convolutional Neural Networks with VGG16 architecture is proposed to classify the hand drawn electronic components and digitize them for better legibility. It is most preferable during online classes and presentations. For training and testing, custom online hand drawn dataset is given, which consists of 15 different symbols each of 1100 symbols. In this approach a user-friendly GUI is provided for drawing circuit symbols which is very helpful for the user rather than picking placing and drawing the diagram. The custom hand-made dataset is trained, tested and accuracy is calculated. With this approach along with the individual symbol recognition, complete circuit is also reconstructed and gives 99.2% accuracy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Wearing a mask has become mandatory to protect ourselves from infectious diseases caused by viruses. Today, we are facing a pandemic crisis due to COVID-19 virus. It worsens the lives of living things particularly human beings. The whole world felt stagnant from its normalcy. The educational institutions are particularly affected by this pandemic situation for not conducting the direct classes. To avoid this scenario, they are willing to conduct classes with some guidelines such as social distancing, wearing masks, and sanitizing the hands. We have considered wearing a mask is more important than the remaining two aspects. We are providing a solution with the help of the ResNet50 deep learning network to check whether the students have worn a mask in a classroom in order to prevent them from illness. Deep learning is an advancement of machine learning technique which gives more accurate results than the machine learning algorithms. The performance of our implemented deep learning based face mask detection system is discussed. The live video of the classroom is taken and analysed for recognizing the student's face with and without mask and generating the name of the students without wearing a mask.
ABSTRACT
This study was aimed to summarize the current data on clinicolaboratory features, treatment, intensive care needs, and outcome of pediatric inflammatory multisystem syndrome temporally associated with severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2; PIMS-TS) or multisystem inflammatory syndrome in children (MIS-C). Articles published in PubMed, Web of Science, Scopus, Google Scholar, and novel coronavirus disease 2019 (COVID-19) research database of World Health Organization (WHO), Centers for Disease Control and Prevention (CDC) database, and Cochrane COVID-19 study register between December 1, 2019 and July 10, 2020. Observational studies involving patients <21 years with PIMS-TS or MIS-C were reported the clinicolaboratory features, treatment, intensive care needs, and outcome. The search identified 422 citations and finally 18 studies with 833 participants that were included in this study, and pooled estimate was calculated for parameters of interest utilizing random effect model. The median age was 9 (range: 8-11) years. Fever, gastrointestinal symptoms, rash, conjunctival injection, and respiratory symptoms were common clinical features. Majority (84%) had positive SARS-CoV-2 antibody test and only one-third had positive reverse transcript polymerase chain reaction (RT-PCR). The most common laboratory abnormalities noted were elevated C-reactive protein (CRP), D-dimer, procalcitonin, brain natriuretic peptide (BNP), fibrinogen, ferritin, troponin, interleukin 6 (IL-6), lymphopenia, hypoalbuminemia, and thrombocytopenia. Cardiovascular complications included shock (65%), myocardial dysfunction (61%), myocarditis (65%), and coronary artery abnormalities (39%). Three-fourths of children required admission to pediatric intensive care unit (PICU) where they received vasoactive medications (61%) and mechanical ventilation (25%). Treatment strategies used included intravenous immunoglobulin (IVIg; 82%), steroids (54%), antiplatelet drugs (64%), and anticoagulation (51%). Mortality for patients with PIMS-TS or MIS-C was low ( n = 13). In this systematic review, we highlight key clinical features, laboratory findings, therapeutic strategies, intensive care needs, and observed outcomes for patients with PIMS-TS or MIS-C. Commonly observed clinical manifestations include fever, gastrointestinal symptoms, mucocutaneous findings, cardiac dysfunction, shock, and evidence of hyperinflammation. The majority of children required PICU admission, received immunomodulatory treatment, and had good outcome with low mortality.