Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System.

With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and smart medical systems have gained popularity. The Internet of Things (IoT) has revolutionized communication and data collection by incorporating smart sensors for data collection from diverse sources. In addition, it utilizes artificial intelligence (AI) approaches to control a large volume of data for better use, storing, managing, and making decisions. In this research, a health monitoring system based on AI and IoT is designed to deal with the data of heart patients. The system monitors the heart patient's activities, which helps to inform patients about their health status. Moreover, the system can perform disease classification using machine learning models. Experimental results reveal that the proposed system can perform real-time monitoring of patients and classify diseases with higher accuracy.

Evaluation of Willingness to Accept COVID-19 Vaccine and Willingness to Pay among Pakistani Parents for Their Children Aged 5 to 11 Years: Findings and Implications.

Vaccines are the most efficient and cost-effective tool to halt the transmission and prevention of COVID-19. The current study examined the willingness of parents to vaccinate their children against COVID-19. This was a cross-sectional study that used a questionnaire based on the Health Belief Model, previous history of COVID-19, willingness to accept, and willingness to pay for the COVID-19 vaccine. The questionnaire was administered among parents of children aged 5 to 11 years. Descriptive statistics, χ2 tests, and regression analysis were carried out for data analysis. A total of 474 respondents participated in this survey with a response rate of 67.7%. In our study, a majority of the respondents exhibited a willingness to accept the COVID-19 vaccine for their children (Definitely yes/Probably yes = 252, 53.2%); nevertheless, 229 (48.3%) respondents were unwilling to pay for it. More than three-quarters of the respondents were worried about the probability of COVID-19 infection in their children (n = 361, 76.2%) and were afraid of COVID-19-associated complications (n = 391, 82.5%). Likewise, most respondents showed their concerns regarding the effectiveness of the vaccine (n = 351, 74.1%), vaccine safety (n = 351, 74.1%), and the halal nature of the vaccine (n = 309, 65.2%). Respondents who were aged 40 to 50 years (odds ratio [OR]: 0.101, 95% CI: 0.38-0.268; P < 0.001), family income > 50,000 PKR (OR: 0.680, 95% CI: 0.321-1.442; P = 0.012), and location (OR: 0.324, 95% CI: 0.167-0.628; P = 0.001) were the factors that were likely to impact vaccine acceptance among parents. Education-based interventions are urgently required to improve COVID-19 vaccination acceptance among parents for their children.

Face mask detection using deep convolutional neural network and multi-stage image processing

• A customized image dataset is built for research on face mask detection. • The dataset is manually labeled to provide high annotation accuracy. • For Face mask detection customized CNN with multi-step image processing is used. • The performance of the proposed CNN is compared with YOLO v3 and Faster R-CNN. • Two publicly available datasets including MAFA and MOXA used for validation. Face mask detection has several applications including real-time surveillance, biometrics, etc. Face mask detection is also useful for surveillance of the public to ensure face mask wearing in public places. Ensuring that people are wearing a face mask is not possible with monitoring staff;instead, automatic systems are a much better choice for face mask detection and monitoring to help manage public behaviour and contribute to restricting the outbreak of COVID-19. Despite the availability of several such systems, the lack of a real image dataset is a big hurdle to validating state-of-the-art face mask detection systems. In addition, using the simulated datasets lack the analysis needed for real-world scenarios. This study builds a new dataset namely RILFD by taking real pictures using a camera and annotating them with two labels (with mask, without mask) which are publicly available for future research. In addition, this study investigates various machine learning models and off-the-shelf deep learning models YOLOv3 and Faster R-CNN for the detection of face masks. The customized CNN models in combination with the 4 steps of image processing are proposed for face mask detection. The proposed approach outperforms other models and proved its robustness with a 97.5% of accuracy score in face mask detection on the RILFD dataset and two publicly available datasets (MAFA and MOXA). [ FROM AUTHOR] Copyright of Image & Vision Computing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Temporal analysis and opinion dynamics of COVID-19 vaccination tweets using diverse feature engineering techniques.

The outbreak of the COVID-19 pandemic has also triggered a tsunami of news, instructions, and precautionary measures related to the disease on social media platforms. Despite the considerable support on social media, a large number of fake propaganda and conspiracies are also circulated. People also reacted to COVID-19 vaccination on social media and expressed their opinions, perceptions, and conceptions. The present research work aims to explore the opinion dynamics of the general public about COVID-19 vaccination to help the administration authorities to devise policies to increase vaccination acceptance. For this purpose, a framework is proposed to perform sentiment analysis of COVID-19 vaccination-related tweets. The influence of term frequency-inverse document frequency, bag of words (BoW), Word2Vec, and combination of TF-IDF and BoW are explored with classifiers including random forest, gradient boosting machine, extra tree classifier (ETC), logistic regression, Naïve Bayes, stochastic gradient descent, multilayer perceptron, convolutional neural network (CNN), bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), and recurrent neural network (RNN). Results reveal that ETC outperforms using BoW with a 92% of accuracy and is the most suitable approach for sentiment analysis of COVID-19-related tweets. Opinion dynamics show that sentiments in favor of vaccination have increased over time.

ETCNN: Extra Tree and Convolutional Neural Network-based Ensemble Model for COVID-19 Tweets Sentiment Classification.

Pandemics influence people negatively and people experience fear and disappointment. With the global outspread of COVID-19, the sentiments of the general public are substantially influenced, and analyzing their sentiments could help to devise corresponding policies to alleviate negative sentiments. Often the data collected from social media platforms is unstructured leading to low classification accuracy. This study brings forward an ensemble model where the benefits of handcrafted features and automatic feature extraction are combined by machine learning and deep learning models. Unstructured data is obtained, preprocessed, and annotated using TextBlob and VADER before training machine learning models. Similarly, the efficiency of Word2Vec, TF, and TF-IDF features is also analyzed. Results reveal the better performance of the extra tree classifier when trained with TF-IDF features from TextBlob annotated data. Overall, machine learning models perform better with TF-IDF and TextBlob. The proposed model obtains superior performance using both annotation techniques with 0.97 and 0.95 scores of accuracy using TextBlob and VADER respectively with Word2Vec features. Results reveal that use of machine learning and deep learning models together with a voting criterion tends to yield better results than other machine learning models. Analysis of sentiments indicates that predominantly people possess negative sentiments regarding COVID-19.

Industry 4.0 technologies and their applications in fighting COVID-19 pandemic using deep learning techniques.

The disease known as COVID-19 has turned into a pandemic and spread all over the world. The fourth industrial revolution known as Industry 4.0 includes digitization, the Internet of Things, and artificial intelligence. Industry 4.0 has the potential to fulfil customized requirements during the COVID-19 emergency crises. The development of a prediction framework can help health authorities to react appropriately and rapidly. Clinical imaging like X-rays and computed tomography (CT) can play a significant part in the early diagnosis of COVID-19 patients that will help with appropriate treatment. The X-ray images could help in developing an automated system for the rapid identification of COVID-19 patients. This study makes use of a deep convolutional neural network (CNN) to extract significant features and discriminate X-ray images of infected patients from non-infected ones. Multiple image processing techniques are used to extract a region of interest (ROI) from the entire X-ray image. The ImageDataGenerator class is used to overcome the small dataset size and generate ten thousand augmented images. The performance of the proposed approach has been compared with state-of-the-art VGG16, AlexNet, and InceptionV3 models. Results demonstrate that the proposed CNN model outperforms other baseline models with high accuracy values: 97.68% for two classes, 89.85% for three classes, and 84.76% for four classes. This system allows COVID-19 patients to be processed by an automated screening system with minimal human contact.

Customer Prioritization Integrated Supply Chain Optimization Model with Outsourcing Strategies.

Pre-COVID-19, most of the supply chains functioned with more capacity than demand. However, COVID-19 changed traditional supply chains' dynamics, resulting in more demand than their production capacity. This article presents a multiobjective and multiperiod supply chain network design along with customer prioritization, keeping in view price discounts and outsourcing strategies to deal with the situation when demand exceeds the production capacity. Initially, a multiperiod, multiobjective supply chain network is designed that incorporates prices discounts, customer prioritization, and outsourcing strategies. The main objectives are profit and prioritization maximization and time minimization. The introduction of the prioritization objective function having customer ranking as a parameter and considering less capacity than demand and outsourcing differentiates this model from the literature. A four-valued neutrosophic multiobjective optimization method is introduced to solve the model developed. To validate the model, a case study of the supply chain of a surgical mask is presented as the real-life application of research. The research findings are useful for the managers to make price discounts and preferred customer prioritization decisions under uncertainty and imbalance between supply and demand. In future, the logic in the proposed model can be used to create web application for optimal decision-making in supply chains.

A CNN based coronavirus disease prediction system for chest X-rays

Coronavirus disease (COVID-19) proliferated globally in early 2020, causing existential dread in the whole world. Radiography is crucial in the clinical staging and diagnosis of COVID-19 and offers high potential to improve healthcare plans for tackling the pandemic. However high variations in infection characteristics and low contrast between normal and infected regions pose great challenges in preparing radiological reports. To address these challenges, this study presents CODISC-CNN (CNN based Coronavirus DIsease Prediction System for Chest X-rays) that can automatically extract the features from chest X-ray images for the disease prediction. However, to get the infected region of X-ray, edges of the images are detected by applying image preprocessing. Furthermore, to attenuate the shortage of labeled datasets data augmentation has been adapted. Extensive experiments have been performed to classify X-ray images into two classes (Normal and COVID), three classes (Normal, COVID, and Virus Bacteria), and four classes (Normal, COVID, and Virus Bacteria, and Virus Pneumonia) with the accuracy of 97%, 89%, and 84% respectively. The proposed CNN-based model outperforms many cutting-edge classification models and boosts state-of-the-art performance.

A Portable Device for LAMP Based Detection of SARS-CoV-2.

This paper reports the design, development, and testing of a novel, yet simple and low-cost portable device for the rapid detection of SARS-CoV-2. The device performs loop mediated isothermal amplification (LAMP) and provides visually distinguishable images of the fluorescence emitted from the samples. The device utilises an aluminium block embedded with a cartridge heater for isothermal heating of the sample and a single-board computer and camera for fluorescence detection. The device demonstrates promising results within 20 min using clinically relevant starting concentrations of the synthetic template. Time-to-signal data for this device are considerably lower compared to standard quantitative Polymerase Chain Reaction(qPCR) machine (~10-20 min vs. >38 min) for 1 × 102 starting template copy number. The device in its fully optimized and characterized state can potentially be used as simple to operate, rapid, sensitive, and inexpensive platform for population screening as well as point-of-need severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) detection and patient management.

Prophylactic potential of honey and Nigella sativa L. against hospital and community-based SARS-CoV-2 spread: a structured summary of a study protocol for a randomised controlled trial.

OBJECTIVES: Considering the therapeutic potential of honey and Nigella sativa (HNS) in coronavirus disease 2019 (COVID-19) patients, the objective of the study is defined to evaluate the prophylactic role of HNS. TRIAL DESIGN: The study is a randomized, placebo-controlled, adaptive clinical trial with parallel group design, superiority framework with an allocation ratio of 1:1 among experimental (HNS) and placebo group. An interim analysis will be done when half of the patients have been recruited to evaluate the need to adapt sample size, efficacy, and futility of the trial. PARTICIPANTS: All asymptomatic patients with hospital or community based COVID-19 exposure will be screened if they have had 4 days exposure to a confirmed case. Non-pregnant adults with significant exposure level will be enrolled in the study High-risk exposure (<6 feet distance for >10min without face protection) Moderate exposure (<6 feet distance for >10min with face protection) Subjects with acute or chronic infection, COVID-19 vaccinated, and allergy to HNS will be excluded from the study. Recruitment will be done at Shaikh Zayed Post-Graduate Medical Institute, Ali Clinic and Doctors Lounge in Lahore (Pakistan). INTERVENTION AND COMPARATOR: In this clinical study, patients will receive either raw natural honey (0.5 g) and encapsulated organic Nigella sativa seeds (40 mg) per kg body weight per day or empty capsule with and 30 ml of 5% dextrose water as a placebo for 14 days. Both the natural products will be certified for standardization by Government College University (Botany department). Furthermore, each patient will be given standard care therapy according to version 3.0 of the COVID-19 clinical management guidelines by the Ministry of National Health Services of Pakistan. MAIN OUTCOMES: Primary outcome will be Incidence of COVID-19 cases within 14 days of randomisation. Secondary endpoints include incidence of COVID-19-related symptoms, hospitalizations, and deaths along with the severity of COVID-19-related symptoms till 14th day of randomization. RANDOMISATION: Participants will be randomized into experimental and control groups (1:1 allocation ratio) via the lottery method. There will be stratification based on high risk and moderate risk exposure. BLINDING (MASKING): Quadruple blinding will be ensured for the participants, care providers and outcome accessors. Data analysts will also be blinded to avoid conflict of interest. Site principal investigator will be responsible for ensuring masking. NUMBERS TO BE RANDOMISED (SAMPLE SIZE): 1000 participants will be enrolled in the study with 1:1 allocation. TRIAL STATUS: The final protocol version 1.4 was approved by institutional review board of Shaikh Zayed Post-Graduate Medical Complex on February 15, 2021. The trial recruitment was started on March 05, 2021, with a trial completion date of February 15, 2022. TRIAL REGISTRATION: Clinical trial was registered on February 23, 2021, www.clinicaltrials.gov with registration ID NCT04767087 . FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). With the intention of expediting dissemination of this trial, the conventional formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol. The study protocol has been reported in accordance with the Standard Protocol Items: Recommendations for Clinical Interventional Trials (SPIRIT) guidelines.

An integrated framework for COVID-19 classification based on classical and quantum transfer learning from a chest radiograph.

COVID-19 is a quickly spreading over 10 million persons globally. The overall number of infected patients worldwide is estimated to be around 133,381,413 people. Infection rate is being increased on daily basis. It has also caused a devastating effect on the world economy and public health. Early stage detection of this disease is mandatory to reduce the mortality rate. Artificial intelligence performs a vital role for COVID-19 detection at an initial stage using chest radiographs. The proposed methods comprise of the two phases. Deep features (DFs) are derived from its last fully connected layers of pre-trained models like AlexNet and MobileNet in phase-I. Later these feature vectors are fused serially. Best features are selected through feature selection method of PCA and passed to the SVM and KNN for classification. In phase-II, quantum transfer learning model is utilized, in which a pre-trained ResNet-18 model is applied for DF collection and then these features are supplied as an input to the 4-qubit quantum circuit for model training with the tuned hyperparameters. The proposed technique is evaluated on two publicly available x-ray imaging datasets. The proposed methodology achieved an accuracy index of 99.0% with three classes including corona virus-positive images, normal images, and pneumonia radiographs. In comparison to other recently published work, the experimental findings show that the proposed approach outperforms it.

A novel RdRp-based colorimetric RT-LAMP assay for rapid and sensitive detection of SARS-CoV-2 in clinical and sewage samples from Pakistan.

Novel corona virus SARS-CoV-2, causing coronavirus disease 2019 (COVID-19), has become a global health challenge particularly for developing countries like Pakistan where overcrowded cities, inadequate sanitation, little health awareness and poor socioeconomic conditions exist. The SARS-CoV-2 has been known to spread primarily through direct contact and respiratory droplets. However, detection of SARS-CoV-2 in stool and sewage have raised the possibility of fecal-oral mode of transmission. Currently, quantitative reverse-transcriptase PCR (qRT-PCR) is the only method being used for SARS-CoV-2 detection, which requires expensive instrumentation, dedicated laboratory setup, highly skilled staff, and several hours to report results. Considering the high transmissibility and rapid spread, a robust, sensitive, specific and cheaper assay for rapid SARS-CoV-2 detection is highly needed. Herein, we report a novel colorimetric RT-LAMP assay for naked-eye detection of SARS-COV-2 in clinical as well as sewage samples. Our SARS-CoV-2 RdRp-based LAMP assay could successfully detect the virus RNA in 26/28 (93%) of RT-PCR positive COVID-19 clinical samples with 100% specificity (n = 7) within 20 min. We also tested the effect of various additives on the performance of LAMP assay and found that addition of 1 mg/ml bovine serum albumin (BSA) could increase the sensitivity of assay up to 101 copies of target sequence. Moreover, we also successfully applied this assay to detect SARS-CoV-2 in sewage waters collected from those areas of Lahore, a city of Punjab province of Pakistan, declared as virus hotspots by local government. Our optimized LAMP assay could provide a sensitive first tier strategy for SARS-CoV-2 screening and can potentially help diagnostic laboratories in better handling of high sample turnout during pandemic situation. By providing rapid naked-eye SARS-CoV-2 detection in sewage samples, this assay may support pandemic readiness and emergency response to any possible virus outbreaks in future.

Saliva Exhibits High Sensitivity and Specificity for the Detection of SARS-COV-2.

In the wake of the COVID-19 pandemic, it is crucial to assess the application of a multitude of effective diagnostic specimens for conducting mass testing, for accurate diagnosis and to formulate strategies for its prevention and control. As one of the most versatile and amenable specimen options, saliva offers great advantages for widespread screening strategies due to its non-invasive properties, cost-effectiveness, excellent stability and minimal risk of cross-infection. This review attempts to outline the scientific rationale for detection of SARS-COV-2 in saliva specimens. By combining the data obtained from ten chosen published clinical studies, we calculated the pooled sensitivity and specificity using an online calculator. Through evidence, we established that SARS-COV-2 is detectable in saliva with a high degree of diagnostic sensitivity (87%) and specificity (98%). We also presented a review of emerging technologies approved by the FDA for detection of SARS-COV-2 in oral fluids (saliva and sputum) using polymerase chain reaction methods. Given the challenges involved in obtaining invasive specimens from the naso- and oropharynx, saliva can serve as an easy to collect diagnostic specimen for screening in the work environment, schools and for home testing. Furthermore, saliva offers the opportunity to screen early cases that can be missed by invasive sampling.

The Role of Internet of Things to Control the Outbreak of COVID-19 Pandemic.

Currently, COVID-19 pandemic is the major cause of disease burden globally. So, there is a need for an urgent solution to fight against this pandemic. Internet of Things (IoT) has the ability of data transmission without human interaction. This technology enables devices to connect in the hospitals and other planned locations to combat this situation. This article provides a road map by highlighting the IoT applications that can help to control it. This study also proposes a real-time identification and monitoring of COVID-19 patients. The proposed framework consists of four components using the cloud architecture: 1) data collection of disease symptoms (using IoT-based devices); 2) health center or quarantine center (data collected using IoT devices); 3) data warehouse (analysis using machine learning models); and 4) health professionals (provide treatment). To predict the severity level of COVID-19 patients on the basis of IoT-based real-time data, we experimented with five machine learning models. The results reveal that random forest outperformed among all other models. IoT applications will help management, health professionals, and patients to investigate the symptoms of contagious disease and manage COVID-19 +ve patients worldwide.

COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images.

COVID-19 pandemic is widely spreading over the entire world and has established significant community spread. Fostering a prediction system can help prepare the officials to respond properly and quickly. Medical imaging like X-ray and computed tomography (CT) can play an important role in the early prediction of COVID-19 patients that will help the timely treatment of the patients. The x-ray images from COVID-19 patients reveal the pneumonia infections that can be used to identify the patients of COVID-19. This study presents the use of Convolutional Neural Network (CNN) that extracts the features from chest x-ray images for the prediction. Three filters are applied to get the edges from the images that help to get the desired segmented target with the infected area of the x-ray. To cope with the smaller size of the training dataset, Keras' ImageDataGenerator class is used to generate ten thousand augmented images. Classification is performed with two, three, and four classes where the four-class problem has X-ray images from COVID-19, normal people, virus pneumonia, and bacterial pneumonia. Results demonstrate that the proposed CNN model can predict COVID-19 patients with high accuracy. It can help automate screening of the patients for COVID-19 with minimal contact, especially areas where the influx of patients can not be treated by the available medical staff. The performance comparison of the proposed approach with VGG16 and AlexNet shows that classification results for two and four classes are competitive and identical for three-class classification.

Detection of the SARS-CoV-2 humanized antibody with paper-based ELISA.

This work reports the development of a rapid, simple and inexpensive colorimetric paper-based assay for the detection of the severe acute respiratory symptom coronavirus 2 (SARS-CoV-2) humanized antibody. The paper device was prepared with lamination for easy sample handling and coated with the recombinant SARS-CoV-2 nucleocapsid antigen. This assay employed a colorimetric reaction, which is followed by horseradish peroxidase (HRP) conjugated detecting antibody in the presence of the 3,3',5,5'-tetramethylbenzidine (TMB) substrate. The colorimetric readout was evaluated and quantified for specificity and sensitivity. The characterization of this assay includes determining the linear regression curve, the limit of detection (LOD), the repeatability, and testing complex biological samples. We found that the LOD of the assay was 9.00 ng µL-1 (0.112 IU mL-1). The relative standard deviation was approximately 10% for a sample number of n = 3. We believe that our proof-of-concept assay has the potential to be developed for clinical screening of the SARS-CoV-2 humanized antibody as a tool to confirm infected active cases or to confirm SARS-CoV-2 immune cases during the process of vaccine development.