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The microfluidic paper-based analytical devices (μPADs) have grown-up swiftly over the decade due to its low cost, simple fabrication procedure, resource-limitedness, non-toxicity and their environmentally benign nature. The μPADs, also identified as point-of-care devices or health care devices have successfully applied in several fields such as diagnostics, biological, food safety, environmental, electrochemical and most importantly colorimetric/fluorimetric sensors, owing to the attractive passive motions of analyte without any external forces. In recent years, a large number of colorimetric and fluorimetric probes have been reported that can selectively recognize the analytes in μPADs. However, there is no organized review on its structure-activity relationship. In this review, we have focused to summarize the colorimetric and fluorimetric probes utilized in μPADs. This review discuss about the relationships between the structure and functions of various probes as signaling units of the efficient μPADs. The probes including nanomaterials, nanozymes, polymers and organic molecules, their structural activity with regard to sensing performances along with their limit of detection are also discussed. This review is expected to assist readers for better understanding of the sensing mechanisms of various chemo and bio-probes utilized in μPADs, as well as promote their advancement in the field. On the other hand, this review also helps the researchers for enhancement of μPADs and paves way for synergistic application of existing molecular probes as an effective diagnostic tool for the worldwide pandemic novel corona virus COVID-19. [Display omitted] • Traditional fabrications methods for µPADs are summarized. • Discusses about the hindrances of commercial wax printing method for fabrication of µPADs. • Various sensory materials are categorized and discussed along with their structure activity properties. • Highlights about the most promising material that could be a probable probe for µPADs. • The vision towards employing µPADs for COVID19 diagnosis is also discussed. [ABSTRACT FROM AUTHOR]

Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In this paper, we extensively study the role of artificial intelligence and machine learning in delivering efficient responses to the COVID-19 pandemic almost four years after its start. In this regard, we examine a large number of critical studies conducted by various academic and research communities from multiple disciplines, as well as practical implementations of artificial intelligence algorithms that suggest potential solutions in investigating different COVID-19 decision-making scenarios. We identify numerous areas where artificial intelligence and machine learning can impact this context, including diagnosis (using chest X-ray imaging and CT imaging), severity, tracking, treatment, and the drug industry. Furthermore, we analyse the dilemma's limits, restrictions, and hazards. [ABSTRACT FROM AUTHOR]

The recent pandemic caused by the Coronavirus Disease (COVID-19) first surfaced in Wuhan, China in December 2019. This paper presents an expert system for the diagnosis of COVID-19 based on its symptoms to aid people in taking precautionary measures. When experts are not available, an expert system that can effectively diagnose the disease is crucial. It takes the place of one or more experts in decision-making and problem-solving. An expert system for diagnosis of COVID-19 is a system developed to recognize early COVID-19 symptoms that individuals may experience by allowing users to directly check the disease with results that can serve as a foundation for additional testing. This study's primary goal is to identify useful COVID-19 detection patterns or knowledge by examining the historical data we have obtained from the Kaggle dataset. The patterns are presented as rules, which are given to the expert system after consultation with a domain expert. A total of 1,048,575 pieces of data were used for model training and testing. To detect COVID-19 disease, we employ a PART rule-based algorithm, which performed 92.47% accurately in a 10-fold cross-validation test. We can therefore draw the conclusion that the algorithm produces a promising result and that the expert system aids in the diagnosis of the disease. The system offers a suggestion in line with the identified symptom. [ABSTRACT FROM AUTHOR]

COVID-19 is a dangerous disease that directly damages human health, with the properties of severely contagious and highly variable. It is endangering the health and safety of people all around the world. Thus, it compels governments to seek rapid detection, diagnosis and treatment, and epidemic forecasting approaches under the consumption of considerable human resources, material, and financial resources, for the purpose of curbing its development. In view of diverse merits, such as flexibility, rapidity, and non-intrusion, artificial intelligence (AI) techniques have unparalleled advantages in the rapid, non-contact auxiliary diagnosis and epidemic prediction of COVID-19. This paper reviews the AI's technical advances and clinical applications in the COVID-19 epidemic, including computer-aided diagnosis and epidemic prediction, especially the pipelines of medical imaging and analytical techniques. The survey aims to comprehensively investigate the application of AI technologies in the fight against the epidemic and attempt to organize related works in a globally understandable way. This survey also summarizes current challenging issues in the diagnosis and prediction of COVID-19 with AI technologies and puts forward some suggestions for future work. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has sparked a barrage of primary research and reviews. We investigated the publishing process, time and resource wasting, and assessed the methodological quality of the reviews on artificial intelligence techniques to diagnose COVID-19 in medical images. We searched nine databases from inception until 1 September 2020. Two independent reviewers did all steps of identification, extraction, and methodological credibility assessment of records. Out of 725 records, 22 reviews analysing 165 primary studies met the inclusion criteria. This review covers 174,277 participants in total, including 19,170 diagnosed with COVID-19. The methodological credibility of all eligible studies was rated as critically low: 95% of papers had significant flaws in reporting quality. On average, 7.24 (range: 0–45) new papers were included in each subsequent review, and 14% of studies did not include any new paper into consideration. Almost three-quarters of the studies included less than 10% of available studies. More than half of the reviews did not comment on the previously published reviews at all. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Such information chaos is alarming. It is high time to draw conclusions from what we experienced and prepare for future pandemics. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been a global event affecting all aspects of human life and society, including acoustic aspects. In this Special Issue on COVID-19 and acoustics, we present 48 papers discussing the acoustical impacts of the pandemic and how we deal with it. The papers are divided into seven categories which include: physical masking and speech production, speech perception, noise, the underwater soundscape, the urban soundscape, pathogen transmissibility, and medical diagnosis. [ABSTRACT FROM AUTHOR]

In March 2020, the World Health Organization (WHO) declared COVID-19 a global epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real-time reverse transcription–polymerase chain reaction (RT-PCR) tests with a turnaround time of 2–3 days. To enhance diagnostic accuracy, medical professionals use medical imaging alongside RT-PCR. A positive result on both RT-PCR and medical imaging confirms a COVID-19 diagnosis. Imaging modalities like chest X-ray (CXR), computed tomography (CT) scans, and ultrasound are widely utilized for rapid and precise COVID-19 diagnoses. However, interpreting COVID-19 from these images is time-consuming and susceptible to human error. Therefore, leveraging artificial intelligence (AI) methods, particularly deep learning (DL) models, can deliver consistent, high-performance results. Unlike conventional machine learning (ML), DL models automate all stages of feature extraction, selection, and classification. This paper presents a comprehensive review of using DL techniques for diagnosing COVID-19 from medical imaging. The introduction provides an overview of diagnosing the coronavirus using medical imaging, highlighting associated challenges. Subsequently, the paper delves into key aspects of Computer-Aided Diagnosis Systems (CADS) based on DL methods for diagnosing COVID-19, covering segmentation, classification, explainable AI (XAI), and predictive research. Additionally, it reviews the rehabilitation systems such as the Internet of Medical Things (IoMT) in the context of COVID-19. In another section, uncertainty quantification (UQ) research is showcased, focusing on DL models for the diagnosis of Covid-19. Crucial challenges and future research directions are outlined in another section. Finally, discussion and conclusion sections are also provided at the end of the paper. [ABSTRACT FROM AUTHOR]

Since the first case of COVID‐19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID‐19 and COVID‐19 findings from computed tomography images using pre‐trained models using two different datasets. COVID‐19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy‐paving pattern, ground‐glass opacity, ground‐glass opacity and consolidation, ground‐glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre‐trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient‐weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID‐19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID‐19 in the lungs was an innovative and successful approach. [ABSTRACT FROM AUTHOR]

Coronavirus is an infectious disease induced by extreme acute respiratory syndrome coronavirus 2. Novel coronaviruses can lead to mild to serious symptoms, like tiredness, nausea, fever, dry cough and breathlessness. Coronavirus symptoms are close to influenza, pneumonia and common cold. So Coronavirus can only be confirmed with a diagnostic test. 218 countries and territories worldwide have reported a total of 59.6 million active cases of the COVID-19 and 1.4 million deaths as of November 24, 2020. Rapid, accurate and early medical diagnosis of the disease is vital at this stage. Researchers analyzed the CT and X-ray findings from a large number of patients with coronavirus pneumonia to draw their conclusions. In this paper, we applied Support Vector Machine (SVM) classifier. After that we moved on to deep transfer learning models such as VGG16 and Xception which are implemented using Keras and Tensor flow to detect positive coronavirus patient using X-ray images. VGG16 and Xception show better performances as compared to SVM. In our work, Xception gained an accuracy of 97.46% with 98% f-score. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has opened numerous challenges for scientists to use massive data to develop an automatic diagnostic tool for COVID-19. Since the outbreak in January 2020, COVID-19 has caused a substantial destructive impact on society and human life. Numerous studies have been conducted in search of a suitable solution to test COVID-19. Artificial intelligence (AI) based research is not behind in this race, and many AI-based models have been proposed. This paper proposes a lightweight convolutional neural network (CNN) model to classify COVID and Non_COVID patients by analyzing the hidden features in the X-Ray images. The model has been evaluated with different standard metrics to prove the reliability of the model. The model obtained 98.78%, 93.22%, and 92.7% accuracy in the training, validation, and testing phases. In addition, the model achieved 0.964 scores in the Area Under Curve (AUC) metric. We compared the model with four state-of-art pre-trained models (VGG16, InceptionV3, DenseNet121, and EfficientNetB6). The evaluation results demonstrate that the proposed CNN model is a candidate for an automatic diagnostic tool for the classification of COVID-19 patients using chest X-ray images. This research proposes a technique to classify COVID-19 patients and does not claim any medical diagnosis accuracy. [ABSTRACT FROM AUTHOR]

Recently, the whole world was hit by COVID‐19 pandemic that led to health emergency everywhere. During the peak of the early waves of the pandemic, medical and healthcare departments were overwhelmed by the number of COVID‐19 cases that exceeds their capacity. Therefore, new rules and techniques are urgently required to help in receiving, filtering and diagnosing patients. One of the decisive steps in the fight against COVID‐19 is the ability to detect patients early enough and selectively put them under special care. Symptoms of this disease can be observed in chest X‐rays. However, it is sometimes difficult and tricky to differentiate "only" pneumonia patients from COVID‐19 patients. Machine‐learning can be very helpful in carrying out this task. In this paper, we tackle the problem of COVID‐19 diagnostics following a data‐centric approach. For this purpose, we construct a diversified dataset of chest X‐ray images from publicly available datasets and by applying data augmentation techniques. Then, we employ a transfer learning approach based on a pre‐trained convolutional neural network (DenseNet‐169) to detect COVID‐19 in chest X‐ray images. In addition to that, we employ Gradient‐weighted Class Activation Mapping (GradCAM) to provide visual inspection and explanation of the predictions made by our deep learning model. The results were evaluated against various metrics such as sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and the confusion matrix. The resulting models has achieved an average detection accuracy close to 98.82%. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The outbreak of coronavirus disease (COVID-19) has been a nightmare to citizens, hospitals, healthcare practitioners, and the economy in 2020. The overwhelming number of confirmed cases and suspected cases put forward an unprecedented challenge to the hospital’s capacity of management and medical resource distribution. To reduce the possibility of cross-infection and attend a patient according to his severity level, expertly diagnosis and sophisticated medical examinations are often required but hard to fulfil during a pandemic. To facilitate the assessment of a patient’s severity, this paper proposes a multi-modality feature learning and fusion model for end-to-end covid patient severity prediction using the blood test supported electronic medical record (EMR) and chest computerized tomography (CT) scan images. To evaluate a patient’s severity by the co-occurrence of salient clinical features, the High-order Factorization Network (HoFN) is proposed to learn the impact of a set of clinical features without tedious feature engineering. On the other hand, an attention-based deep convolutional neural network (CNN) using pre-trained parameters are used to process the lung CT images. Finally, to achieve cohesion of cross-modality representation, we design a loss function to shift deep features of both-modality into the same feature space which improves the model’s performance and robustness when one modality is absent. Experimental results demonstrate that the proposed multi-modality feature learning and fusion model achieves high performance in an authentic scenario. [ABSTRACT FROM AUTHOR]

A new epidemic of acute respiratory viral pneumonia was discovered in central China at the end of 2019. The disease was given the name coronavirus disease 2019 (COVID-19), and the virus that caused this disease was known as severe acute respiratory syndrome coronavirus (SARS-CoV-2). So far, diagnostic methods have been focused on (a) human antibody detection, (b) viral antigen detection and (c) viral gene detection, the latter using RT-PCR being the most accurate approach. In this paper, we present a summary of the COVID-19 pandemic, clinical features and epidemiology and pathogenesis. Also, we focus on the recent advances in bioanalytical diagnostic methods based on various techniques for SARS-CoV-2 sensing that have recently been published (2020–2021). Furthermore, we present the mechanisms, advantages and disadvantages of the most common biosensors for COVID-19 detection, which include optical, electrochemical and piezoelectric biosensors as well as wearable and smart nanobiosensors, immunosensors, aptasensors and genosensors. [ABSTRACT FROM AUTHOR]

Since the outbreak of COVID-19 in 2019, the rapid spread of the epidemic has brought huge challenges to medical institutions. If the pathological region in the COVID-19 CT image can be automatically segmented, it will help doctors quickly determine the patient's infection, thereby speeding up the diagnosis process. To be able to automatically segment the infected area, we proposed a new network structure and named QC-HC U-Net. First, we combine residual connection and dense connection to form a new connection method and apply it to the encoder and the decoder. Second, we choose to add Hypercolumns in the decoder section. Compared with the benchmark 3D U-Net, the improved network can effectively avoid vanishing gradient while extracting more features. To improve the situation of insufficient data, resampling and data enhancement methods are selected in this paper to expand the datasets. We used 63 cases of MSD lung tumor data for training and testing, continuously verified to ensure the training effect of this model, and then selected 20 cases of public COVID-19 data for training and testing. Experimental results showed that in the segmentation of COVID-19, the specificity and sensitivity were 85.3% and 83.6%, respectively, and in the segmentation of MSD lung tumors, the specificity and sensitivity were 81.45% and 80.93%, respectively, without any fitting. [ABSTRACT FROM AUTHOR]

The coronaviruses are a deadly family of epidemic viruses that can spread from one individual to another very quickly, infecting masses. The literature on epidemics indicates that the early diagnosis of a coronavirus infection can lead to a reduction in mortality rates. To prevent coronavirus disease 2019 (COVID-19) from spreading, the regular identification and monitoring of infected patients are needed. In this regard, wireless body area networks (WBANs) can be used in conjunction with machine learning and the Internet of Things (IoT) to identify and monitor the human body for health-related information, which in turn can aid in the early diagnosis of diseases. This paper proposes a novel coronavirus-body area network (CoV-BAN) model based on IoT technology as a real-time health monitoring system for the detection of the early stages of coronavirus infection using a number of wearable biosensors to examine the health status of the patient. The proposed CoV-BAN model is tested with five machine learning-based classification methods, including random forest, logistic regression, Naive Bayes, support vector machine and multi-layer perceptron classifiers, to optimize the accuracy of the diagnosis of COVID-19. For the long-term sustainability of the sensor devices, the development of energy-efficient WBAN is critical. To address this issue, a long-range (LoRa)-based IoT program is used to receive biosensor signals from the patient and transmit them to the cloud directly for monitoring. The experimental results indicate that the proposed model using the random forest classifier outperforms models using the other classifiers, with an average accuracy of 88.6%. In addition, power consumption is reduced when LoRa technology is used as a relay node. [ABSTRACT FROM AUTHOR]

Aim: The COVID‐19 pandemic led to widespread disruption of colorectal cancer services during 2020. Established cancer referral pathways were modified in response to reduced diagnostic availability. The aim of this paper is to assess the impact of COVID‐19 on colorectal cancer referral, presentation and stage. Methods: This was a single centre, retrospective cohort study performed at a tertiary referral centre. Patients diagnosed and managed with colorectal adenocarcinoma between January and December 2020 were compared with patients from 2018 and 2019 in terms of demographics, mode of presentation and pathological cancer staging. Results: In all, 272 patients were diagnosed with colorectal adenocarcinoma during 2020 compared with 282 in 2019 and 257 in 2018. Patients in all years were comparable for age, gender and tumour location (P > 0.05). There was a significant decrease in urgent suspected cancer referrals, diagnostic colonoscopy and radiological imaging performed between March and June 2020 compared with previous years. More patients presented as emergencies (P = 0.03) with increased rates of large bowel obstruction in 2020 compared with 2018–2019 (P = 0.01). The distribution of TNM grade was similar across the 3 years but more T4 cancers were diagnosed in 2020 versus 2018–2019 (P = 0.03). Conclusion: This study demonstrates that a relatively short‐term impact on the colorectal cancer referral pathway can have significant consequences on patient presentation leading to higher risk emergency presentation and surgery at a more advanced stage. It is therefore critical that efforts are made to make this pathway more robust to minimize the impact of other future adverse events and to consolidate the benefits of earlier diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Background: Severe acute respiratory syndrome-coronavirus (SARS-CoV) and SARS-CoV-2 might affect the hypothalamic-pituitary-adrenal axis. This paper presents a rare case of pituitary insufficiency diagnosed after CoV disease (COVID)-19. Case Report: On December 7, 2020, a 67-year-old male patient presented to the Endocrinology Department of Lokman Hekim University with weakness, weight loss, and abdominal pain lasting for 3 weeks. In his medical history, he had a positive real-time polymerase chain reaction test result for SARS-CoV-2 based on the nasopharyngeal swab analyzed on September 30. Central hypothyroidism, secondary adrenal insufficiency, hypogonadotropic hypogonadism, and a low dehydroepiandrosterone-sulfate level were detected in the laboratory examinations. Steroid therapy was initiated. Conclusion: Non-specific adrenal insufficiency symptoms such as fatigue and weakness, and psychosomatic symptoms can be seen during the course of COVID-19 or the post-COVID-19 period. Physicians should be aware about the systemic effects of SARS-CoV-2 infection and follow-up patients with a history of COVID-19 accordingly. [ABSTRACT FROM AUTHOR]

Background: Mortality is a key component of the natural history of COVID-19 infection. Surveillance data on COVID-19 deaths and case diagnoses are widely available in the public domain, but they are not used to model time to death because they typically do not link diagnosis and death at an individual level. This paper demonstrates that by comparing the unlinked patterns of new diagnoses and deaths over age and time, age-specific mortality and time to death may be estimated using a statistical method called deconvolution.Methods: Age-specific data were analysed on 816 deaths among 6235 cases over age 50 years in Victoria, Australia, from the period January through December 2020. Deconvolution was applied assuming logistic dependence of case fatality risk (CFR) on age and a gamma time to death distribution. Non-parametric deconvolution analyses stratified into separate age groups were used to assess the model assumptions.Results: It was found that age-specific CFR rose from 2.9% at age 65 years (95% CI:2.2 - 3.5) to 40.0% at age 95 years (CI: 36.6 - 43.6). The estimated mean time between diagnosis and death was 18.1 days (CI: 16.9 - 19.3) and showed no evidence of varying by age (heterogeneity P = 0.97). The estimated 90% percentile of time to death was 33.3 days (CI: 30.4 - 36.3; heterogeneity P = 0.85). The final age-specific model provided a good fit to the observed age-stratified mortality patterns.Conclusions: Deconvolution was demonstrated to be a powerful analysis method that could be applied to extensive data sources worldwide. Such analyses can inform transmission dynamics models and CFR assessment in emerging outbreaks. Based on these Australian data it is concluded that death from COVID-19 occurs within three weeks of diagnosis on average but takes five weeks in 10% of fatal cases. Fatality risk is negligible in the young but rises above 40% in the elderly, while time to death does not seem to vary by age. [ABSTRACT FROM AUTHOR]

Background: In Lebanon, HIV is concentrated in both native and refugee communities of men who have sex with men (MSM). For over 10 years, the National AIDS Program (NAP) has offered HIV voluntary counselling and testing through a partnership with nongovernmental organizations (NGOs). In 2018, implementation of HIV self-tests (HIVST) was introduced, and this self-care intervention has been further scaled up during the coronavirus disease 2019 (COVID-19) pandemic. This paper (1) describes the effectiveness of implementing HIVST in Lebanon, and (2) discusses how the success of HIVST implementation has been reflected during the COVID-19 pandemic.Methods: The NAP conducted a series of workshops (July-November 2018) to introduce HIVST services for healthcare workers working at different NGOs. The workshops highlighted that HIVST would be distributed for free, that it would be confidential and voluntary, and that participants were encouraged to notify the NGOs of their results, which would be kept strictly confidential. NGOs collected data anonymously and confidentially from beneficiaries (age, consistency of condom use and HIV testing history), who were asked to call back with the results of their HIVST. At the NAP, data were combined, aggregated and analysed.Results: In 2019, the NGOs distributed 1103/1380 (79.9%) HIVST kits to their beneficiaries. The NGOs collected feedback on 111 kit results, of which two were HIV-positive. Feedback about HIVST results from beneficiaries was low (111/1103) due to noncompliance of beneficiaries and the lack of human and financial resources in the NGOs. From January through May 2020, a total of 625/780 HIVST kits (80.1%) were distributed. This period was divided into pre-COVID-19 and during COVID-19. The follow-up with the beneficiaries during COVID-19 was much improved because of the absence of on-site activities, shifting more efforts towards HIVST (449/625). There have been no reports of social harm related to HIVST.Conclusion: HIVST implementation in Lebanon serves as an example of introducing a self-care intervention as part of a community-led effort. In order to maintain HIVST services at the same improved level, reorganization of care is needed within each NGO following the adaptation process due to COVID-19, along with continuous monitoring and evaluation of HIVST reported data. [ABSTRACT FROM AUTHOR]

AIMS: Aims: The Coronavirus Disease 2019 (COVID-19) caused not only global pandemic, but it also pointed at unprepared health care systems and countermeasures were introduced under the pressure of urgent circumstances. This review is focused on discussion and critical evaluation of instrumental tools for COVID-19 diagnosis that were developed in the last months. METHODS: Survey of actual literature and scientific reports was made. The most substantial analytical and diagnostical methods were identified and described. Principles and limitations of the methods are described, and actual papers are cited in this review. RESULTS: Analytical and diagnostical methods like Polymerase Chain Reaction (PCR), Loop-mediated isothermal Amplification (LAMP), Lateral Flow Immunochromatography Assay (LFIA), Enzyme-Linked Immunosorbent Assay (ELISA), biosensors and ChemiLuminescence ImmunoAssay (CLIA) are discussed for assay of viral particles, antigens and specific host antibodies in blood, serum, plasma, nasopharyngeal swab and other samples in order to diagnose COVID-19. CONCLUSIONS: The Coronavirus Disease 2019 (COVID-19) is an emerging disease that has spread over the world since the end of year 2019. The global epidemic pointed at the necessity to introduce sensitive methods for instrumental diagnosis of COVID-19 and distinguishing it from the other viral diseases. (Tab. 2, Ref. 96). [ABSTRACT FROM AUTHOR]

In this paper, we describe a case series of four patients who were admitted with emergencies related to aortic aneurysms over a 3-day period and were treated with endovascular repair. The first patient was an 81-year-old female with a history of abdominal pain and a ruptured aortic aneurysm diagnosed by AngioCT-scan. The second patient was a 63-year-old male with a history of oral digestive bleeding and an AngioCT-scan showing an aortoenteric fistula. The third patient was a 77-year-old female with sudden-onset abdominal pain and ruptured right common iliac aneurysm. The fourth patient presented with abdominal pain and an AngioCT-scan showed aortic rupture. All four patients were discharged with no major complications or surgical mortality. These case series show that despite the Covid-19 pandemic situation, since elective surgeries decreased, vascular emergencies have increased. [ABSTRACT FROM AUTHOR]

Since COVID-19 pandemic has been continuously rising and spreading, several original contributions and review articles on COVID-19 started to appear in the literature. The review articles are mainly focus on the current status of the pandemic along with current status of the corona diagnosis and treatment process. Due to some disadvantages of the currently used methods, the improvement on the novel promising diagnosis and treatment methods of corona virus is very important issue. In this review, after briefly discussing the status of current diagnosis and treatment methods, we present to the scientific community, novel promising methods in the diagnosis and treatment of COVID-19. As with other novel approaches, first, the diagnosis potential of mass spectroscopy and optical spectroscopic methods such as UV/visible, infrared, and Raman spectroscopy coupled with chemometrics will be discussed for the corona virus infected samples based on the relevant literature. In vibrational spectroscopy studies, due to complexity of the data, multivariate analysis methods are also applied to data. The application of multivariate analysis tools that can be used to extract useful information from the data for diagnostic and characterisation purposes is also included in this review. The reviewed methods include hierarchical cluster analysis, principal component analysis, linear and quadratic discriminant analysis, support vector machine algorithm, and one form of neural networks namely deep learning method. Second, novel treatment methods such as photodynamic therapy and the use of nanoparticles in the in-corona virus therapy will be discussed. Finally, the advantages of novel promising diagnosis and treatment methods in COVID-19, over standard methods will be discussed. One of the main aims of this paper is to encourage the scientific community to explore the potential of this novel tools for their use in corona virus characterization, diagnosis, and treatment. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) dissemination occurred from December 2019 and quickly spread to all countries. Infected patients with COVID-19 have had a wide range of symptoms, ranging from mild to severe illness. The most mortality was observed in patients with underlying disease and over 45 years. World statistics have shown that the COVID-19 outbreak is most expanded in Middle Eastern, West Asian, European, North, and South American countries, and is least expanded in African countries. Therefore, the aim of the paper was the evaluation of six African countries including Mali, Mauritania, Niger, Guinea, Togo, and Djibouti to find why this disease is least expanded in African countries. Study was conducted by Questioner for countries health organizers to define their different aspect exposure and fight with COVID-19 including epidemiology, clinical aspects of the disease, case definitions, diagnosis laboratory confirmation, and referral of cases by the portal of entry, case management, and disease prevention in these countries. According to this opinion review, due to the low international flights and low domestic travel, the spread, and prevalence of COVID-19 was low and the return of the immigrants of these countries has caused the spread of COVID-19 among these countries. Experience, preparation, and impact of previous infections epidemic such as the Ebola virus epidemic would have beneficial, which have promoted certain reflexes among people that cause low dissemination in these countries. [ABSTRACT FROM AUTHOR]

COVID-19 is a rapidly growing pandemic that has grown from a few cases in Wuhan, China to millions of infections and hundreds of thousands of deaths worldwide within a few months. Sub-Saharan Africa is not spared. Radiology has a key role to play in the diagnosis and management of COVID-19 as literature from Wuhan and Italy demonstrates. We therefore share some critical knowledge and practice areas for radiological suspicion and diagnosis. In addition, emphasis on how guarding against healthcare acquired infections (HAIs) by applying "red" and "green" principle is addressed. Given that pandemics such as COVID-19 can worsen the strain on the scantily available radiological resources in this region, we share some practical points that can be applied to manage these precious resources also needed for other essential services. We have noted that radiology does not feature in many main COVID-19 guidelines, regionally and internationally. This paper therefore suggests areas of collaboration for radiology with other clinical and management teams. We note from our local experience that radiology can play a role in COVID-19 surveillance. [ABSTRACT FROM AUTHOR]

Background: Since the beginning of the COVID-19 outbreak in December 2019, a substantial body of COVID-19 medical literature has been generated. As of June 2020, gaps and longitudinal trends in the COVID-19 medical literature remain unidentified, despite potential benefits for research prioritisation and policy setting in both the COVID-19 pandemic and future large-scale public health crises.Methods: In this paper, we searched PubMed and Embase for medical literature on COVID-19 between 1 January and 24 March 2020. We characterised the growth of the early COVID-19 medical literature using evidence maps and bibliometric analyses to elicit cross-sectional and longitudinal trends and systematically identify gaps.Results: The early COVID-19 medical literature originated primarily from Asia and focused mainly on clinical features and diagnosis of the disease. Many areas of potential research remain underexplored, such as mental health, the use of novel technologies and artificial intelligence, pathophysiology of COVID-19 within different body systems, and indirect effects of COVID-19 on the care of non-COVID-19 patients. Few articles involved research collaboration at the international level (24.7%). The median submission-to-publication duration was 8 days (interquartile range: 4-16).Conclusions: Although in its early phase, COVID-19 research has generated a large volume of publications. However, there are still knowledge gaps yet to be filled and areas for improvement for the global research community. Our analysis of early COVID-19 research may be valuable in informing research prioritisation and policy planning both in the current COVID-19 pandemic and similar global health crises. [ABSTRACT FROM AUTHOR]

To manage the propagation of infectious diseases, particularly fast-spreading pandemics, it is necessary to provide information about possible infected places and individuals, however, it needs diagnostic tests and is time-consuming and expensive. To smooth these issues, and motivated by the current Coronavirus disease (COVID-19) pandemic, in this paper, we propose a learning-based system and a hidden Markov model (i) to assess hazardous places of a contagious disease, and (ii) to predict the probability of individuals' infection. To this end, we track the trajectories of individuals in an environment. For evaluating the models and the approaches, we use the Covid-19 outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by their movement trajectories over a period of time. The simulation results demonstrate that by adjusting the communicable disease parameters, the detector system and the predictor system are able to correctly assess the hazardous places and determine the infection possibility of individuals and cluster them accurately with high probability, i.e., on average more than 96%. In general, the proposed approaches to assessing hazardous places and predicting the infection possibility of individuals can be applied to contagious diseases by tailoring them to the influential features of the disease. • Utilizing the movement trajectories of individuals in a city to manage infection disease. • Proposing a learning-based system to assess hazardous places of a contagious disease. • Proposing a hidden Markov model to predict the probability of individuals infection. • Applying the Covid-19 outbreak in an urban environment as a case study. [Display omitted] [ABSTRACT FROM AUTHOR]

To manage the propagation of infectious diseases, particularly fast-spreading pandemics, it is necessary to provide information about possible infected places and individuals, however, it needs diagnostic tests and is time-consuming and expensive. To smooth these issues, and motivated by the current Coronavirus disease (COVID-19) pandemic, in this paper, we propose a learning-based system and a hidden Markov model (i) to assess hazardous places of a contagious disease, and (ii) to predict the probability of individuals' infection. To this end, we track the trajectories of individuals in an environment. For evaluating the models and the approaches, we use the Covid-19 outbreak in an urban environment as a case study. Individuals in a closed population are explicitly represented by their movement trajectories over a period of time. The simulation results demonstrate that by adjusting the communicable disease parameters, the detector system and the predictor system are able to correctly assess the hazardous places and determine the infection possibility of individuals and cluster them accurately with high probability, i.e., on average more than 96%. In general, the proposed approaches to assessing hazardous places and predicting the infection possibility of individuals can be applied to contagious diseases by tailoring them to the influential features of the disease. • Utilizing the movement trajectories of individuals in a city to manage infection disease. • Proposing a learning-based system to assess hazardous places of a contagious disease. • Proposing a hidden Markov model to predict the probability of individuals infection. • Applying the Covid-19 outbreak in an urban environment as a case study. [Display omitted] [ABSTRACT FROM AUTHOR]

Vare et al. in their manuscript reported the incidence, cumulative mortality, and factors affecting the outcome of COVID-19-associated mucormycosis (CAM) from western India. The reported incidence of CAM in this report, that is, 13.6 cases per 1000 cases of moderate-severe COVID-19 infection, is a debatable figure as the paper does not specify if the 67 CAM patients belonged to the same pool of the 4910 COVID-19 patients treated at the same hospital. It is alarming to note that there were patients in this cohort who despite having a confirmed diagnosis of mucormycosis were unable to receive the drug of choice (amphotericin-B), most likely due to unavailability of the drug. [Extracted from the article]

In the on-going COVID-19 pandemic, pooled testing of samples by RT-PCR has been recommended at certain scenarios to increase labs' testing capacity and reduce cost of testing. This paper describes the evaluation of bi-directional matrix pooling strategies with clinical samples in a 5 × 5 and 10 × 10 matrix. Nasopharyngeal swab samples in viral transport medium (VTM) previously tested (positive or negative) by real time RT-PCR for SARS-CoV-2 were used for these experiments. Ten sets of 5 × 5 (250 samples) and ten sets of 10 × 10 (1000 samples) pooling of samples in both directions was done with known positive samples introduced at random positions. Extracted nucleic acid was tested for SARS-CoV-2 E-gene by RT-PCR. Sensitivity or concordance and feasibility of matrix pooling were assessed in comparison to direct RT-PCR testing. In comparison to direct testing, the overall concordance was 86.6% for 5 × 5 pooling, 73.3% for 10 × 10 with 200 µL extraction volume and 86.6% for 10 × 10 with 400 µL extraction volume. Bi-directional matrix pooling can be adopted with advantage over conventional direct or pool testing for COVID-19 by RT-PCR under the following conditions: i) sample positivity rate of ≤ 5%, ii) matrix pool size of 8–10 samples, iii) use of min. 40 µL VTM from each sample and iv) utilization of automated liquid handling equipment, if available, for sample addition to avoid human errors. • Evaluation of new pooling strategy was carried out for COVID-19 RT-PCR testing. • Pools of 5 or 10 samples tested in 5 × 5 or 10 × 10 bi-directional matrix in which positive samples could be identified without de-pooling all the samples of a positive pool. • In comparison to direct testing, the overall concordance was 86.6% for 5 × 5 pooling, 73.3% for 10 × 10 with 200 µL extraction volume and 86.6% for 10 × 10 with 400 µL extraction volume. [ABSTRACT FROM AUTHOR]

Almost 80% of people confronting COVID-19 recover from COVID-19 disease without any particular treatments. They experience heterogeneous symptoms; a wide range of respiratory symptoms, cough, dyspnea, fever, and viral pneumonia. However, some others need urgent intervention and special treatment to get rid of this widespread disease. So far, there isn't any unique drug for the potential treatment of COVID 19. However, some available therapeutic drugs used for other diseases seem beneficial for the COVID-19 treatment. On the other hand, there is a robust global concern for developing an efficient COVID-19 vaccine to control the COVID-19 pandemic sustainably. According to the WHO report, since 8 October 2021, 320 vaccines have been in progress. 194 vaccines are in the pre-clinical development stage that 126 of them are in clinical progression. Here, in this paper, we have comprehensively reviewed the most recent and updated information about coronavirus and its mutations, all the potential therapeutic approaches for treating COVID-19, developed diagnostic systems for COVID- 19 and the available COVID-19 vaccines and their mechanism of action. [Display omitted] [ABSTRACT FROM AUTHOR]

• Joint segmentation and detection framework for COVID-19. • Context enhancement module for semantic segmentation. • Context enhancement at multiple resolutions. • Optimize segmentation and detection results through post-processing. • Obtained state-of-the-art results for COVID-19 segmentation and detection. The fast pandemics of coronavirus disease (COVID-19) has led to a devastating influence on global public health. In order to treat the disease, medical imaging emerges as a useful tool for diagnosis. However, the computed tomography (CT) diagnosis of COVID-19 requires experts' extensive clinical experience. Therefore, it is essential to achieve rapid and accurate segmentation and detection of COVID-19. This paper proposes a simple yet efficient and general-purpose network, called Sequential Region Generation Network (SRGNet), to jointly detect and segment the lesion areas of COVID-19. SRGNet can make full use of the supervised segmentation information and then outputs multi-scale segmentation predictions. Through this, high-quality lesion-areas suggestions can be generated on the predicted segmentation maps, reducing the diagnosis cost. Simultaneously, the detection results conversely refine the segmentation map by a post-processing procedure, which significantly improves the segmentation accuracy. The superiorities of our SRGNet over the state-of-the-art methods are validated through extensive experiments on the built COVID-19 database. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has shown immense potential to fight COVID-19 in many ways. This paper focuses primarily on AI's role in managing COVID-19 using digital images, clinical and laboratory data analysis, and a summary of the most recent articles published last year. We surveyed the use of AI for COVID-19 detection, screening, diagnosis, the progression of severity, mortality, drug repurposing, and other tasks. We started with the technical overview of all models used to fight the COVID-19 pandemic and ended with a brief statement of the current state-of-the-art, limitations, and challenges. [ABSTRACT FROM AUTHOR]

• Immediate requirement for fast and sensitively identification of COVID-19. • Development of new, low-cost and reliable methods for identifying COVID-19. • Importance of rapid and specific diagnosis in the control and treatment of COVID-19. • Awareness with the advantages of biosensor technology and their importance in pandemic screening. Undoubtedly, the coronavirus pandemic is one of the most influential events not only in medicine but also in the economic field in the world. Rapid transmission and high mortality rates, as well as prolonged and asymptomatic communal periods, are the most important reasons for the global panic due to coronavirus. Since coronavirus treatment and specific vaccines are not yet available, early detection of the virus is critical. A rapid and accurate diagnosis can play a crucial role in the treatment and control of the COVID 19 disease. Serological, ELISA, and molecular-based tests, including PCR and RT-PCR, are among the most important routine methods for detecting coronaviruses. False-positive/negative results, low sensitivity and specificity, and the need for advanced equipment are among the disadvantages and problems of routine methods. To eliminate the drawbacks of routine methods, new technologies are being developed. Biosensors are one of the most important ones. This paper is a summary of the up-to-date states of innovative bio-sensing tools for the ultrasensitive detection of coronaviruses (COVID 19) with encouraging uses for future challenges in disease diagnosis. [ABSTRACT FROM AUTHOR]

The current coronavirus disease 2019 (COVID-19) outbreak is considered as one of the biggest public health challenges and medical emergencies of the century. A global health emergency demands an urgent development of rapid diagnostic tools and advanced therapeutics for the mitigation of COVID-19. To cope with the current crisis, nanotechnology offers a number of approaches based on abundance and versatile functioning. Despite major developments in early diagnostics and control of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is still a need to find effective nanomaterials with low cost, high stability and easy use. Nanozymes are nanomaterials with innate enzyme-like characteristics and exhibit great potential for various biomedical applications such as disease diagnosis and anti-viral agents. Overall the potential and contribution of nanozymes in the fight against SARS-CoV-2 infection i.e., rapid detection, inhibition of the virus at various stages, and effective vaccine development strategies, is not fully explored. This paper discusses the utility and potential of nanozymes from the perspective of COVID-19. Moreover, future research directions and potential applications of nanozymes are highlighted to overcome the challenges related to early diagnosis and therapeutics development for the SARS-CoV-2. We anticipate the current perspective will play an effective role in the existing response to the COVID-19 crisis. [ABSTRACT FROM AUTHOR]