Search

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]

Background and Objectives: In the era of the COVID-19 pandemic, telemedicine, so far underestimated, has gained in value. Currently, telemedicine is not only a telephone or chat consultation, but also the possibility of the remote recording of signals (such as ECG, saturation, and heart rate) or even remote auscultation of the lungs. The objective of this review article is to present a potential role for, and disseminate knowledge of, telemedicine during the COVID-19 pandemic. Material and Methods: In order to analyze the research material in accordance with PRISMA guidelines, a systematic search of the ScienceDirect, Web of Science, and PubMed databases was conducted. Out of the total number of 363 papers identified, 22 original articles were subjected to analysis. Results: This article presents the possibilities of remote patient registration, which contributes to an improvement in remote diagnostics and diagnoses. Conclusions: Telemedicine is, although not always and not by everyone, an accepted form of providing medical services. It cannot replace direct patient–doctor contact, but it can undoubtedly contribute to accelerating diagnoses and improving their quality at a distance. [ABSTRACT FROM AUTHOR]

Prior studies found reduced incidences of cardiovascular diagnoses and treatments in the initial phase of the COVID-19 pandemic. However, these studies included a limited number of outcomes and did not consider pre-pandemic trends. This study aimed to describe trends in the incidence of cardiovascular diagnoses and treatments over the years 2012–2021 in Israel and to compare the two years of the COVID-19 period with the preceding 8 years. In this retrospective, population-based study, carried out within Clalit Health Services, the incidence rates of cardiovascular outcomes were calculated for individuals aged ≥ 25 (~2.7 million adults per year) during the first (Y1, 3/2020–2/2021) and second (Y2, 3/2021–2/2022) years of COVID-19 and the 8 years prior (3/2012–2/2020). Declines were observed in Y1 compared to 2019 in all diagnoses and treatments: STEMI (−16.3%; 95% CI: −16.6, −16.1), non-STEMI (−16.4%; −16.6, −16.2), AF (−14.1%; −14.2, −14.0), CHF (−7.8%; −7.9, −7.7), CVA (−5.0%; −5.0, −4.9), catheterization (−64.7%; −65.2, −64.2), CABG (−77.7%; −79.2, −76.2), ablation (−21.2%; −22.0, −20.4), pacemaker implantation (−39.3%; −40.7, −37.9), and defibrillator insertion (−12.5%; −13.1, −12.0). Compared with expected rates based on pre-pandemic trends, observed rates were within expected ranges (CHF, CVA, and ablation), less than expected (STEMI, non-STEMI, AF, catheterization, CABG, and pacemaker insertion), or more than expected (defibrillator insertion). In Y2, STEMI, catheterization, and CABG returned to expected rates; non-STEMI and AF were lower than expected; and CHF, CVA, ablation, and pacemaker and defibrillator implantations were higher than expected. Several cardiovascular diagnoses and treatment trends were interrupted by COVID-19. The long-term consequences of these changes should be considered by health policymakers. [ABSTRACT FROM AUTHOR]

Pathogen discovery plays a crucial role in the fields of infectious diseases, clinical microbiology, and public health. During the past four years, the global response to the COVID-19 pandemic highlighted the importance of early and accurate identification of novel pathogens for effective management and prevention of outbreaks. The post-COVID era has ushered in a new phase of infectious disease research, marked by accelerated advancements in pathogen discovery. This review encapsulates the recent innovations and paradigm shifts that have reshaped the landscape of pathogen discovery in response to the COVID-19 pandemic. Primarily, we summarize the latest technology innovations, applications, and causation proving strategies that enable rapid and accurate pathogen discovery for both acute and historical infections. We also explored the significance and the latest trends and approaches being employed for effective implementation of pathogen discovery from various clinical and environmental samples. Furthermore, we emphasize the collaborative nature of the pandemic response, which has led to the establishment of global networks for pathogen discovery. [ABSTRACT FROM AUTHOR]

In this paper, we approach the problem of detecting and diagnosing COVID-19 infections using multisource scan images including CT and X-ray scans to assist the healthcare system during the COVID-19 pandemic. Here, a computer-aided diagnosis (CAD) system is proposed that utilizes analysis of the CT or X-ray to diagnose the impact of damage in the respiratory system per infected case. The CAD was utilized and optimized by hyper-parameters for shallow learning, e.g., SVM and deep learning. For the deep learning, mini-batch stochastic gradient descent was used to overcome fitting problems during transfer learning. The optimal parameter list values were found using the naïve Bayes technique. Our contributions are (i) a comparison among the detection rates of pre-trained CNN models, (ii) a suggested hybrid deep learning with shallow machine learning, (iii) an extensive analysis of the results of COVID-19 transition and informative conclusions through developing various transfer techniques, and (iv) a comparison of the accuracy of the previous models with the systems of the present study. The effectiveness of the proposed CAD is demonstrated using three datasets, either using an intense learning model as a fully end-to-end solution or using a hybrid deep learning model. Six experiments were designed to illustrate the superior performance of our suggested CAD when compared to other similar approaches. Our system achieves 99.94, 99.6, 100, 97.41, 99.23, and 98.94 accuracy for binary and three-class labels for the CT and two CXR datasets. [ABSTRACT FROM AUTHOR]

Coronavirus (COVID-19) is the most prevalent coronavirus infection with respiratory symptoms such as fever, cough, dyspnea, pneumonia, and weariness being typical in the early stages. On the other hand, COVID-19 has a direct impact on the circulatory and respiratory systems as it causes a failure to some human organs or severe respiratory distress in extreme circumstances. Early diagnosis of COVID-19 is extremely important for the medical community to limit its spread. For a large number of suspected cases, manual diagnostic methods based on the analysis of chest images are insufficient. Faced with this situation, artificial intelligence (AI) techniques have shown great potential in automatic diagnostic tasks. This paper aims at proposing a fast and precise medical diagnosis support system (MDSS) that can distinguish COVID-19 precisely in chest-X-ray images. This MDSS uses a concatenation technique that aims to combine pre-trained convolutional neural networks (CNN) depend on the transfer learning (TL) technique to build a highly accurate model. The models enable storage and application of knowledge learned from a pre-trained CNN to a new task, viz., COVID-19 case detection. For this purpose, we employed the concatenation method to aggregate the performances of numerous pre-trained models to confirm the reliability of the proposed method for identifying the patients with COVID-19 disease from X-ray images. The proposed system was trialed on a dataset that included four classes: normal, viral-pneumonia, tuberculosis, and COVID-19 cases. Various general evaluation methods were used to evaluate the effectiveness of the proposed model. The first proposed model achieved an accuracy rate of 99.80% while the second model reached an accuracy of 99.71%. [ABSTRACT FROM AUTHOR]

The COVID-19 infection caused by the new SARS-CoV-2 virus has been linked to a broad spectrum of symptoms, from a mild cough to life-threatening pneumonia. As we learn more about this unusual COVID-19 epidemic, new issues are emerging and being reported daily. Mucormycosis, also known as zygomycosis or phycomycosis, causes severe fungal illness to individuals with a weakened immune system. It is a devastating fungal infection, and the most frequent kind is the rhino cerebral type. As a devastating second wave of COVID-19 sweeps India, doctors report several instances involving a strange illness—sometimes known as the "black fungus"—among returning and recovered COVID-19 patients. This paper analyzes the existing statistical data to address the severity of prevalence and further notes the nano-based diagnostic parameters, clinical presentations, its connection with other conditions like diabetes, hypertension, and GI disorders, and the importance of anti-fungal therapy in treating the same. Anti-fungal therapies, as well as surgical interventions, are currently used for the treatment of the disease. Proper and timely diagnosis is necessary, along with the reduction in the spread of COVID-19. From the review, it was found that timely pharmacologic interventions and early diagnosis by using a nano-based diagnostic kit can help control the disease. Additionally, this paper provides novel information about the nanotechnology approaches such as fungal detection biosensors, nucleic acids-based testing, point-of-care tests, and galactomannans detection, in the diagnosis of mucormycosis, and thereby reinforces the need for further research on the topic. [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]

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]

Laryngeal adenoid cystic carcinoma (LACC) is a sporadic neoplasm, especially if supraglottic. The COVID-19 pandemic worsened the presenting stage of many cancers and impacted their prognosis negatively. Here, a case of a patient with adenoid cystic carcinoma (ACC) with delayed diagnosis and a rapid deterioration with distant metastasis due to the COVID-19 pandemic is illustrated. Next, we present a literature review of this rare glottic ACC. The COVID-19 pandemic worsened the stage of presentation of many cancers and adversely affected their prognosis. The present case had a rapidly lethal course, undoubtedly due to the diagnosis delay caused by the COVID-19 pandemic, which impacted the prognosis of this rare glottic ACC. Strict follow-up is recommended for any suspicious clinical findings, as an early diagnosis will improve the disease prognosis, and to consider the influence of the COVID-19 pandemic, especially on the timing of common diagnostic and therapeutic procedures for oncological diseases. In the post-COVID-19 era, it is important to generate new diagnostic scenarios to achieve an increasingly rapid diagnosis of oncological diseases, especially the rare ones, through screening or similar procedures. [ABSTRACT FROM AUTHOR]

Simple Summary: Dogs are considered man's best friend, with many owners sharing a close relationship with their dogs. This affection increased during the COVID-19 pandemic, as owners spent more time with their canines during the lockdowns and quarantines. During this time, owners noted how their dogs helped reduce their stress and anxiety. Yet, of concern is the known natural infection/transmission of SARS-CoV-2 reported in numerous animals (e.g., cats, minks, deer), necessitating the need to determine if dogs can contract or spread SARS-CoV-2. In this review, we focused on the human–canine interface of COVID-19 and the positives and negatives of this interface. We found that dogs can get COVID-19 from their owners but are asymptomatic when infected—it is unclear if it affects the health of dogs. Research is inconclusive on if dogs can transmit SARS-CoV-2 to their owners. Although dogs could be hosts, they are beneficial beyond just stress relief. Sniffer dogs can rapidly detect COVID-19-positive individuals in crowds, with almost 80% success in distinguishing between positive and negative people. Overall, we emphasize that more research is needed to understand the role of dogs in the interspecies spread and even contraction of SARS-CoV-2. COVID-19 is one of the deadliest epidemics. This pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but the role of dogs in spreading the disease in human society is poorly understood. This review sheds light on the limited susceptibility of dogs to COVID-19 infections which is likely attributed to the relatively low levels of angiotensin-converting enzyme 2 (ACE2) in the respiratory tract and the phylogenetic distance of ACE2 in dogs from the human ACE2 receptor. The low levels of ACE2 affect the binding affinity between spike and ACE2 proteins resulting in it being uncommon for dogs to spread the disease. To demonstrate the role of dogs in spreading COVID-19, we reviewed the epidemiological studies and prevalence of SARS-CoV-2 in dogs. Additionally, we discussed the use of detection dogs as a rapid and reliable method for effectively discriminating between SARS-CoV-2 infected and non-infected individuals using different types of samples (secretions, saliva, and sweat). We considered the available information on COVID-19 in the human–dog interfaces involving the possibility of transmission of COVID-19 to dogs by infected individuals and vice versa, the human–dog behavior changes, and the importance of preventive measures because the risk of transmission by domestic dogs remains a concern. [ABSTRACT FROM AUTHOR]

COVID-19 is a rapidly spreading pandemic, and early detection is important to halting the spread of infection. Recently, the outbreak of this virus has severely affected people around the world with increasing death rates. The increased death rates are because of its spreading nature among people, mainly through physical interactions. Therefore, it is very important to control the spreading of the virus and detect people's symptoms during the initial stages so proper preventive measures can be taken in good time. In response to COVID-19, revolutionary automation such as deep learning, machine learning, image processing, and medical images such as chest radiography (CXR) and computed tomography (CT) have been developed in this environment. Currently, the coronavirus is identified via an RT-PCR test. Alternative solutions are required due to the lengthy moratorium period and the large number of false-negative estimations. To prevent the spreading of the virus, we propose the Vehicle-based COVID-19 Detection System to reveal the related symptoms of a person in the vehicles. Moreover, deep extreme machine learning is applied. The proposed system uses headaches, flu, fever, cough, chest pain, shortness of breath, tiredness, nasal congestion, diarrhea, breathing difficulty, and pneumonia. The symptoms are considered parameters to reveal the presence of COVID-19 in a person. Our proposed approach in Vehicles will make it easier for governments to perform COVID-19 tests timely in cities. Due to the ambiguous nature of symptoms in humans, we utilize fuzzy modeling for simulation. The suggested COVID-19 detection model achieved an accuracy of more than 90%. [ABSTRACT FROM AUTHOR]

Accurate and early detection of causes of pneumonia is important for implementing fast treatment and preventive strategies, reducing the burden of infections, and establishing more effective ways of interventions. After the outbreak of COVID-19, the new cases of pneumonia and conditions of breathing problems called acute respiratory distress syndrome have increased. Chest radiography, known as CXR or simply X-ray has become a significant source to diagnose COVID-19-infected pneumonia in designated institutions and hospitals. It is essential to develop automated computer systems to assist doctors and medical experts to diagnose pneumonia in a fast and reliable manner. In this work, we propose a deep learning (DL)-based computer-aided diagnosis system for rapid and easy detection of pneumonia using X-ray images. To improve classification accuracy and faster conversion of the models, we employ transfer learning and parallel computing techniques using well-known DL models such as VGG19 and ResNet50. Experiments are conducted on the large COVID-QU-Ex dataset of X-ray images with three classes, such as COVID-19-infected pneumonia, non-COVID-19 infections (other viral and bacterial pneumonia), and normal (uninfected) images. The proposed model outperformed compared methodologies, achieving an average classification accuracy of 96.6%. Experimental results demonstrate that the proposed method is effective in diagnosing pneumonia using X-ray images. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 is an emerging infectious disease of zoonotic origin that caused the coronavirus disease in late 2019 and triggered a pandemic that has severely affected human health and caused millions of deaths. Early and massive diagnosis of SARS-CoV-2 infected patients is the key to preventing the spread of the virus and controlling the outbreak. Lateral flow immunoassays (LFIA) are the simplest biosensors. These devices are clinical diagnostic tools that can detect various analytes, including viruses and antibodies, with high sensitivity and specificity. This review summarizes the advantages, limitations, and evolution of LFIA during the SARS-CoV-2 pandemic and the challenges of improving these diagnostic devices. [ABSTRACT FROM AUTHOR]

This app project was aimed to remotely deliver diagnoses and disease-progression information to COVID-19 patients to help minimize risk during this and future pandemics. Data collected from chest computed tomography (CT) scans of COVID-19-infected patients were shared through the app. In this article, we focused on image preprocessing techniques to identify and highlight areas with ground glass opacity (GGO) and pulmonary infiltrates (PIs) in CT image sequences of COVID-19 cases. Convolutional neural networks (CNNs) were used to classify the disease progression of pneumonia. Each GGO and PI pattern was highlighted with saliency map fusion, and the resulting map was used to train and test a CNN classification scheme with three classes. In addition to patients, this information was shared between the respiratory triage/radiologist and the COVID-19 multidisciplinary teams with the application so that the severity of the disease could be understood through CT and medical diagnosis. The three-class, disease-level COVID-19 classification results exhibited a macro-precision of more than 94.89% in a two-fold cross-validation. Both the segmentation and classification results were comparable to those made by a medical specialist. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 coronavirus, also known as the disease-causing agent for COVID-19, is a virulent pathogen that may infect people and certain animals. The global spread of COVID-19 and its emerging variation necessitates the development of rapid, reliable, simple, and low-cost diagnostic tools. Many methodologies and devices have been developed for the highly sensitive, selective, cost-effective, and rapid diagnosis of COVID-19. This review organizes the diagnosis platforms into four groups: imaging, molecular-based detection, serological testing, and biosensors. Each platform's principle, advancement, utilization, and challenges for monitoring SARS-CoV-2 are discussed in detail. In addition, an overview of the impact of variants on detection, commercially available kits, and readout signal analysis has been presented. This review will expand our understanding of developing advanced diagnostic approaches to evolve into susceptible, precise, and reproducible technologies to combat any future outbreak. [ABSTRACT FROM AUTHOR]

Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown restrictions, and decrease the workload on healthcare structures. The present tools to detect COVID-19 experience numerous shortcomings. Therefore, novel diagnostic tools are to be examined to enhance diagnostic accuracy and avoid the limitations of these tools. Earlier studies indicated multiple structures of cardiovascular alterations in COVID-19 cases which motivated the realization of using ECG data as a tool for diagnosing the novel coronavirus. This study introduced a novel automated diagnostic tool based on ECG data to diagnose COVID-19. The introduced tool utilizes ten deep learning (DL) models of various architectures. It obtains significant features from the last fully connected layer of each DL model and then combines them. Afterward, the tool presents a hybrid feature selection based on the chi-square test and sequential search to select significant features. Finally, it employs several machine learning classifiers to perform two classification levels. A binary level to differentiate between normal and COVID-19 cases, and a multiclass to discriminate COVID-19 cases from normal and other cardiac complications. The proposed tool reached an accuracy of 98.2% and 91.6% for binary and multiclass levels, respectively. This performance indicates that the ECG could be used as an alternative means of diagnosis of COVID-19. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has hugely disrupted healthcare provision, including oncology services. To evaluate the effects of the pandemic on referral routes leading to diagnosis, treatments, and prognosis in patients with pancreatic ductal adenocarcinoma, we performed a retrospective cohort study at a single tertiary centre in the UK. The patients were identified from the weekly hepatopancreatobiliary multidisciplinary team meetings between February 2018 and March 2021. The demographic, referral, and treatment data for each patient and date of death, where applicable, were extracted from the electronic patient record. The patients (n = 203) were divided into "pre-pandemic" and "pandemic" cohorts based on a referral date cut-off of 23rd March 2020. The median survival was 7.4 months [4.9–9.3] in the "pre-pandemic" cohort (n = 125), halving to 3.3 months [2.2–6.0], (p = 0.015) in the "pandemic" cohort (n = 78). There was no significant difference in patient characteristics between the two cohorts. There was a trend toward increased emergency presentations at diagnosis and reduced use of surgical resection in the "pandemic" cohort. This small-scale study suggested that the COVID-19 pandemic is associated with a halving of median survival in pancreatic ductal adenocarcinoma. Urgent further studies are required to confirm these findings and examine corresponding effects in other cancer types. [ABSTRACT FROM AUTHOR]

Sample preparation is an essential step for nearly every type of biochemical analysis in use today. Among the most important of these analyses is the diagnosis of diseases, since their treatment may rely greatly on time and, in the case of infectious diseases, containing their spread within a population to prevent outbreaks. To address this, many different methods have been developed for use in the wide variety of settings for which they are needed. In this work, we have reviewed the literature and report on a broad range of methods that have been developed in recent years and their applications to point-of-care (POC), high-throughput screening, and low-resource and traditional clinical settings for diagnosis, including some of those that were developed in response to the coronavirus disease 2019 (COVID-19) pandemic. In addition to covering alternative approaches and improvements to traditional sample preparation techniques such as extractions and separations, techniques that have been developed with focuses on integration with smart devices, laboratory automation, and biosensors are also discussed. [ABSTRACT FROM AUTHOR]

Individuals with cancer are vulnerable to infection with SARS-CoV-2, the virus causing COVID-19. Physical distancing, the reallocation of health care resources, and the implementation of procedures to reduce the spread of COVID-19 may also have serious consequences for people with cancer. We evaluated the impact of COVID-19 on new cancer diagnoses and oncology care in Manitoba, Canada using an interrupted time series design and data from the Manitoba Cancer Registry and CancerCare Manitoba's (CCMB) electronic medical record. In April 2020, there was a 23% decrease in new cancer diagnoses, a 21% decrease in pathology reports, and a 43% reduction in surgical resections. There was no difference in new cancer diagnoses by August 2020, surgery by July 2020, and pathology reports by September 2020. From April 2020 to June 2021, there was a 13% decrease in radiotherapy (RT) fractions, an 18% decrease in UCC visits, and a 52% decrease in in-person visits. There was no change in intravenous chemotherapy visits per month, first RT visits, or overall patient visits. The impact of COVID-19 on shifts in the stage at diagnosis and survival will be assessed in future analyses. [ABSTRACT FROM AUTHOR]

A conceptual artificial intelligence (AI)-enabled framework is presented in this study involving triangulation of various diagnostic methods for management of coronavirus disease 2019 (COVID-19) and its associated comorbidities in resource-limited settings (RLS). The proposed AI-enabled framework will afford capabilities to harness low-cost polymerase chain reaction (PCR)-based molecular diagnostics, radiological image-based assessments, and end-user provided information for the detection of COVID-19 cases and management of symptomatic patients. It will support self-data capture, clinical risk stratification, explanation-based intelligent recommendations for patient triage, disease diagnosis, patient treatment, contact tracing, and case management. This will enable communication with end-users in local languages through cheap and accessible means, such as WhatsApp/Telegram, social media, and SMS, with careful consideration of the need for personal data protection. The objective of the AI-enabled framework is to leverage multimodal diagnostics of COVID-19 and associated comorbidities in RLS for the diagnosis and management of COVID-19 cases and general support for pandemic recovery. We intend to test the feasibility of implementing the proposed framework through community engagement in sub-Saharan African (SSA) countries where many people are living with pre-existing comorbidities. A multimodal approach to disease diagnostics enabling access to point-of-care testing is required to reduce fragmentation of essential services across the continuum of COVID-19 care. [ABSTRACT FROM AUTHOR]

The analysis of viral nucleic acids (NA), DNA or RNA, is a crucial issue in the diagnosis of infections and the treatment and prevention of related human diseases. Conventional nucleic acid tests (NATs) require multistep approaches starting from the purification of the pathogen genetic material in biological samples to the end of its detection, basically performed by the consolidated polymerase chain reaction (PCR), by the use of specialized instruments and dedicated laboratories. However, since the current NATs are too constraining and time and cost consuming, the research is evolving towards more integrated, decentralized, user-friendly, and low-cost methods. These will allow the implementation of massive diagnoses addressing the growing demand of fast and accurate viral analysis facing such global alerts as the pandemic of coronavirus disease of the recent period. Silicon-based technology and microfluidics, in this sense, brought an important step up, leading to the introduction of the genetic point-of-care (PoC) systems. This review goes through the evolution of the analytical methods for the viral NA diagnosis of infection diseases, highlighting both advantages and drawbacks of the innovative emerging technologies versus the conventional approaches. [ABSTRACT FROM AUTHOR]

Simple Summary: The SARS-CoV-2 pandemic drastically changed daily life activities and medical practice and a total reorganization of healthcare activities was required. People changed their daily habits and undertook a more sedentary lifestyle, leading to significant consequences in knee pathologies. The aim of this study was to evaluate the outpatient activity for knee pathologies before and after lockdown, in order to understand better which kind of pathologies orthopedic field is going to face. Further studies are required to validate this result with more in-depth clinical and radiological elements and to evaluate any differences in surgical treatment. Background: The SARS-CoV-2 pandemic drastically changed daily life activities and medical practice, leading to a reorganization of healthcare activities. People spent two months in home-isolation, changing their daily habits and undertaking a more sedentary lifestyle. Change in lifestyle is related to important consequences in knee pathologies. The aim of this study was to evaluate the outpatient activity for knee pathologies before and after lockdown in terms of incidence, severity, diagnosis, and treatment. Methods: Medical records of patients with knee pathology in outpatient follow-up at IRCCS Istituto Ortopedico Galeazzi in Milan (Italy) were analyzed in the time frame 4 May–4 September 2020 and compared with patients examined between 4 May and 4 September 2019. Results: A significant increase of knee diagnoses associated to patellofemoral disorders in 2020 was found (p = 0.004). In addition, physiotherapy was significantly more prescribed in 2020 than in 2019 (p = 0.012). Conclusions: The SARS-CoV-2 pandemic lockdown did not drastically change knee pathology, but it may have had an impact on it, highlighting a summary worsening of patellofemoral disorders associated with other knee diagnoses. Further studies are required to validate this result. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic and the related lockdown measures presented a significant risk to physical and mental wellbeing in affected populations. Women with polycystic ovary syndrome (PCOS) are predisposed to several cardio-metabolic risk factors which increase the susceptibility to severe COVID-19 and also exhibit increased likelihood of impaired mental health wellbeing. Therefore, these women who usually receive care from multiple primary and specialist healthcare services may be disproportionately impacted by this pandemic and the related restrictions. This study aimed to explore the lived experience of the first UK national lockdown as a woman with PCOS. Methods: As part of a larger cross-sectional study, 12 women with PCOS living in the UK during the first national COVID-19 lockdown were recruited to a qualitative study. Telephone interviews were conducted in June/July of 2020, and data collected were subjected to thematic analysis. Results: Five themes were identified. "My PCOS Journey" describes participants' experiences of diagnosis, treatment and ongoing management of their PCOS. "Living Through Lockdown" describes the overall experience and impact of the lockdown on all aspects of participants' lives. "Self-care and Managing Symptoms" describe multiple challenges to living well with PCOS during the lockdown, including lack of access to supplies and services, and disruption to weight management. "Healthcare on Hold" describes the uncertainty and anxiety associated with delays in accessing specialised healthcare for a range of PCOS aspects, including fertility treatment. "Exacerbating Existing Issues" captures the worsening of pre-existing mental health issues, and an increase in health anxiety and feelings of isolation. Conclusion: For the women with PCOS in this study, the COVID-19 pandemic and the first national lockdown was mostly experienced as adding to the pre-existing challenges of living with their condition. The mental health impact experienced by the study participants was increased due to lack of access to their normal support strategies, limitations on healthcare services and uncertainty about their risk of COVID-19. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has wreaked havoc globally and still persists even after a year of its initial outbreak. Several reasons can be considered: people are in close contact with each other, i.e., at a short range (1 m), and the healthcare system is not sufficiently developed or does not have enough facilities to manage and fight the pandemic, even in developed countries such as the USA and the U.K. and countries in Europe. There is a great need in healthcare for remote monitoring of COVID-19 symptoms. In the past year, a number of IoT-based devices and wearables have been introduced by researchers, providing good results in terms of high accuracy in diagnosing patients in the prodromal phase and in monitoring the symptoms of patients, i.e., respiratory rate, heart rate, temperature, etc. In this systematic review, we analyzed these wearables and their need in the healthcare system. The research was conducted using three databases: IEEE Xplore®, Web of Science®, and PubMed Central®, between December 2019 and June 2021. This article was based on the PRISMA guidelines. Initially, 1100 articles were identified while searching the scientific literature regarding this topic. After screening, ultimately, 70 articles were fully evaluated and included in this review. These articles were divided into two categories. The first one belongs to the on-body sensors (wearables), their types and positions, and the use of AI technology with ehealth wearables in different scenarios from screening to contact tracing. In the second category, we discuss the problems and solutions with respect to utilizing these wearables globally. This systematic review provides an extensive overview of wearable systems for the remote management and automated assessment of COVID-19, taking into account the reliability and acceptability of the implemented technologies. [ABSTRACT FROM AUTHOR]

Background: Mucormycosis is a life-threatening invasive fungal infection most commonly observed in immunocompromised patients. Throughout the COVID-19 pandemic, a growing number of Mucorales associated infections, now termed COVID-19 associated mucormycosis (CAM), have been reported. Despite an increase in fatality reports, no cases of rhino-orbital CAM complicated with gangrenous bone necrosis have been described in the literature to date. Case: A 56-year-old male with a recent COVID-19 diagnosis developed rhino-orbital mucormycosis after 22 days of treatment with dexamethasone. Cultures and histopathological assessment of tissue biopsy confirmed the diagnosis. The patient survived after treatment with amphotericin B. Conclusions: Mucormycosis is an invasive fungal infection affecting mostly immunocompromised patients. Along with the COVID-19 pandemic, the inappropriate use of steroids, in addition to concurrent risk factors, such as diabetes, has led to an increase in the occurrence of these devastating mycoses, leading to the development of severe presentations and complications, as observed in many cases. Early diagnosis and prompt treatment are crucial in order to avoid dissemination and fatal outcomes. [ABSTRACT FROM AUTHOR]

Background: Because of the new pandemic caused by the novel coronavirus disease (COVID-19), the demand for telemedicine and telemonitoring solutions has been exponentially raised. Because of its special advantage to treat patients in an emergency without physical presence at a hospital via video conferencing, telemedicine has been used to overcome distance barriers and to improve access to special domains like neurology. In these pandemic times, telemedicine has been also employed as a support for the diagnosis and treatment of adult-onset dementia disorders including Alzheimer's disease. Objective: In this study, we carried out a systematic literature analysis to clarify if the neuropsychological tests traditionally employed in face-to-face (FTF) contexts are reliable via telemedicine. Methods: A systematic literature search for the past 20 years (2001–2020) was carried out through the medical databases PubMed (Medline) and the Cumulative Index to Nursing and Allied Health Literature (CINAHL). The quality assessment was conducted by adopting the Newcastle Ottawa Scale (NOS) and only studies with a NOS ≥ 7 were included in this review. Results: The Mini-Mental State Examination (MMSE) results do not differ when tests are administered in the traditional FTF modality or by videoconference, and only negligible minor changes in the scoring system were noticeable. Other neuropsychological tests used to support the diagnosis of AD and dementia such as the Token Test, the Comprehension of Words and Phrases (ACWP), the Controlled Oral Word Association Test showed high reliability between the two modalities considered. No differences in the reliability concerning the living setting or education of the subjects were reported. Conclusions: The MMSE, which is the main screening test for dementia, can be administered via telemedicine with minor adaptation in the scoring system. Telemedicine use for other neuropsychological tests also resulted in general reliability and enough accuracy. Cognitive assessment by videoconference is accepted and appreciated and therefore can be used for dementia diagnosis in case of difficulties to performing FTF assessments. This approach can be useful given a personalized medicine approach for the treatment of adult-onset dementia disorders. [ABSTRACT FROM AUTHOR]

As demonstrated with the novel coronavirus pandemic, rapid and accurate diagnosis is key to determine the clinical characteristic of a disease and to improve vaccine development. Once the infected person is identified, hematological findings may be used to predict disease outcome and offer the correct treatment. Rapid and accurate diagnosis and clinical parameters are pivotal to track infections during clinical trials and set protection status. This is also applicable for re-emerging diseases like dengue fever, which causes outbreaks in Asia and Latin America every 4 to 5 years. Some areas in the US are also endemic for the transmission of dengue virus (DENV), the causal agent of dengue fever. However, significant number of DENV infections in rural areas are diagnosed solely by clinical and hematological findings because of the lack of availability of ELISA or PCR-based tests or the infrastructure to implement them in the near future. Rapid diagnostic tests (RDT) are a less sensitive, yet they represent a timely way of detecting DENV infections. The purpose of this study was to determine whether there is an association between hematological findings and the probability for an NS1-based DENV RDT to detect the DENV NS1 antigen. We also aimed to describe the hematological parameters that are associated with the diagnosis through each test. [ABSTRACT FROM AUTHOR]

Background: To measure patient flow at our Pediatric Emergency Department (PED) during the Italian lockdown, with particular care in terms of otolaryngological (ENT)-related diagnoses. Methods: A retrospective evaluation of electronic charts of children admitted to our PED in the City Center of Milan (Italy) for any disease. The outcome was to compare distribution of diagnoses performed at our PED during 21 February–3 May 2019 (period 1) to 21 February–3 May 2020 (period 2). Results: A total of 4538 children were evaluated during period 1 compared to 1310 during period 2. A statistically significant overall effect on diagnosis between the study periods was attested (p-value < 0.001; pseudo R2 = 0.010), ENT-related diagnoses being more frequently documented in period 1 (80.4% vs. 19.5%; p-value < 0.001), as well as those related to middle ear infections (92.8% vs. 7.2%; p-value < 0.001). Non-complicated acute otitis media more frequently occurred in period 1 (92.0% vs. 8.0%; p-value < 0.001); no significant difference in the number of complicated middle ear infections occurred (95.8% vs. 4.2%). Conclusions: The exceptional circumstances of the Italian lockdown resulted in a significant decrease in patients' attendance to our PED, especially when considering diagnoses related to any ENT disorder, middle ear disease, and non-complicated middle ear infection. [ABSTRACT FROM AUTHOR]

Type 1 diabetes mellitus (T1DM) represents one of the most frequent chronic illnesses affecting children. The early diagnosis of this disease is crucial, as it plays a key role in preventing the development of a life-threatening acute complication: diabetic ketoacidosis. The etiopathogenetic role of viral infections has long been suggested and emerging data are pointing towards a complex bidirectional relationship between diabetes and COVID-19. The aim of this study is to assess the impact of the COVID-19 pandemic on the incidence and severity of new T1DM cases in children in Romania. We analyzed the differences between a group of 312 patients diagnosed with T1DM in the period 2003–2019 and a group of 147 children diagnosed during the pandemic. The data were investigated using statistical analysis of a series of relevant variables. The total number of newly diagnosed T1DM increased by 30.08% in the period March 2020–February 2021 compared to the previous years. The patients in the pandemic group had a higher mean age at the onset of T1DM, were less frequently living in an urban area, and presented a higher mean value of HbA1c. Diabetic ketoacidosis at the onset of T1DM was 67.40% more frequent, and a higher percentage of these patients presented with a severe form. The duration of T1DM symptoms did not differ significantly between the two groups. A number of 8 patients associated SARS-CoV-2 infection at the time of T1DM diagnosis. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) global pandemic created an unprecedented public health emergency. Early recognition of an infected person and disruption of the transmission pathway are the keys to controlling this major public health threat around the world. The scientifically reliable screening method is an RT-PCR test that is performed on an ororhinopharyngeal swab in the laboratory. In the current severe SARS-CoV-2 pandemic, it is necessary to identify devices for rapid diagnosis to reduce the spread of the disease. The aim of this study was to provide a qualitative, rapid, sensitive, and specific method for a diagnosis of SARS-CoV-2 infection based on the recognition of specific antigens of the SARS-CoV-2 virus. The device was built by assembling commercially available and custom-made semi-finished products. The method was performed in environments outside the laboratory, i.e., "patient side," with an immediate chemocolorimetric response or with a digital reader using an ELISA method. [ABSTRACT FROM AUTHOR]

Since early 2020, the COVID-19 pandemic has caused an excess in morbidity and mortality rates worldwide. Containment strategies rely firstly on rapid and sensitive laboratory diagnosis, with molecular detection of the viral genome in respiratory samples being the gold standard. The reliability of diagnostic protocols could be affected by SARS-CoV-2 genetic variability. In fact, mutations occurring during SARS-CoV-2 genomic evolution can involve the regions targeted by the diagnostic probes. Following a review of the literature and an in silico analysis of the most recently described virus variants (including the UK B 1.1.7 and the South Africa 501Y.V2 variants), we conclude that the described genetic variability should have minimal or no effect on the sensitivity of existing diagnostic protocols for SARS-CoV-2 genome detection. However, given the continuous emergence of new variants, the situation should be monitored in the future, and protocols including multiple targets should be preferred. [ABSTRACT FROM AUTHOR]

The recent COVID-19 pandemic has dramatically changed the world in the last months, leading to a serious global emergency related to a novel coronavirus infection that affects both sexes of all ages ubiquitously. Advanced age, cardiovascular comorbidity, and viral load have been hypothesized as some of the risk factors for severity, but their role in patients affected with other diseases, in particular immune disorders, such as sarcoidosis, and the specific interaction between these two diseases remains unclear. The two conditions might share similar imaging findings but have distinctive features that are here described. The recent development of complex imaging softwares, called deep learning techniques, opens new scenarios for the diagnosis and management. [ABSTRACT FROM AUTHOR]

Primary percutaneous coronary intervention (PPCI) is one of the important clinical procedures that have been affected by the COVID-19 pandemic. In this study, we aimed to assess the incidence and impact of COVID-19 on in-hospital clinical outcome of ST elevation myocardial infarction (STEMI) patients managed with PPCI. This observational retrospective study was conducted on consecutive STEMI patients who presented to the International Cardiac Center (ICC) hospital, Alexandria, Egypt between 1 February and 31 October 2020. A group of STEMI patients presented during the same period in 2019 was also assessed (control group) and data was used for comparison. The inclusion criteria were established diagnosis of STEMI requiring PPCI.A total of 634 patients were included in the study. During the COVID-19 period, the number of PPCI procedures was reduced by 25.7% compared with previous year (mean 30.0 ± 4.01 vs. 40.4 ± 5.3 case/month) and the time from first medical contact to Needle (FMC-to-N) was longer (125.0 ± 53.6 vs. 52.6 ± 22.8 min, p = 0.001). Also, during COVID-19, the in-hospital mortality was higher (7.4 vs. 4.6%, p = 0.036) as was the incidence of re-infarction (12.2 vs. 7.7%, p = 0.041) and the need for revascularization (15.9 vs. 10.7%, p = 0.046). The incidence of heart failure, stroke, and bleeding was not different between groups, but hospital stay was longer during COVID-19 (6.85 ± 4.22 vs. 3.5 ± 2.3 day, p = 0.0025). Conclusion: At the ICC, COVID-19 pandemic contributed significantly to the PPCI management of STEMI patients with decreased number and delayed procedures. COVID-19 was also associated with higher in-hospital mortality, rate of re-infarction, need for revascularization, and longer hospital stay. [ABSTRACT FROM AUTHOR]

The outbreak of novel coronavirus disease (2019-nCoV or COVID-19) is responsible for severe health emergency throughout the world. The attack of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is found to be responsible for COVID-19. The World Health Organization has declared the ongoing global public health emergency as a pandemic. The whole world fights against this invincible enemy in various capacities to restore economy, lifestyle, and safe life. Enormous amount of scientific research work(s), administrative strategies, and economic measurements are in place to create a successful step against COVID-19. Furthermore, differences in opinion, facts, and implementation methods laid additional layers of complexities in this battle against survival. Thus, a timely overview of the recent, important, and overall inclusive developments against this pandemic is a pressing need for better understanding and dealing with COVID-19. In this review, we have systematically summarized the epidemiological studies, clinical features, biological properties, diagnostic methods, treatment modalities, and preventive measurements related to COVID-19. [ABSTRACT FROM AUTHOR]