SARS-CoV-2 detection in patients with influenza-like illness

Sampling and collector biases as taphonomic filters: an overview

Sampling (or sample) bias is a widespread concern in scientific research, across several disciplines. The concept of sampling bias originated in statistical studies. The consequence of a biased sample is that scientists will conclude about a population different from their target. In paleontology, sampling bias is typically related to fieldwork context. Human factors, known as sullegic (e.g. collection method, historic resampling) and trephic (transport, and curatorial processes) factors can generate bias. Other factor is the ugly fossil syndrome (i.e. choosing based on completeness of the specimens, or according to the researcher interest). Thus, sampling implies information loss. Biased samples add artificial results and can be considered an additional taphonomic filter. Therefore, sampling bias and the collector role and choices are frequently linked and almost indistinguishable. Compared to the treatment of this topic in other research fields, little related discussion has been held in vertebrate paleontology, especially regarding what happens at the interface between the biosphere, lithosphere, and anthroposphere, and during the transition between the anthroposphere and the patrisphere (museums). Numerous questions still arise. As a community, we must pay attention, to minimize the loss of information, from field activities to cataloging.

Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic

Zoonotic spillovers of viruses have occurred through the animal trade worldwide. The start of the COVID-19 pandemic was traced epidemiologically to the Huanan Seafood Wholesale Market. Here, we analyze environmental qPCR and sequencing data collected in the Huanan market in early 2020. We demonstrate that market-linked severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genetic diversity is consistent with market emergence and find increased SARS-CoV-2 positivity near and within a wildlife stall. We identify wildlife DNA in all SARS-CoV-2-positive samples from this stall, including species such as civets, bamboo rats, and raccoon dogs, previously identified as possible intermediate hosts. We also detect animal viruses that infect raccoon dogs, civets, and bamboo rats. Combining metagenomic and phylogenetic approaches, we recover genotypes of market animals and compare them with those from farms and other markets. This analysis provides the genetic basis for a shortlist of potential intermediate hosts of SARS-CoV-2 to prioritize for serological and viral sampling.

Pathogen shape: Implication on pathogenicity via respiratory deposition

The shape of environmental aerosols contributes to the discrepancy in their dynamic behavior compared to spherical particles, which have received inadequate consideration. We reported deposition patterns of aerosols and aerosol-transmissible pathogens in real human respiratory systems, taking into account their actual shape, using a validated computational-based model. We found that the shape of the aerosols significantly influenced its deposits and accessibility within the respiratory system, significantly in the tracheobronchial region. As an example, we estimated that over 180 % of differences in deposits in the trachea and bronchi were attributable to pathogens shape, inferring the underlying pathogenicity difference of these regions. These findings, capturing the spatial heterogeneity of pathogens and aerosols deposition in human respiratory system, have major implication for understanding the evolution of aerosol-related disease.

CNN-IKOA: convolutional neural network with improved Kepler optimization algorithm for image segmentation: experimental validation and numerical exploration

Chest diseases, especially COVID-19, have quickly spread throughout the world and caused many deaths. Finding a rapid and accurate diagnostic tool was indispensable to combating these diseases. Therefore, scientists have thought of combining chest X-ray (CXR) images with deep learning techniques to rapidly detect people infected with COVID-19 or any other chest disease. Image segmentation as a preprocessing step has an essential role in improving the performance of these deep learning techniques, as it could separate the most relevant features to better train these techniques. Therefore, several approaches were proposed to tackle the image segmentation problem accurately. Among these methods, the multilevel thresholding-based image segmentation methods won significant interest due to their simplicity, accuracy, and relatively low storage requirements. However, with increasing threshold levels, the traditional methods have failed to achieve accurate segmented features in a reasonable amount of time. Therefore, researchers have recently used metaheuristic algorithms to tackle this problem, but the existing algorithms still suffer from slow convergence speed and stagnation into local minima as the number of threshold levels increases. Therefore, this study presents an alternative image segmentation technique based on an enhanced version of the Kepler optimization algorithm (KOA), namely IKOA, to better segment the CXR images at small, medium, and high threshold levels. Ten CXR images are used to assess the performance of IKOA at ten threshold levels (T-5, T-7, T-8, T-10, T-12, T-15, T-18, T-20, T-25, and T-30). To observe its effectiveness, it is compared to several metaheuristic algorithms in terms of several performance indicators. The experimental outcomes disclose the superiority of IKOA over all the compared algorithms. Furthermore, the IKOA-based segmented CXR images at eight different threshold levels are used to train a newly proposed CNN model called CNN-IKOA to find out the effectiveness of the segmentation step. Five performance indicators, namely overall accuracy, precision, recall, F1-score, and specificity, are used to disclose the CNN-IKOA’s effectiveness. CNN-IKOA, according to the experimental outcomes, could achieve outstanding outcomes for the images segmented at T-12, where it could reach 94.88% for overall accuracy, 96.57% for specificity, 95.40% for precision, and 95.40% for recall.

Repurposing antibiotics as potent multi-drug candidates for SARS-CoV-2 delta and omicron variants: molecular docking and dynamics

There is a daunting public health emergency due to the emergence and rapid global spread of the new omicron variants of SARS-CoV-2. The variants differ in many characteristics, such as transmissibility, antigenicity and the immune system of the human hosts' shifting responses. This change in characteristics raises concern, as it leads to unknown consequences and also raises doubts about the efficacy of the currently available vaccines. As of March 2022, there are five variants of SARS-CoV-2 disseminating: the alpha, the beta, the gamma, the delta and the omicron variant. The omicron variant has more than 30 mutations on the spike protein, which is used by the virus to enter the host cell and is also used as a target for the vaccines. In this work, we studied the possible anti-COVID-19 effect of two molecules by molecular docking using Autodock Vina and molecular dynamic simulations using Gromacs 2020 software. We docked amoxicillin and clavulanate to the main protease (Mpro), the RNA-dependent RNA polymerase (RdRp) and the spike protein receptor-binding domain (SRBD) of the wild type with the two variants (delta and omicron) of SARS-CoV-2. The docking results show that the ligands bound tightly with the SRBD of the omicron variant, while the dynamic simulation revealed the ability of amoxicillin to bind to the SRBD of both variants' delta and omicron. The high number of mutations that occurred in both variants increases the affinity of amoxicillin towards them.Communicated by Ramaswamy H. Sarma.

Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic

Zoonotic spillovers of viruses have occurred through the animal trade worldwide. The start of the COVID-19 pandemic was traced epidemiologically to the Huanan Wholesale Seafood Market, the site with the most reported wildlife vendors in the city of Wuhan, China. Here, we analyze publicly available qPCR and sequencing data from environmental samples collected in the Huanan market in early 2020. We demonstrate that the SARS-CoV-2 genetic diversity linked to this market is consistent with market emergence, and find increased SARS-CoV-2 positivity near and within a particular wildlife stall. We identify wildlife DNA in all SARS-CoV-2 positive samples from this stall. This includes species such as civets, bamboo rats, porcupines, hedgehogs, and one species, raccoon dogs, known to be capable of SARS-CoV-2 transmission. We also detect other animal viruses that infect raccoon dogs, civets, and bamboo rats. Combining metagenomic and phylogenetic approaches, we recover genotypes of market animals and compare them to those from other markets. This analysis provides the genetic basis for a short list of potential intermediate hosts of SARS-CoV-2 to prioritize for retrospective serological testing and viral sampling.

Greenspace exposure is conducive to the resilience of public sentiment during the COVID-19 pandemic

The coronavirus disease 2019 (COVID-19) pandemic significantly impacts people's sentiment and mental health, threatening their health and lives. We gathered 4.17 million geotagged social media posts from Weibo and scrutinized the nuances of the collective sentiments of netizens in four megacities in China during the first pandemic wave (from 1 December 2019 to 30 April 2020). Our findings suggest that the COVID-19 outbreak significantly reduced the Sentiment Index (SI) in China's cities, and the collective sentiments expressed in Wuhan were even more negative than those in the other three megacities. We explored the uncharted impacts of exposure to three geographical environment factors (GEFs) on SIs. Public exposure to greenspaces increased, while exposure to indoor built spaces decreased during the lockdown period. The exposure to sidewalks increased in rural areas but decreased in the main urban areas. The contributions of various GEFs to the SIs were the lowest during the lockdown period, and SIs were strongly affected by the pandemic. However, greenspace had the most potent effect on SIs, improving public sentiment resilience and mitigating mental health risks.

Interventions to support mental health in people with long COVID: a scoping review

INTRODUCTION

Long COVID (LC) is a multisystem disease with symptoms lasting weeks or months beyond the acute COVID-19 infection. Several manifestations are reported by people with LC, including effects on mental health, with varying degrees of psychological distress and disturbances to daily activities. Research conducted to identify effective interventions to support mental health among people with LC has been limited by the breadth and scope of studies.

AIM

This review aims to identify interventions being tested to support mental health of people with LC.

METHODS

A scoping review was conducted by searching five databases for articles published between January 2020 and early October 2022 to identify research evaluating interventions focused on improving mental health symptoms associated with LC. Results from all sources were checked for eligibility by two reviewers, and agreements were resolved by discussion. Gray literature and reference list of included studies and relevant reviews were scrutinised to identify any additional studies. Data extraction was conducted by one reviewer and checked by another reviewer for accuracy.

RESULTS

Of the 940 studies identified, 17 were included, the design of which varied but included mainly case studies (n = 6) and clinical trials (n = 5). Several interventions were described, ranging from single interventions (e.g., pharmacologic) to more holistic, comprehensive suites of services (pharmacologic and non-pharmacologic). Several mental health outcomes were measured, mostly anxiety and depression. All included studies were reported to be associated with improvements in participants' mental health outcomes.

CONCLUSION

This scoping review identified studies reporting on a variety of interventions to support mental health among people with LC. Although positive changes were reported by all studies, some were case studies and thus their findings must be interpreted with caution. There is a need for more research to be conducted to identify the impact of interventions on mental health of people with LC.

Significant impact of COVID-19 pandemic on the circulation of respiratory viruses in Tunisia, 2020-2021

Background

The COVID-19 pandemic changed the typical patterns of respiratory infections globally. While SARS-CoV-2 illness exhibited explosive growth since 2020, the activity of other respiratory viruses fell below historical seasonal norms. The objective of this study was to assess the prevalence of seasonal respiratory viruses during the COVID-19 pandemic in Tunisia.

Methods

This is a retrospective cross-sectional study including 284 nasopharyngeal samples tested negative for SARS-CoV-2 during the period October 2020-May 2021. All samples were screened for fifteen common respiratory viruses. Either a fast syndromic approach using Biofire FILM ARRAY respiratory 2.1 (RP2.1) Panel, or end-point multiplex RT-PCRs detecting RNA viruses and Real-Time PCR detecting Adenoviruses were used.

Results

Overall, 30.6% (87/284) of samples were positive for at least one virus. Mixed infections were detected in 3.4% of positive cases. Enterovirus/Rhinovirus (HEV/HRV) was the most detected virus throughout the study period, especially during December 2020 (33.3% of all HEV/HRV being detected). During the 2020-2021 winter season, neither Respiratory Syncytial Virus nor Influenza Viruses circulation was observed. Metapneumovirus and Parainfluenza Viruses infections were detected during the spring season. The highest rate of respiratory viruses detection was observed in children and adults aged [0-10] years (50%) and [31-40] years (40%). HEV/HRV was the most detected virus regardless of age group.

Conclusions

Public health measures used to prevent SARS-CoV-2 spread in Tunisia were also effective to reduce transmission of the other respiratory viruses, especially Influenza. The higher resistance of HEV/HRV in the environment could explain their predominance and continuous circulation during this period.

Exploring the pharmacological aspects of natural phytochemicals against SARS-CoV-2 Nsp14 through an in silico approach

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), possesses an important bifunctional nonstructural protein (nsp14) with a C-terminal N7-methyltransferase (N7-MTase) domain and an N-terminal domain with exoribonuclease (ExoN) activity that is required for maintaining high-fidelity viral replication. Viruses use the error-prone replication mechanism, which results in high mutation rates, to adapt quickly to stressful situations. The efficiency with which nsp14 removes mismatched nucleotides due to the presence of ExoN activity protects viruses from mutagenesis. We investigated the pharmacological role of the phytochemicals (Baicalein, Bavachinin, Emodin, Kazinol F, Lycorine, Sinigrin, Procyanidin A2, Tanshinone IIA, Tanshinone IIB, Tomentin A, and Tomentin E) against the highly conserved nsp14 protein using docking-based computational analyses in search of new potential natural drug targets. The selected eleven phytochemicals failed to bind the active site of N7-Mtase in the global docking study, while the local docking study identified the top five phytochemicals with high binding energy scores ranging from - 9.0 to - 6.4 kcal/mol. Procyanidin A2 and Tomentin A showed the highest docking score of - 9.0 and - 8.1 kcal/mol, respectively. Local docking of isoform variants was also conducted, yielding the top five phytochemicals, with Procyanidin A1 having the highest binding energy value of - 9.1 kcal/mol. The phytochemicals were later tested for pharmacokinetics and pharmacodynamics analysis for Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) which resulted in choosing Tomentin A as a potential candidate. The molecular dynamics simulations studies of nsp14 revealed significant conformational changes upon complex formation with the identified compound, implying that these phytochemicals could be used as safe nutraceuticals which will impart long-term immunological competence in the human population against CoVs.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00143-7.

Portable Chest Radiograph: A Boon for Critically Ill Patients With COVID-19 Pneumonia

OBJECTIVE

In the present study, we evaluated the role of portable chest radiographs in critically ill patients with COVID-19 pneumonia in whom computed tomography (CT) of the chest was not feasible.

METHODS

A retrospective chest X-ray study of patients under investigation for COVID-19 was performed in our dedicated COVID hospital (DCH) during the exponential growth phase of the COVID-19 outbreak (August-October, 2020). A total of 562 on-bed chest radiographs were examined comprising 289 patients (critically ill who couldn't be mobilized for CT) along with positive reverse transcription-polymerase chain reaction (RT-PCR) tests. We categorized each chest radiograph as progressive, with changes, or improvement in appearance for COVID-19, utilizing well-documented COVID-19 imaging patterns.

RESULTS

 In our study, portable radiographs provided optimum image quality for diagnosing pneumonia, in critically ill patients. Although less informative than CT, nevertheless radiographs detected serious complications like pneumothorax or lung cavitation and estimated the evolution of pneumonia.

CONCLUSION

A portable chest X-ray is a simple but reliable alternative for critically ill SARS-CoV-2 patients who could not undergo chest CT. With the help of portable chest radiographs, we could monitor the severity of the disease as well as different complications with minimal radiation exposure which would help in identifying the prognosis of the patient and thus help in medical management.

HLA-I and HLA-II Peptidomes of SARS-CoV-2: A Review

The adaptive (T-cell-mediated) immune response is a key player in determining the clinical outcome, in addition to neutralizing antibodies, after SARS-CoV-2 infection, as well as supporting the efficacy of vaccines. T cells recognize viral-derived peptides bound to major histocompatibility complexes (MHCs) so that they initiate cell-mediated immunity against SARS-CoV-2 infection or can support developing a high-affinity antibody response. SARS-CoV-2-derived peptides bound to MHCs are characterized via bioinformatics or mass spectrometry on the whole proteome scale, named immunopeptidomics. They can identify potential vaccine targets or therapeutic approaches for SARS-CoV-2 or else may reveal the heterogeneity of clinical outcomes. SARS-CoV-2 epitopes that are naturally processed and presented on the human leukocyte antigen class I (HLA-I) and class II (HLA-II) were identified for immunopeptidomics. Most of the identified SARS-CoV-2 epitopes were canonical and out-of-frame peptides derived from spike and nucleocapsid proteins, followed by membrane proteins, whereby many of which are not caught by existing vaccines and could elicit effective responses of T cells in vivo. This review addresses the detection of SARS-CoV-2 viral epitopes on HLA-I and HLA-II using bioinformatics prediction and mass spectrometry (HLA peptidomics). Profiling the HLA-I and HLA-II peptidomes of SARS-CoV-2 is also detailed.

A high-frequency QCM biosensing platform for label-free detection of the SARS-CoV-2 spike receptor-binding domain: an aptasensor and an immunosensor

Herein, high-frequency quartz crystal microbalance biosensing platforms were constructed using an aptamer and antibody as bioreceptors for fast and label-free detection of the SARS-CoV-2 RBD.

Utilisation of deep learning for COVID-19 diagnosis

The COVID-19 pandemic that began in 2019 has resulted in millions of deaths worldwide. Over this period, the economic and healthcare consequences of COVID-19 infection in survivors of acute COVID-19 infection have become apparent. During the course of the pandemic, computer analysis of medical images and data have been widely used by the medical research community. In particular, deep-learning methods, which are artificial intelligence (AI)-based approaches, have been frequently employed. This paper provides a review of deep-learning-based AI techniques for COVID-19 diagnosis using chest radiography and computed tomography. Thirty papers published from February 2020 to March 2022 that used two-dimensional (2D)/three-dimensional (3D) deep convolutional neural networks combined with transfer learning for COVID-19 detection were reviewed. The review describes how deep-learning methods detect COVID-19, and several limitations of the proposed methods are highlighted.

Epidemiological investigation of the first locally acquired case of COVID-19 identified by influenza surveillance-Taiwan, February 2020

BACKGROUND

COVID-19 and influenza have similar clinical presentations that can range from mild to severe disease. The World Health Organization recommends that countries use existing influenza surveillance to monitor COVID-19 transmission in communities. We aim to describe the surveillance and investigation of COVID-19 at the early stage of the pandemic in Taiwan.

METHODS

In February 2020, the Taiwan Centers for Disease Control enhanced COVID-19 surveillance through its existing influenza surveillance. We retrospectively tested patients for SARS-CoV-2 who had symptoms of severe complicated influenza but were negative in influenza testing. We conducted an epidemiological investigation and contact tracing for the index patient and secondary cases to prevent virus transmission.

RESULTS

We identified the first COVID-19 patient on February 15 through enhanced COVID-19 surveillance. He had no history of traveling abroad and an unclear history of contact with COVID-19 cases. He presented with influenza-like illness on January 27 and was hospitalized from February 3 to 15. We identified 39 close contacts of the index patient, including 11 family members and 28 healthcare workers. In total, four close family contacts of the index patient tested positive for SARS-CoV-2. An additional 84 close contacts of the four secondary cases were identified and traced; none was diagnosed with COVID-19.

CONCLUSIONS

We recommend enhancing COVID-19 surveillance by testing patients with influenza-like illness. To prevent the spread of COVID-19, we recommend using appropriate personal protective equipment when in close contact with patients who present with influenza-like illness or when caring for patients with pneumonia of unknown etiology.

Rapid and simultaneous detection of SARS-CoV-2 and influenza A using vertical flow assay based on AAO and SERS nanotags

A highly sensitive SERS-VFA sensor was developed to determine SARS-CoV-2 or influenza A infection simultaneously, especially during co-pandemics.

A Survey on Applications of Artificial Intelligence in Fighting Against COVID-19

The COVID-19 pandemic caused by the SARS-CoV-2 virus has spread rapidly worldwide, leading to a global outbreak. Most governments, enterprises, and scientific research institutions are participating in the COVID-19 struggle to curb the spread of the pandemic. As a powerful tool against COVID-19, artificial intelligence (AI) technologies are widely used in combating this pandemic. In this survey, we investigate the main scope and contributions of AI in combating COVID-19 from the aspects of disease detection and diagnosis, virology and pathogenesis, drug and vaccine development, and epidemic and transmission prediction. In addition, we summarize the available data and resources that can be used for AI-based COVID-19 research. Finally, the main challenges and potential directions of AI in fighting against COVID-19 are discussed. Currently, AI mainly focuses on medical image inspection, genomics, drug development, and transmission prediction, and thus AI still has great potential in this field. This survey presents medical and AI researchers with a comprehensive view of the existing and potential applications of AI technology in combating COVID-19 with the goal of inspiring researchers to continue to maximize the advantages of AI and big data to fight COVID-19.

Dual-mode SERS-based lateral flow assay strips for simultaneous diagnosis of SARS-CoV-2 and influenza a virus

Since COVID-19 and flu have similar symptoms, they are difficult to distinguish without an accurate diagnosis. Therefore, it is critical to quickly and accurately determine which virus was infected and take appropriate treatments when a person has an infection. This study developed a dual-mode surface-enhanced Raman scattering (SERS)-based LFA strip that can diagnose SARS-CoV-2 and influenza A virus with high accuracy to reduce the false-negative problem of the commercial colorimetric LFA strip. Furthermore, using a single strip, it is feasible to detect SARS-CoV-2 and influenza A virus simultaneously. A clinical test was performed on 39 patient samples (28 SARS-CoV-2 positives, 6 influenza A virus positives, and 5 negatives), evaluating the clinical efficacy of the proposed dual-mode SERS-LFA strip. Our assay results for clinical samples show that the dual-mode LFA strip significantly reduced the false-negative rate for both SARS-CoV-2 and influenza A virus.

COVID-19 INFLUENCE OVER BRAZILIAN AGRICULTURAL COMMODITIES AND DOLLAR–REAL EXCHANGE

This study evaluates the Brazilian agricultural commodities market and the dollar–real exchange price variation using the multifractal detrended fluctuations analysis methodology. We investigated the period from January 1, 2019 to September 25, 2019, outside the COVID-19 pandemic, and from January 1, 2020 to September 25, 2020, during the COVID-19 pandemic. We verified the fluctuations of commodities and dollar–real exchange prices during the pandemic caused by COVID-19 showed a record price. The results of Hurst exponent and multifractal parameters [Formula: see text], [Formula: see text], and [Formula: see text] indicate that during the COVID-19 pandemic, sugar was the most efficient commodity, while pork the less one. Compared to the identical months in 2019, the dollar–real exchange was the most efficient market, while ethanol was the least efficient.

The Huanan Seafood Wholesale Market in Wuhan was the early epicenter of the COVID-19 pandemic

Understanding how severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in 2019 is critical to preventing future zoonotic outbreaks before they become the next pandemic. The Huanan Seafood Wholesale Market in Wuhan, China, was identified as a likely source of cases in early reports, but later this conclusion became controversial. We show here that the earliest known COVID-19 cases from December 2019, including those without reported direct links, were geographically centered on this market. We report that live SARS-CoV-2-susceptible mammals were sold at the market in late 2019 and that within the market, SARS-CoV-2-positive environmental samples were spatially associated with vendors selling live mammals. Although there is insufficient evidence to define upstream events, and exact circumstances remain obscure, our analyses indicate that the emergence of SARS-CoV-2 occurred through the live wildlife trade in China and show that the Huanan market was the epicenter of the COVID-19 pandemic.

Validation of a noninvasive aMMP‐8 point‐of‐care diagnostic methodology in COVID‐19 patients with periodontal disease

Objectives

The aim of this study was to validate an active matrix metalloproteinase (MMP‐8) point‐of‐care diagnostic tool in COVID‐19 patients with periodontal disease.

Subjects, Materials, and Methods

Seventy‐two COVID‐19‐positive and 30 COVID‐19‐negative subjects were enrolled in the study. Demographic data were recorded, periodontal examination carried out, and chairside tests run for evaluating the expression of active MMP‐8 (aMMP‐8) in the site with maximum periodontal breakdown via gingival crevicular fluid sampling as well as via a mouth rinse‐based kit for general disease activity. In COVID‐19‐positive patients, the kits were run again once the patients turned COVID‐19 negative.

Results

The overall (n = 102) sensitivity/specificity of the mouthrinse‐based kits to detect periodontal disease was 79.41%/36.76% and that of site‐specific kits was 64.71%/55.88% while adjusting for age, gender, and smoking status increased the sensitivity and specificity (82.35%/76.47% and 73.53%/88.24, respectively). Receiver operating characteristic (ROC) analysis for the adjusted model revealed very good area under the ROC curve 0.746–0.869 (p < .001) and 0.740–0.872 (p < .001) (the aMMP‐8 mouth rinse and site‐specific kits, respectively). No statistically significant difference was observed in the distribution of results of aMMP‐8 mouth rinse test (p = .302) and aMMP‐8 site‐specific test (p = .189) once the subjects recovered from COVID‐19.

Conclusions

The findings of the present study support the aMMP‐8 point‐of‐care testing (PoCT) kits as screening tools for periodontitis in COVID‐19 patients. The overall screening accuracy can be further increased by utilizing adjunctively risk factors of periodontitis. The reported noninvasive, user‐friendly, and objective PoCT diagnostic methodology may provide a way of stratifying risk groups, deciding upon referrals, and in the institution of diligent oral hygiene regimens.

A community-level study on COVID-19 transmission and policy interventions in Wuhan, China

The specific factors and response strategies that affect COVID-19 transmission in local communities remain under-explored in the current literature due to a lack of data. Based on primary COVID-19 data collected at the community level in Wuhan, China, our study contributes a community-level investigation on COVID-19 transmission and response strategies by addressing two research questions: 1) What community factors are associated with viral transmission? and 2) What are the key mechanisms behind policy interventions towards controlling viral transmission within local communities? We conducted two sets of analyses to address these two questions-quantitative analyses of the relationship between community factors and viral transmission and qualitative analyses of policy interventions on community transmission. Our findings show that the viral spread in local communities is irrelevant to the built environment of a community and its socioeconomic position but is related to its demographic composition. Specifically, groups under the age of 18 play an important role in viral transmission. Moreover, a series of community shutdown management initiatives (e.g., group buying, delivering supplies, and self-reporting of health conditions) play an important role in curbing viral transmission at the local level that can be applied to other geographic contexts.

Artificial Intelligence-Based Data-Driven Strategy to Accelerate Research, Development, and Clinical Trials of COVID Vaccine

The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5-15 years to 1-2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools.

The Ecology of Viral Emergence

The coronavirus disease 2019 (COVID-19) pandemic has had a profound impact on human health, economic well-being, and societal function. It is essential that we use this generational experience to better understand the processes that underpin the emergence of COVID-19 and other zoonotic diseases. Herein, I review the mechanisms that determine why and how viruses emerge in new hosts, as well as the barriers to this process. I show that traditional studies of virus emergence have an inherent anthropocentric bias, with disease in humans considered the inevitable outcome of virus emergence, when in reality viruses are integral components of a global ecosystem characterized by continual host jumping with humans also transmitting their viruses to other animals. I illustrate these points using coronaviruses, including severe acute respiratory syndrome coronavirus 2, as a case study. I also outline the potential steps that can be followed to help mitigate and prevent future pandemics, with combating climate change a central component. Expected final online publication date for the Annual Review of Virology, Volume 9 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Recent advances in aptamer applications for analytical biochemistry

Aptamers are typically defined as relatively short (20-60 nucleotides) single-stranded DNA or RNA molecules that bind with high affinity and specificity to various types of targets. Aptamers are frequently referred to as "synthetic antibodies" but are easier to obtain, less expensive to produce, and in several ways more versatile than antibodies. The beginnings of aptamers date back to 1990, and since then there has been a continual increase in aptamer publications. The intent of the present account was to focus on recent original research publications, i.e., those appearing in 2019 through April 2020, when this account was written. A Google Scholar search of this recent literature was performed for relevance-ranking of articles. New methods for selection of aptamers were not included. Nine categories of applications were organized and representative examples of each are given. Finally, an outlook is offered focusing on "faster, better, cheaper" application performance factors as key drivers for future innovations in aptamer applications.

SERS-based dual-mode DNA aptasensors for rapid classification of SARS-CoV-2 and influenza A/H1N1 infection

We developed a dual-mode surface-enhanced Raman scattering (SERS)-based aptasensor that can accurately diagnose and distinguish severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza A/H1N1 at the same time. Herein, DNA aptamers that selectively bind to SARS-CoV-2 and influenza A/H1N1 were immobilized together on Au nanopopcorn substrate. Raman reporters (Cy3 and RRX), attached to the terminal of DNA aptamers, could generate strong SERS signals in the nanogap of the Au nanopopcorn substrate. Additionally, the internal standard Raman reporter (4-MBA) was immobilized on the Au nanopopcorn substrate along with aptamer DNAs to reduce errors caused by changes in the measurement environment. When SARS-CoV-2 or influenza A virus approaches the Au nanopopcorn substrate, the corresponding DNA aptamer selectively detaches from the substrate due to the significant binding affinity between the corresponding DNA aptamer and the virus. As a result, the related SERS intensity decreases with increasing target virus concentration. Thus, it is possible to determine whether a suspected patient is infected with SARS-CoV-2 or influenza A using this SERS-based DNA aptasensor. Furthermore, this sensor enables a quantitative evaluation of the target virus concentration with high sensitivity without being affected by cross-reactivity. Therefore, this SERS-based diagnostic platform is considered a conceptually new diagnostic tool that rapidly discriminates against these two respiratory diseases to prevent their spread.

Bacterial microbiota in upper respiratory tract of COVID-19 and influenza patients

The upper respiratory tract is inhabited by diverse range of commensal microbiota which plays a role in protecting the mucosal surface from pathogens. Alterations of the bacterial community from respiratory viral infections could increase the susceptibility to secondary infections and disease severities. We compared the upper respiratory bacterial profiles among Thai patients with influenza or COVID-19 by using 16S rDNA high-throughput sequencing based on MiSeq platform. The Chao1 richness was not significantly different among groups, whereas the Shannon diversity of Flu A and Flu B groups were significantly lower than Non-Flu & COVID-19 group. The beta diversity revealed that the microbial communities of influenza (Flu A and Flu B), COVID-19, and Non-Flu & COVID-19 were significantly different; however, the comparison of the community structure was similar between Flu A and Flu B groups. The bacterial classification revealed that Enterobacteriaceae was predominant in influenza patients, while Staphylococcus and Pseudomonas were significantly enriched in the COVID-19 patients. These implied that respiratory viral infections might be related to alteration of upper respiratory bacterial community and susceptibility to secondary bacterial infections. Moreover, the bacteria that observed in Non-Flu & COVID-19 patients had high abundance of Streptococcus, Prevotella, Veillonella, and Fusobacterium. This study provides the basic knowledge for further investigation of the relationship between upper respiratory microbiota and respiratory disease which might be useful for better understanding the mechanism of viral infectious diseases.

Effectiveness of tests to detect the presence of SARS-CoV-2 virus, and antibodies to SARS-CoV-2, to inform COVID-19 diagnosis: a rapid systematic review

OBJECTIVES

We undertook a rapid systematic review with the aim of identifying evidence that could be used to answer the following research questions: (1) What is the clinical effectiveness of tests that detect the presence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to inform COVID-19 diagnosis? (2) What is the clinical effectiveness of tests that detect the presence of antibodies to the SARS-CoV-2 virus to inform COVID-19 diagnosis?

DESIGN AND SETTING

Systematic review and meta-analysis of studies of diagnostic test accuracy. We systematically searched for all published evidence on the effectiveness of tests for the presence of SARS-CoV-2 virus, or antibodies to SARS-CoV-2, up to 4 May 2020, and assessed relevant studies for risks of bias using the QUADAS-2 framework.

MAIN OUTCOME MEASURES

Measures of diagnostic accuracy (sensitivity, specificity, positive/negative predictive value) were the main outcomes of interest. We also included studies that reported influence of testing on subsequent patient management, and that reported virus/antibody detection rates where these facilitated comparisons of testing in different settings, different populations or using different sampling methods.

RESULTS

38 studies on SARS-CoV-2 virus testing and 25 studies on SARS-CoV-2 antibody testing were identified. We identified high or unclear risks of bias in the majority of studies, most commonly as a result of unclear methods of patient selection and test conduct, or because of the use of a reference standard that may not definitively diagnose COVID-19. The majority were in hospital settings, in patients with confirmed or suspected COVID-19 infection. Pooled analysis of 16 studies (3818 patients) estimated a sensitivity of 87.8% (95% CI 81.5% to 92.2%) for an initial reverse-transcriptase PCR test. For antibody tests, 10 studies reported diagnostic accuracy outcomes: sensitivity ranged from 18.4% to 96.1% and specificity 88.9% to 100%. However, the lack of a true reference standard for SARS-CoV-2 diagnosis makes it challenging to assess the true diagnostic accuracy of these tests. Eighteen studies reporting different sampling methods suggest that for virus tests, the type of sample obtained/type of tissue sampled could influence test accuracy. Finally, we searched for, but did not identify, any evidence on how any test influences subsequent patient management.

CONCLUSIONS

Evidence is rapidly emerging on the effectiveness of tests for COVID-19 diagnosis and management, but important uncertainties about their effectiveness and most appropriate application remain. Estimates of diagnostic accuracy should be interpreted bearing in mind the absence of a definitive reference standard to diagnose or rule out COVID-19 infection. More evidence is needed about the effectiveness of testing outside of hospital settings and in mild or asymptomatic cases. Implementation of public health strategies centred on COVID-19 testing provides opportunities to explore these important areas of research.

Curse of La Corona: unravelling the scientific and psychological conundrums of the 21st century pandemic

Microbes possess a tremendous potential to interact with their surroundings and have continued to shape the future of all life forms existing on earth. Of all the groups of microbes, viruses are the most nefarious creatures which cannot be solely classified as living or non-living but still pose the greatest threats to the biosphere. Viruses are minuscule, diverse and are probably the only entities that exhibit non-mutualistic association with other lifeforms while retaining their ability to infect and hijack any of the existing living being on the planet. The latest global devastation, caused by novel SARS-CoV-2, is unparalleled in the last century. This review encompasses the mysterious origin of this virus by tracking its lineage, which may help to decode the conundrum of SARS-CoV-2 and shed more light on its epidemiology. The implications and the challenge posed by this virus to the scientific community to the medical community and the economy at large are reflected. Also discussed is the paradigm shift brought upon by the COVID-19 pandemic on the human psyche and their behaviour.

Total infectome characterization of respiratory infections in pre-COVID-19 Wuhan, China

At the end of 2019 Wuhan witnessed an outbreak of "atypical pneumonia" that later developed into a global pandemic. Metagenomic sequencing rapidly revealed the causative agent of this outbreak to be a novel coronavirus denoted SARS-CoV-2. To provide a snapshot of the pathogens in pneumonia-associated respiratory samples from Wuhan prior to the emergence of SARS-CoV-2, we collected bronchoalveolar lavage fluid samples from 408 patients presenting with pneumonia and acute respiratory infections at the Central Hospital of Wuhan between 2016 and 2017. Unbiased total RNA sequencing was performed to reveal their "total infectome", including viruses, bacteria and fungi. We identified 35 pathogen species, comprising 13 RNA viruses, 3 DNA viruses, 16 bacteria and 3 fungi, often at high abundance and including multiple co-infections (13.5%). SARS-CoV-2 was not present. These data depict a stable core infectome comprising common respiratory pathogens such as rhinoviruses and influenza viruses, an atypical respiratory virus (EV-D68), and a single case of a sporadic zoonotic pathogen-Chlamydia psittaci. Samples from patients experiencing respiratory disease on average had higher pathogen abundance than healthy controls. Phylogenetic analyses of individual pathogens revealed multiple origins and global transmission histories, highlighting the connectedness of the Wuhan population. This study provides a comprehensive overview of the pathogens associated with acute respiratory infections and pneumonia, which were more diverse and complex than obtained using targeted PCR or qPCR approaches. These data also suggest that SARS-CoV-2 or closely related viruses were absent from Wuhan in 2016-2017.

Using an Administrative and Clinical Database to Determine the Early Spread of COVID-19 at the US Department of Veterans Affairs during the Beginning of the 2019-2020 Flu Season: A Retrospective Longitudinal Study

BACKGROUND

Previous studies examining the early spread of COVID-19 have used influenza-like illnesses (ILIs) to determine the early spread of COVID-19. We used COVID-19 case definition to identify COVID-like symptoms (CLS) independently of other influenza-like illnesses (ILIs).

METHODS

Using data from Emergency Department (ED) visits at VA Medical Centers in CA, TX, and FL, we compared weekly rates of CLS, ILIs, and non-influenza ILIs encounters during five consecutive flu seasons (2015-2020) and estimated the risk of developing each illness during the first 23 weeks of the 2019-2020 season compared to previous seasons.

RESULTS

Patients with CLS were significantly more likely to visit the ED during the first 23 weeks of the 2019-2020 compared to prior seasons, while ED visits for influenza and non-influenza ILIs did not differ substantially. Adjusted CLS risk was significantly lower for all seasons relative to the 2019-2020 season: RR15-16 = 0.72, 0.75, 0.72; RR16-17 = 0.81, 0.77, 0.79; RR17-18 = 0.80, 0.89, 0.83; RR18-19 = 0.82, 0.96, 0.81, in CA, TX, and FL, respectively.

CONCLUSIONS

The observed increase in ED visits for CLS indicates the likely spread of COVID-19 in the US earlier than previously reported. VA data could potentially help identify emerging infectious diseases and supplement existing syndromic surveillance systems.

Advanced Point-of-Care Testing Technologies for Human Acute Respiratory Virus Detection

The ever-growing global threats to human life caused by the human acute respiratory virus (RV) infections have cost billions of lives, created a significant economic burden, and shaped society for centuries. The timely response to emerging RVs could save human lives and reduce the medical care burden. The development of RV detection technologies is essential for potentially preventing RV pandemic and epidemics. However, commonly used detection technologies lack sensitivity, specificity, and speed, thus often failing to provide the rapid turnaround times. To address this problem, new technologies are devised to address the performance inadequacies of the traditional methods. These emerging technologies offer improvements in convenience, speed, flexibility, and portability of point-of-care test (POCT). Herein, recent developments in POCT are comprehensively reviewed for eight typical acute respiratory viruses. This review discusses the challenges and opportunities of various recognition and detection strategies and discusses these according to their detection principles, including nucleic acid amplification, optical POCT, electrochemistry, lateral flow assays, microfluidics, enzyme-linked immunosorbent assays, and microarrays. The importance of limits of detection, throughput, portability, and specificity when testing clinical samples in resource-limited settings is emphasized. Finally, the evaluation of commercial POCT kits for both essential RV diagnosis and clinical-oriented practices is included.

Impact of Coinfection With SARS-CoV-2 and Influenza on Disease Severity: A Systematic Review and Meta-Analysis

Background: Although coinfection with influenza in COVID-19 patients has drawn considerable attention, it is still not completely understood whether simultaneously infected with these two viruses influences disease severity. We therefore aimed to estimate the impact of coinfected with SARS-CoV-2 and influenza on the disease outcomes compared with the single infection of SARS-CoV-2. Materials and Methods: We searched the PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure Database (CNKI) to identify relevant articles up to July 9, 2021. Studies that assessed the effect of SARS-CoV-2 and influenza coinfection on disease outcomes or those with sufficient data to calculate risk factors were included. Risk effects were pooled using fixed or random effects model. Results: We ultimately identified 12 studies with 9,498 patients to evaluate the risk effects of SARS-CoV-2 and influenza coinfection on disease severity. Results indicated that coinfection was not significantly associated with mortality (OR = 0.85, 95%CI: 0.51, 1.43; p = 0.55, I ² = 76.00%). However, mortality was found significantly decreased in the studies from China (OR = 0.51, 95%CI: 0.39, 0.68; I ² = 26.50%), while significantly increased outside China (OR = 1.56, 95%CI: 1.12, 2.19; I ² = 1.00%). Moreover, a lower risk for critical outcomes was detected among coinfection patients (OR = 0.64, 95%CI: 0.43, 0.97; p = 0.04, I ² = 0.00%). Additionally, coinfection patients presented different laboratory indexes compared with the single SARS-CoV-2 infection, including lymphocyte counts and APTT. Conclusion: Our study revealed that coinfection with SARS-CoV-2 and influenza had no effect on overall mortality. However, risk for critical outcomes was lower in coinfection patients and different associations were detected in the studies from different regions and specific laboratory indexes. Further studies on influenza strains and the order of infection were warranted. Systematic testing for influenza coinfection in COVID-19 patients and influenza vaccination should be recommended.

Efficacy and safety of the combination of modern medicine and traditional Chinese medicine in pulmonary fibrosis caused by novel coronavirus disease: A protocol for Bayesian network meta-analysis

BACKGROUND

Novel coronavirus disease (COVID-19) is a kind of pulmonary inflammation induced by New Coronavirus. It seriously threatens people's health and safety. Clinical studies have found that some patients have different degrees of inflammation after discharge from hospital, especially in patients with severe inflammatory lung fibrosis. Early combination of Chinese medicine and modern medicine has important clinical significance. There are still many deficiencies in the current research. We studied the effectiveness of the combination of traditional Chinese medicine and modern medicine in the treatment of pulmonary fibrosis caused by COVID-19, and proposed a network meta-analysis (NMA) scheme.

METHODS

According to the search strategy, we will search Chinese and English databases to collect all randomized controlled trials of traditional Chinese medicine combined with modern drugs or only using traditional Chinese medicine for new coronavirus-19-induced pulmonary fibrosis between December 1, 2019 and November 15, 2021. First, the literature was screened according to the eligibility criteria, endnotex9 was used to manage the literature, and the Cochrane Collaboration's tool was used to assess the quality of the included literature. Revman 5.3, Stata 14.2, and gemtc14.3 meta-analysis software was then used for data processing and analysis, and the grading of recommendations assessment will be used to develop and evaluate a hierarchy for classifying the quality of evidence for NMA.

RESULTS

Through the analysis, the ranking of efficacy and safety of various treatments for pulmonary fibrosis caused by COVID-19 will be drawn, thus providing stronger evidence support for the choice of clinical treatment methods.

CONCLUSION

Traditional Chinese medicine (TCM) combined with modern drugs has played a positive role in the treatment of pulmonary fibrosis caused by COVID-19, and this study may provide more references for the clinical medication of pulmonary fibrosis caused by COVID-19.

INPLASY REGISTRATION NUMBER

INPLASY2021110061.

The Domestic Violence during the COVID-19 Pandemic: Scoping Review

Introduction The Covid-19 pandemic had brought unexpected challenges in all sectors of life, from the social, economic to health sectors, particularly public health. One of the most worrying effects of Covid-19 is the increase in cases of domestic violence. Aim : This scoping review was to determine trends in domestic violence during the Covid-19 pandemic and strategies for handling it   Method The method used in this paper is the Scoping Review study. We searched for relevant articles on 4 databases (PubMed, ScienceDirect, ProQuest and EBSco), in English, published in 2019-2021. Of the 434 articles identified, there were 6 articles that met the inclusion criteria.   Result There were 6 themes we got related to domestic violence during the Covid-19 pandemic, namely: 1) The number of cases of domestic violence during the Covid-19 pandemic; 2) Risk factors for domestic violence; 3) Victims and perpetrators of domestic violence; 4) Types of domestic violence; 5) Impact of domestic violence; 6) Strategies for handling domestic violence during the Covid-19 pandemic.   Conclusion The Covid-19 pandemic had an impact on domestic violence cases, where the majority of victims were women and most of whom experienced recurrent domestic violence. The majority of the articles in this scoping review were quantitative research, we recommend that future research can be examined deeper into the understanding, experiences and needs of victims and perpetrators of domestic violence during the Covid-19 pandemic including the roles of cross-sectors and obstacles experienced in handling domestic violence in the future. the covid-19 pandemic. The results of this study are expected to provide input in making policies related to the prevention and reduction of domestic violence cases during the Covid-19 pandemic.  

Recent omics-based computational methods for COVID-19 drug discovery and repurposing

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the main reason for the increasing number of deaths worldwide. Although strict quarantine measures were followed in many countries, the disease situation is still intractable. Thus, it is needed to utilize all possible means to confront this pandemic. Therefore, researchers are in a race against the time to produce potential treatments to cure or reduce the increasing infections of COVID-19. Computational methods are widely proving rapid successes in biological related problems, including diagnosis and treatment of diseases. Many efforts in recent months utilized Artificial Intelligence (AI) techniques in the context of fighting the spread of COVID-19. Providing periodic reviews and discussions of recent efforts saves the time of researchers and helps to link their endeavors for a faster and efficient confrontation of the pandemic. In this review, we discuss the recent promising studies that used Omics-based data and utilized AI algorithms and other computational tools to achieve this goal. We review the established datasets and the developed methods that were basically directed to new or repurposed drugs, vaccinations and diagnosis. The tools and methods varied depending on the level of details in the available information such as structures, sequences or metabolic data.

Reciprocal circulation pattern of SARS-CoV-2 and influenza viruses during the influenza seasons 2019/2020 and 2020/2021 in the Bavarian Influenza Sentinel (Germany)

The corona virus disease-2019 (COVID-19) pandemic began in Wuhan, China, and quickly spread around the world. The pandemic overlapped with two consecutive influenza seasons (2019/2020 and 2020/2021). This provided the opportunity to study community circulation of influenza viruses and severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) in outpatients with acute respiratory infections during these two seasons within the Bavarian Influenza Sentinel (BIS) in Bavaria, Germany. From September to March, oropharyngeal swabs collected at BIS were analysed for influenza viruses and SARS-CoV-2 by real-time polymerase chain reaction. In BIS 2019/2020, 1376 swabs were tested for influenza viruses. The average positive rate was 37.6%, with a maximum of over 60% (in January). The predominant influenza viruses were Influenza A(H1N1)pdm09 (n = 202), Influenza A(H3N2) (n = 144) and Influenza B Victoria lineage (n = 129). In all, 610 of these BIS swabs contained sufficient material to retrospectively test for SARS-CoV-2. SARS-CoV-2 RNA was not detectable in any of these swabs. In BIS 2020/2021, 470 swabs were tested for influenza viruses and 457 for SARS-CoV-2. Only three swabs (0.6%) were positive for Influenza, while SARS-CoV-2 was found in 30 swabs (6.6%). We showed that no circulation of SARS-CoV-2 was detectable in BIS during the 2019/2020 influenza season, while virtually no influenza viruses were found in BIS 2020/2021 during the COVID-19 pandemic.

Recent advances in detection technologies for COVID-19

Corona Virus Disease 2019 (COVID-19) is a highly infectious respiratory illness that was caused by the SARS-CoV-2. It spread around the world in just a few months and became a worldwide pandemic. Quick and accurate diagnosis of infected patients is very important for controlling transmission. In addition to the commonly used Real-time reverse-transcription polymerase chain reaction (RT-PCR) detection techniques, other diagnostic techniques are also emerging endlessly. This article reviews the current diagnostic methods for COVID-19 and discusses their advantages and disadvantages. It provides an important reference for the diagnosis of COVID-19.

Retrospective screening for SARS-CoV-2 among influenza-like illness hospitalizations: 2018-2019 and 2019-2020 seasons, Valencia region, Spain

On 9 March 2020, the World Health Organization (WHO) Global Influenza Programme (GIP) asked participant sites on the Global Influenza Hospital Surveillance Network (GIHSN) to contribute to data collection concerning severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). We re‐analysed 5833 viral RNA archived samples collected prospectively from hospital admissions for influenza‐like illness (ILI) in the Valencia Region of Spain by the Valencia Hospital Surveillance Network for the Study of Influenza and Other Respiratory Viruses (VAHNSI) network (four hospitals, catchment area population 1 118 732) during the pre‐pandemic 2018/2019 (n = 4010) and pandemic 2019/2020 (n = 1823) influenza seasons for the presence of SARS‐CoV‐2. We did not find evidence for community‐acquired SARS‐CoV‐2 infection in hospital admissions for ILI in our region before early March 2020.

Overview of Technologies Implemented During the First Wave of the COVID-19 Pandemic: Scoping Review

BACKGROUND

Technologies have been extensively implemented to provide health care services for all types of clinical conditions during the COVID-19 pandemic. While several reviews have been conducted regarding technologies used during the COVID-19 pandemic, they were limited by focusing either on a specific technology (or features) or proposed rather than implemented technologies.

OBJECTIVE

This review aims to provide an overview of technologies, as reported in the literature, implemented during the first wave of the COVID-19 pandemic.

METHODS

We conducted a scoping review using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) Extension for Scoping Reviews. Studies were retrieved by searching 8 electronic databases, checking the reference lists of included studies and relevant reviews (backward reference list checking), and checking studies that cited included studies (forward reference list checking). The search terms were chosen based on the target intervention (ie, technologies) and the target disease (ie, COVID-19). We included English publications that focused on technologies or digital tools implemented during the COVID-19 pandemic to provide health-related services regardless of target health condition, user, or setting. Two reviewers independently assessed the eligibility of studies and extracted data from eligible papers. We used a narrative approach to synthesize extracted data.

RESULTS

Of 7374 retrieved papers, 126 were deemed eligible. Telemedicine was the most common type of technology (107/126, 84.9%) implemented in the first wave of the COVID-19 pandemic, and the most common mode of telemedicine was synchronous (100/108, 92.6%). The most common purpose of the technologies was providing consultation (75/126, 59.5%), followed by following up with patients (45/126, 35.7%), and monitoring their health status (22/126, 17.4%). Zoom (22/126, 17.5%) and WhatsApp (12/126, 9.5%) were the most commonly used videoconferencing and social media platforms, respectively. Both health care professionals and health consumers were the most common target users (103/126, 81.7%). The health condition most frequently targeted was COVID-19 (38/126, 30.2%), followed by any physical health conditions (21/126, 16.7%), and mental health conditions (13/126, 10.3%). Technologies were web-based in 84.1% of the studies (106/126). Technologies could be used through 11 modes, and the most common were mobile apps (86/126, 68.3%), desktop apps (73/126, 57.9%), telephone calls (49/126, 38.9%), and websites (45/126, 35.7%).

CONCLUSIONS

Technologies played a crucial role in mitigating the challenges faced during the COVID-19 pandemic. We did not find papers describing the implementation of other technologies (eg, contact-tracing apps, drones, blockchain) during the first wave. Furthermore, technologies in this review were used for other purposes (eg, drugs and vaccines discovery, social distancing, and immunity passport). Future research on studies on these technologies and purposes is recommended, and further reviews are required to investigate technologies implemented in subsequent waves of the pandemic.

The clinical association between Periodontitis and COVID-19

OBJECTIVES

The study aimed to clinically assess the association between periodontitis and COVID-19-related outcomes.

MATERIAL AND METHODS

Data pertaining to patient demographics, medical history, blood parameters, periodontal clinical examination and aMMP-8 point-of-care diagnostics (both site-level and patient-level) was recorded for eighty-two COVID-19-positive patients. COVID-19-related outcomes such as COVID-19 pneumonia, death/survival, types of hospital admission and need of assisted ventilation were also assessed.

RESULTS

Males were predominantly afflicted with COVID-19, with advanced age exhibiting a greater association with the presence of periodontitis. Higher severity of periodontitis led to 7.45 odds of requiring assisted ventilation, 36.52 odds of hospital admission, 14.58 odds of being deceased and 4.42 odds of COVID-19-related pneumonia. The aMMP-8 mouthrinse kit was slightly more sensitive but less specific than aMMP-8 site-specific tests.

CONCLUSIONS

Based on the findings of the present study, periodontitis seems to be related to poorer COVID-19-related outcomes. However, within the constraints of this work, a direct causality may not be established. Periodontitis, by means of skewing the systemic condition for a number of comorbidities, may eventually influence COVID-19 outcomes in an indirect manner.

CLINICAL RELEVANCE

The study is the first to clinically, and by means of a validated point-of-care diagnostic methodology, assess the association between periodontal health and COVID-19-related outcomes. Assessment of the periodontal status of individuals can aid in the identification of risk groups during the pandemic along with reinforcing the need to maintain oral hygiene and seeking periodontal care.