Detecting SARS-CoV-2 in the Breath of COVID-19 Patients

Machine learning enabled detection of COVID-19 pneumonia using exhaled breath analysis: a proof-of-concept study

Detection of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) relies on real-time-reverse-transcriptase polymerase chain reaction (RT-PCR) on nasopharyngeal swabs. The false-negative rate of RT-PCR can be high when viral burden and infection is localized distally in the lower airways and lung parenchyma. An alternate safe, simple and accessible method for sampling the lower airways is needed to aid in the early and rapid diagnosis of COVID-19 pneumonia. In a prospective unblinded observational study, patients admitted with a positive RT-PCR and symptoms of SARS-CoV-2 infection were enrolled from three hospitals in Ontario, Canada. Healthy individuals or hospitalized patients with negative RT-PCR and without respiratory symptoms were enrolled into the control group. Breath samples were collected and analyzed by laser absorption spectroscopy (LAS) for volatile organic compounds (VOCs) and classified by machine learning (ML) approaches to identify unique LAS-spectra patterns (breathprints) for SARS-CoV-2. Of the 135 patients enrolled, 115 patients provided analyzable breath samples. Using LAS-breathprints to train ML classifier models resulted in an accuracy of 72.2%-81.7% in differentiating between SARS-CoV2 positive and negative groups. The performance was consistent across subgroups of different age, sex, body mass index, SARS-CoV-2 variants, time of disease onset and oxygen requirement. The overall performance was higher than compared to VOC-trained classifier model, which had an accuracy of 63%-74.7%. This study demonstrates that a ML-based breathprint model using LAS analysis of exhaled breath may be a valuable non-invasive method for studying the lower airways and detecting SARS-CoV-2 and other respiratory pathogens. The technology and the ML approach can be easily deployed in any setting with minimal training. This will greatly improve access and scalability to meet surge capacity; allow early and rapid detection to inform therapy; and offers great versatility in developing new classifier models quickly for future outbreaks.

Advancements in Airborne Viral Nucleic Acid Detection with Wearable Devices

Wearable health sensors for an expanding range of physiological parameters have experienced rapid development in recent years and are poised to disrupt the way healthcare is tracked and administered. The monitoring of environmental contaminants with wearable technologies is an additional layer of personal and public healthcare and is also receiving increased focus. Wearable sensors that detect exposure to airborne viruses could alert wearers of viral exposure and prompt proactive testing and minimization of viral spread, benefitting their own health and decreasing community risk. With the high levels of asymptomatic spread of COVID-19 observed during the pandemic, such devices could dramatically enhance our pandemic response capabilities in the future. To facilitate advancements in this area, this review summarizes recent research on airborne viral detection using wearable sensing devices as well as technologies suitable for wearables. Since the low concentration of viral particles in the air poses significant challenges to detection, methods for airborne viral particle collection and viral sensing are discussed in detail. A special focus is placed on nucleic acid-based viral sensing mechanisms due to their enhanced ability to discriminate between viral subtypes. Important considerations for integrating airborne viral collection and sensing on a single wearable device are also discussed.

At the crossroads of epidemiology and biology: Bridging the gap between SARS-CoV-2 viral strain properties and epidemic wave characteristics

The COVID-19 pandemic has given rise to numerous articles from different scientific fields (epidemiology, virology, immunology, airflow physics …) without any effort to link these different insights. In this review, we aim to establish relationships between epidemiological data and the characteristics of the virus strain responsible for the epidemic wave concerned. We have carried out this study on the Wuhan, Alpha, Delta and Omicron strains allowing us to illustrate the evolution of the relationships we have highlighted according to these different viral strains. We addressed the following questions. 1) How can the mean infectious dose (one quantum, by definition in epidemiology) be measured and expressed as an amount of viral RNA molecules (in genome units, GU) or as a number of replicative viral particles (in plaque-forming units, PFU)? 2) How many infectious quanta are exhaled by an infected person per unit of time? 3) How many infectious quanta are exhaled, on average, integrated over the whole contagious period? 4) How do these quantities relate to the epidemic reproduction rate R as measured in epidemiology, and to the viral load, as measured by molecular biological methods? 5) How has the infectious dose evolved with the different strains of SARS-CoV-2? We make use of state-of-the-art modelling, reviewed and explained in the appendix of the article (Supplemental Information, SI), to answer these questions using data from the literature in both epidemiology and virology. We have considered the modification of these relationships according to the vaccination status of the population.

Oxylipin concentration shift in exhaled breath condensate (EBC) of SARS-CoV-2 infected patients

Infection of airway epithelial cells with severe acute respiratory coronavirus 2 (SARS-CoV-2) can lead to severe respiratory tract damage and lung injury with hypoxia. It is challenging to sample the lower airways non-invasively and the capability to identify a highly representative specimen that can be collected in a non-invasive way would provide opportunities to investigate metabolomic consequences of COVID-19 disease. In the present study, we performed a targeted metabolomic approach using liquid chromatography coupled with high resolution chromatography (LC-MS) on exhaled breath condensate (EBC) collected from hospitalized COVID-19 patients (COVID+) and negative controls, both non-hospitalized and hospitalized for other reasons (COVID-). We were able to noninvasively identify and quantify inflammatory oxylipin shifts and dysregulation that may ultimately be used to monitor COVID-19 disease progression or severity and response to therapy. We also expected EBC-based biochemical oxylipin changes associated with COVID-19 host response to infection. The results indicated ten targeted oxylipins showing significative differences between SAR-CoV-2 infected EBC samples and negative control subjects. These compounds were prostaglandins A2 and D2, LXA4, 5-HETE, 12-HETE, 15-HETE, 5-HEPE, 9-HODE, 13-oxoODE and 19(20)-EpDPA, which are associated with specific pathways (i.e. P450, COX, 15-LOX) related to inflammatory and oxidative stress processes. Moreover, all these compounds were up-regulated by COVID+, meaning their concentrations were higher in subjects with SAR-CoV-2 infection. Given that many COVID-19 symptoms are inflammatory in nature, this is interesting insight into the pathophysiology of the disease. Breath monitoring of these and other EBC metabolites presents an interesting opportunity to monitor key indicators of disease progression and severity.

Evaluation of droplet digital qRT-PCR (dd qRT-PCR) for quantification of SARS CoV-2 RNA in stool and urine specimens of COVID-19 patients

Introduction

There have been a few reports of viral load detection in stool and urine samples of patients with coronavirus disease 2019 (COVID-19), and the transmission of the virus through faecal oral route. For clinical diagnosis and treatment, the widely used reverse transcription-polymerase chain reaction (qRT-PCR) method has some limitations.

Methods

The aim of our study to assess the presence and concentration of SARS CoV-2 RNA in stool and urine samples from COVID-19 patients with mild, moderate, and severe disease, we compared a traditional qRT-PCR approach with a ddPCR. ddPCR and qRT-PCR-based target gene analysis were performed on 107 COVID-19-confirmed patients paired samples (N1 and N2). The MagMax magnetic beads base method was used to isolate RNA. Real-time qRT-PCR and dd PCR were performed on all patients.

Results and Discussion

The average cycle threshold (Ct) of qRT-PCR was highly correlated with the average copy number of 327.10 copies/l analyzed in ddPCR. In ddPCR, urine samples showed 27.1% positivity while for stool it was 100%.

Conclusion

This study's findings not only show that SARS CoV-2 is present in urine and faeces, but also suggest that low concentrations of the viral target ddPCR make it easier to identify positive samples and help resolve for cases of inconclusive diagnosis.

BIS-guided sedation prevents the cough reaction of patients under general anaesthesia caused by extubation: a randomized controlled trial

Background

The multiple modes of SARS-CoV-2 transmission including airborne, droplet, contact and faecal–oral transmissions that cause coronavirus disease 2019 (COVID-19) contribute to a public threat to the lives of people worldwide. Heavy aerosol production by coughing and the big peak expiratory flow in patients with respiratory infections (especially SARS-CoV-2) during recovery from general anaesthesia are the highest risk factors for infection in healthcare workers. To perform sedation before extubation significantly reduced the incidence of coughing during recovery from general anaesthesia. However, there are few studies on endotracheal tube removal under BIS-guided sedation in postanaesthesia care unit (PACU). We speculated that the BIS-guided sedation with dexmedetomidine and propofol would better prevent coughing caused by tracheal extubation and reducing peak expiratory flow.

Methods

Patients with general anaesthesia were randomly assigned to Group S (dexmedetomidine was infused in the operating room for 30 min, and the bispectral index (BIS) value was maintained 60–70 by infusion propofol at 0.5~1.5 μg/ml in the PACU until the endotracheal tubes were pulled out) and Group C (no dexmedetomidine and propofol treatment, replaced with the saline treatment). The incidence of coughing, agitation and active extubation, endotracheal tube tolerance and the peak expiratory flow at spontaneous breathing and at extubation were assessed.

Results

A total of 101 patients were randomly assigned to Group S (51 cases) and Group C (50 cases). The incidence of coughing, agitation and active extubation was significantly lower (1(51), 0(51) and 0(51), respectively) in Group S than (11(50), 8(50) and 5(50), respectively) in Group C (p < 0.05 or p < 0.01, respectively); the scores of cough were significantly reduced (1(1, 1)) in Group S than (1(1, 2)) in Group C (p < 0.01); and the endotracheal tube tolerance was significantly improved (0(0, 1)) in Group S than (1(1, 3)) in Group C (p < 0.001). The peak expiratory flow at spontaneous breathing and at extubation was significantly reduced (5(5, 7) and 6.5(6, 8), respectively) in Group S than (8(5, 10) and 21(9, 32)) in Group C (p < 0.001).

Conclusions

BIS-guided sedation with dexmedetomidine and propofol significantly prevented coughing and reduced peak expiratory flow during recovery from general anaesthesia, which may play an important role in preventing medical staff from contracting COVID-19.

Trial registration

Chinese Clinical Trial Registry: ChiCTR2200058429 (registration date: 09-04-2022) “retrospectively registered”.

Potential Use of Exhaled Breath Condensate for Diagnosis of SARS-CoV-2 Infections: A Systematic Review and Meta-Analysis

Background. Reverse-transcriptase polymerase chain reaction (RT-qPCR) assays performed on respiratory samples collected through nasal swabs still represent the gold standard for COVID-19 diagnosis. Alternative methods to this invasive and time-consuming options are still being inquired, including the collection of airways lining fluids through exhaled breath condensate (EBC). Materials and Methods. We performed a systematic review and meta-analysis in order to explore the reliability of EBC as a way to collect respiratory specimens for RT-qPCR for diagnosis of COVID-19. Results. A total of 4 studies (205 specimens), were ultimately collected, with a pooled sensitivity of 69.5% (95%CI 26.8–93.4), and a pooled specificity of 98.3% (95%CI 87.8–99.8), associated with high heterogeneity and scarce diagnostic agreement with the gold standard represented by nasal swabs (Cohen’s kappa = 0.585). Discussion. Even though non-invasive options for diagnosis of COVID-19 are still necessary, EBC-based RT-qPCR showed scarce diagnostic performances, ultimately impairing its implementation in real-world settings. However, as few studies have been carried out to date, and the studies included in the present review are characterized by low numbers and low sample power, further research are requested to fully characterize the actual reliability of EBC-based RT-qPCR in the diagnosis of COVID-19.

Detection of SARS-CoV-2 in exhaled breath from non-hospitalized COVID-19-infected individuals

The diagnosis of COVID-19 is based on detection of SARS-CoV-2 in oro-/nasopharyngel swabs, but due to discomfort and minor risk during the swab procedure, detection of SARS-CoV-2 has been investigated in other biological matrixes. In this proof-of-concept study, individuals with confirmed SARS-CoV-2 infection performed a daily air sample for five days. Air samples were obtained through a non-invasive electrostatic air sampler. Detection of SARS-CoV-2 RNA was determined with qRT-PCR. The association of positive samples with different exposures was evaluated through mixed-effect models. We obtained 665 air samples from 111 included participants with confirmed SARS-CoV-2 infection. Overall, 52 individuals (46.8%) had at least one positive air sample, and 129 (19.4%) air samples were positive for SARS-CoV-2. Participants with symptoms or a symptom duration ≤ four days had significantly higher odds of having a positive air sample. Cycle threshold values were significantly lower in samples obtained ≤ 4 days from symptom onset. Neither variant of SARS-CoV-2 nor method of air sampling were associated with a positive air sample. We demonstrate that SARS-CoV-2 is detectable in human breath by electrostatic air sampling with the highest detection rate closest to symptom onset. We suggest further evaluation of the air sampling technique to increase sensitivity.

Metabolic profile of exhaled breath condensate from the pneumonia patients

PURPOSE OF THE STUDY

Exhaled breath condensate (EBC) is increasingly being used for disease diagnosis and environmental exposure assessment as a noninvasive method reducing the risk of exposure. The purpose of this study was to investigate the application of a new sample type of EBC in pneumonia by metabolomics and to explore differential metabolites and potential metabolic pathways.

MATERIALS AND METHODS

A case-control study was performed at the Peking University Third Hospital from August to December 2020. C-MS/MS analyses were performed on EBC samples using a UHPLC system.

RESULTS

Totally 22 patients with pneumonia and 24 healthy controls were recruited. Using untargeted metabolomics based on LC-MS/MS analysis, 25 kinds of differential metabolites were found. Through a comprehensive analysis of the pathways in which the differential metabolites were located, the key pathway with the highest correlation with the difference of metabolites was taurine and hypotaurine metabolism.

CONCLUSIONS

The study implicates that the hypotaurine/taurine metabolic pathway may play a role on the development of pneumonia through metabolism analysis on EBC and the 3-Sulfinoalanine may be used as a biomarker in the diagnosis of pneumonia.

Aluminium Gauze Reduces SARS-CoV-2 Viral Load in Non-Woven Masks Worn by Patients with COVID-19

Background: Aluminium reduces severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) survival in experimental settings. It is unknown whether adding an aluminium gauze to a mask reduces the SARS-CoV-2 RNA load in the mask and whether SARS-CoV-2 is detectable in the breath that permeates through such a mask in clinical settings. Methods: Patients admitted to Kishiwada City Hospital, Osaka, Japan, between July 2021 and September 2021 were enrolled in the study. Non-woven masks comprising filters with 99% viral filtration efficacy and aluminium and cotton gauzes attached to plastic collection cases were developed. All participants wore the experimental mask models for three hours. Results: Twenty-nine patients who wore the final model masks were analysed in this study. The Ct values of the nucleocapsid gene and envelope gene of SARS-CoV-2 were significantly higher in the aluminium gauze than in the cotton gauze. SARS-CoV-2 RNA was detected in the masks of 8 out of 12 vaccinated patients (66.7%). Although breath condensates were collected behind both aluminium and cotton gauzes, SARS-CoV-2 RNA was not detected in these condensates. Conclusions: Our study indicated that non-woven masks with an aluminium gauze may obstruct SARS-CoV-2 transmission in clinical settings better than non-woven masks with cotton gauzes.

Review of non-invasive detection of SARS-CoV-2 and other respiratory pathogens in exhaled breath condensate

In 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged to cause high viral infectivity and severe respiratory illness in humans (COVID-19). Worldwide, limited pandemic mitigation strategies, including lack of diagnostic test availability, resulted in COVID-19 overrunning health systems and spreading throughout the global population. Currently, proximal respiratory tract (PRT) specimens such as nasopharyngeal swabs are used to diagnose COVID-19 because of their relative ease of collection and applicability in large scale screening. However, localization of SARS-CoV-2 in the distal respiratory tract (DRT) is associated with more severe infection and symptoms. Exhaled breath condensate (EBC) is a sample matrix comprising aerosolized droplets originating from alveolar lining fluid that are further diluted in the DRT and then PRT and collected via condensation during tidal breathing. The COVID-19 pandemic has resulted in recent resurgence of interest in EBC collection as an alternative, non-invasive sampling method for the staging and accurate detection of SARS-CoV-2 infections. Herein, we review the potential utility of EBC collection for detection of SARS-CoV-2 and other respiratory infections. While much remains to be discovered in fundamental EBC physiology, pathogen-airway interactions, and optimal sampling protocols, EBC, combined with emerging detection methods, presents a promising non-invasive sample matrix for detection of SARS-CoV-2.

Detection of SARS-CoV-2 infection by exhaled breath spectral analysis: Introducing a ready-to-use point-of-care mass screening method

BACKGROUND

The current SARS-CoV-2 pandemic created an urgent need for rapid, infection screening applied to large numbers of asymptomatic individuals. To date, nasal/throat swab polymerase chain reaction (PCR) is considered the "gold standard". However, this is inconducive to mass, point-of-care (POC) testing due to person discomfort during sampling and a prolonged result turnaround. Breath testing for disease specific organic compounds potentially offers a practical, rapid, non-invasive, POC solution. The study compares the Breath of Health, Ltd. (BOH) breath analysis system to PCR's ability to screen asymptomatic individuals for SARS-CoV-2 infection. The BOH system is mobile and combines Fourier-transform infrared (FTIR) spectroscopy with artificial intelligence (AI) to generate results within 2 min and 15 s. In contrast to prior SARS-CoV-2 breath analysis research, this study focuses on diagnosing SARS-CoV-2 via disease specific spectrometric profiles rather than through identifying the disease specific molecules.

METHODS

Asymptomatic emergency room patients with suspected SARS-CoV-2 exposure in two leading Israeli hospitals were selected between February through April 2021. All were tested via nasal/throat-swab PCR and BOH breath analysis. In total, 297 patients were sampled (mean age 57·08 ± SD 18·86, 156 males, 139 females, 2 unknowns). Of these, 96 were PCR-positive (44 males, 50 females, 2 unknowns), 201 were PCR-negative (112 males, 89 females). One hundred samples were used for AI identification of SARS-CoV-2 distinguishing spectroscopic wave-number patterns and diagnostic algorithm creation. Algorithm validation was tested in 100 proof-of-concept samples (34 PCR-positive, 66 PCR-negative) by comparing PCR with AI algorithm-based breath-test results determined by a blinded medical expert. One hundred additional samples (12 true PCR-positive, 85 true PCR-negative, 3 confounder false PCR-positive [not included in the 297 total samples]) were evaluated by two blinded medical experts for further algorithm validation and inter-expert correlation.

FINDINGS

The BOH system identified three distinguishing wave numbers for SARS-CoV-2 infection. In the first phase, the single expert identified the first 100 samples correctly, yielding a 1:1 FTIR/AI:PCR correlation. The two-expert second-phase also yielded 1:1 FTIR/AI:PCR correlation for 97 non-confounders and null correlation for the 3 confounders. Inter-expert correlation was 1:1 for all results. In total, the FTIR/AI algorithm demonstrated 100% sensitivity and specificity for SARS-CoV-2 detection when compared with PCR.

INTERPRETATION

The SARS-CoV-2 method of breath analysis via FTIR with AI-based algorithm demonstrated high PCR correlation in screening for asymptomatic individuals. This is the first practical, rapid, POC breath analysis solution with such high PCR correlation in asymptomatic individuals. Further validation is required with a larger sample size.

FUNDING

Breath of Health Ltd, Rehovot, Israel provided study funding.

Breathing new life into clinical testing and diagnostics: perspectives on volatile biomarkers from breath

Human breath offers several benefits for diagnostic applications, including simple, noninvasive collection. Breath is a rich source of clinically-relevant biological information; this includes a volatile fraction, where greater than 1,000 volatile organic compounds (VOCs) have been described so far, and breath aerosols that carry nucleic acids, proteins, signaling molecules, and pathogens. Many of these factors, especially VOCs, are delivered to the lung by the systemic circulation, and diffusion of candidate biomarkers from blood into breath allows systematic profiling of organismal health. Biomarkers on breath offer the capability to advance early detection and precision medicine in areas of global clinical need. Breath tests are noninvasive and can be performed at home or in a primary care setting, which makes them well-suited for the kind of public screening program that could dramatically improve the early detection of conditions such as lung cancer. Since measurements of VOCs on breath largely report on metabolic changes, this too aids in the early detection of a broader range of illnesses and can be used to detect metabolic shifts that could be targeted through precision medicine. Furthermore, the ability to perform frequent sampling has envisioned applications in monitoring treatment responses. Breath has been investigated in respiratory, liver, gut, and neurological diseases and in contexts as diverse as infectious diseases and cancer. Preclinical research studies using breath have been ongoing for some time, yet only a few breath-based diagnostics tests are currently available and in widespread clinical use. Most recently, tests assessing the gut microbiome using hydrogen and methane on breath, in addition to tests using urea to detect Helicobacter pylori infections have been released, yet there are many more applications of breath tests still to be realized. Here, we discuss the strengths of breath as a clinical sampling matrix and the technical challenges to be addressed in developing it for clinical use. Historically, a lack of standardized methodologies has delayed the discovery and validation of biomarker candidates, resulting in a proliferation of early-stage pilot studies. We will explore how advancements in breath collection and analysis are in the process of driving renewed progress in the field, particularly in the context of gastrointestinal and chronic liver disease. Finally, we will provide a forward-looking outlook for developing the next generation of clinically relevant breath tests and how they may emerge into clinical practice.

Detection of SARS-CoV-2 RNA in Bivalve Mollusks by Droplet Digital RT-PCR (dd RT-PCR)

Bivalve shellfish are readily contaminated by human pathogens present in waters impacted by municipal sewage, and the detection of SARS-CoV-2 in feces of infected patients and in wastewater has drawn attention to the possible presence of the virus in bivalves. The aim of this study was to collect data on SARS-CoV-2 prevalence in bivalve mollusks from harvesting areas of Campania region. A total of 179 samples were collected between September 2019 and April 2021 and were tested using droplet digital RT-PCR (dd RT-PCR) and real-time RT-PCR. Combining results obtained with different assays, SARS-CoV-2 presence was detected in 27/179 (15.1%) of samples. A median viral concentration of 1.1 × 102 and 1.4 × 102 g.c./g was obtained using either Orf1b nsp14 or RdRp/gene E, respectively. Positive results were unevenly distributed among harvesting areas and over time, positive samples being more frequent after January 2021. Partial sequencing of the spike region was achieved for five samples, one of which displaying mutations characteristic of the Alpha variant (lineage B.1.1.7). This study confirms that bivalve mollusks may bioaccumulate SARS-CoV-2 to detectable levels and that they may represent a valuable approach to track SARS-CoV-2 in water bodies and to monitor outbreak trends and viral diversity.

Detection of Covid-19 through a Heptanal Biomarker Using Transition Metal Doped Graphene

A rapid and noninvasive way to monitor the spread of COVID-19 is the detection of SARS-CoV-2 biomarkers from exhaled breath. Heptanal was identified as a key biomarker which was significantly elevated in the breath of SARS-CoV-2 patients. In this study, the adsorption behaviors of heptanal on pristine and transition metal (Pd, Pt, and Ag) doped graphene were studied based on density functional theory. The results indicated that heptanal was weakly adsorbed on pristine graphene with an adsorption energy of -0.015 eV while it was strongly adsorbed on Pd-, Pt-, and Ag-doped graphene with adsorption energies of -0.404, - 0.356, and -0.755 eV, respectively. Also, the electronic properties of Pd-, Pt-, and Ag-doped graphene changed more dramatically after heptanal adsorption than pristine graphene. The recovery times were estimated to be 6.13 × 10-6, 9.57 × 10-7, and 4.83 s for Pd-, Pt-, and Ag-doped graphene, respectively, showing that Pd-, Pt-, and Ag-doped graphene are suitable as reversible sensors. Our results conclude that Pd-, Pt-, and Ag-doped graphene are potential candidates as gas sensors for heptanal detection, and Ag-doped graphene is the most promising one.

Sampling for SARS-CoV-2 Aerosols in Hospital Patient Rooms

Evidence varies as to how far aerosols spread from individuals infected with SARS-CoV-2 in hospital rooms. We investigated the presence of aerosols containing SARS-CoV-2 inside of dedicated COVID-19 patient rooms. Three National Institute for Occupational Safety and Health BC 251 two-stage cyclone samplers were set up in each patient room for a six-hour sampling period. Samplers were place on tripods, which each held two samplers at various heights above the floor. Extracted samples underwent reverse transcription polymerase chain reaction for selected gene regions of the SARS-CoV-2 virus nucleocapsid. Patient medical data were compared between participants in rooms where virus-containing aerosols were detected and those where they were not. Of 576 aerosols samples collected from 19 different rooms across 32 participants, 3% (19) were positive for SARS-CoV-2, the majority from near the head and foot of the bed. Seven of the positive samples were collected inside a single patient room. No significant differences in participant clinical characteristics were found between patients in rooms with positive and negative aerosol samples. SARS-CoV-2 viral aerosols were detected from the patient rooms of nine participants (28%). These findings provide reassurance that personal protective equipment that was recommended for this virus is appropriate given its spread in hospital rooms.

One-Step, Wash-free, Nanoparticle Clustering-Based Magnetic Particle Spectroscopy Bioassay Method for Detection of SARS-CoV-2 Spike and Nucleocapsid Proteins in the Liquid Phase

With the ongoing global pandemic of coronavirus disease 2019 (COVID-19), there is an increasing quest for more accessible, easy-to-use, rapid, inexpensive, and high-accuracy diagnostic tools. Traditional disease diagnostic methods such as qRT-PCR (quantitative reverse transcription-PCR) and ELISA (enzyme-linked immunosorbent assay) require multiple steps, trained technicians, and long turnaround time that may worsen the disease surveillance and pandemic control. In sight of this situation, a rapid, one-step, easy-to-use, and high-accuracy diagnostic platform will be valuable for future epidemic control, especially for regions with scarce medical resources. Herein, we report a magnetic particle spectroscopy (MPS) platform for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) biomarkers: spike and nucleocapsid proteins. This technique monitors the dynamic magnetic responses of magnetic nanoparticles (MNPs) and uses their higher harmonics as a measure of the nanoparticles' binding states. By anchoring polyclonal antibodies (pAbs) onto MNP surfaces, these nanoparticles function as nanoprobes to specifically bind to target analytes (SARS-CoV-2 spike and nucleocapsid proteins in this work) and form nanoparticle clusters. This binding event causes detectable changes in higher harmonics and allows for quantitative and qualitative detection of target analytes in the liquid phase. We have achieved detection limits of 1.56 nM (equivalent to 125 fmole) and 12.5 nM (equivalent to 1 pmole) for detecting SARS-CoV-2 spike and nucleocapsid proteins, respectively. This MPS platform combined with the one-step, wash-free, nanoparticle clustering-based assay method is intrinsically versatile and allows for the detection of a variety of other disease biomarkers by simply changing the surface functional groups on MNPs.

Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity

Efforts to mitigate the COVID-19 crisis revealed that fast, accurate, and scalable testing is crucial for curbing the current impact and that of future pandemics. We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification. An ultrasensitive interferometric method was used to image four virus types with nanoscale optical path-length sensitivity. Pairing these data with fluorescence images for ground truth, we trained semantic segmentation models based on U-Net, a particular type of convolutional neural network. The trained network was applied to classify the viruses from the interferometric images only, containing simultaneously SARS-CoV-2, H1N1 (influenza-A virus), HAdV (adenovirus), and ZIKV (Zika virus). Remarkably, due to the nanoscale sensitivity in the input data, the neural network was able to identify SARS-CoV-2 vs. the other viruses with 96% accuracy. The inference time for each image is 60 ms, on a common graphic-processing unit. This approach of directly imaging unlabeled viral particles may provide an extremely fast test, of less than a minute per patient. As the imaging instrument operates on regular glass slides, we envision this method as potentially testing on patient breath condensates. The necessary high throughput can be achieved by translating concepts from digital pathology, where a microscope can scan hundreds of slides automatically.

A comprehensive review on current COVID-19 detection methods: From lab care to point of care diagnosis

Without a doubt, the current global pandemic affects all walks of our life. It affected almost every age group all over the world with a disease named COVID-19, declared as a global pandemic by WHO in early 2020. Due to the high transmission and moderate mortality rate of this virus, it is also regarded as the panic-zone virus. This potentially deadly virus has pointed up the significance of COVID-19 research. Due to the rapid transmission of COVID-19, early detection is very crucial. Presently, there are different conventional techniques are available for coronavirus detection like CT-scan, PCR, Sequencing, CRISPR, ELISA, LFA, LAMP. The urgent need for rapid, accurate, and cost-effective detection and the requirement to cut off shortcomings of traditional detection methods, make scientists realize to advance new technologies. Biosensors are one of the reliable platforms for accurate, early diagnosis. In this article, we have pointed recent diagnosis approaches for COVID-19. The review includes basic virology of SARS-CoV-2 mainly clinical and pathological features. We have also briefly discussed different types of biosensors, their working principles, and current advancement for COVID-19 detection and prevention.