Rapid and low-cost sampling for detection of airborne SARS-CoV-2 in dehumidifier condensate

Diagnosis of COVID-19 with simultaneous accurate prediction of cardiac abnormalities from chest computed tomographic images

COVID-19 has potential consequences on the pulmonary and cardiovascular health of millions of infected people worldwide. Chest computed tomographic (CT) imaging has remained the first line of diagnosis for individuals infected with SARS-CoV-2. However, differentiating COVID-19 from other types of pneumonia and predicting associated cardiovascular complications from the same chest-CT images have remained challenging. In this study, we have first used transfer learning method to distinguish COVID-19 from other pneumonia and healthy cases with 99.2% accuracy. Next, we have developed another CNN-based deep learning approach to automatically predict the risk of cardiovascular disease (CVD) in COVID-19 patients compared to the normal subjects with 97.97% accuracy. Our model was further validated against cardiac CT-based markers including cardiac thoracic ratio (CTR), pulmonary artery to aorta ratio (PA/A), and presence of calcified plaque. Thus, we successfully demonstrate that CT-based deep learning algorithms can be employed as a dual screening diagnostic tool to diagnose COVID-19 and differentiate it from other pneumonia, and also predicts CVD risk associated with COVID-19 infection.

On-site airborne pathogen detection for infection risk mitigation

Human-infecting pathogens that transmit through the air pose a significant threat to public health. As a prominent instance, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the COVID-19 pandemic has affected the world in an unprecedented manner over the past few years. Despite the dissipating pandemic gloom, the lessons we have learned in dealing with pathogen-laden aerosols should be thoroughly reviewed because the airborne transmission risk may have been grossly underestimated. From a bioanalytical chemistry perspective, on-site airborne pathogen detection can be an effective non-pharmaceutic intervention (NPI) strategy, with on-site airborne pathogen detection and early-stage infection risk evaluation reducing the spread of disease and enabling life-saving decisions to be made. In light of this, we summarize the recent advances in highly efficient pathogen-laden aerosol sampling approaches, bioanalytical sensing technologies, and the prospects for airborne pathogen exposure measurement and evidence-based transmission interventions. We also discuss open challenges facing general bioaerosols detection, such as handling complex aerosol samples, improving sensitivity for airborne pathogen quantification, and establishing a risk assessment system with high spatiotemporal resolution for mitigating airborne transmission risks. This review provides a multidisciplinary outlook for future opportunities to improve the on-site airborne pathogen detection techniques, thereby enhancing the preparedness for more on-site bioaerosols measurement scenarios, such as monitoring high-risk pathogens on airplanes, weaponized pathogen aerosols, influenza variants at the workplace, and pollutant correlated with sick building syndromes.

Highly-Specific Single-Stranded Oligonucleotides and Functional Nanoprobes for Clinical Determination of Chlamydia Trachomatis and Neisseria Gonorrhoeae Infections

Early detection of Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) is the key to controlling the spread of these bacterial infections. An important step in developing biosensors involves identifying reliable sensing probes against specific genetic targets for CT and NG. Here, the authors have designed single-stranded oligonucleotides (ssDNAs) targeting mutually conserved genetic regions of cryptic plasmid and chromosomal DNA of both CT and NG. The 5'- and 3'- ends of these ssDNAs are differentially functionalized with thiol groups and coupled with gold nanoparticles (AuNP) to develop absorbance-based assay. The AuNPs agglomerate selectively in the presence of its target DNA sequence and demonstrate a change in their surface plasmon resonance. The optimized assay is then used to detect both CT and NG DNA extracted from 60 anonymized clinical samples with a clinical sensitivity of ∼100%. The limit of detection of the assays are found to be 7 and 5 copies/µL for CT and NG respectively. Furthermore, it can successfully detect the DNA levels of these two bacteria without the need for DNA extraction and via a lateral flow-based platform. These assays thus hold the potential to be employed in clinics for rapid and efficient monitoring of sexually transmitted infections.

Detection of SARS-COV-2 by functionally imprinted micelles

The near real-time detection of airborne particles-of-interest is needed for avoiding current/future threats. The incorporation of imprinted particles into a micelle-based electrochemical cell produced a signal when brought into contact with particle analytes (such as SARS-COV-2), previously imprinted onto the structure. Nanoamp scales of signals were generated from what may've been individual virus-micelle interactions. The system showed selectivity when tested against similar size and morphology particles. The technology was compatible with airborne aerosol sampling techniques. Overall, the application of imprinted micelle technology could provide near real-time detection methods to a host of possible analytes of interest in the field.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1557/s43579-022-00242-0.

Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward

Coronavirus disease 2019 (COVID-19) is a transmitted respiratory disease caused by the infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although humankind has experienced several outbreaks of infectious diseases, the COVID-19 pandemic has the highest rate of infection and has had high levels of social and economic repercussions. The current COVID-19 pandemic has highlighted the limitations of existing virological tests, which have failed to be adopted at a rate to properly slow the rapid spread of SARS-CoV-2. Pandemic preparedness has developed as a focus of many governments around the world in the event of a future outbreak. Despite the largely widespread availability of vaccines, the importance of testing has not diminished to monitor the evolution of the virus and the resulting stages of the pandemic. Therefore, developing diagnostic technology that serves as a line of defense has become imperative. In particular, that test should satisfy three criteria to be widely adopted: simplicity, economic feasibility, and accessibility. At the heart of it all, it must enable early diagnosis in the course of infection to reduce spread. However, diagnostic manufacturers need guidance on the optimal characteristics of a virological test to ensure pandemic preparedness and to aid in the effective treatment of viral infections. Nanomaterials are a decisive element in developing COVID-19 diagnostic kits as well as a key contributor to enhance the performance of existing tests. Our objective is to develop a profile of the criteria that should be available in a platform as the target product. In this work, virus detection tests were evaluated from the perspective of the COVID-19 pandemic, and then we generalized the requirements to develop a target product profile for a platform for virus detection.

A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering

In this study, we explored machine learning approaches for predictive diagnosis using surface-enhanced Raman scattering (SERS), applied to the detection of COVID-19 infection in biological samples. To do this, we utilized SERS data collected from 20 patients at the University of Maryland Baltimore School of Medicine. As a preprocessing step, the positive-negative labels are obtained using Polymerase Chain Reaction (PCR) testing. First, we compared the performance of linear and nonlinear dimensionality techniques for projecting the high-dimensional Raman spectra to a low-dimensional space where a smaller number of variables defines each sample. The appropriate number of reduced features used was obtained by comparing the mean accuracy from a 10-fold cross-validation. Finally, we employed Gaussian process (GP) classification, a probabilistic machine learning approach, to correctly predict the occurrence of a negative or positive sample as a function of the low-dimensional space variables. As opposed to providing rigid class labels, the GP classifier provides a probability (ranging from zero to one) that a given sample is positive or negative. In practice, the proposed framework can be used to provide high-throughput rapid testing, and a follow-up PCR can be used for confirmation in cases where the model's uncertainty is unacceptably high.

Probing the mutation independent interaction of DNA probes with SARS-CoV-2 variants through a combination of surface-enhanced Raman scattering and machine learning

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) evolution has been characterized by the emergence of sets of mutations impacting the virus characteristics, such as transmissibility and antigenicity, presumably in response to the changing immune profile of the human population. The presence of mutations in the SARS-CoV-2 virus can potentially impact therapeutic and diagnostic test performances. We design and develop here a unique set of DNA probes i.e., antisense oligonucleotides (ASOs) which can interact with genetic sequences of the virus irrespective of its ongoing mutations. The probes, developed herein, target a specific segment of the nucleocapsid phosphoprotein (N) gene of SARS-CoV-2 with high binding efficiency which do not mutate among the known variants. Further probing into the interaction profile of the ASOs reveals that the ASO-RNA hybridization remains unaltered even for a hypothetical single point mutation at the target RNA site and diminished only in case of the hypothetical double or triple point mutations. The mechanism of interaction among the ASOs and SARS-CoV-2 RNA is then explored with a combination of surface-enhanced Raman scattering (SERS) and machine learning techniques. It has been observed that the technique, described herein, could efficiently discriminate between clinically positive and negative samples with ∼100% sensitivity and ∼90% specificity up to 63 copies/mL of SARS-CoV-2 RNA concentration. Thus, this study establishes N gene targeted ASOs as the fundamental machinery to efficiently detect all the current SARS-CoV-2 variants regardless of their mutations.

Prototyping a low-cost residential air quality device using ultraviolet germicidal irradiation (UVGI) light

Many New Zealand residential dwellings suffer from dampness and fungi during the winter, which can cause respiratory health problems. This can be due to poor insulation and ventilation, and the situation worsens when residents cannot afford to heat the dwelling. The main aim of this paper is to modify an existing dehumidifier so that it can remove moisture, heat the living space and reduce fungi growth and bacteria. To achieve that, we installed ultraviolet germicidal lights (UVGI) in an existing dehumidifier with a total cost of USD $150.7 (NZD $213.76). The UVGI lights are known to be efficient in destroying the DNA of fungi and bacteria. The results show that the device reduced the fungi growth and did increase the room temperature because the dehumidifier captured two litres of water over 24 h of testing. The proposed device did achieve a reduction in particulate matters, from 0.9 μ g / m 3 to 0.14 μ g / m 3 and an acceptable range of relative humidity below 50%, which reduces the favourable conditions for fungi growth. Therefore, our proposed low-cost device does improve the indoor air quality (IAQ) in the living space.

A rapid RNA extraction-free lateral flow assay for molecular point-of-care detection of SARS-CoV-2 augmented by chemical probes

The coronavirus disease 2019 (COVID-19) pandemic has highlighted the major shortcoming of healthcare systems globally in their inability to diagnose the disease rapidly and accurately. At present, the molecular approaches for diagnosing COVID-19 primarily use reverse transcriptase polymerase chain reaction (RT-PCR) to create and amplify cDNA from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA. Although molecular tests are reported to be specific, false negatives are quite common. Furthermore, literally all these tests require a step involving RNA isolation which does not make them point-of-care (POC) in the true sense. Here, we report a lateral flow strip-based RNA extraction and amplification-free nucleic acid test (NAT) for rapid diagnosis of positive COVID-19 cases at POC. The assay uses highly specific 6-carboxyfluorescein (6-FAM) and biotin labeled antisense oligonucleotides (ASOs) as probes those are designed to target N-gene sequence of SARS-CoV-2. Additionally, we utilized cysteamine capped gold-nanoparticles (Cyst-AuNPs) to augment the signal further for enhanced sensitivity. Without any large-stationary equipment and highly trained staffers, the entire sample-to-answer approach in our case would take less than 30 min from a patient swab sample collection to final diagnostic result. Moreover, when evaluated with 60 clinical samples and verified with an FDA-approved TaqPath RT-PCR kit for COVID-19 diagnosis, the assay obtained almost 99.99% accuracy and specificity. We anticipate that the newly established low-cost amplification-free detection of SARS-CoV-2 RNA will aid in the development of a platform technology for rapid and POC diagnosis of COVID-19 and other pathogens.

Biosensor detection of airborne respiratory viruses such as SARS-CoV-2

Airborne SARS-CoV-2 transmission represents a significant route for possible human infection that is not yet fully understood. Viruses in droplets and aerosols are difficult to detect because they are typically present in low amounts. In addition, the current techniques used, such as RT-PCR and virus culturing, require large amounts of time to get results. Biosensor technology can provide rapid, handheld, and point-of-care systems that can identify virus presence quickly and accurately. This paper reviews the background of airborne virus transmission and the characteristics of SARS-CoV-2, its relative risk for transmission even at distances greater than the currently suggested 6 feet (or 2 m) physical distancing. Publications on biosensor technology that may be applied to the detection of airborne SARS-CoV-2 and other respiratory viruses are also summarized. Based on the current research we believe that there is a pressing need for continued research into handheld and rapid methods for sensitive collection and detection of airborne viruses. We propose a paper-based microfluidic chip and immunofluorescence assay as one method that could be investigated as a low-cost and portable option.