Real-time environmental surveillance of SARS-CoV-2 aerosols

Multi-scenario surveillance of respiratory viruses in aerosols with sub-single-copy spatial resolution

Highly sensitive airborne virus monitoring is critical for preventing and containing epidemics. However, the detection of airborne viruses at ultra-low concentrations remains challenging due to the lack of ultra-sensitive methods and easy-to-deployment equipment. Here, we present an integrated microfluidic cartridge that can accurately detect SARS-COV-2, Influenza A, B, and respiratory syncytial virus with a sensitivity of 10 copies/mL. When integrated with a high-flow aerosol sampler, our microdevice can achieve a sub-single-copy spatial resolution of 0.83 copies/m3 for airborne virus surveillance with an air flow rate of 400 L/min and a sampling time of 30 minutes. We then designed a series of virus-in-aerosols monitoring systems (RIAMs), including versions of a multi-site sampling RIAMs (M-RIAMs), a stationary real-time RIAMs (S-RIAMs), and a roaming real-time RIAMs (R-RIAMs) for different application scenarios. Using M-RIAMs, we performed a comprehensive evaluation of 210 environmental samples from COVID-19 patient wards, including 30 aerosol samples. The highest positive detection rate of aerosol samples (60%) proved the aerosol-based SARS-CoV-2 monitoring represents an effective method for spatial risk assessment. The detection of 78 aerosol samples in real-world settings via S-RIAMs confirmed its reliability for ultra-sensitive and continuous airborne virus monitoring. Therefore, RIAMs shows the potential as an effective solution for mitigating the risk of airborne virus transmission.

Electrostatic microfiltration (EM) enriches and recovers viable microorganisms at low-abundance in large-volume samples and enhances downstream detection

Rapid and sensitive detection of pathogens in various samples is crucial for disease diagnosis, environmental surveillance, as well as food and water safety monitoring. However, the low abundance of pathogens (<10 CFU) in large volume (1 mL-1 L) samples containing vast backgrounds critically limits the sensitivity of even the most advanced techniques, such as digital PCR. Therefore, there is a critical need for sample preparation that can enrich low-abundance pathogens from complex and large-volume samples. This study develops an efficient electrostatic microfiltration (EM)-based sample preparation technique capable of processing ultra-large-volume (≥500 mL) samples at high throughput (≥10 mL min-1). This approach achieves a significant enrichment (>8000×) of extremely-low-abundance pathogens (down to level of 0.02 CFU mL-1, i.e., 10 CFU in 500 mL). Furthermore, EM-enabled sample preparation facilitates digital amplification techniques sensitively detecting broad pathogens, including bacteria, fungi, and viruses from various samples, in a rapid (≤3 h) sample-to-result workflow. Notably, the operational ease, portability, and compatibility/integrability with various downstream detection platforms highlight its great potential for widespread applications across diverse settings.

Functionalized N95 Face Mask with a Chemical-Free Paper-Based Collector for Exhaled Breath Analysis: SARS-CoV-2 Detection with a Printed Immunosensor as a Case Study

Exhaled breath electrochemical sensing is a promising biomedical technology owing to its portability, painlessness, cost-effectiveness, and user-friendliness. Here, we present a novel approach for target analysis in exhaled breath by integrating a comfortable paper-based collector into an N95 face mask, providing a universal solution for analyzing several biomarkers. As a model analyte, we detected SARS-CoV-2 spike protein from the exhaled breath by sampling the target analyte into the collector, followed by its detection out of the N95 face mask using a magnetic bead-based electrochemical immunosensor. This approach was designed to avoid any contact between humans and the chemicals. To simulate human exhaled breath, untreated saliva samples were nebulized on the paper collector, revealing a detection limit of 1 ng/mL and a wide linear range of 3.7-10,000 ng/mL. Additionally, the developed immunosensor exhibited high selectivity toward the SARS-CoV-2 spike protein, compared to other airborne microorganisms, and the SARS-CoV-2 nucleocapsid protein. Accuracy assessments were conducted by analyzing the simulated breath samples spiked with varying concentrations of SARS-CoV-2 spike protein, resulting in satisfactory recovery values (ranging from 97 ± 4 to 118 ± 1%). Finally, the paper-based hybrid immunosensor was successfully applied for the detection of SARS-CoV-2 in real human exhaled breath samples. The position of the collector in the N95 mask was evaluated as well as the ability of this paper-based analytical tool to identify the positive patient.

A High-Avidity, Thermostable, and Low-Cost Synthetic Capture for Ultrasensitive Detection and Quantification of Viral Antigens and Aerosols

Lateral flow assays (LFAs) are currently the most popular point-of-care diagnostics, rapidly transforming disease diagnosis from expensive doctor checkups and laboratory-based tests to potential on-the-shelf commodities. Yet, their sensitive element, a monoclonal antibody, is expensive to formulate, and their long-term storage depends on refrigeration technology that cannot be met in resource-limited areas. In this work, LCB1 affibodies (antibody mimetic miniproteins) were conjugated to bovine serum albumin (BSA) to afford a high-avidity synthetic capture (LCB1-BSA) capable of detecting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and virus like particles (VLPs). Substituting the monoclonal antibody 2B04 for LCB1-BSA (stable up to 60 °C) significantly improved the thermal stability, shelf life, and affordability of plasmonic-fluor-based LFAs (p-LFAs). Furthermore, this substitution significantly improved the sensitivity of p-LFAs toward the spike protein and VLPs with precise quantitative ability over 2 and 3 orders of magnitude, respectively. LCB1-BSA sensors could detect VLPs at 100-fold lower concentrations, and this improvement, combined with their robust nature, enabled us to develop an aerosol sampling technology to detect aerosolized viral particles. Synthetic captures like LCB1-BSA can increase the ultrasensitivity, availability, sustainability, and long-term accuracy of LFAs while also decreasing their manufacturing costs.

Monitoring the ventilation of living spaces to assess the risk of airborne transmission of infection using a novel Pocket CO2 Logger to track carbon dioxide concentrations in Tokyo

We employed carbon dioxide (CO2) concentration monitoring using mobile devices to identify location-specific risks for airborne infection transmission. We lent a newly developed, portable Pocket CO2 Logger to 10 participants, to be carried at all times, for an average of 8 days. The participants recorded their location at any given time as cinema, gym, hall, home, hospital, other indoors, other outgoings, pub, restaurant, university, store, transportation, or workplace. Generalized linear mixed model was used for statistical analysis, with the objective variable set to the logarithm of CO2 concentration. Analysis was performed by assigning participant identification as the random effect and location as the fixed effect. The data were collected per participant (seven males, four females), resulting in a total of 12,253 records. Statistical analysis identified three relatively poorly ventilated locations (median values > 1,000 ppm) that contributed significantly (p < 0.0001) to CO2 concentrations: homes (1,316 ppm), halls (1,173 ppm), and gyms (1005ppm). In contrast, two locations were identified to contribute significantly (p < 0.0001) to CO2 concentrations but had relatively low average values (<1,000 ppm): workplaces (705 ppm) and stores (620 ppm). The Pocket CO2 Logger can be used to visualize airborne infectious transmission risk by location to help guide recommendation regarding infectious disease policies, such as restrictions on human flow and ventilation measures and guidelines. In the future, large-scale surveys are expected to utilize the global positioning system, Wi-Fi, or Bluetooth of an individual's smartphone to improve ease and accuracy.

Recent Advances in Development of Biosensors for Monitoring of Airborne Microorganisms

Background

The early detection of infectious microorganisms is crucial for preventing and controlling the transmission of diseases. This article provides a comprehensive review of biosensors based on various diagnostic methods for measuring airborne pathogens.

Objective

This article aims to explore recent advancements in the field of biosensors tailored for the detection and monitoring of airborne microorganisms, offering insights into emerging technologies and their potential applications in environmental surveillance and public health management.

Materials and Methods

The study summarizes the research conducted on novel methods of detecting airborne microorganisms using different biological sensors, as well as the application of signal amplification technologies such as polymerase chain reaction (PCR), immunoassay reactions, molecular imprinted polymers (MIP) technique, lectin and cascade reactions, and nanomaterials.

Results

Antibody and PCR detection methods are effective for specific microbial strains, but they have limitations including limited stability, high cost, and the need for skilled operators with basic knowledge of the target structure. Biosensors based on MIP and lectin offer a low-cost, stable, sensitive, and selective alternative to antibodies and PCR. However, challenges remain, such as the detection of small gas molecules by MIP and the lower sensitivity of lectins compared to antibodies. Additionally, achieving high sensitivity in complex environments poses difficulties for both methods.

Conclusion

The development of sensitive, reliable, accessible, portable, and inexpensive biosensors holds great potential for clinical and environmental applications, including disease diagnosis, treatment monitoring, and point-of-care testing, offering a promising future in this field. This review presents an overview of biosensor detection principles, covering component identification, energy conversion principles, and signal amplification. Additionally, it summarizes the research and applications of biosensors in the detection of airborne microorganisms. The latest advancements and future trends in biosensor detection of airborne microorganisms are also analyzed.

Digital Surface-Enhanced Raman Spectroscopy-Lateral Flow Test Dipstick: Ultrasensitive, Rapid Virus Quantification in Environmental Dust

This study introduces a novel surface-enhanced Raman spectroscopy (SERS)-based lateral flow test (LFT) dipstick that integrates digital analysis for highly sensitive and rapid viral quantification. The SERS-LFT dipsticks, incorporating gold-silver core-shell nanoparticle probes, enable pixel-based digital analysis of large-area SERS scans. Such an approach enables ultralow-level detection of viruses that readily distinguishes positive signals from background noise at the pixel level. The developed digital SERS-LFTs demonstrate limits of detection (LODs) of 180 fg for SARS-CoV-2 spike protein, 120 fg for nucleocapsid protein, and 7 plaque forming units for intact virus, all within <30 min. Importantly, digital SERS-LFT methods maintain their robustness and their LODs in the presence of indoor dust, thus underscoring their potential for accurate and reliable virus diagnosis and quantification in real-world environmental settings.

Machine learning-driven SERS fingerprinting of disintegrated viral components for rapid detection of SARS-CoV-2 in environmental dust

Surveillance of airborne viruses in crowded indoor spaces is crucial for managing outbreaks, as highlighted by the SARS-CoV-2 pandemic. However, the rapid and on-site detection of fast-mutating viruses, such as SARS-CoV-2, in complex environmental backgrounds remains challenging. Our study introduces a machine learning (ML)-driven surface-enhanced Raman spectroscopy (SERS) approach for detecting viruses within environmental dust matrices. By decomposing intact virions into individual structural components via a Raman-background-free lysis protocol and concentrating them into nanogap SERS hotspots, we significantly enhance the SERS signal intensity and fingerprint information density from viral structural components. Utilizing Principal Component Analysis (PCA), we establish a robust connection between the SERS data of these structural components and their biological sequences, laying a solid foundation for virus detection through SERS. Furthermore, we demonstrate reliable quantitative detection of SARS-CoV-2 using identified SARS-CoV-2 peaks at concentrations down to 102 pfu/ml through Gaussian Process Regression (GPR) and a digital SERS methodology. Finally, applying a Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA) algorithm, we identify SARS-CoV-2, influenza A virus, and Zika virus within an environmental dust background with over 86% accuracy. Therefore, our ML-driven SERS approach holds promise for rapid environmental virus monitoring to manage future outbreaks.

Reduction of acute respiratory infections in day-care by non-pharmaceutical interventions: a narrative review

Objective

Children who start in day-care have 2-4 times as many respiratory infections compared to children who are cared for at home, and day-care staff are among the employees with the highest absenteeism. The extensive new knowledge that has been generated in the COVID-19 era should be used in the prevention measures we prioritize. The purpose of this narrative review is to answer the questions: Which respiratory viruses are the most significant in day-care centers and similar indoor environments? What do we know about the transmission route of these viruses? What evidence is there for the effectiveness of different non-pharmaceutical prevention measures?

Design

Literature searches with different terms related to respiratory infections in humans, mitigation strategies, viral transmission mechanisms, and with special focus on day-care, kindergarten or child nurseries, were conducted in PubMed database and Web of Science. Searches with each of the main viruses in combination with transmission, infectivity, and infectious spread were conducted separately supplemented through the references of articles that were retrieved.

Results

Five viruses were found to be responsible for ≈95% of respiratory infections: rhinovirus, (RV), influenza virus (IV), respiratory syncytial virus (RSV), coronavirus (CoV), and adenovirus (AdV). Novel research, emerged during the COVID-19 pandemic, suggests that most respiratory viruses are primarily transmitted in an airborne manner carried by aerosols (microdroplets).

Conclusion

Since airborne transmission is dominant for the most common respiratory viruses, the most important preventive measures consist of better indoor air quality that reduces viral concentrations and viability by appropriate ventilation strategies. Furthermore, control of the relative humidity and temperature, which ensures optimal respiratory functionality and, together with low resident density (or mask use) and increased time outdoors, can reduce the occurrence of respiratory infections.

Minimum Minutes Machine-Learning Microfluidic Microbe Monitoring Method (M7)

Frequent outbreaks of viral diseases have brought substantial negative impacts on society and the economy, and they are very difficult to detect, as the concentration of viral aerosols in the air is low and the composition is complex. The traditional detection method is manually collection and re-detection, being cumbersome and time-consuming. Here we propose a virus aerosol detection method based on microfluidic inertial separation and spectroscopic analysis technology to rapidly and accurately detect aerosol particles in the air. The microfluidic chip is designed based on the principles of inertial separation and laminar flow characteristics, resulting in an average separation efficiency of 95.99% for 2 μm particles. We build a microfluidic chip composite spectrometer detection platform to capture the spectral information on aerosol particles dynamically. By employing machine-learning techniques, we can accurately classify different types of aerosol particles. The entire experiment took less than 30 min as compared with hours by PCR detection. Furthermore, our model achieves an accuracy of 97.87% in identifying virus aerosols, which is comparable to the results obtained from PCR detection.

Post COVID-19 Reflections and Questions: How Prepared Are We for the Next Pandemic?

While the end of the COVID-19 pandemic was announced earlier in 2023 by WHO, the currently dominating COVID-19 virus variants, such as the omicron sub-lineages XBB [...].

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.