Development of a real-time SARS-CoV-2 biosensing system to improve health-worker safety
We focused on real-time detection of airborne SARS-CoV-2 and developed a new air sampler prototype as well as a novel optical biosensing platform to monitor airborne virus concentrations in healthcare settings and public spaces. Based on this data, we performed an infection risk assessment and gained a better understanding of virus aerosol behaviour.
As the SARS-CoV-2 virus continues to spread and new variants emerge, monitoring airborne viruses remains crucial in key settings like hospitals and public spaces. Novel, real-time biosensing systems for pathogens that are suitable for on-site monitoring are a great benefit to ensure the safety of key locations such as healthcare facilities. Such systems help to detect outbreaks and places of problematic air quality. Measuring the airborne SARS-CoV-2 grants a deeper understanding of airborne virus transmission and allows for infection risk assessment.
The primary goal was to develop a reliable tool for real-time monitoring of airborne SARS-CoV-2. The viral aerosol sampling system and photothermal biosensor should enable fast on-site detection and be applied in a real-world scenario. This project also aimed to model the infection risk based on the collected data, as well as provide a better understanding of virus aerosol dynamics. The system should be useful to gather epidemiological data in the absence of widespread testing and help to inform safety protocols, such as mask mandates in key areas, especially healthcare settings.
We successfully validated a biosensing platform for detecting airborne SARS-CoV-2 using synthetic sequences and clinical samples including the WHO SARS-CoV-2 RNA standard for validation. The system was then employed in a hospital setting as a proof of concept. The results showed varying concentrations of the virus in different areas of a hospital, indicating factors like mask usage, ventilation, and individual virus shedding affect airborne virus levels. We also detected virus presence in hospital hallways, even with closed isolation room doors.
Further we started collecting daily samples of key locations to monitor the virus concentration over time. These results were shared via QR-code for real-time monitoring. In a nursing home, increased virus levels were correlated with staff members later testing positive for Covid-19. This shows that the system can detect problematic air conditions.
Long-term monitoring was performed during spring of 2023 in a hospital cafeteria, where the collected data mirrored the epidemiological situation, showing that the system can be used to monitor infection waves. We further aimed to quantify the infection risks in the measured areas based on exposure time, PPE use, and activity levels. It demonstrated that even in low-risk scenarios, there's a risk of infection without masks.
To complement this, we conducted controlled lab experiments with a surrogate coronavirus (HCoV-229E). This provided valuable data for comparison.
We improved our air sampling setup by adding additional sensors for monitoring air quality parameters, such as CO2, humidity, and airborne particulate matter.
We also studied different primer sets for SARS-CoV-2 PCR tests, recommending specific sets for cost-effective and reliable SARS-CoV-2 identification. Finally, a clinical mask study showed no positive cases, possibly due to low infection rates during the study period. Focus group discussions highlighted challenges like communication difficulties and mental strain due to pandemic uncertainties.
The study successfully validated the biosensing platform for SARS-CoV-2 detection. It demonstrated varying virus concentrations in different areas of healthcare facilities. Notably, even low-risk scenarios posed infection risks without masks.
Specific contribution to tackle the current pandemic
In this project, we developed and applied a new tool for monitoring airborne SARS-CoV-2 in real-time. It provides an early warning of problematic air quality and likely places of transmission. The data can also inform decision makers on the necessity and efficacy of safety measures. The data collected correlated well with the epidemiological situation, offering an alternative to widespread population testing. Applying this tool in healthcare settings allows for the improvement of staff and patient safety as well as increase our basic understanding of airborne virus transmission.
Development of a real-time biosensing system of SARS-CoV-2 to improve health-worker safety during the COVID-19 pandemic
Recent results demonstrated an increased risk of COVID-19 infection among healthcare workers (HCW), particularly when access to personal protective equipment (PPE) was inadequate. During the COVID-19 pandemic, access to PPE has become complicated by a surge in worldwide demand combined with production limitations and logistical barriers. Since their introduction in hospitals in the 1990s, filtering facepiece (FFP) masks, mostly of the FFP2 type, are used by HCWs to protect themselves against bioaerosols due to tuberculosis, measles and selected respiratory viruses. The COVID-19 pandemic due to the novel SARS-CoV-2 has sparked debate around judicious and safe use of face masks for the protection of HCWs who provide direct care for COVID-19 patients. At the heart of the discussion is the question whether SARS-CoV-2 is transmitted by droplets or aerosols, or by both. While the former are large (>5µm) and fall rapidly to the ground, the latter are small (<5µm) and can stay in the air and travel much farther than the 1-2 metres normally considered a safe distance to infected patients. Today, we have little information on physical spread and infectious dose of SARS-CoV-2, and the discussion about the choice of face masks is based on indirect data. The starting hypothesis of this project is that decision-making regarding mask-wearing for HCW in the current situation of inadequate mask supply, coupled with uncertainty regarding airborne COVID-19 transmission, can be improved if direct detection of SARS-CoV-2 in aerosols can be implemented in clinical situations where aerosolisation is expected. This would be achieved by installing biosensors. Currently, the reverse transcription polymerase chain reaction (RT-PCR) technology is the most sensitive method for SARS-CoV-2 detection in respiratory secretions and it is routinely used to diagnose COVID-19. A reliable biosensing system that can detect SARS-CoV-2 rapidly, quantitatively and in real-time, supplementing RT-PCR technology would significantly help to understand SARS-CoV-2 transmission and inform recommendations for safe and practical use of PPE, and particularly face masks. In this project, we will validate a novel dual-functional, localized surface plasmon resonance (LSPR) biosensor to improve detection and on-line monitoring of SARS-CoV-2 in clinical settings. The two-dimensional gold nano-islands (AuNIs), functionalized with complementary DNA, can perform sensitive detection of selected sequences from SARS-CoV-2 through nucleic acid hybridization. For better sensing performance, the plasmonic photothermal effect, generated by the same AuNIs chip, and an additional laser irradiance can elevate the local temperature and facilitate the specific discrimination of two similar gene sequences. We aim to integrate a bioaerosol sampling system with a specific biosensor, to allow continuous real-time monitoring the shedding of SARS-CoV-2 virus in droplets or aerosols, aiming to rapidly and continuously collect airborne virus with a high collection efficiency and stable microbial recovery. The collected virus can be efficiently enriched in the sampling liquid and subsequently introduced into the integrated micro-system for virus lysis and nucleic acid extraction. This system is to be tested in clinical situations and with real COVID-19 patients with the aim to understand transmission of SARS-CoV-2 in patient surroundings. In parallel, a cluster-randomised, controlled, cross-over study will evaluate the benefits of wearing surgical masks vs. FFP2 masks during COVID-19 patient care (outside aerosol-generating procedures). To date, no study has combined virus detection technology with a cluster-randomised trial to address the question of appropriate face mask usage in COVID-19 care.