Robot-based Sample Tube Handling for Automated Pre- and Post-processing of SARS-CoV-2 PCR Tests

The SARS-CoV-2 pandemic worldwide has led to millions of infections with partly severe complications and death. The diagnosis is based on a PCR test of a saliva sample and is carried out in specialised laboratories. While the actual PCR analysis is usually automated, sample preparation and post-processing often involve manual handling steps. The paper presents an approach for robot-based pre- and post-processing of sample tubes. A seven-axis industrial robot is equipped with a multifunctional gripper and enabled for the application. In addition, machine-learning based image processing solutions are be integrated to increase flexibility and robustness. The implementation shows the feasibility of the approach in principle. The achieved cycle time does not fulfil the set requirements completely, but the implementation provides numerous approaches for optimisation. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

Detection of SARS-CoV-2 in exhaled air using non-invasive embedded strips in masks.

BACKGROUND: SARS-CoV-2 emerged in 2019 and resulted in a pandemic causing millions of infections worldwide. Gold-standard for SARS-CoV-2 detection uses quantitative RT-qPCR on respiratory secretions to detect viral RNA (vRNA). Acquiring these samples is invasive, can be painful for those with xerostomia and other health conditions, and sample quality can vary greatly. Frequently only symptomatic individuals are tested even though asymptomatic individuals can have comparable viral loads and efficiently transmit virus. METHODS: We utilized a non-invasive approach to detect SARS-CoV-2 in individuals, using polyvinyl alcohol (PVA) strips embedded in KN95 masks. PVA strips were tested for SARS-CoV-2 vRNA via qRT-PCR and infectious virus. RESULTS: We show efficient recovery of vRNA and infectious virus from virus-spiked PVA with detection limits comparable to nasal swab samples. In infected individuals, we detect both human and SARS-CoV-2 RNA on PVA strips, however, these levels are not correlated with length of time mask was worn, number of times coughed or sneezed, or level of virus in nasal swab samples. We successfully cultured and deep-sequenced PVA-associated virus. CONCLUSIONS: These results demonstrate the feasibility of using PVA-embedded masks as a non-invasive platform for detecting SARS-CoV-2 in exhaled air in COVID-positive individuals regardless of symptom status.

SARS-CoV-2 Detection in air samples from inside heating, ventilation, and air conditioning (HVAC) systems- COVID surveillance in student dorms.

BACKGROUND: The COVID-19 pandemic affected universities and institutions and caused campus shutdowns with a transition to online teaching models. To detect infections that might spread on campus, we pursued research towards detecting SARS-CoV-2 in air samples inside student dorms. METHODS: We sampled air in 2 large dormitories for 3.5 months and a separate isolation suite containing a student who had tested positive for COVID-19. We developed novel techniques employing 4 methods to collect air samples: Filter Cassettes, Button Sampler, BioSampler, and AerosolSense sampler combined with direct qRT-PCR SARS-CoV-2 analysis. RESULTS: For the 2 large dorms with the normal student population, we detected SARS-CoV-2 in 11 samples. When compared with student nasal swab qRT-PCR testing, we detected SARS-CoV-2 in air samples when a PCR positive COVID-19 student was living on the same floor of the sampling location with a detection rate of 75%. For the isolation dorm, we had a 100% SARS-CoV-2 detection rate with AerosolSense sampler. CONCLUSIONS: Our data suggest air sampling may be an important SARS-CoV-2 surveillance technique, especially for buildings with congregant living settings (dorms, correctional facilities, barracks). Future building designs and public health policies should consider implementation of Heating, Ventilation, and Air Conditioning surveillance.