On-Chip Nucleic Acid Purification Followed by ddPCR for SARS-CoV-2 Detection.

We developed a microfluidic chip integrated with nucleic acid purification and droplet-based digital polymerase chain reaction (ddPCR) modules to realize a 'sample-in, result-out' infectious virus diagnosis. The whole process involved pulling magnetic beads through drops in an oil-enclosed environment. The purified nucleic acids were dispensed into microdroplets by a concentric-ring, oil-water-mixing, flow-focusing droplets generator driven under negative pressure conditions. Microdroplets were generated with good uniformity (CV = 5.8%), adjustable diameters (50-200 µm), and controllable flow rates (0-0.3 µL/s). Further verification was provided by quantitative detection of plasmids. We observed a linear correlation of R2 = 0.9998 in the concentration range from 10 to 105 copies/µL. Finally, this chip was applied to quantify the nucleic acid concentrations of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The measured nucleic acid recovery rate of 75 ± 8.8% and detection limit of 10 copies/µL proved its on-chip purification and accurate detection abilities. This chip can potentially be a valuable tool in point-of-care testing.

Establishment of wastewater-based SARS-CoV-2 monitoring system over two years: Case studies in South Korea.

With the global COVID-19 pandemic, wastewater surveillance has received a considerable attention as a method for the early identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater treatment plant (WWTP) and sewer systems. For the first time in Korea, this study utilized the wastewater surveillance technique to monitor the COVID-19 outbreak. Sampling efforts were carried out at the WWTPs in the capital city of Korea, Seoul, and Daegu the place where the first severe outbreak was reported. The RNA of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been extracted from the collected wastewater influent and primary sewage sludge samples. The outcomes were contrasted with the COVID-19 cases in the WWTPs served area. Additionally, whole transcriptome sequencing was used to compare the microbial community alterations before and after the COVID-19 outbreak and SARS-CoV-2 variations. The results demonstrated that the changes in SARS-CoV-2 RNA concentrations in the influent and sludge matched the trends of reported COVID-19 cases, especially sludge showed high-resolution data, which is well-matched when fewer COVID-19 cases (0-250) are reported. Interestingly, one month before the clinical report, we found that the SARS-CoV-2 Beta variant (South Africa, B.1.351) in the wastewater. In addition, the Aeromonas bacterial species was dominated (21.2%) among other bacterial species in wastewater after the COVID-19 outbreak, suggesting a potential indirect microbial indicator of the COVID-19 outbreak.

The fate of SARS-CoV-2 viral RNA in coastal New England wastewater treatment plants

Municipal sewage carries SARS-CoV-2 viruses shed in the human stool by infected individuals to wastewater treatment plants (WWTPs). It is well-established that increasing prevalence of COVID-19 in a community increases the viral load in its WWTPs. Despite the fact that wastewater treatment facilities serve a critical role in protecting downstream human and environmental health through removal or inactivation of the virus, little is known about the fate of the virus along the treatment train. To assess the efficacy of differing WWTP size and treatment processes in viral RNA removal we quantified two SARS-CoV-2 nucleocapsid (N) biomarkers (N1 and N2) in both liquid and solids phases for multiple treatment train locations from seven coastal New England WWTPs. SARS-CoV-2 biomarkers were commonly detected in the influent, primary treated, and sludge samples (returned activated sludge, waste activated sludge, and digested sludge), and not detected after secondary clarification processes or disinfection. Solid fractions had 470 to 3,700-fold higher concentrations of viral biomarkers than liquid fractions, suggesting considerably higher affinity of the virus for the solid phase. Our findings indicate that a variety of wastewater treatment designs are efficient at achieving high removal of SARS CoV-2 from effluent;however, quantifiable viral RNA was commonly detected in wastewater solids at various points in the facility. This study supports the important role municipal wastewater treatment facilities serve in reducing the discharge of SARS-CoV-2 viral fragments to the environment and highlights the need to better understand the fate of this virus in wastewater solids.

Detection of SARS-CoV-2 RNA in wastewater and comparison to COVID-19 cases in two sewersheds, North Carolina, USA

Wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be useful for monitoring population-wide coronavirus disease 2019 (COVID-19) infections, especially given asymptomatic infections and limitations in diagnostic testing. We aimed to detect SARS-CoV-2 RNA in wastewater and compare viral concentrations to COVID-19 case numbers in the respective counties and sewersheds. Influent 24-hour composite wastewater samples were collected from July to December 2020 from two municipal wastewater treatment plants serving different population sizes in Orange and Chatham Counties in North Carolina. After a concentration step via HA filtration, SARS-CoV-2 RNA was detected and quantified by reverse transcription droplet digital polymerise chain reaction (RT-ddPCR) and quantitative PCR (RT-qPCR), targeting the N1 and N2 nucleocapsid genes. SARS-CoV-2 RNA was detected by RT-ddPCR in 100 % (24/24) and 79 % (19/24) of influent wastewater samples from the larger and smaller plants, respectively. In comparison, viral RNA was detected by RT-qPCR in 41.7 % (10/24) and 8.3 % (2/24) of samples from the larger and smaller plants, respectively. Positivity rates and method agreement further increased for the RT-qPCR assay when samples with positive signals below the limit of detection were counted as positive. The wastewater data from the larger plant generally correlated (square similar to 0.5, p < 0.05) with, and even anticipated, the trends in reported COVID-19 cases, with a notable spike in measured viral RNA preceding a spike in cases when students returned to a college campus in the Orange County sewershed. Correlations were generally higher when using estimates of sewershed-level case data rather than county-level data. This work supports use of wastewater surveillance for tracking COVID-19 disease trends, especially in identifying spikes in cases. Wastewater-based epidemiology can be a valuable resource for tracking disease trends, allocating resources, and evaluating policy in the fight against current and future pandemics.

Long-term study on wastewater SARS-CoV-2 surveillance across United Arab Emirates.

Wastewater-based epidemiology (WBE) demonstrates an efficient tool to monitor and predict SARS-CoV-2 community distribution. Many countries across the world have adopted the technique, however, most of these studies were conducted for a short duration with a limited sampling size. In this study, long-term reliability and quantification of wastewater SARS-CoV-2 surveillance is reported via analyzing 16,858 samples collected from 453 different locations across the United Arab Emirates (UAE) from May 2020 to June 2022. The collected composite samples were first incubated at 60 °C followed by filtration, concentration, and then RNA extraction using commercially available kits. The extracted RNA was then analyzed by one-step RT-qPCR and RT-ddPCR, and the data was compared to the reported clinical cases. The average positivity rate in the wastewater samples was found to be 60.61 % (8.41-96.77 %), however, the positivity rate obtained from the RT-ddPCR was significantly higher than the RT-qPCR suggesting higher sensitivity of RT-ddPCR. Time-lagged correlation analysis indicated an increase in positive cases in the wastewater samples when the clinical positive cases declined suggesting that wastewater data are highly affected by the unreported asymptomatic, pre-symptomatic and recovering individuals. The weekly SARS-CoV-2 viral count in the wastewater samples are positively correlated with the diagnosed new clinical cases throughout the studied period and the studied locations. Viral count in wastewater peaked approximately one to two weeks prior to the peaks appearing in active clinical cases indicating that wastewater viral concentrations are effective in predicting clinical cases. Overall, this study further confirms the long-term sensitivity and robust approach of WBE to detect trends in SARS-CoV-2 spread and helps contribute to pandemic management.

Characterizing SARS-CoV-2 transcription of subgenomic and genomic RNAs during early human infection using multiplexed droplet digital PCR.

BACKGROUND: Control of SARS-CoV-2 (SCV-2) transmission requires understanding SCV-2 replication dynamics. METHODS: We developed a multiplexed droplet digital PCR (ddPCR) assay to quantify SCV-2 subgenomic RNAs (sgRNAs), which are only produced during active viral replication, and discriminate them from genomic RNAs (gRNAs). We applied the assay to specimens from 144 people with single nasopharyngeal samples and 27 people with >1 sample. Results were compared to qPCR and viral culture. RESULTS: sgRNAs were quantifiable across a range of qPCR cycle threshold (Ct) values and correlated with Ct values. The ratio of sgRNA:gRNA was stable across a wide range of Ct values, whereas adjusted amounts of N sgRNA to a human housekeeping gene declined with higher Ct values. Adjusted sgRNA and gRNA amounts were quantifiable in culture-negative samples, although levels were significantly lower than in culture-positive samples. Daily testing of 6 persons revealed that sgRNA is concordant with culture results during the first week of infection but may be discordant with culture later in infection. Further, sgRNA:gRNA is constant during infection despite changes in viral culture. CONCLUSIONS: Ct values from qPCR correlate with active viral replication. More work is needed to understand why some cultures are negative despite presence of sgRNA.

Development of a Droplet Digital PCR to Monitor SARS-CoV-2 Omicron Variant BA.2 in Wastewater Samples.

Wastewater-based surveillance can be used as a complementary method to other SARS-CoV-2 surveillance systems. It allows the emergence and spread of infections and SARS-CoV-2 variants to be monitored in time and place. This study presents an RT-ddPCR method that targets the T19I amino acid mutation in the spike protein of the SARS-CoV-2 genomes, which is specific to the BA.2 variant (omicron). The T19I assay was evaluated both in silico and in vitro for its inclusivity, sensitivity, and specificity. Moreover, wastewater samples were used as a proof of concept to monitor and quantify the emergence of the BA.2 variant from January until May 2022 in the Brussels-Capital Region which covers a population of more than 1.2 million inhabitants. The in silico analysis showed that more than 99% of the BA.2 genomes could be characterized using the T19I assay. Subsequently, the sensitivity and specificity of the T19I assay were successfully experimentally evaluated. Thanks to our specific method design, the positive signal from the mutant probe and wild-type probe of the T19I assay was measured and the proportion of genomes with the T19I mutation, characteristic of the BA.2 mutant, compared to the entire SARS-CoV-2 population was calculated. The applicability of the proposed RT-ddPCR method was evaluated to monitor and quantify the emergence of the BA.2 variant over time. To validate this assay as a proof of concept, the measurement of the proportion of a specific circulating variant with genomes containing the T19I mutation in comparison to the total viral population was carried out in wastewater samples from wastewater treatment plants in the Brussels-Capital Region in the winter and spring of 2022. This emergence and proportional increase in BA.2 genomes correspond to what was observed in the surveillance using respiratory samples; however, the emergence was observed slightly earlier, which suggests that wastewater sampling could be an early warning system and could be an interesting alternative to extensive human testing.

Comparison of Reverse Transcription (RT)-Quantitative PCR and RT-Droplet Digital PCR for Detection of Genomic and Subgenomic SARS-CoV-2 RNA.

Most individuals acutely infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exhibit mild symptoms. However, 10 to 20% of those infected develop long-term symptoms, referred to as post-coronavirus disease 2019 (COVID-19) condition (PCC). One hypothesis is that PCC might be exacerbated by viral persistence in tissue sanctuaries. Therefore, the accurate detection and quantification of SARS-CoV-2 are not only necessary for viral load monitoring but also crucial for detecting long-term viral persistence and determining whether viral replication is occurring in tissue reservoirs. In this study, the sensitivity and robustness of reverse transcription (RT)-droplet digital PCR (ddPCR) and RT-quantitative PCR (qPCR) techniques have been compared for the detection and quantification of SARS-CoV-2 genomic and subgenomic RNAs from oropharyngeal swabs taken from confirmed SARS-CoV-2-positive, SARS-CoV-2-exposed, and nonexposed individuals as well as from samples from mice infected with SARS-CoV-2. Our data demonstrated that both techniques presented equivalent results in the mid- and high-viral-load ranges. Additionally, RT-ddPCR was more sensitive than RT-qPCR in the low-viral-load range, allowing the accurate detection of positive results in individuals exposed to the virus. Overall, these data suggest that RT-ddPCR might be an alternative to RT-qPCR for detecting low viral loads in samples and for assessing viral persistence in samples from individuals with PCC. IMPORTANCE We developed one-step reverse transcription (RT)-droplet digital PCR (ddPCR) protocols to detect SARS-CoV-2 RNA and compared them to the gold-standard RT-quantitative PCR (RT-qPCR) method. RT-ddPCR was more sensitive than RT-qPCR in the low-viral-load range, while both techniques were equivalent in the mid- and high-viral-load ranges. Overall, these results suggest that RT-ddPCR might be a viable alternative to RT-qPCR when it comes to detecting low viral loads in samples, which is a highly relevant issue for determining viral persistence in as-yet-unknown tissue reservoirs in individuals suffering from post-COVID conditions or long COVID.

Comparison of the methods for isolation and detection of SARS-CoV-2 RNA in municipal wastewater.

Introduction: Coronavirus SARS-CoV-2 is a causative agent responsible for the current global pandemic situation known as COVID-19. Clinical manifestations of COVID-19 include a wide range of symptoms from mild (i.e., cough, fever, dyspnea) to severe pneumonia-like respiratory symptoms. SARS-CoV-2 has been demonstrated to be detectable in the stool of COVID-19 patients. Waste-based epidemiology (WBE) has been shown as a promising approach for early detection and monitoring of SARS-CoV-2 in the local population performed via collection, isolation, and detection of viral pathogens from environmental sources. Methods: In order to select the optimal protocol for monitoring the COVID-19 epidemiological situation in region Turiec, Slovakia, we (1) compared methods for SARS-CoV-2 separation and isolation, including virus precipitation by polyethylene glycol (PEG), virus purification via ultrafiltration (Vivaspin®) and subsequent isolation by NucleoSpin RNA Virus kit (Macherey-Nagel), and direct isolation from wastewater (Zymo Environ Water RNA Kit); (2) evaluated the impact of water freezing on SARS- CoV-2 separation, isolation, and detection; (3) evaluated the role of wastewater filtration on virus stability; and (4) determined appropriate methods including reverse transcription-droplet digital PCR (RT-ddPCR) and real-time quantitative polymerase chain reaction (RT-qPCR) (targeting the same genes, i.e., RdRp and gene E) for quantitative detection of SARS-CoV-2 in wastewater samples. Results: (1) Usage of Zymo Environ Water RNA Kit provided superior quality of isolated RNA in comparison with both ultracentrifugation and PEG precipitation. (2) Freezing of wastewater samples significantly reduces the RNA yield. (3) Filtering is counterproductive when Zymo Environ Water RNA Kit is used. (4) According to the specificity and sensitivity, the RT-ddPCR outperforms RT-qPCR. Discussion: The results of our study suggest that WBE is a valuable early warning alert and represents a non-invasive approach to monitor viral pathogens, thus protects public health on a regional and national level. In addition, we have shown that the sensitivity of testing the samples with a nearer detection limit can be improved by selecting the appropriate combination of enrichment, isolation, and detection methods.

Rise and fall of SARS-CoV-2 variants in Rotterdam: Comparison of wastewater and clinical surveillance.

Monitoring of SARS-CoV-2 in wastewater (WW) is a promising tool for epidemiological surveillance, correlating not only viral RNA levels with the infection dynamics within the population, but also to viral diversity. However, the complex mixture of viral lineages in WW samples makes tracking of specific variants or lineages circulating in the population a challenging task. We sequenced sewage samples of 9 WW-catchment areas within the city of Rotterdam, used specific signature mutations from individual SARS-CoV-2 lineages to estimate their relative abundances in WW and compared them against those observed in clinical genomic surveillance of infected individuals between September 2020 and December 2021. We showed that especially for dominant lineages, the median of the frequencies of signature mutations coincides with the occurrence of those lineages in Rotterdam's clinical genomic surveillance. This, along with digital droplet RT-PCR targeting signature mutations of specific variants of concern (VOCs), showed that several VOCs emerged, became dominant and were replaced by the next VOC in Rotterdam at different time points during the study. In addition, single nucleotide variant (SNV) analysis provided evidence that spatio-temporal clusters can also be discerned from WW samples. We were able to detect specific SNVs in sewage, including one resulting in the Q183H amino acid change in the Spike gene, that was not captured by clinical genomic surveillance. Our results highlight the potential use of WW samples for genomic surveillance, increasing the set of epidemiological tools to monitor SARS-CoV-2 diversity.

Development of multiplex RT-ddPCR assays for detection of SARS-CoV-2 and other common respiratory virus infections.

BACKGROUND: Measures for mitigation of Coronavirus Disease 2019 (COVID-19) were set to reduce the spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). SARS-CoV-2 and other respiratory viruses share similar transmission routes and some common clinical manifestations. Co-circulation of SARS-CoV-2 and other common respiratory viruses is imminent. Therefore, development of multiplex assays for detecting these respiratory viruses is essential for being prepared for future outbreaks of respiratory viruses. METHODS: A panel of three reverse transcription droplet digital PCR (RT-ddPCR) assays were developed to detect 15 different human respiratory viruses. Evaluations of its performance were demonstrated. A total of 100 local and 98 imported COVID-19 cases in Hong Kong were screened for co-infection with other common respiratory viruses. RESULTS: All detected viral targets showed distinct signal clusters using the multiplex RT-ddPCR assays. These assays have a broad range of linearity and good intra-/inter-assay reproducibility for each target. The lower limits of quantification for all targets were ≤46 copies per reaction. Six imported cases of COVID-19 were found to be co-infected with other respiratory viruses, whereas no local case of co-infection was observed. CONCLUSIONS: The multiplex RT-ddPCR assays were demonstrated to be useful for screening of respiratory virus co-infections. The strict preventive measures applied in Hong Kong may be effective in limiting the circulation of other human respiratory viruses. The multiplex assays developed in this study can achieve a robust detection method for clinical and research purposes.

Digital Droplet PCR to Track SARS-CoV-2 Outbreak in a Hospital Transitional Care Unit.

We describe an outbreak of SARS-CoV-2 on a transition unit composed of elderly patients awaiting placement. Environmental and patient sample analyses using digital droplet PCR (ddPCR) suggested possible fomite transmission and a high viral burden source from a few individual patients. This outbreak illustrates challenges inherent to this specific patient population.

SARS-CoV-2 concentration in wastewater consistently predicts trends in COVID-19 case counts by at least two days across multiple WWTP scales.

Wastewater surveillance of SARS-CoV-2 has proven instrumental in mitigating the spread of COVID-19 by providing an economical and equitable approach to disease surveillance. Here, we analyze the correlation of SARS-CoV-2 RNA in influents of seven wastewater plants (WWTPs) across the state of South Carolina with corresponding daily case counts to determine whether underlying characteristics of WWTPs and sewershed populations predict stronger correlations. The populations served by these WWTPs have varying social vulnerability and represent 24% of the South Carolina population. The study spanned 15 months from April 19, 2020, to July 1, 2021, which includes the administration of the first COVID-19 vaccines. SARS-CoV-2 RNA concentrations were measured by either reverse transcription quantitative PCR (RT-qPCR) or droplet digital PCR (RT-ddPCR). Although populations served and average flow rate varied across WWTPs, the strongest correlation was identified for six of the seven WWTPs when daily case counts were lagged two days after the measured SARS-CoV-2 RNA concentration in wastewater. The weakest correlation was found for WWTP 6, which had the lowest ratio of population served to average flow rate, indicating that the SARS-CoV-2 signal was too dilute for a robust correlation. Smoothing daily case counts by a 7-day moving average improved correlation strength between case counts and SARS-CoV-2 RNA concentration in wastewater while dampening the effect of lag-time optimization. Correlation strength between cases and SARS-CoV-2 RNA was compared for cases determined at the ZIP-code and sewershed levels. The strength of correlations using ZIP-code-level versus sewershed-level cases were not statistically different across WWTPs. Results indicate that wastewater surveillance, even without normalization to fecal indicators, is a strong predictor of clinical cases by at least two days, especially when SARS-CoV-2 RNA is measured using RT-ddPCR. Furthermore, the ratio of population served to flow rate may be a useful metric to assess whether a WWTP is suitable for a surveillance program.

Deep Learning-Assisted Droplet Digital PCR for Quantitative Detection of Human Coronavirus.

Since coronavirus disease 2019 (COVID-19) pandemic rapidly spread worldwide, there is an urgent demand for accurate and suitable nucleic acid detection technology. Although the conventional threshold-based algorithms have been used for processing images of droplet digital polymerase chain reaction (ddPCR), there are still challenges from noise and irregular size of droplets. Here, we present a combined method of the mask region convolutional neural network (Mask R-CNN)-based image detection algorithm and Gaussian mixture model (GMM)-based thresholding algorithm. This novel approach significantly reduces false detection rate and achieves highly accurate prediction model in a ddPCR image processing. We demonstrated that how deep learning improved the overall performance in a ddPCR image processing. Therefore, our study could be a promising method in nucleic acid detection technology.

Detection of SARS-CoV-2 RNA in wastewater and comparison to COVID-19 cases in two sewersheds, North Carolina, USA.

Wastewater surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may be useful for monitoring population-wide coronavirus disease 2019 (COVID-19) infections, especially given asymptomatic infections and limitations in diagnostic testing. We aimed to detect SARS-CoV-2 RNA in wastewater and compare viral concentrations to COVID-19 case numbers in the respective counties and sewersheds. Influent 24-hour composite wastewater samples were collected from July to December 2020 from two municipal wastewater treatment plants serving different population sizes in Orange and Chatham Counties in North Carolina. After a concentration step via HA filtration, SARS-CoV-2 RNA was detected and quantified by reverse transcription droplet digital polymerase chain reaction (RT-ddPCR) and quantitative PCR (RT-qPCR), targeting the N1 and N2 nucleocapsid genes. SARS-CoV-2 RNA was detected by RT-ddPCR in 100 % (24/24) and 79 % (19/24) of influent wastewater samples from the larger and smaller plants, respectively. In comparison, viral RNA was detected by RT-qPCR in 41.7 % (10/24) and 8.3 % (2/24) of samples from the larger and smaller plants, respectively. Positivity rates and method agreement further increased for the RT-qPCR assay when samples with positive signals below the limit of detection were counted as positive. The wastewater data from the larger plant generally correlated (⍴ ~0.5, p < 0.05) with, and even anticipated, the trends in reported COVID-19 cases, with a notable spike in measured viral RNA preceding a spike in cases when students returned to a college campus in the Orange County sewershed. Correlations were generally higher when using estimates of sewershed-level case data rather than county-level data. This work supports use of wastewater surveillance for tracking COVID-19 disease trends, especially in identifying spikes in cases. Wastewater-based epidemiology can be a valuable resource for tracking disease trends, allocating resources, and evaluating policy in the fight against current and future pandemics.

Neuro-Axonal Damage and Alteration of Blood-Brain Barrier Integrity in COVID-19 Patients.

Neurofilament light chain (NfL) is a specific biomarker of neuro-axonal damage. Matrix metalloproteinases (MMPs) are zinc-dependent enzymes involved in blood-brain barrier (BBB) integrity. We explored neuro-axonal damage, alteration of BBB integrity and SARS-CoV-2 RNA presence in COVID-19 patients with severe neurological symptoms (neuro-COVID) as well as neuro-axonal damage in COVID-19 patients without severe neurological symptoms according to disease severity and after recovery, comparing the obtained findings with healthy donors (HD). Overall, COVID-19 patients (n = 55) showed higher plasma NfL levels compared to HD (n = 31) (p < 0.0001), especially those who developed ARDS (n = 28) (p = 0.0005). After recovery, plasma NfL levels were still higher in ARDS patients compared to HD (p = 0.0037). In neuro-COVID patients (n = 12), higher CSF and plasma NfL, and CSF MMP-2 levels in ARDS than non-ARDS group were observed (p = 0.0357, p = 0.0346 and p = 0.0303, respectively). SARS-CoV-2 RNA was detected in four CSF and two plasma samples. SARS-CoV-2 RNA detection was not associated to increased CSF NfL and MMP levels. During COVID-19, ARDS could be associated to CNS damage and alteration of BBB integrity in the absence of SARS-CoV-2 RNA detection in CSF or blood. CNS damage was still detectable after discharge in blood of COVID-19 patients who developed ARDS during hospitalization.

Circulating Ubiquitous RNA, A Highly Predictive and Prognostic Biomarker in Hospitalized Coronavirus Disease 2019 (COVID-19) Patients.

BACKGROUND: Approximately 15-30% of hospitalized coronavirus disease 2019 (COVID-19) patients develop acute respiratory distress syndrome, systemic tissue injury, and/or multi-organ failure leading to death in around 45% of cases. There is a clear need for biomarkers that quantify tissue injury, predict clinical outcomes, and guide the clinical management of hospitalized COVID-19 patients. METHODS: We herein report the quantification by droplet-based digital polymerase chain reaction (ddPCR) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNAemia and the plasmatic release of a ubiquitous human intracellular marker, the ribonuclease P (RNase P) in order to evaluate tissue injury and cell lysis in the plasma of 139 COVID-19 hospitalized patients at admission. RESULTS: We confirmed that SARS-CoV-2 RNAemia was associated with clinical severity of COVID-19 patients. In addition, we showed that plasmatic RNase P RNAemia at admission was also highly correlated with disease severity (P < .001) and invasive mechanical ventilation status (P < .001) but not with pulmonary severity. Altogether, these results indicate a consequent cell lysis process in severe and critical patients but not systematically due to lung cell death. Finally, the plasmatic RNase P RNA value was also significantly associated with overall survival. CONCLUSIONS: Viral and ubiquitous blood biomarkers monitored by ddPCR could be useful for the clinical monitoring and the management of hospitalized COVID-19 patients. Moreover, these results could pave the way for new and more personalized circulating biomarkers in COVID-19, and more generally in infectious diseases, specific from each patient organ injury profile.

The Entero-Mammary Pathway and Perinatal Transmission of Gut Microbiota and SARS-CoV-2.

COVID-19 is a severe respiratory disease threatening pregnant women, which increases the possibility of adverse pregnancy outcomes. Several recent studies have demonstrated the ability of SARS-CoV-2 to infect the mother enterocytes, disturbing the gut microbiota diversity. The aim of this study was to characterize the entero-mammary microbiota of women in the presence of the virus during delivery. Fifty mother-neonate pairs were included in a transversal descriptive work. The presence of SARS-CoV-2 RNA was detected in nasopharyngeal, mother rectal swabs (MRS) and neonate rectal swabs (NRS) collected from the pairs, and human colostrum (HC) samples collected from mothers. The microbiota diversity was characterized by high-throughput DNA sequencing of V3-16S rRNA gene libraries prepared from HC, MRS, and NRS. Data were analyzed with QIIME2 and R. Our results indicate that several bacterial taxa are highly abundant in MRS positive for SARS-CoV-2 RNA. These bacteria mostly belong to the Firmicutes phylum; for instance, the families Bifidobacteriaceae, Oscillospiraceae, and Microbacteriaceae have been previously associated with anti-inflammatory effects, which could explain the capability of women to overcome the infection. All samples, both positive and negative for SARS-CoV-2, featured a high abundance of the Firmicutes phylum. Further data analysis showed that nearly 20% of the bacterial diversity found in HC was also identified in MRS. Spearman correlation analysis highlighted that some genera of the Proteobacteria and Actinobacteria phyla were negatively correlated with MRS and NRS (p < 0.005). This study provides new insights into the gut microbiota of pregnant women and their potential association with a better outcome during SARS-CoV-2 infection.

Novel aerosol detection platform for SARS­CoV­2: Based on specific magnetic nanoparticles adsorption sampling and digital droplet PCR detection.

The SARS­CoV­2 virus is released from an infectious source (such as a sick person) and adsorbed on aerosols, which can form pathogenic microorganism aerosols, which can affect human health through airborne transmission. Efficient sampling and accurate detection of microorganisms in aerosols are the premise and basis for studying their properties and evaluating their hazard. In this study, we built a set of sub-micron aerosol detection platform, and carried out a simulation experiment on the SARS­CoV­2 aerosol in the air by wet-wall cyclone combined with immunomagnetic nanoparticle adsorption sampling and ddPCR. The feasibility of the system in aerosol detection was verified, and the influencing factors in the detection process were experimentally tested. As a result, the sampling efficiency was 29.77%, and extraction efficiency was 98.57%. The minimum detection limit per unit volume of aerosols was 250 copies (102 copies/mL, concentration factor 2.5).

A Propidium Monoazide (PMAxx)-Droplet Digital PCR (ddPCR) for the Detection of Viable Burkholderia cepacia Complex in Nuclease-Free Water and Antiseptics

Pharmaceutical products contaminated with Burkholderia cepacia complex (BCC) strains constitute a serious health issue for susceptible individuals. New detection methods to distinguish DNA from viable cells are required to ensure pharmaceutical product quality and safety. In this study, we have assessed a droplet digital PCR (ddPCR) with a variant propidium monoazide (PMAxx) for selective detection of live/dead BCC cells in autoclaved nuclease-free water after 365 days, in 0.001% chlorhexidine gluconate (CHX), and in 0.005% benzalkonium chloride (BZK) solutions after 184 days. Using 10 μM PMAxx and 5 min light exposure, a proportion of dead BCC was quantified by ddPCR. The detection limit of culture-based method was 104 CFU/mL, equivalent to 9.7 pg/μL for B. cenocepacia J2315, while that of ddPCR was 9.7 fg/μL. The true positive rate from nuclease-free water and CHX using PMAxx-ddPCR assay was 60.0% and 38.3%, respectively, compared to 85.0% and 74.6% without PMAxx (p < 0.05), respectively. However, in BZK-treated cells, no difference in the detection rate was observed between the ddPCR assay on samples treated with PMAxx (67.1%) and without PMAxx (63.3%). This study shows that the PMAxx-ddPCR assay provides a better tool for selective detection of live BCC cells in non-sterile pharmaceutical products.