Predicting COVID-19 cases using SARS-CoV-2 RNA in air, surface swab and wastewater samples.

Genomic footprints of pathogens shed by infected individuals can be traced in environmental samples, which can serve as a noninvasive method of infectious disease surveillance. The research evaluates the efficacy of environmental monitoring of SARS-CoV-2 RNA in air, surface swabs and wastewater to predict COVID-19 cases. Using a prospective experimental design, air, surface swabs, and wastewater samples were collected from a college dormitory housing roughly 500 students from March to May 2021 at the University of Miami, Coral Gables, FL. Students were randomly screened for COVID-19 during the study period. SARS-CoV-2 concentration in environmental samples was quantified using Volcano 2nd Generation-qPCR. Descriptive analyses were conducted to examine the associations between time-lagged SARS-CoV-2 in environmental samples and COVID-19 cases. SARS-CoV-2 was detected in air, surface swab and wastewater samples on 52 (63.4 %), 40 (50.0 %) and 57 (68.6 %) days, respectively. On 19 (24 %) of 78 days SARS-CoV-2 was detected in all three sample types. COVID-19 cases were reported on 11 days during the study period and SARS-CoV-2 was also detected two days before the case diagnosis on all 11 (100 %), 9 (81.8 %) and 8 (72.7 %) days in air, surface swab and wastewater samples, respectively. SARS-CoV-2 detection in environmental samples was an indicator of the presence of local COVID-19 cases and a 3-day lead indicator for a potential outbreak at the dormitory building scale. Proactive environmental surveillance of SARS-CoV-2 or other pathogens in multiple environmental media has potential to guide targeted measures to contain and/or mitigate infectious disease outbreaks within communities.

Relationships between SARS-CoV-2 in Wastewater and COVID-19 Clinical Cases and Hospitalizations, with and without Normalization against Indicators of Human Waste.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in wastewater has been used to track community infections of coronavirus disease-2019 (COVID-19), providing critical information for public health interventions. Since levels in wastewater are dependent upon human inputs, we hypothesize that tracking infections can be improved by normalizing wastewater concentrations against indicators of human waste [Pepper Mild Mottle Virus (PMMoV), ß-2 Microglobulin (B2M), and fecal coliform]. In this study, we analyzed SARS-CoV-2 and indicators of human waste in wastewater from two sewersheds of different scales: a University campus and a wastewater treatment plant. Wastewater data were combined with complementary COVID-19 case tracking to evaluate the efficiency of wastewater surveillance for forecasting new COVID-19 cases and, for the larger scale, hospitalizations. Results show that the normalization of SARS-CoV-2 levels by PMMoV and B2M resulted in improved correlations with COVID-19 cases for campus data using volcano second generation (V2G)-qPCR chemistry (r s = 0.69 without normalization, r s = 0.73 with normalization). Mixed results were obtained for normalization by PMMoV for samples collected at the community scale. Overall benefits from normalizing with measures of human waste depend upon qPCR chemistry and improves with smaller sewershed scale. We recommend further studies that evaluate the efficacy of additional normalization targets.

Combining Pentoxifylline With Vedolizumab for Crohn's Disease: Results of a Randomised, Placebo-controlled Pilot Study.

BACKGROUND AND AIMS: The efficacy of current biologics may be limited by targeting only one pathway. Pentoxifylline [PTX] interferes with tumour necrosis factor [TNF] gene expression. We performed a randomised, placebo-controlled pilot study to determine if PTX plus vedolizumab [VDZ] in patients with Crohn's disease [CD] is safe and improves response compared with VDZ monotherapy. METHODS: Thirty adult patients with active CD were randomised to VDZ/PTX or VDZ/placebo and followed for 24 weeks. Endoscopic activity and inflammatory cytokines were measured at baseline and Week 24. Descriptive statistics were used to determine estimates of effect. RESULTS: Demographics were similar but baseline disease activity was higher in the VDZ/PTX group. There was no difference in clinical remission at Week 14 (60.0% vs 66.67%, odds ratio [OR] 0.76, 95% confidence interval [CI] 0.16, 3.51) or steroid-free clinical remission at Week 24 in patients receiving VDZ/PTX. Improved clinical response was noted in the VDZ/PTX group at Weeks 6, 14, and 24 [Week 6: 20% vs 6.67%, Week 14: 26.67% vs 6.67%, Week 24: 40% vs 20%]. The rate of endoscopic remission was similar between the groups [40% vs 33.33%], with a greater mean decrease in Simple Endoscopic Score-CD [SES-CD] and C-reactive protein [CRP] with VDZ/PTX [SES-CD -3.17 vs -0.15, CRP -5.56 vs 0.46]. An increase in serum TNF-α concentration was observed with VDZ/placebo group; PTX mitigated this effect. No serious adverse events occurred. CONCLUSIONS: VDZ/PTX did not provide benefit over VDZ monotherapy in clinical or endoscopic remission but appeared to improve clinical response and was safe. These data should inform a fully powered study.

COVID-19 Prediction using Genomic Footprint of SARS-CoV-2 in Air, Surface Swab and Wastewater Samples.

Importance: Genomic footprints of pathogens shed by infected individuals can be traced in environmental samples. Analysis of these samples can be employed for noninvasive surveillance of infectious diseases. Objective: To evaluate the efficacy of environmental surveillance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) for predicting COVID-19 cases in a college dormitory. Design: Using a prospective experimental design, air, surface swabs, and wastewater samples were collected from a college dormitory from March to May 2021. Students were randomly screened for COVID-19 during the study period. SARS-CoV-2 in environmental samples was concentrated with electronegative filtration and quantified using Volcano 2 nd Generation-qPCR. Descriptive analyses were conducted to examine the associations between time-lagged SARS-CoV-2 in environmental samples and clinically diagnosed COVID-19 cases. Setting: This study was conducted in a residential dormitory at the University of Miami, Coral Gables campus, FL, USA. The dormitory housed about 500 students. Participants: Students from the dormitory were randomly screened, for COVID-19 for 2-3 days / week while entering or exiting the dormitory. Main Outcome: Clinically diagnosed COVID-19 cases were of our main interest. We hypothesized that SARS-CoV-2 detection in environmental samples was an indicator of the presence of local COVID-19 cases in the dormitory, and SARS-CoV-2 can be detected in the environmental samples several days prior to the clinical diagnosis of COVID-19 cases. Results: SARS-CoV-2 genomic footprints were detected in air, surface swab and wastewater samples on 52 (63.4%), 40 (50.0%) and 57 (68.6%) days, respectively, during the study period. On 19 (24%) of 78 days SARS-CoV-2 was detected in all three sample types. Clinically diagnosed COVID-19 cases were reported on 11 days during the study period and SARS-CoV-2 was also detected two days before the case diagnosis on all 11 (100%), 9 (81.8%) and 8 (72.7%) days in air, surface swab and wastewater samples, respectively. Conclusion: Proactive environmental surveillance of SARS-CoV-2 or other pathogens in a community/public setting has potential to guide targeted measures to contain and/or mitigate infectious disease outbreaks. Key Points: Question: How effective is environmental surveillance of SARS-CoV-2 in public places for early detection of COVID-19 cases in a community?Findings: All clinically confirmed COVID-19 cases were predicted with the aid of 2 day lagged SARS-CoV-2 in environmental samples in a college dormitory. However, the prediction efficiency varied by sample type: best prediction by air samples, followed by wastewater and surface swab samples. SARS-CoV-2 was also detected in these samples even on days without any reported cases of COVID-19, suggesting underreporting of COVID-19 cases.Meaning: SARS-CoV-2 can be detected in environmental samples several days prior to clinical reporting of COVID-19 cases. Thus, proactive environmental surveillance of microbiome in public places can serve as a mean for early detection of location-time specific outbreaks of infectious diseases. It can also be used for underreporting of infectious diseases.

Lessons learned from SARS-CoV-2 measurements in wastewater.

Standardized protocols for wastewater-based surveillance (WBS) for the RNA of SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, are being developed and refined worldwide for early detection of disease outbreaks. We report here on lessons learned from establishing a WBS program for SARS-CoV-2 integrated with a human surveillance program for COVID-19. We have established WBS at three campuses of a university, including student residential dormitories and a hospital that treats COVID-19 patients. Lessons learned from this WBS program address the variability of water quality, new detection technologies, the range of detectable viral loads in wastewater, and the predictive value of integrating environmental and human surveillance data. Data from our WBS program indicated that water quality was statistically different between sewer sampling sites, with more variability observed in wastewater coming from individual buildings compared to clusters of buildings. A new detection technology was developed based upon the use of a novel polymerase called V2G. Detectable levels of SARS-CoV-2 in wastewater varied from 102 to 106 genomic copies (gc) per liter of raw wastewater (L). Integration of environmental and human surveillance data indicate that WBS detection of 100 gc/L of SARS-CoV-2 RNA in wastewater was associated with a positivity rate of 4% as detected by human surveillance in the wastewater catchment area, though confidence intervals were wide (ß ~ 8.99 ∗ ln(100); 95% CI = 0.90-17.08; p < 0.05). Our data also suggest that early detection of COVID-19 surges based on correlations between viral load in wastewater and human disease incidence could benefit by increasing the wastewater sample collection frequency from weekly to daily. Coupling simpler and faster detection technology with more frequent sampling has the potential to improve the predictive potential of using WBS of SARS-CoV-2 for early detection of the onset of COVID-19.

Advanced Statistics: Multiple Logistic Regression, Cox Proportional Hazards, and Propensity Scores.

Background: Randomized controlled trials (RCTs) are generally regarded as the gold standard for demonstrating causality because they effectively mitigate bias from both known and unknown confounders. However, conducting an RCT is not always feasible because of logistical and ethical considerations. This is especially true when evaluating surgical interventions, and non-randomized study designs must be utilized instead. Methods: Statistical methods that adjust for baseline differences in non-randomized studies were reviewed. Results: The three methods used most commonly to adjust for confounding factors are multiple logistic regression, Cox proportional hazard, and propensity scoring. Multiple logistic regression (MLR) is implemented to analyze the influence of categorical and/or continuous variables on a single dichotomous outcome. The model controls for multiple covariates while also quantifying the magnitude of each covariate's influence on the outcome. Selecting which variables to include in a model should be the most important consideration, and authors must report how and why variables were chosen. Cox proportional hazards modeling is conceptually similar to logistic regression and is used when analyzing survival data. When applied to survival curves, Cox proportional hazards can adjust for baseline group differences and provide a hazard ratio to quantify the effect that any single factor contributes to the survival curve. Propensity scores (PS) range from zero to one and are defined as the probability of receiving an intervention based on observed baseline characteristics. Propensity score matching (PSM) is especially useful when the outcome of interest is a rare event. Treated and untreated subjects with similar propensity scores are paired, forming balanced samples for further analysis. Conclusions: The method by which to address confounding should be selected according to the data format and sample size. Reporting of methods should provide justification for selected covariates, confirmation that data did not violate model assumptions, and measures of model performance.

Umbilical cord mesenchymal stem cells for COVID-19 acute respiratory distress syndrome: A double-blind, phase 1/2a, randomized controlled trial.

Acute respiratory distress syndrome (ARDS) in COVID-19 is associated with high mortality. Mesenchymal stem cells are known to exert immunomodulatory and anti-inflammatory effects and could yield beneficial effects in COVID-19 ARDS. The objective of this study was to determine safety and explore efficacy of umbilical cord mesenchymal stem cell (UC-MSC) infusions in subjects with COVID-19 ARDS. A double-blind, phase 1/2a, randomized, controlled trial was performed. Randomization and stratification by ARDS severity was used to foster balance among groups. All subjects were analyzed under intention to treat design. Twenty-four subjects were randomized 1:1 to either UC-MSC treatment (n = 12) or the control group (n = 12). Subjects in the UC-MSC treatment group received two intravenous infusions (at day 0 and 3) of 100 ± 20 × 106 UC-MSCs; controls received two infusions of vehicle solution. Both groups received best standard of care. Primary endpoint was safety (adverse events [AEs]) within 6 hours; cardiac arrest or death within 24 hours postinfusion). Secondary endpoints included patient survival at 31 days after the first infusion and time to recovery. No difference was observed between groups in infusion-associated AEs. No serious adverse events (SAEs) were observed related to UC-MSC infusions. UC-MSC infusions in COVID-19 ARDS were found to be safe. Inflammatory cytokines were significantly decreased in UC-MSC-treated subjects at day 6. Treatment was associated with significantly improved patient survival (91% vs 42%, P = .015), SAE-free survival (P = .008), and time to recovery (P = .03). UC-MSC infusions are safe and could be beneficial in treating subjects with COVID-19 ARDS.