The spatial and temporal distribution of SARS-CoV-2 from the built environment of COVID-19 patient rooms: A multicentre prospective study.

BACKGROUND: SARS-CoV-2 can be detected from the built environment (e.g., floors), but it is unknown how the viral burden surrounding an infected patient changes over space and time. Characterizing these data can help advance our understanding and interpretation of surface swabs from the built environment. METHODS: We conducted a prospective study at two hospitals in Ontario, Canada between January 19, 2022 and February 11, 2022. We performed serial floor sampling for SARS-CoV-2 in rooms of patients newly hospitalized with COVID-19 in the past 48 hours. We sampled the floor twice daily until the occupant moved to another room, was discharged, or 96 hours had elapsed. Floor sampling locations included 1 metre (m) from the hospital bed, 2 m from the hospital bed, and at the room's threshold to the hallway (typically 3 to 5 m from the hospital bed). The samples were analyzed for the presence of SARS-CoV-2 using quantitative reverse transcriptase polymerase chain reaction (RT-qPCR). We calculated the sensitivity of detecting SARS-CoV-2 in a patient with COVID-19, and we evaluated how the percentage of positive swabs and the cycle threshold of the swabs changed over time. We also compared the cycle threshold between the two hospitals. RESULTS: Over the 6-week study period we collected 164 floor swabs from the rooms of 13 patients. The overall percentage of swabs positive for SARS-CoV-2 was 93% and the median cycle threshold was 33.4 (interquartile range [IQR]: 30.8, 37.2). On day 0 of swabbing the percentage of swabs positive for SARS-CoV-2 was 88% and the median cycle threshold was 33.6 (IQR: 31.8, 38.2) compared to swabs performed on day 2 or later where the percentage of swabs positive for SARS-CoV-2 was 98% and the cycle threshold was 33.2 (IQR: 30.6, 35.6). We found that viral detection did not change with increasing time (since the first sample collection) over the sampling period, Odds Ratio (OR) 1.65 per day (95% CI 0.68, 4.02; p = 0.27). Similarly, viral detection did not change with increasing distance from the patient's bed (1 m, 2 m, or 3 m), OR 0.85 per metre (95% CI 0.38, 1.88; p = 0.69). The cycle threshold was lower (i.e., more virus) in The Ottawa Hospital (median quantification cycle [Cq] 30.8) where floors were cleaned once daily compared to the Toronto hospital (median Cq 37.2) where floors were cleaned twice daily. CONCLUSIONS: We were able to detect SARS-CoV-2 on the floors in rooms of patients with COVID-19. The viral burden did not vary over time or by distance from the patient's bed. These results suggest floor swabbing for the detection of SARS-CoV-2 in a built environment such as a hospital room is both accurate and robust to variation in sampling location and duration of occupancy.

EMERGE: Evaluating the value of Measuring Random Plasma Glucose Values for Managing Hyperglycemia in the Inpatient Setting.

IMPORTANCE: A diagnosis of diabetes is considered when a patient has hyperglycemia with a random plasma glucose ≥200 mg/dL. However, in the inpatient setting, hyperglycemia is frequently non-specific, especially among patients who are acutely unwell. As a result, patients with transient hyperglycemia may be incorrectly labeled as having diabetes, leading to unnecessary treatment, and potential harm. DESIGN, SETTING, AND PARTICIPANTS: We conducted a multicenter cohort study of patients hospitalized at six hospitals in Ontario, Canada, and identified those with a glucose value ≥200 mg/dL (including standing measurements and randomly drawn). We validated a definition for diabetes using manual chart review that included physician notes, pharmacy notes, home medications, and hemoglobin A1C. Among patients with a glucose value ≥200 mg/dL (11.1 mmol/L), we identified patients without diabetes who received a diabetes medication, and the number who experienced hypoglycemia during the same admission. MAIN OUTCOMES AND MEASURES: To determine the diagnostic value of using random blood glucose to diagnose diabetes in the inpatient setting, and its impact on patient outcomes. RESULTS: We identified 328,786 hospitalizations from hospital between 2010 and 2020. A blood glucose value of ≥200 mg/dL (11.1 mmol/L) had a positive predictive value of 68% and a negative predictive value of 90% for a diagnosis of diabetes. Of the 76,967 patients with an elevated glucose value reported, 16,787 (21.8%) did not have diabetes, and of these, 5375 (32%) received a diabetes medication. Hypoglycemia was frequently reported among the 5375 patients that received a diabetes medication, with 1406 (26.2%) experiencing hypoglycemia and 405 (7.5%) experiencing severe hypoglycemia. CONCLUSIONS AND RELEVANCE: Hyperglycemia in hospital is common but does not necessarily indicate a patient has diabetes. Furthermore, it can lead to treatment with diabetes medications with potential harm. Our findings highlight that clinicians should be cautious when responding to elevated random plasma glucose tests in the inpatient setting.

Detection of Covid-19 Outbreaks Using Built Environment Testing for SARS-CoV-2.

Built Environment Testing for SARS-CoV-2Wastewater testing has proven to be a valuable tool for forecasting Covid-19 outbreaks. Fralick et al. now report that swabbing of surfaces (i.e., floors) for SARS-CoV-2 may provide a similar benefit for predicting outbreaks in long-term care homes.

The Landscape of COVID-19 Research in the United States: a Cross-sectional Study of Randomized Trials Registered on ClinicalTrials.Gov.

IMPORTANCE: SARS-CoV-2 has infected over 200 million people worldwide, resulting in more than 4 million deaths. Randomized controlled trials are the single best tool to identify effective treatments against this novel pathogen. OBJECTIVE: To describe the characteristics of randomized controlled trials of treatments for COVID-19 in the United States launched in the first 9 months of the pandemic. Design, Setting, and Participants We conducted a cross-sectional study of all completed or actively enrolling randomized, interventional, clinical trials for the treatment of COVID-19 in the United States registered on www.clinicaltrials.gov as of August 10, 2020. We excluded trials of vaccines and other interventions intended to prevent COVID-19. Main Outcomes and Measures We used descriptive statistics to characterize the clinical trials and the statistical power for the available studies. For the late-phase trials (i.e., phase 3 and 2/3 studies), we compared the geographic distribution of the clinical trials with the geographic distribution of people diagnosed with COVID-19. RESULTS: We identified 200 randomized controlled trials of treatments for people with COVID-19. Across all trials, 87 (43.5%) were single-center, 64 (32.0%) were unblinded, and 80 (40.0%) were sponsored by industry. The most common treatments included monoclonal antibodies (N=46 trials), small molecule immunomodulators (N=28), antiviral medications (N=24 trials), and hydroxychloroquine (N=20 trials). Of the 9 trials completed by August 2020, the median sample size was 450 (IQR 67-1113); of the 191 ongoing trials, the median planned sample size was 150 (IQR 60-400). Of the late-phase trials (N=54), the most common primary outcome was a severity scale (N=23, 42.6%), followed by a composite of mortality and ventilation (N=10, 18.5%), and mortality alone (N=6, 11.1%). Among these late-phase trials, all trials of antivirals, monoclonal antibodies, or chloroquine/hydroxychloroquine had a power of less than 25% to detect a 20% relative risk reduction in mortality. Had the individual trials for a given class of treatments instead formed a single trial, the power to detect that same reduction in mortality would have been greater than 98%. There was large variability in access to trials with the highest number of trials per capita in the Northeast and the lowest in the Midwest. CONCLUSIONS AND RELEVANCE: A large number of randomized trials were launched early in the pandemic to evaluate treatments for COVID-19. However, many trials were underpowered for important clinical endpoints and substantial geographic disparities were observed, highlighting the importance of improving national clinical trial infrastructure.

Clustering Continuous Wavelet Transform Characteristics of Heart Rate Variability through Unsupervised Learning.

The analysis and interpretation of physiological signals acquired non-invasively are increasingly important in Smart Health, precision medicine, and medical research. However, this analysis is hampered due to the length, complexity, and inter-subject variation of these signals, and, consequently, dimensionality reduction and clustering offer substantial benefits. Machine learning, used widely in biomedicine, is increasingly being applied to physiological time series. Among the applications of unsupervised learning, clustering is one of the most important. In this paper, an unsupervised autoen-coder architecture, deep convolutional embedded clustering, is presented as a data-driven approach to study time-frequency characteristics of heart rate variability records. An autoen-coder network is trained on continuous wavelet transforms of heart rate variability signals calculated from publicly-available annotated ECG records with a wide variety of conditions. The latent variables learned by the clustering autoencoder are low-dimensional representations of wavelet transform characteristics that can be visualized and further analyzed. The results indicate that the learned clusters correspond to beat morphologies in the electrocardiogram in many cases, but also that the reduced dimensions of the time-frequency features can potentially provide additional insights into cardiac activity and the autonomic nervous system.

Sensitive Detection of Immunoglobulin G Stability Using in Real-Time Isothermal Differential Scanning Fluorimetry: Determinants of Protein Stability for Antibody-Based Therapeutics.

Protein instability is a major obstacle in the production and delivery of monoclonal antibody-based therapies for cancer. This study presents real-time isothermal differential scanning fluorimetry as an emerging method to evaluate the stability of human immunoglobulin G protein with high sensitivity. The stability of polyclonal human immunoglobulin G against urea-induced denaturation was assessed following: (1) oxidation by the free-radical generator 2,2-Azobis[2-amidinopropane]dihydrochloride and (2) in selected storage buffers. Significant differences in immunoglobulin G stability were detected by real-time isothermal differential scanning fluorimetry when the immunoglobulin G was stored in 1,4-Piperazinediethanesulfonic acid buffer compared to phosphate-buffered saline, with half-maximal rate of denaturation occurring at a higher urea concentration in 1,4-Piperazinediethanesulfonic acid than phosphate-buffered saline (Knd;PIPES = 3.56 ± 0.09 M, Knd;PBS = 2.94 ± 0.08 M; P < .01), but differential scanning fluorimetry did not detect differences in unfolding temperature (Tm;PIPES = 70.5 ± 0.3°C, Tm;PBS = 69.7 ± 0.2°C). The effects of 2,2-Azobis[2-amidinopropane]dihydrochloride-induced oxidation on immunoglobulin G stability were analyzed by real-time isothermal differential scanning fluorimetry; the oxidized protein showed greater sensitivity to urea (Knd;CNTRL = 3.96 ± 0.19 M, Knd;AAPH = 3.49 ± 0.07 M; P < .05). Similarly, differential scanning fluorimetry indicated greater thermal sensitivity of oxidized immunoglobulin G (Tm;CNTRL = 70.5 ± 0.3°C, Tm;AAPH = 62.9 ± 0.1°C; P < .001). However, a third method for assessing protein stability, pulse proteolysis, proved to be substantially less sensitive and did not detect significant effects of 2,2-Azobis[2-amidinopropane]dihydrochloride on the half-maximal concentration of urea needed to denature immunoglobulin G (Cm;CNTRL= 6.8 ± 0.1 M; Cm;AAPH = 6.4 ± 0.7 M). Overall these results demonstrate the merit of using real-time isothermal differential scanning fluorimetry as a rapid and sensitive technique for the evaluation of protein stability in solution using a quantitative real-time thermocycler.

The hibernating South American marsupial, Dromiciops gliroides, displays torpor-sensitive microRNA expression patterns.

When faced with adverse environmental conditions, the marsupial Dromiciops gliroides uses either daily or seasonal torpor to support survival and is the only known hibernating mammal in South America. As the sole living representative of the ancient Order Microbiotheria, this species can provide crucial information about the evolutionary origins and biochemical mechanisms of hibernation. Hibernation is a complex energy-saving strategy that involves changes in gene expression that are elicited in part by microRNAs. To better elucidate the role of microRNAs in orchestrating hypometabolism, a modified stem-loop technique and quantitative PCR were used to characterize the relative expression levels of 85 microRNAs in liver and skeletal muscle of control and torpid D. gliroides. Thirty-nine microRNAs were differentially regulated during torpor; of these, 35 were downregulated in liver and 11 were differentially expressed in skeletal muscle. Bioinformatic analysis predicted that the downregulated liver microRNAs were associated with activation of MAPK, PI3K-Akt and mTOR pathways, suggesting their importance in facilitating marsupial torpor. In skeletal muscle, hibernation-responsive microRNAs were predicted to regulate focal adhesion, ErbB, and mTOR pathways, indicating a promotion of muscle maintenance mechanisms. These tissue-specific responses suggest that microRNAs regulate key molecular pathways that facilitate hibernation, thermoregulation, and prevention of muscle disuse atrophy.