Development of a universal real-time RT-PCR assay for detection of pan-SARS-coronaviruses with an RNA-based internal control.

The current pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exemplifies the critical need for rapid diagnostic assays to prompt intensified virological monitoring both in human and wild animal populations. To date, there are no clinical validated assays for pan-SARS-coronavirus (pan-SARS-CoV) detection. Here, we suggest an innovative primer design strategy for the diagnosis of pan-SARS-CoVs targeting the envelope (E) gene using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Furthermore, we developed a new primer-probe set targeting human ß2-microglobulin (B2M) as an RNA-based internal control for process efficacy. The universal RT-qPCR assay demonstrated no false-positive amplifications with other human coronaviruses or 20 common respiratory viruses, and its limit of detection (LOD) was 159.16 copies/ml at 95% detection probability. In clinical validation, the assay delivered 100% sensitive results in the detection of SARS-CoV-2-positive oropharyngeal samples (n = 120), including three variants of concern (Wuhan, Delta, and Omicron). Taken together, this universal RT-qPCR assay provides a highly sensitive, robust, and rapid detection of SARS-CoV-1, SARS-CoV-2, and animal-derived SARS-related CoVs.

Secondary bloodstream infection in critically ill patients with COVID-19.

OBJECTIVE: Secondary infection, especially bloodstream infection, is an important cause of death in critically ill patients with COVID-19. We aimed to describe secondary bloodstream infection (SBI) in critically ill adults with COVID-19 in the intensive care unit (ICU) and to explore risk factors related to SBI. METHODS: We reviewed all SBI cases among critically ill patients with COVID-19 from 12 February 2020 to 24 March 2020 in the COVID-19 ICU of Jingmen First People's Hospital. We compared risk factors associated with bloodstream infection in this study. All SBIs were confirmed by blood culture. RESULTS: We identified five cases of SBI among the 32 patients: three with Enterococcus faecium, one mixed septicemia (E. faecium and Candida albicans), and one C. parapsilosis. There were no significant differences between the SBI group and non-SBI group. Significant risk factors for SBI were extracorporeal membrane oxygenation, central venous catheter, indwelling urethral catheter, and nasogastric tube. CONCLUSIONS: Our findings confirmed that the incidence of secondary infection, particularly SBI, and mortality are high among critically ill patients with COVID-19. We showed that long-term hospitalization and invasive procedures such as tracheotomy, central venous catheter, indwelling urethral catheter, and nasogastric tube are risk factors for SBI and other complications.

DW-UNet: Loss Balance under Local-Patch for 3D Infection Segmentation from COVID-19 CT Images.

(1) Background: COVID-19 has been global epidemic. This work aims to extract 3D infection from COVID-19 CT images; (2) Methods: Firstly, COVID-19 CT images are processed with lung region extraction and data enhancement. In this strategy, gradient changes of voxels in different directions respond to geometric characteristics. Due to the complexity of tubular tissues in lung region, they are clustered to the lung parenchyma center based on their filtered possibility. Thus, infection is improved after data enhancement. Then, deep weighted UNet is established to refining 3D infection texture, and weighted loss function is introduced. It changes cost calculation of different samples, causing target samples to dominate convergence direction. Finally, the trained network effectively extracts 3D infection from CT images by adjusting driving strategy of different samples. (3) Results: Using Accuracy, Precision, Recall and Coincidence rate, 20 subjects from a private dataset and eight subjects from Kaggle Competition COVID-19 CT dataset tested this method in hold-out validation framework. This work achieved good performance both in the private dataset (99.94-00.02%, 60.42-11.25%, 70.79-09.35% and 63.15-08.35%) and public dataset (99.73-00.12%, 77.02-06.06%, 41.23-08.61% and 52.50-08.18%). We also applied some extra indicators to test data augmentation and different models. The statistical tests have verified the significant difference of different models. (4) Conclusions: This study provides a COVID-19 infection segmentation technology, which provides an important prerequisite for the quantitative analysis of COVID-19 CT images.

Early predictors and screening tool developing for severe patients with COVID-19.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a declared global pandemic, causing a lot of death. How to quickly screen risk population for severe patients is essential for decreasing the mortality. Many of the predictors might not be available in all hospitals, so it is necessary to develop a simpler screening tool with predictors which can be easily obtained for wide wise. METHODS: This retrospective study included all the 813 confirmed cases diagnosed with COVID-19 before March 2nd, 2020 in a city of Hubei Province in China. Data of the COVID-19 patients including clinical and epidemiological features were collected through Chinese Disease Control and Prevention Information System. Predictors were selected by logistic regression, and then categorized to four different level risk factors. A screening tool for severe patient with COVID-19 was developed and tested by ROC curve. RESULTS: Seven early predictors for severe patients with COVID-19 were selected, including chronic kidney disease (OR 14.7), age above 60 (OR 5.6), lymphocyte count less than < 0.8 × 109 per L (OR 2.5), Neutrophil to Lymphocyte Ratio larger than 4.7 (OR 2.2), high fever with temperature ≥ 38.5℃ (OR 2.2), male (OR 2.2), cardiovascular related diseases (OR 2.0). The Area Under the ROC Curve of the screening tool developed by above seven predictors was 0.798 (95% CI 0.747-0.849), and its best cut-off value is > 4.5, with sensitivity 72.0% and specificity 75.3%. CONCLUSIONS: This newly developed screening tool can be a good choice for early prediction and alert for severe case especially in the condition of overload health service.

Cumulative oxygen deficit is a novel predictor for the timing of invasive mechanical ventilation in COVID-19 patients with respiratory distress.

BACKGROUND AND OBJECTIVES: The timing of invasive mechanical ventilation (IMV) is controversial in COVID-19 patients with acute respiratory hypoxemia. The study aimed to develop a novel predictor called cumulative oxygen deficit (COD) for the risk stratification. METHODS: The study was conducted in four designated hospitals for treating COVID-19 patients in Jingmen, Wuhan, from January to March 2020. COD was defined to account for both the magnitude and duration of hypoxemia. A higher value of COD indicated more oxygen deficit. The predictive performance of COD was calculated in multivariable Cox regression models. RESULTS: A number of 111 patients including 80 in the non-IMV group and 31 in the IMV group were included. Patients with IMV had substantially lower PaO2 (62 (49, 89) vs. 90.5 (68, 125.25) mmHg; p < 0.001), and higher COD (-6.87 (-29.36, 52.38) vs. -231.68 (-1040.78, 119.83) mmHg·day) than patients without IMV. As compared to patients with COD < 0, patients with COD > 30 mmHg·day had higher risk of fatality (HR: 3.79, 95% CI [2.57-16.93]; p = 0.037), and those with COD > 50 mmHg·day were 10 times more likely to die (HR: 10.45, 95% CI [1.28-85.37]; p = 0.029). CONCLUSIONS: The study developed a novel predictor COD which considered both magnitude and duration of hypoxemia, to assist risk stratification of COVID-19 patients with acute respiratory distress.