Cell viability is a physiological status connected to cell membrane integrity and cytoplasmic topography, which is profoundly important for fundamental biological research and practical biomedical applications. A conventional method for assessing cell viability is through cell staining analysis. However, cell staining involves laborious and complicated processing procedures and is normally cytotoxic. Intrinsic cellular phenotypes thus provide new avenues for measuring cell viability in a stain-free and non-toxic manner. In this work, we present a label-free non-destructive impedance-based approach for cell viability assessment by simultaneously characterizing multiple electrical cellular phenotypes in a high-throughput manner (>1000 cells per min). A novel concept called the complex opacity spectrum is introduced for improving the discrimination of live and dead cells. The analysis of the complex opacity spectrum leads to the discovery of two frequency ranges that are optimized for characterizing membranous and cytoplasmic electrical phenotypes. The present impedance-based approach has successfully discriminated between living and dead cells in two different experimental scenarios, including mixed living and dead cells in both homogenous and heterogeneous cell samples. This impedance-based single cell phenotyping technique provides highly accurate and consistent cell viability analysis, which has been validated by commercial fluorescence-based flow cytometry (∼1% difference) using heterogeneous cell samples. This label-free high-throughput cell viability analysis strategy will have broad applications in the field of biology and medicine.

A predictive model to automatically identify the earliest determinants of both hospital discharge and mortality in hospitalized COVID-19 patients could be of great assistance to caregivers if the predictive information is generated and made available in the immediate hours following admission.

To identify the most important predictors of hospital discharge and mortality from measurements at admission for hospitalized COVID-19 patients.

Observational cohort study.

Electronic records from hospitalized patients.

Patients admitted between March 3

and August 24

with COVID-19 in Johns Hopkins Health System hospitals.

216 phenotypic variables collected within 48 hours of admission.

We used age-stratified (<60 and >=60 years) random survival forests with competing risks to identify the most important predictors of death and discharge. Fine-Gray competing risk regression (FGR) models were then constructed based on the most important RSF-derived covariates.

Of 2212 patients, 1913 were dischah accuracy based on just 8-10 variables, and the probability of hospital discharge increased over the course of the pandemic.Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) transmission is uncontrolled in many parts of the world, compounded in some areas by higher transmission potential of the B1.1.7 variant now seen in 50 countries. It is unclear whether responses to SARS-CoV-2 vaccines based on the prototypic strain will be impacted by mutations found in B.1.1.7. Here we assessed immune responses following vaccination with mRNA-based vaccine BNT162b2. We measured neutralising antibody responses following a single immunization using pseudoviruses expressing the wild-type Spike protein or the 8 amino acid mutations found in the B.1.1.7 spike protein. The vaccine sera exhibited a broad range of neutralising titres against the wild-type pseudoviruses that were modestly reduced against B.1.1.7 variant. This reduction was also evident in sera from some convalescent patients. Decreased B.1.1.7 neutralisation was also observed with monoclonal antibodies targeting the N-terminal domain (9 out of 10), the Receptor Binding Motif (RBM) (5 out of 31), but not in neutralising mAbs binding outside the RBM. GSK3368715 in vivo Introduction of the E484K mutation in a B.1.1.7 background to reflect newly emerging viruses in the UK led to a more substantial loss of neutralising activity by vaccine-elicited antibodies and mAbs (19 out of 31) over that conferred by the B.1.1.7 mutations alone. E484K emergence on a B.1.1.7 background represents a threat to the vaccine BNT162b.

The COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an unprecedented event requiring rapid adaptation to changing clinical circumstances. Convalescent immune plasma (CIP) is a promising treatment that can be mobilized rapidly in a pandemic setting.

We tested whether administration of SARS-CoV-2 CIP at hospital admission could reduce the rate of ICU transfer or 28 day mortality.

In a single-arm phase II study, patients >18 years-old with respiratory symptoms documented with COVID-19 infection who were admitted to a non-ICU bed were administered two units of CIP within 72 hours of admission. Detection of respiratory tract SARS-CoV-2 by polymerase chain reaction and circulating anti-SARS-CoV-2 antibody titers were measured before and at time points after CIP transfusion.

Twenty-nine patients were transfused CIP and forty-eight contemporaneous controls were identified with comparable baseline characteristics. Levels of anti-SARS-CoV-2 IgG, IgM, and IgA anti-spike, anti-receptor-binding domain, and anti-nucleocapsid significantly increased from baseline to post-transfusion for all proteins tested. In patients transfused with CIP, the rate of ICU transfer was 13.8% compared to 27.1% for controls with a hazard ratio 0.506 (95% CI 0.165-1.554), and 28-day mortality was 6.9% compared to 10.4% for controls, hazard ratio 0.640 (95% CI 0.124-3.298).

Transfusion of high-titer CIP to patients early after admission with COVID-19 respiratory disease was associated with reduced ICU transfer and 28-day mortality but was not statistically significant. Follow up randomized trials may inform the use of CIP for COVID-19 or future coronavirus pandemics.

Transfusion of high-titer CIP to patients early after admission with COVID-19 respiratory disease was associated with reduced ICU transfer and 28-day mortality but was not statistically significant. Follow up randomized trials may inform the use of CIP for COVID-19 or future coronavirus pandemics.