Newcastle disease virus vector-based SARS-CoV-2 vaccine candidate AVX/COVID-12 activates T cells and is recognized by antibodies from COVID-19 patients and vaccinated individuals.

Introduction: Several effective vaccines for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been developed and implemented in the population. However, the current production capacity falls short of meeting global demand. Therefore, it is crucial to further develop novel vaccine platforms that can bridge the distribution gap. AVX/COVID-12 is a vector-based vaccine that utilizes the Newcastle Disease virus (NDV) to present the SARS-CoV-2 spike protein to the immune system. Methods: This study aims to analyze the antigenicity of the vaccine candidate by examining antibody binding and T-cell activation in individuals infected with SARS-CoV-2 or variants of concern (VOCs), as well as in healthy volunteers who received coronavirus disease 2019 (COVID-19) vaccinations. Results: Our findings indicate that the vaccine effectively binds antibodies and activates T-cells in individuals who received 2 or 3 doses of BNT162b2 or AZ/ChAdOx-1-S vaccines. Furthermore, the stimulation of T-cells from patients and vaccine recipients with AVX/COVID-12 resulted in their proliferation and secretion of interferon-gamma (IFN-γ) in both CD4+ and CD8+ T-cells. Discussion: The AVX/COVID-12 vectored vaccine candidate demonstrates the ability to stimulate robust cellular responses and is recognized by antibodies primed by the spike protein present in SARS-CoV-2 viruses that infected patients, as well as in the mRNA BNT162b2 and AZ/ChAdOx-1-S vaccines. These results support the inclusion of the AVX/COVID-12 vaccine as a booster in vaccination programs aimed at addressing COVID-19 caused by SARS-CoV-2 and its VOCs.

Accurate diagnosis of sepsis using a neural network: Pilot study using routine clinical variables.

BACKGROUND AND OBJECTIVES: Sepsis is a severe infection that increases mortality risk and is one if the main causes of death in intensive care units. Accurate detection is key to successful interventions, but diagnosis of sepsis is complicated because the initial signs and symptoms are not specific. Biomarkers that have been proposed have low specificity and sensitivity, are expensive, and not available in every hospital. In this study, we propose the use of artificial intelligence in the form of a neural network to diagnose sepsis using only common laboratory tests and vital signs that are routine and widely available. METHODS: A retrospective, cross sectional cohort of 113 patients from an intensive care unit, each with 48 routinely evaluated vital signs and biochemical parameters was used to train, validate and test a neural network with 48 inputs, 10 neurons in a single hidden layer and one output. The sensitivity and specificity of the neural network as a point sampled diagnostic test was calculated. RESULTS: All but one case were correctly diagnosed by the neural network, with 91% sensitivity and 100% specificity in the validation data set, and 100% sensitivity and specificity in the test data set. CONCLUSIONS: The designed neural network system can identify patients with sepsis, with minimal resources using standard laboratory tests widely available in most health care facilities. This should reduce the burden on the medical staff of a difficult diagnosis and should improve outcomes for patients with sepsis.