Enhanced Label-Free Nanoplasmonic Cytokine Detection in SARS-CoV-2 Induced Inflammation Using Rationally Designed Peptide Aptamer.

Rapid and precise serum cytokine quantification provides immense clinical significance in monitoring the immune status of patients in rapidly evolving infectious/inflammatory disorders, examplified by the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. However, real-time information on predictive cytokine biomarkers to guide targetable immune pathways in pathogenic inflammation is critically lacking, because of the insufficient detection range and detection limit in current label-free cytokine immunoassays. In this work, we report a highly sensitive localized surface plasmon resonance imaging (LSPRi) immunoassay for label-free Interleukin 6 (IL-6) detection utilizing rationally designed peptide aptamers as the capture interface. Benefiting from its characteristically smaller dimension and direct functionalization on the sensing surface via Au-S bonding, the peptide-aptamer-based LSPRi immunoassay achieved enhanced label-free serum IL-6 detection with a record-breaking limit of detection down to 4.6 pg/mL, and a wide dynamic range of ∼6 orders of magnitude (values from 4.6 to 1 × 106 pg/mL were observed). The immunoassay was validated in vitro for label-free analysis of SARS-CoV-2 induced inflammation, and further applied in rapid quantification of serum IL-6 profiles in COVID-19 patients. Our peptide aptamer LSPRi immunoassay demonstrates great potency in label-free cytokine detection with unprecedented sensing capability to provide accurate and timely interpretation of the inflammatory status and disease progression, and determination of prognosis.

Machine-Learning-Assisted Microfluidic Nanoplasmonic Digital Immunoassay for Cytokine Storm Profiling in COVID-19 Patients.

Cytokine storm, known as an exaggerated hyperactive immune response characterized by elevated release of cytokines, has been described as a feature associated with life-threatening complications in COVID-19 patients. A critical evaluation of a cytokine storm and its mechanistic linkage to COVID-19 requires innovative immunoassay technology capable of rapid, sensitive, selective detection of multiple cytokines across a wide dynamic range at high-throughput. In this study, we report a machine-learning-assisted microfluidic nanoplasmonic digital immunoassay to meet the rising demand for cytokine storm monitoring in COVID-19 patients. Specifically, the assay was carried out using a facile one-step sandwich immunoassay format with three notable features: (i) a microfluidic microarray patterning technique for high-throughput, multiantibody-arrayed biosensing chip fabrication; (ii) an ultrasensitive nanoplasmonic digital imaging technology utilizing 100 nm silver nanocubes (AgNCs) for signal transduction; (iii) a rapid and accurate machine-learning-based image processing method for digital signal analysis. The developed immunoassay allows simultaneous detection of six cytokines in a single run with wide working ranges of 1-10,000 pg mL^-1 and ultralow detection limits down to 0.46-1.36 pg mL^-1 using a minimum of 3 µL serum samples. The whole chip can afford a 6-plex assay of 8 different samples with 6 repeats in each sample for a total of 288 sensing spots in less than 100 min. The image processing method enhanced by convolutional neural network (CNN) dramatically shortens the processing time ∼6,000 fold with a much simpler procedure while maintaining high statistical accuracy compared to the conventional manual counting approach. The immunoassay was validated by the gold-standard enzyme-linked immunosorbent assay (ELISA) and utilized for serum cytokine profiling of COVID-19 positive patients. Our results demonstrate the nanoplasmonic digital immunoassay as a promising practical tool for comprehensive characterization of cytokine storm in patients that holds great promise as an intelligent immunoassay for next generation immune monitoring.

Outcomes for the first wave of hospitalised patients with COVID-19 in the South Australian context: a retrospective audit.

BACKGROUND: The first case of corona virus disease (COVID-19) was detected in South Australia on 1 February 2020. The Royal Adelaide Hospital (RAH) is the state's designated quarantine hospital. AIM: To determine the characteristics, outcomes and predictors of outcomes for hospitalised patients with coronavirus disease (COVID-19) within the RAH. METHODS: We performed a retrospective audit of 103 patients diagnosed with COVID-19 who were discharged from the RAH between 14 February and 21 May 2020. We collected demographic, clinical and laboratory data through an audit of electronic medical records. The main outcome measures were: (i) the need for oxygen supplementation; (ii) need for intensive care unit (ICU) care; and (iii) death in hospital. RESULTS: The median age of patients was 60 years (range 19-85). A total of 55 (53%) patients was male. All patients were independent at baseline; 37 (36%) patients suffered from hypertension. Cardiovascular disease, respiratory disease and diabetes were present in fewer than 19 (18%) patients. Obesity was present in 24 (23%) patients; 39 (38%) patients required supplemental oxygen, 18 (17%) required ICU care and 4 (4%) patients died. Older patients were significantly more at risk of oxygen requirement (median 68 vs 57.5 years, P < 0.01), ICU admission (median 66.5 vs 60 years, P = 0.04) and death (median 74.5 vs 60 years, P = 0.02). We did not find a statistically significant association between gender, body mass index and poor outcomes. Lactate dehydrogenase (LDH) was the only parameter at admission associated with oxygen requirement, ICU care and death. Peak LDH, aspartate aminotransferase, alanine aminotransferase, C-reactive protein and neutrophil lymphocyte ratio were significantly associated with oxygen requirement, ICU admission and death (P < 0.05 for all of the above laboratory markers). CONCLUSIONS: Although our sample size was small, we found that certain comorbidities and laboratory values were associated with poor outcomes. This occurred in a setting where care was not influenced by limited hospital and intensive care beds.