First reported case of late recurrence of pulmonary mucormycosis in a renal transplant recipient with poorly controlled diabetes mellitus.

Pulmonary mucormycosis (PM) is a rare, life-threatening fungal infection usually affecting immunocompromised patients. Its incidence is rising, with a recent outbreak associated with COVID-19 co-infection. Amphotericin B along with early surgery are considered the standard treatment. Recurrence has been reported in patients without adequate treatment and without permanent reversal of predisposing factors. We report a case of late recurrence of PM in a renal transplant recipient. In 2012, he was diagnosed with PM. Imaging at the time showed a lingular mass. He was treated with antifungal for 1 year until complete radiological resolution. Surgical intervention was considered but no further follow-up action was taken. In 2020, he presented with fever and haemoptysis. Imaging again showed a lingular mass, which was confirmed to be PM by bronchoscopic lung biopsy. This case highlights the importance of secondary antifungal prophylaxis for PM if permanent reversal of immunosuppression is not possible.

Author Correction: ORF8 and ORF3b antibodies are accurate serological markers of early and late SARS-CoV-2 infection.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph.

Triaging and prioritising patients for RT-PCR test had been essential in the management of COVID-19 in resource-scarce countries. In this study, we applied machine learning (ML) to the task of detection of SARS-CoV-2 infection using basic laboratory markers. We performed the statistical analysis and trained an ML model on a retrospective cohort of 5148 patients from 24 hospitals in Hong Kong to classify COVID-19 and other aetiology of pneumonia. We validated the model on three temporal validation sets from different waves of infection in Hong Kong. For predicting SARS-CoV-2 infection, the ML model achieved high AUCs and specificity but low sensitivity in all three validation sets (AUC: 89.9-95.8%; Sensitivity: 55.5-77.8%; Specificity: 91.5-98.3%). When used in adjunction with radiologist interpretations of chest radiographs, the sensitivity was over 90% while keeping moderate specificity. Our study showed that machine learning model based on readily available laboratory markers could achieve high accuracy in predicting SARS-CoV-2 infection.

ORF8 and ORF3b antibodies are accurate serological markers of early and late SARS-CoV-2 infection.

The SARS-CoV-2 virus emerged in December 2019 and has caused a worldwide pandemic due to the lack of any pre-existing immunity. Accurate serology testing is urgently needed to help diagnose infection, determine past exposure of populations and assess the response to a future vaccine. The landscape of antibody responses to SARS-CoV-2 is unknown. In this study, we utilized the luciferase immunoprecipitation system to assess the antibody responses to 15 different SARS-CoV-2 antigens in patients with COVID-19. We identified new targets of the immune response to SARS-CoV-2 and show that nucleocapsid, open reading frame (ORF)8 and ORF3b elicit the strongest specific antibody responses. ORF8 and ORF3b antibodies, taken together as a cluster of points, identified 96.5% of COVID-19 samples at early and late time points of disease with 99.5% specificity. Our findings could be used to develop second-generation diagnostic tests to improve serological assays for COVID-19 and are important in understanding pathogenicity.