BACKGROUND: The ongoing worldwide outbreak of the 2019-nCoV is markedly similar to the severe acute respiratory syndrome (SARS) outbreak 17 years ago. During the 2002-2003 SARS outbreak, healthcare workers formed a special population of patients. Although virus-specific IgG play important roles in virus neutralization and prevention against future infection, limited information is available regarding the long term persistence of IgG after infection with SARS-like coronavirus. METHODS: A long-term prospective cohort study followed 34 SARS-CoV-infected healthcare workers from a hospital with clustered infected cases during the 2002-2003 SARS outbreak in Guangzhou, China, with a 13-year follow-up. Serum samples were collected annually from 2003-2015. Twenty SARS-CoV-infected and 40 non-infected healthcare workers were enrolled in 2015, and their serum samples were collected. All sera were tested for IgG antibodies with ELISA using whole virus and a recombinant nucleocapsid protein of SARS-CoV, as a diagnostic antigen. RESULTS: Anti SARS-CoV IgG was found to persist for up to 12 years. IgG titers typically peaked in 2004, declining rapidly from 2004-2006, and then continued to decline at a slower rate. IgG titers in SARS-CoV-infected healthcare workers remained at a significantly high level until 2015. Patients treated with corticosteroids at the time of infection were found to have lower IgG titers than those without. CONCLUSIONS: IgG antibodies against SARS-CoV can persist for at least 12 years. The presence of SARS-CoV IgG might provide protection against SARS-CoV and other betacoronavirus. This study provides valuable information regarding humoral immune responses against SARS-CoV and the 2019-nCoV.

Early detection of cancer aims to reduce cancer deaths. Unfortunately, many established cancer screening technologies are not suitable for use in low- and middle-income countries (LMICs) due to cost, complexity, and dependency on extensive medical infrastructure. We aimed to assess the performance and robustness of a protein assay (OncoSeek) for multi-cancer early detection (MCED) that is likely to be more practical in LMICs. This observational study comprises a retrospective analysis on the data generated from the routine clinical testings at SeekIn and Sun Yat-sen Memorial Hospital. 7565 participants (954 with cancer and 6611 without) from the two sites were divided into training and independent validation cohort. The second validation cohort (1005 with cancer and 812 without) was from Johns Hopkins University School of Medicine. Patients with cancer prior to therapy were eligible for inclusion in the study. Individuals with no history of cancer were enrolled from the participating sites as the non-cancer group. One tube of peripheral blood was collected from each participant and quantified a panel of seven selected protein tumour markers (PTMs) by a common clinical electrochemiluminescence immunoassay analyser. An algorithm named OncoSeek was established using artificial intelligence (AI) to distinguish patients with cancer from those without cancer by calculating the probability of cancer (POC) index based on the quantification results of the seven PTMs and clinical information including sex and age of the individuals and to predict the possible affected tissue of origin (TOO) for those who have been detected with cancer signals in blood. Between November 2012 and May 2022, 7565 participants were enrolled at SeekIn and Sun Yat-sen Memorial Hospital. The conventional clinical method, which relies only on a single threshold for each PTM, would suffer from a high false positive rate that accumulates as the number of markers increased. OncoSeek was empowered by AI technology to significantly reduce the false positive rate, increasing the specificity from 56.9% (95% confidence interval [CI]: 55.8–58.0) to 92.9% (92.3–93.5). In all cancer types, the overall sensitivity of OncoSeek was 51.7% (49.4–53.9), resulting in 84.3% (83.5–85.0) accuracy. The performance was generally consistent in the training and the two validation cohorts. The sensitivities ranged from 37.1% to 77.6% for the detection of the nine common cancer types (breast, colorectum, liver, lung, lymphoma, oesophagus, ovary, pancreas, and stomach), which account for ∼59.2% of global cancer deaths annually. Furthermore, it has shown excellent sensitivity in several high-mortality cancer types for which routine screening tests are lacking in the clinic, such as the sensitivity of pancreatic cancer which was 77.6% (69.3–84.6). The overall accuracy of TOO prediction in the true positives was 66.8%, which could assist the clinical diagnostic workup. OncoSeek significantly outperforms the conventional clinical method, representing a novel blood-based test for MCED which is non-invasive, easy, efficient, and robust. Moreover, the accuracy of TOO facilitates the follow-up diagnostic workup. The National Key Research and Development Programme of China.

Aims Metagenomic next‐generation sequencing (mNGS) has been utilized for diagnosing infectious diseases. It is a culture‐free and hypothesis‐free nucleic acid test for diagnosing all pathogens with known genomic sequences, including bacteria, fungi, viruses, and parasites. While this technique greatly expands the clinical capacity of pathogen detection, it is a second‐line choice due to lengthy procedures and microbial contaminations introduced from wet‐lab processes. As a result, we aimed to reduce the hands‐on time and exogenous contaminations in mNGS. Methods and Results We developed a device (NGSmaster) that automates the wet‐lab workflow, including nucleic acid extraction, PCR‐free library preparation and purification. It shortens the sample‐to‐results time to 16 and 18.5 hours for DNA and RNA sequencing, respectively. We used it to test cultured bacteria for validation of the workflow and bioinformatic pipeline. We also compared PCR‐free with PCR‐based library prep and discovered no differences in microbial reads. Moreover, we analyzed results by automation and manual testing and found that automation can significantly reduce microbial contaminations. Finally, we tested artificial and clinical samples and showed mNGS results were concordant with traditional culture. Conclusion NGSmaster can fulfill the microbiological diagnostic needs in a variety of sample types. Significance and Impact of Study This study opens up an opportunity of performing in‐house mNGS to reduce turnaround time and workload, instead of transferring potentially contagious specimen to a third‐party laboratory.

Bacterial biofilms do serious harm to the diabetic foot ulcer (DFU) because they play a crucial role in infection invasion and spread. Staphylococcus aureus, the predominant Gram-positive bacteria in diabetic foot infection (DFI), is often associated with colonization and biofilm formation. Through biofilm formation tests in vitro, we observed that S. aureus bacteria isolated from DFU wounds were more prone to form biofilms than those from non-diabetic patients, while there was no difference in blood sugar between the biofilm (+) diabetics (DB+) and biofilm (-) diabetics (DB-). Furthermore, we found that advanced glycation end products (AGEs) promoted the biofilm formation of S. aureus in clinical isolates and laboratory strains in vitro, including a methicillin-resistant strain. Analysis of biofilm components demonstrated that the biofilms formed mainly by increasing extracellular DNA (eDNA) release; remarkably, the S. aureus global regulator sigB was upregulated, and its downstream factor lrgA was downregulated after AGE treatments. Mechanism studies using a sigB-deleted mutant (Newman-ΔsigB) confirmed that AGEs decreased expression of lrgA via induction of sigB, which is responsible for eDNA release and is a required component for S. aureus biofilm development. In conclusion, the present study suggests that AGEs promote S. aureus biofilm formation via an eDNA-dependent pathway by regulating sigB. The data generated by this study will provide experimental proof and theoretical support to improve DFU infection healing.