Ye Lin

Bio: Ye Lin is an academic researcher from Guangzhou Medical University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 2, co-authored 7 publications receiving 1060 citations.

Development and Clinical Application of A Rapid IgM-IgG Combined Antibody Test for SARS-CoV-2 Infection Diagnosis

Abstract: The outbreak of the novel coronavirus disease (COVID-19) quickly spread all over China and to more than 20 other countries. Although the virus (severe acute respiratory syndrome coronavirus [SARS-Cov-2]) nucleic acid real-time polymerase chain reaction (PCR) test has become the standard method for diagnosis of SARS-CoV-2 infection, these real-time PCR test kits have many limitations. In addition, high false-negative rates were reported. There is an urgent need for an accurate and rapid test method to quickly identify a large number of infected patients and asymptomatic carriers to prevent virus transmission and assure timely treatment of patients. We have developed a rapid and simple point-of-care lateral flow immunoassay that can detect immunoglobulin M (IgM) and IgG antibodies simultaneously against SARS-CoV-2 virus in human blood within 15 minutes which can detect patients at different infection stages. With this test kit, we carried out clinical studies to validate its clinical efficacy uses. The clinical detection sensitivity and specificity of this test were measured using blood samples collected from 397 PCR confirmed COVID-19 patients and 128 negative patients at eight different clinical sites. The overall testing sensitivity was 88.66% and specificity was 90.63%. In addition, we evaluated clinical diagnosis results obtained from different types of venous and fingerstick blood samples. The results indicated great detection consistency among samples from fingerstick blood, serum and plasma of venous blood. The IgM-IgG combined assay has better utility and sensitivity compared with a single IgM or IgG test. It can be used for the rapid screening of SARS-CoV-2 carriers, symptomatic or asymptomatic, in hospitals, clinics, and test laboratories.

Trends of pulmonary fungal infections from 2013 to 2019: an AI-based real-world observational study in Guangzhou, China.

Abstract: Recently, the prevalence trend of pulmonary fungal infection (PFI) has rapidly increased. Changes in the risk factors for, distributions of underlying diseases associated with and clinical characteristics of some individual PFIs have been reported in the past decade. However, data regarding PFIs remain uncertain. This study reports the epidemiological characteristics and trends of PFIs over time in recent years. We applied an automated natural language processing (NLP) system to extract clinically relevant information from the electronic health records (EHRs) of PFI patients at the First Affiliated Hospital of Guangzhou Medical University. Then, a trend analysis was performed. From January 1, 2013, to December 31, 2019, 40,504 inpatients and 219,414 outpatients with respiratory diseases were screened, in which 1368 inpatients and 1313 outpatients with PFI were identified. These patients were from throughout the country, but most patients were from southern China. Upward trends in PFIs were observed in both hospitalized patients and outpatients (P<0.05). The stratification by age showed that the incidence of hospitalized patients aged 14-30 years exhibited the most obvious upward trend, increasing from 9.5 per 1000 patients in 2013 to 88.3 per 1000 patients in 2019. Aspergillosis (56.69%) was the most common PFI, but notably, the incidence rates of Talaromyces marneffei, which used to be considered uncommon, exhibited the most rapid increases. In younger PFI patients, the incidence and trend of PFIs have increased. Infection by previously uncommon pathogens has also gradually increased. Increased attention should be paid to young PFI patients and uncommon PFI pathogen infections.

Clinical findings of Talaromyces marneffei infection among patients with anti-interferon-γ immunodeficiency: a prospective cohort study.

Abstract: Background Talaromyces marneffei (T. marneffei) infection has been associated with adult-onset immunodeficiency due to anti-IFN-γ autoantibodies. We aimed to investigate the clinical features of non-HIV-infected patients with T. marneffei infection in southern China. Methods Between January 2018 and September 2020, we enrolled patients with T. marneffei infection who were HIV-negative (group TM, n = 42), including anti-IFN-γ autoantibody-positive (group TMP, n = 22) and anti-IFN-γ autoantibody-negative (group TMN, n = 20) patients and healthy controls (group HC, n = 40). Anti-IFN-γ autoantibodies were detected by ELISA. Clinical characteristics and clinical laboratory parameters were recorded. Results Compared with anti-IFN-γ autoantibody-negative patients with T. marneffei infection, anti-IFN-γ autoantibody-positive patients did not have underlying respiratory disease; more frequently exhibited dissemination of systemic infections with severe pleural effusion; had higher WBC counts, C-reactive protein levels, erythrocyte sedimentation rates, and neutrophil and CD8+ T cell counts; had lower hemoglobin levels; and were more likely to have other intracellular pathogen infections. Most of these patients had poor outcomes despite standardized antimicrobial therapy. Conclusion T. marneffei-infected patients with higher anti-IFN-γ autoantibody titers have more severe disease and complex clinical conditions.

Exhaled Volatile Organic Compounds for Identifying Patients With Chronic Pulmonary Aspergillosis.

Abstract: Background: Diagnosing chronic pulmonary aspergillosis is a major challenge in clinical practice. The development and validation of a novel, sensitive and specific assay for diagnosing chronic pulmonary aspergillosis is urgently needed. Methods: From April 2018 to June 2019, 53 patients with chronic pulmonary aspergillosis (CPA), 32 patients with community-acquired pneumonia (CAP) and 48 healthy controls were recruited from the First Affiliated Hospital of Guangzhou Medical University. Clinical characteristics and samples were collected at enrollment. All exhaled breath samples were analyzed offline using thermal desorption single-photon ionization time-of-flight mass spectrometry; to analyze the metabolic pathways of the characteristic volatile organic compounds, serum samples were subjected to ultrahigh-performance liquid chromatography. Results: We identified characteristic volatile organic compounds in patients with chronic pulmonary aspergillosis, which mainly consisted of phenol, neopentyl alcohol, toluene, limonene and ethylbenzene. These compounds were assessed using a logistic regression model. The sensitivity and specificity were 95.8 and 96.9% for discriminating patients in the CPA group from those in the CAP group and 95.8 and 97.9% for discriminating patients in the CPA group from healthy controls, respectively. The concentration of limonene (m/z 136) correlated significantly positively with anti-Aspergillus fumigatus IgG antibody titers (r = 0.420, P < 0.01). After antifungal treatment, serum IgG and the concentration of limonene (m/z 136) decreased in the subgroup of patients with chronic pulmonary aspergillosis. Conclusions: We identified VOCs that can be used as biomarkers for differential diagnosis and therapeutic response prediction in patients with chronic pulmonary aspergillosis.

Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy.

Abstract: In Italy, 128,948 confirmed cases and 15,887 deaths of people who tested positive for SARS-CoV-2 were registered as of 5 April 2020. Ending the global SARS-CoV-2 pandemic requires implementation of multiple population-wide strategies, including social distancing, testing and contact tracing. We propose a new model that predicts the course of the epidemic to help plan an effective control strategy. The model considers eight stages of infection: susceptible (S), infected (I), diagnosed (D), ailing (A), recognized (R), threatened (T), healed (H) and extinct (E), collectively termed SIDARTHE. Our SIDARTHE model discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non-diagnosed individuals is important because the former are typically isolated and hence less likely to spread the infection. This delineation also helps to explain misperceptions of the case fatality rate and of the epidemic spread. We compare simulation results with real data on the COVID-19 epidemic in Italy, and we model possible scenarios of implementation of countermeasures. Our results demonstrate that restrictive social-distancing measures will need to be combined with widespread testing and contact tracing to end the ongoing COVID-19 pandemic.

Laboratory Diagnosis of COVID-19: Current Issues and Challenges.

Abstract: The COVID-19 outbreak has had a major impact on clinical microbiology laboratories in the past several months. This commentary covers current issues and challenges for the laboratory diagnosis of infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the preanalytical stage, collecting the proper respiratory tract specimen at the right time from the right anatomic site is essential for a prompt and accurate molecular diagnosis of COVID-19. Appropriate measures are required to keep laboratory staff safe while producing reliable test results. In the analytic stage, real-time reverse transcription-PCR (RT-PCR) assays remain the molecular test of choice for the etiologic diagnosis of SARS-CoV-2 infection while antibody-based techniques are being introduced as supplemental tools. In the postanalytical stage, testing results should be carefully interpreted using both molecular and serological findings. Finally, random-access, integrated devices available at the point of care with scalable capacities will facilitate the rapid and accurate diagnosis and monitoring of SARS-CoV-2 infections and greatly assist in the control of this outbreak.

A SIDARTHE Model of COVID-19 Epidemic in Italy

Abstract: In late December 2019, a novel strand of Coronavirus (SARS-CoV-2) causing a severe, potentially fatal respiratory syndrome (COVID-19) was identified in Wuhan, Hubei Province, China and is causing outbreaks in multiple world countries, soon becoming a pandemic. Italy has now become the most hit country outside of Asia: on March 16, 2020, the Italian Civil Protection documented a total of 27980 confirmed cases and 2158 deaths of people tested positive for SARS-CoV-2. In the context of an emerging infectious disease outbreak, it is of paramount importance to predict the trend of the epidemic in order to plan an effective control strategy and to determine its impact. This paper proposes a new epidemic model that discriminates between infected individuals depending on whether they have been diagnosed and on the severity of their symptoms. The distinction between diagnosed and non-diagnosed is important because non-diagnosed individuals are more likely to spread the infection than diagnosed ones, since the latter are typically isolated, and can explain misperceptions of the case fatality rate and of the seriousness of the epidemic phenomenon. Being able to predict the amount of patients that will develop life-threatening symptoms is important since the disease frequently requires hospitalisation (and even Intensive Care Unit admission) and challenges the healthcare system capacity. We show how the basic reproduction number can be redefined in the new framework, thus capturing the potential for epidemic containment. Simulation results are compared with real data on the COVID-19 epidemic in Italy, to show the validity of the model and compare different possible predicted scenarios depending on the adopted countermeasures.