Zhengtu Li

Bio: Zhengtu Li 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 5 publications receiving 1061 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.

Talaromyces marneffei and Mycobacterium tuberculosis co-infection in a patient with high titer anti-interferon-γ autoantibodies: a case report

Abstract: High-titer anti-interferon (IFN)-γ autoantibodies are strongly associated with intracellular pathogens such as nontuberculous mycobacteria and Talaromyces marneffei, but they are not as commonly associated with Talaromyces marneffei co-infected with Mycobacterium tuberculosis.Herein, we report a case of an HIV-negative Chinese man with a severe, disseminated co-infection of Talaromyces marneffei and Mycobacterium tuberculosis, who had a high-titer of anti IFN-γ autoantibodies and a CFI heterozygous nonsense gene mutation. The patient rapidly developed sepsis and died. Through by flow cytometry for CD4+ T cells' intracellular phosphorylated STAT-1 and Th1 cells (CD4+ IFN-γ+ cells), we found that the patient's serum can inhibited IFN γ-induced CD4+ T cells' STAT-1 phosphorylation and Th1 cell differentiation in normal peripheral blood mononuclear cells, but this phenomenon was not observed in normal control's serum. In addition, the higher serum concentration in the culture medium, the more obvious inhibition of Th1 cell differentiation.For HIV-negative individuals with relapsing, refractory, fatal double or multiple intracellular pathogen infections, especially Talaromyces marneffei, clinicians should be aware that if they might be dealing with adult-onset immunodeficiency syndrome due to high-titer anti-IFN-γ autoantibodies. Systematic genetic and immunological investigations should also be performed.

Both simulation and sequencing data reveal coinfections with multiple SARS-CoV-2 variants in the COVID-19 pandemic

Abstract: SARS-CoV-2 is a single-stranded RNA betacoronavirus with a high mutation rate. The rapidly emerging SARS-CoV-2 variants could increase transmissibility and diminish vaccine protection. However, whether coinfection with multiple SARS-CoV-2 variants exists remains controversial. This study collected 12,986 and 4,113 SARS-CoV-2 genomes from the GISAID database on May 11, 2020 (GISAID20May11), and Apr 1, 2021 (GISAID21Apr1), respectively. With single-nucleotide variant (SNV) and network clique analyses, we constructed single-nucleotide polymorphism (SNP) coexistence networks and discovered maximal SNP cliques of sizes 16 and 34 in the GISAID20May11 and GISAID21Apr1 datasets, respectively. Simulating the transmission routes and SNV accumulations, we discovered a linear relationship between the size of the maximal clique and the number of coinfected variants. We deduced that the COVID-19 cases in GISAID20May11 and GISAID21Apr1 were coinfections with 3.20 and 3.42 variants on average, respectively. Additionally, we performed Nanopore sequencing on 42 COVID-19 patients and discovered recurrent heterozygous SNPs in twenty of the patients, including loci 8,782 and 28,144, which were crucial for SARS-CoV-2 lineage divergence. In conclusion, our findings reported SARS-CoV-2 variants coinfection in COVID-19 patients and demonstrated the increasing number of coinfected variants.

Successful Management of Mixed Mycosis in HIV-Negative Patients With Different Immune Status: A Case Series Report

Abstract: Objective The limited information available on mixed mycosis involving the lungs makes the understanding of mixed fungal diseases insufficient and affects prognosis. Our study aims to improve understanding by exploring experience in the successful management of mixed fungal infections. Methods Patients who had two types of mycosis involving the lung at the same disease course were retrospectively enrolled. Results Between September 2011 and December 2019, 17 patients with proven mixed mycosis were enrolled. Four patients were immunocompromised, with one case each of lung transplantation, corticosteroid treatment, STAT3 hyper-IgE syndrome, and anti-IFN-γ autoantibody-associated immunodeficiency syndrome. Among 13 patients who were not immunocompromised, 9 had type 2 diabetes mellitus. Eight cases were coinfection with Mucor and Aspergillus, 4 cases were Cryptococcus and Aspergillus, 2 cases were Talaromyces marneffei and Cryptococcus, 2 cases were Talaromyces marneffei and Aspergillus, and 1 case was Candida and Aspergillus. Seven patients were diagnosed with mixed pulmonary mycosis at almost the same time. Among the remaining 10 patients, the initial treatment was ineffective in four cases, and six patients showed a partial response to the initial antifungal treatment, but the original fungal lesions became re-enlarged. Three patients were admitted to the intensive care unit during hospitalization, and one patient died. Another Mucor coinfection patient died due to treatment refusal. Conclusion Mixed mycosis involving the lungs is not uncommon in patients without apparent immune deficiency diseases. During the management of mycosis, we recommend keeping mixed mycosis in mind for patients with a poor response to initial antifungal treatment, even in immunocompetent populations, and identifying the cause of illness through a rigorous procedure.

Both Simulation and Sequencing Data Reveal Multiple SARS-CoV-2 Variants Coinfection in COVID-19 Pandemic

Abstract: SARS-CoV-2 is a single-stranded RNA betacoronavirus with a high mutation rate. The rapidly emerged SARS-CoV-2 variants could increase the transmissibility, aggravate the severity, and even fade the vaccine protection. Although the coinfections of SARS-CoV-2 with other respiratory pathogens have been reported, whether multiple SARS-CoV-2 variants coinfection exists remains controversial. This study collected 12,986 and 4,113 SARS-CoV-2 genomes from the GISAID database on May 11, 2020 (GISAID20May11) and April 1, 2021 (GISAID21Apr1), respectively. With the single-nucleotide variants (SNV) and network clique analysis, we constructed the single-nucleotide polymorphism (SNP) coexistence networks and noted the SNP number of the maximal clique as the coinfection index. The coinfection indices of GISAID20May11 and GISAID21Apr1 datasets were 16 and 34, respectively. Simulating the transmission routes and the mutation accumulations, we discovered the linear relationship between the coinfection index and the coinfected variant number. Based on the linear relationship, we deduced that the COVID-19 cases in the GISAID20May11 and GISAID21Apr1 datasets were coinfected with 2.20 and 3.42 SARS-CoV-2 variants on average. Additionally, we performed Nanopore sequencing on 42 COVID-19 patients to explore the virus mutational characteristics. We found the heterozygous SNPs in 41 COVID-19 cases, which support the coinfection of SARS-CoV-2 variants and challenge the accuracy of phylogenetic analysis. In conclusion, our findings reported the coinfection of SARS-CoV-2 variants in COVID-19 patients, demonstrated the increased coinfected variants number in the epidemic, and provided clues for the prolonged viral shedding and severe symptoms in some cases.

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.