ArticlePloS one2022
A novel assessment method for COVID-19 humoral immunity duration using serial measurements in naturally infected and vaccinated subjects.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04341142 (Assessment of Serological Techniques for Screening Patients Regarding COVID-19-COVID-SER), which is not on this map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Assessment of Serological Techniques for Screening Patients Regarding COVID-19-COVID-SER
Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Early assessment of antibodies decline in Chagas patients following treatment using a serological multiplex immunoassay.Nature communications · 2024Trial
- SARS-CoV-2 Antibody Levels and Infections in Multiple Vaccinated Employees Over Time.Journal of medical virology · 2025Article
- Serological assessment of the durability of vaccine-mediated protection against SARS-CoV-2 infection.Human vaccines & immunotherapeutics · 2024Observational
- Comparing in vivo bioluminescence imaging and the Multi-Cruzi immunoassay platform to develop improved Chagas disease diagnostic procedures and biomarkers for monitoring parasitological cure.PLoS neglected tropical diseases · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCollecting information on sustainability of immune responses after infection or vaccination is crucial to inform medical decision-making and vaccination strategies. Data on how long-lasting antibodies against SARS-COV-2 could provide a humoral and protective immunity and prevent reinfection with SARS-CoV-2 or its variants is particularly valuable. This study presents a novel method to quantitatively measure and monitor the diversity of SARS-CoV-2 specific antibody profiles over time.
methodsSerum samples from two groups were used in this study: Samples from 20 naturally infected subjects (followed for up to 1 year) and samples from 83 subjects vaccinated with one or two doses of the Pfizer BioNtech vaccine (BNT162b2/BNT162b2) (followed for up to 6 months). The Multi-SARS-CoV-2 assay, a multiparameter serology test developed for the serological confirmation of past-infections, was used to determine the reactivity of six different SARS-CoV-2 antigens. For each subject sample, 3 dilutions (1/50, 1/400 and 1/3200) were defined as an optimal set over the six antigens and their respective linear ranges. This allowed accurate quantification of the corresponding six antibodies. Nonlinear mixed-effects modelling was applied to convert intensity readings from 3 determined dilutions to a single quantification value for each antibody.
resultsMedian half-life for the 20 naturally infected vs 74 vaccinated subjects (two doses) was 120 vs 50 days for RBD, 127 vs 53 days for S1 and 187 vs 86 days for S2 antibodies respectively.
conclusionThe newly proposed method, based on a series of a limited number of dilutions, can convert a conventional qualitative assay into a quantitative assay. This conversion helps define the sustainability of specific immune responses against each relevant viral antigen and can help in defining the protection characteristics after an infection or a vaccination.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.