Evidence map›Paper›PMID 36353634›Full record

ArticleFrontiers in immunology2022

Paola Stolfi, Filippo Castiglione, Enrico Mastrostefano, Immacolata Di Biase, Sebastiano Di Biase, Gianna Palmieri, Antonella Prisco

Open access · goldAbstract read
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
4.9field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed, 50 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 1 country.

Paola StolfiInstitute for Applied Computing, National Research Council of Italy, Rome, Italy.
Filippo CastiglioneInstitute for Applied Computing, National Research Council of Italy, Rome, Italy.
Enrico MastrostefanoInstitute for Applied Computing, National Research Council of Italy, Rome, Italy.
Immacolata Di BiaseMeriGen Res, Naples, Italy.
Sebastiano Di BiaseMeriGen Res, Naples, Italy.
Gianna PalmieriInstitute of Biosciences and BioResources, National Research Council, Naples, Italy.
Antonella PriscoInstitute of Genetics and Biophysics, National Research Council, Naples, Italy.
Institute for High Performance Computing and Networking · ITInstitute of Biosciences and Bioresources · ITInstitute of Genetics and Biophysics · ITNational Research Council · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The immune response to adenoviral COVID-19 vaccines is affected by the interval between doses. The optimal interval is unknown. Aim: We aim to explore in-silico the effect of the interval between vaccine administrations on immunogenicity and to analyze the contribution of pre-existing levels of antibodies, plasma cells, and memory B and T lymphocytes. Methods: We used a stochastic agent-based immune simulation platform to simulate two-dose and three-dose vaccination protocols with an adenoviral vaccine. We identified the model's parameters fitting anti-Spike antibody levels from individuals immunized with the COVID-19 vaccine AstraZeneca (ChAdOx1-S, Vaxzevria). We used several statistical methods, such as principal component analysis and binary classification, to analyze the correlation between pre-existing levels of antibodies, plasma cells, and memory B and T cells to the magnitude of the antibody response following a booster dose. Results and conclusions: We find that the magnitude of the antibody response to a booster depends on the number of pre-existing memory B cells, which, in turn, is highly correlated to the number of T helper cells and plasma cells, and the antibody titers. Pre-existing memory T cytotoxic cells and antibodies directly influence antigen availability hence limiting the magnitude of the immune response. The optimal immunogenicity of the third dose is achieved over a large time window, spanning from 6 to 16 months after the second dose. Interestingly, after any vaccine dose, individuals can be classified into two groups,

Indexed as

COVID-19COVID-19 VaccinesAdenoviridaeHumansImmunization, SecondaryImmunologic MemoryVaccinationCOVID-19 Vaccinesadenoviral COVID-19 vaccineagent-based modeling (ABM)anti-vector immunityboosterimmunological memoryin silicosimulation

Identifiers

PMID36353634
PMCPMC9639861
OpenAlexW4307266380

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.