Evidence map›Paper›PMID 41053351›Full record

ArticleScientific reports2025

Comparing neutralizing antibody activity over time between naïve and convalesced COVID-19 vaccinated individuals.

Lisette de Pillis, Rebecca Caffrey, Ge Chen, Mark Dela, Leif Eldevik, Hong Liu, Joseph P McConnell, Shahrokh Shabahang, Stephen A Varvel

Erratum issuedAbstract readComparative Study
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Lisette de PillisDepartment of Mathematics, Harvey Mudd College, 301 Platt Blvd., Claremont, 91711, CA, USA. depillis@hmc.edu.
Rebecca CaffreyAditxt Inc., 2569 Wyandotte Street, Suite 101, Mountain View, 94043, CA, USA.
Ge ChenAditxt Inc., 2569 Wyandotte Street, Suite 101, Mountain View, 94043, CA, USA.
Mark DelaSan Bernardino County Department of Behavioral Health, 303 E Vanderbilt Way, San Bernardino, 92415, CA, USA. mark.dela@dbh.sbcounty.gov.
Leif EldevikAditxt Inc., 2569 Wyandotte Street, Suite 101, Mountain View, 94043, CA, USA.
Hong LiuAditxt Inc., 2569 Wyandotte Street, Suite 101, Mountain View, 94043, CA, USA.
Joseph P McConnellAditxt Inc., 2569 Wyandotte Street, Suite 101, Mountain View, 94043, CA, USA.
Shahrokh ShabahangAditxt Inc., 2569 Wyandotte Street, Suite 101, Mountain View, 94043, CA, USA.
Stephen A VarvelPearsanta Inc., 737 N. Fifth Street, Suite 200, Richmond, 23219, VA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Longitudinal data comprised of neutralizing antibody (NAb) activity measurements from subjects who received COVID-19 vaccinations were collected between November 2020 and April 2022. To detect differences between convalesced and naïve groups with respect to the evolution of NAb activity since the subject's first COVID-19 vaccine, we initially fit a linear mixed effects model to only the decay section of NAb evolution. We conclude that NAb activity, when restricted to this region, behaves differently between these two groups, with the convalesced group generally having higher neutralizing antibody levels than the naïve group. We then fit a nonlinear mixed effects model over the entire NAb progression using a system of ordinary differential equations described by De Pillis et al. as our structural component to the model. This analysis not only supports the claim that over the entire progression, NAb activity behaves differently for convalesced and naïve groups, but aligns with the linear analysis in confirming that NAb decay is slower in the convalesced group than the naïve group. Finally, we use the estimated parameters from the nonlinear mixed effects model to predict NAb progression for each subject from their last observed measurement to 100 days past this measurement.

Indexed as

Antibodies, NeutralizingAntibodies, ViralCOVID-19COVID-19 VaccinesSARS-CoV-2AdultFemaleHumansLongitudinal StudiesMaleMiddle AgedVaccinationAntibodies, NeutralizingAntibodies, ViralCOVID-19 VaccinesAditxtScoreTMCOVID-19Linear mixed effects modelsMathematical modelsMATLABMonolixNeutralizing antibodyNonlinear mixed effects modelsODEsPredictionRSARS-Cov-2

Identifiers

PMID41053351
PMCPMC12501271

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.