Evidence map›Paper›PMID 35214789›Full record

ArticleVaccines2022

SARS-CoV-2 Spike and Neutralizing Antibody Kinetics 90 Days after Three Doses of BNT162b2 mRNA COVID-19 Vaccine in Singapore.

Chin Shern Lau, Soon Kieng Phua, Ya Li Liang, May Lin Helen Oh, Tar Choon Aw

Open access · goldAbstract read
In one paragraph

Article in Vaccines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
2.5field-weighted citation impact, top 9% 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

21 citing papers in PubMed, 25 citations in OpenAlex.

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  17. The durability of natural infection and vaccine-induced immunity against future infection by SARS-CoV-2.Proceedings of the National Academy of Sciences of the United States of America · 2022
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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

5 authors at 2 institutions in 1 country.

Chin Shern LauDepartment of Laboratory Medicine, Changi General Hospital, Singapore 529889, Singapore.
Soon Kieng PhuaDepartment of Laboratory Medicine, Changi General Hospital, Singapore 529889, Singapore.
Ya Li LiangDepartment of Laboratory Medicine, Changi General Hospital, Singapore 529889, Singapore.
May Lin Helen OhDepartment of Infectious Diseases, Changi General Hospital, Singapore 529889, Singapore.
Tar Choon AwDepartment of Laboratory Medicine, Changi General Hospital, Singapore 529889, Singapore.ORCID 0000-0002-7814-8836
Changi General Hospital · SGNational University of Singapore · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe evaluated the post-booster (BNT162b2) antibody responses in Singapore.

methodsParticipants (

resultsPre-booster median S-Ab/N-Ab titers were 829 BAU/mL/0.83 µg/mL; 2 participants were below manufacturer's N-Ab cut-offs of 0.3 µg/mL (0.192 and 0.229). Both S-Ab and N-Ab titers peaked at 30 days post-booster (median S-Ab 25,220 BAU/mL and N-Ab 30.3 µg/mL) at 30-37× pre-booster median levels. These peak post-booster S-Ab/N-Ab titers were 11× (25,220 vs. 2235 BAU/mL) and 9× (30.3 vs. 3.52 µg/mL) higher than the previously reported peak post-second dose levels. Antibody titers declined to 12,315 BAU/mL (51% decrease) and 14.3 µg/mL (53% decrease) 90 days post-booster. Non-linear regression estimates for S-Ab/N-Ab half-lives were 44/58 days. At 180 days post-booster, S-Ab/N-Ab are estimated to be 2671 BAU/mL/4.83 µg/mL.

conclusionsBoth S-Ab and N-Ab show a good response following post-booster vaccination, with half-lives that may provide a prolonged antibody response.

Indexed as

antibodiesbooster vaccinationSARS-CoV-2

Identifiers

PMID35214789
PMCPMC8879250
OpenAlexW4212902041

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.