Evidence map›Paper›PMID 30959955›Full record

ReviewTropical medicine and infectious disease2019

Pre-Clinical Pregnancy Models for Evaluating Zika Vaccines.

In-Jeong Kim, Marcia A Blackman, Jr-Shiuan Lin

Open access · goldAbstract readReview
In one paragraph

Review in Tropical medicine and infectious disease, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.3field-weighted citation impact, top 19% 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

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. A review on Zika vaccine development.Pathogens and disease · 2024
    Review
  2. Review
  3. Review
  4. Article
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  6. Review
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

3 authors at 1 institution in 1 country.

In-Jeong KimTrudeau Institute, Saranac Lake, NY 12983, USA. ijkim@trudeauinstitute.org.
Marcia A BlackmanTrudeau Institute, Saranac Lake, NY 12983, USA. mblackman@trudeauinstitute.org.
Jr-Shiuan LinTrudeau Institute, Saranac Lake, NY 12983, USA. jslin@trudeauinstitute.org.ORCID 0000-0003-0583-148X
Trudeau Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Zika virus (ZIKV) infection during pregnancy can result in a variety of developmental abnormalities in the fetus, referred to as Congenital Zika Syndrome (CZS). The effects of CZS can range from the loss of the viable fetus to a variety of neurological defects in full-term infants, including microcephaly. The clinical importance of ZIKV-induced CZS has driven an intense effort to develop effective vaccines. Consequently, there are approximately 45 different ZIKV vaccine candidates at various stages of development with several undergoing phase I and II clinical trials. These vaccine candidates have been shown to effectively prevent infection in adult animal models, however, there has been less extensive testing for their ability to block vertical transmission to the fetus during pregnancy or prevent the development of CZS. In addition, it is becoming increasingly difficult to test vaccines in the field as the intensity of the ZIKV epidemic has declined precipitously, making clinical endpoint studies difficult. These ethical and practical challenges in determining efficacy of ZIKV vaccine candidates in preventing CZS have led to increased emphasis on pre-clinical testing in animal pregnancy models. Here we review the current status of pre-clinical pregnancy models for testing the ability of ZIKV vaccines to prevent CZS.

Indexed as

congenital Zika syndromemousenon-human primatepre-clinical pregnancy modelvaccineZika virusZIKV

Identifiers

PMID30959955
PMCPMC6630727
OpenAlexW2933073311

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.