Evidence map›Paper›PMID 42506621›Full record

ReviewVaccines2026

Challenges, Advances, and Future Directions in Nipah Virus Vaccine Development.

Hongshan Xu, Xuanxuan Zhang, Shuai Shang, Fangxuan Chen, Xinyu Liu, Qunying Mao

Abstract readReview
In one paragraph

Review in Vaccines, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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

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

6 authors.

Hongshan XuNational Institutes for Food and Drug Control, Beijing 102629, China.
Xuanxuan ZhangNational Institutes for Food and Drug Control, Beijing 102629, China.
Shuai ShangChangchun Biological Products Research Institute Co., Ltd., Changchun 130012, China.
Fangxuan ChenChangchun Biological Products Research Institute Co., Ltd., Changchun 130012, China.
Xinyu LiuNational Institutes for Food and Drug Control, Beijing 102629, China.
Qunying MaoNational Institutes for Food and Drug Control, Beijing 102629, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nipah virus (NiV) is a highly pathogenic zoonotic pathogen. Since its discovery in 1998, recurrent epidemics have occurred in South and Southeast Asia, with a case fatality rate ranging from 40% to 100%. The outbreak in West Bengal, India in early 2026 has once again highlighted its severe threat to public health. To date, no licensed human vaccines or specific therapeutics against NiV are available worldwide. This review systematically summarizes the breakthroughs in antigen design for NiV vaccines, with a focus on conformational stabilization of prefusion F (pre-F) protein, chimeric G/F antigens, and multivalent nanoparticle strategies. In addition, we comparatively analyze the clinical progress of mainstream vaccine platforms, including viral vectors, mRNA and subunit vaccines. Given the sporadic nature and high mortality of NiV infection, the conventional licensing pathway relying on large-scale phase III clinical trials faces substantial practical obstacles. Accordingly, this article discusses adaptive adjustments in regulatory science. We propose several strategies to accelerate the clinical translation and emergency stockpiling of NiV vaccine candidates, including establishing unified correlates of protection thresholds, coordinating multinational regulatory resources, and optimizing the implementation of Animal Rule.

Indexed as

Animal Ruleantigen designNipah viruspublic healthtechnical platformvaccine development

Identifiers

PMID42506621
PMCPMC13431381

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.