Evidence map›Paper›PMID 42254427›Full record

ReviewFood science & nutrition2026

Immunopotentiation by Natural Extracts: Mechanisms and Applications in Influenza Vaccine Adjuvant Development.

Thi Len Ho, Eun-Ju Ko

Abstract readReview
In one paragraph

Review in Food science & nutrition, 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

2 authors.

Thi Len HoInterdisciplinary Graduate Program in Advanced Convergence Technology & Science Jeju National University Jeju Republic of Korea.
Eun-Ju KoInterdisciplinary Graduate Program in Advanced Convergence Technology & Science Jeju National University Jeju Republic of Korea.ORCID https://orcid.org/0000-0002-1081-904X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the widespread use of seasonal influenza vaccines, their efficacy remains limited particularly among the elderly, immunocompromised individuals, and in the face of antigenically drifted or shifted viral strains. Traditional adjuvants enhance vaccine efficacy but may have limitations related to safety, tolerability, or restricted immune activation. Natural extracts derived from plants, marine organisms, fungi, and algae are gaining attention as novel adjuvants due to their ability to modulate both innate and adaptive immunity with low toxicity. This review highlights key natural compounds such as polysaccharides (fucoidan, chitosan, Advax), saponins (QS-21, Matrix-M), and flavonoids (naringenin) and their mechanisms of action, including activation of dendritic cells, cytokine induction, and enhancement of T and B cell responses. These extracts promote balanced Th1/Th2 polarization, memory cell formation, and improved mucosal and systemic immunity. We also summarize in vitro and in vivo methods used for adjuvant screening, including immune cell activation assays, T/B cell co-culture systems, and murine vaccination models. Natural extract-based adjuvants offer formulation flexibility for various vaccine platforms and delivery routes, including intranasal and intramuscular administration. Their favorable safety profiles, immunostimulatory potency, and capacity to enhance cross-protective immunity position them as strong candidates for next-generation adjuvants for influenza vaccines. Ongoing research may enable their broader application in improving vaccine performance across diverse populations.

Indexed as

immunomodulationinfluenza vaccinenatural adjuvantsnatural extracts

Identifiers

PMID42254427
PMCPMC13239191

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Registered trials

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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.