Evidence map›Paper›PMID 39771997›Full record

ReviewVaccines2024

Advancements in Nanoparticle-Based Adjuvants for Enhanced Tuberculosis Vaccination: A Review.

Jiao Wang, Zian Zhao, Quan Wang, Jingyu Shi, Duo Wai-Chi Wong, James Chung-Wai Cheung

Abstract readReview
In one paragraph

Review in Vaccines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. 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

6 authors.

Jiao WangDepartment of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China.
Zian ZhaoDepartment of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China.ORCID 0009-0001-2376-5298
Quan WangDepartment of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China.
Jingyu ShiDepartment of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China.ORCID 0000-0001-7650-8212
Duo Wai-Chi WongDepartment of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China.ORCID 0000-0002-8805-1157
James Chung-Wai CheungDepartment of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China.ORCID 0000-0001-7446-0569

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tuberculosis (TB) remains a leading cause of morbidity and mortality worldwide, necessitating the development of more effective vaccines. Nanoparticle-based adjuvants represent a promising approach to enhancing tuberculosis vaccine efficacy. This review focuses on the advantages of nanoparticulate-loaded vaccines, emphasizing their ability to improve antigen delivery, safety, and immunogenicity. We discuss the various types of nanoparticles and their unique physicochemical properties that contribute to improved antigen delivery and sustained immune activation. Additionally, we highlight the advantages of nanoparticle-based adjuvants in inducing strong cellular and humoral immunity, enhancing vaccine stability, and reducing adverse effects. Finally, we address current challenges and future perspectives in the application of these novel adjuvants, emphasizing their potential to transform TB vaccine strategies and ultimately contribute to better global health outcomes.

Indexed as

adjuvantsnanoparticlestuberculosisvaccines

Identifiers

PMID39771997
PMCPMC11680411

What OpenQuestion holds

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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.