Evidence map›Paper›PMID 38235403›Full record

ArticleMedical review (2021)2023

Enrichment of nano delivery platforms for mRNA-based nanotherapeutics.

Xiao Liu, Xu Zhang, Jiulong Li, Huan Meng

Abstract read
In one paragraph

Article in Medical review (2021), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

4 authors.

Xiao LiuDepartment of Gastroenterology, Beijing Hospital, National Center of Gerontology, Institute of Geriatrics Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Xu ZhangCAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology, Beijing, China.
Jiulong LiCAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology, Beijing, China.
Huan MengCAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety and CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology, Beijing, China.ORCID https://orcid.org/0000-0001-8844-3938

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lipid-based nanoparticles (LNP) have shown significant progress in delivering mRNA for therapeutics, particularly with the success of coronavirus disease 2019 (COVID-19) vaccines. However, there are still challenges, such as organ-specific targeting, sustained protein expression, immunogenicity, and storage that need to be addressed. Therefore, there is interest in developing additional nano drug delivery systems (DDS) to complement LNP technology. Some of these include polymer, lipid-polymer hybrid, organic/inorganic hybrid nanostructure, and inorganic nanoparticle. In our opinion, LNP technology may not be suitable for every disease scenario in categories such as infection disease, cancer, pulmonary disease, autoimmune disorders and genetic rare disease (among others). This is because different diseases may require distinct administration routes, doses, and treatment durations, as well as considerations for biological barriers that may lower the efficacy and/or exert safety concern. In this perspective, we will highlight the need and potential for enhancing the diversity of nano delivery platforms for mRNA-based nanotherapeutics.

Indexed as

high throughput screeningmRNAnano/bio interfacenano drug delivery systemnanosafetynon-LNP nanocarrier

Identifiers

PMID38235403
PMCPMC10790206

What OpenQuestion holds

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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.