Evidence map›Paper›PMID 42751232›Full record

ArticleACS materials letters2026

Polymerization-Induced Electrostatic Self-Assembly Enables Noncytotoxic Polyplex Formation for Gene Delivery.

Maria A Castillo, Jungyeon Kim, Francesca Patel-Burrows, Xueyuan Li, Antony Adamson, Lee A Fielding, Samuel T Jones

Abstract read
In one paragraph

Article in ACS materials letters, 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

7 authors.

Maria A CastilloDepartment of Materials, University of Manchester, Manchester M13 9PL, United Kingdom.ORCID https://orcid.org/0000-0003-2312-8157
Jungyeon KimDepartment of Materials, University of Manchester, Manchester M13 9PL, United Kingdom.
Francesca Patel-BurrowsDepartment of Materials, University of Manchester, Manchester M13 9PL, United Kingdom.
Xueyuan LiDepartment of Materials, University of Manchester, Manchester M13 9PL, United Kingdom.
Antony AdamsonGenome Editing Unit, University of Manchester, Manchester M13 9PL, United Kingdom.
Lee A FieldingDepartment of Materials, University of Manchester, Manchester M13 9PL, United Kingdom.ORCID https://orcid.org/0000-0002-4958-1155
Samuel T JonesSchool of Chemistry, University of Birmingham, Edgbaston B15 2TT, United Kingdom.ORCID https://orcid.org/0000-0002-3907-0810

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene delivery is a cornerstone of both basic research and gene therapy, where it holds the key to treating genetic disorders at their source. However, it does not adhere to one-size-fits-all solutions. Cargo-tailored and time-saving approaches are urgently needed for different applications. Photoinduced electron/energy transfer reversible addition-fragmentation chain-transfer (PET-RAFT) polymerization is a technique that enables the synthesis of a wide range of well-defined polymers, allowing for rapid reaction condition screening in small volumes. In this work, polymerization-induced electrostatic self-assembly (PIESA) based on PET-RAFT polymerization is demonstrated to facilitate the simultaneous synthesis and assembly of pDNA polyplexes for transfection. The as-prepared polyplexes are shown to be less cytotoxic than their conventionally assembled counterparts and act as more effective gene delivery vehicles in transfection trials. This strategy provides a versatile platform that facilitates rapid screening of cell-compatible copolymer nanoparticle compositions for gene delivery in ultralow volumes.

Identifiers

PMID42751232
PMCPMC13580189

What OpenQuestion holds

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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.