Evidence map›Paper›PMID 41159133›Full record

ArticleRegenerative biomaterials2025

Polylysine-based copolymer self-assemblies featuring acidity-activated structural transformation perceives and relieves sepsis.

Qilong Wu, Chao Fang, Taixia Wang, Qiuxia Peng, Kun Zhang, Dan Wang, Shihao Xu

Abstract read
In one paragraph

Article in Regenerative biomaterials, 2025. 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
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0citing papers in PubMed
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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

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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

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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.

Qilong WuDepartment of Ultrasound Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China.
Chao FangDepartment of Orthopedics and Central Laboratory, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610072, China.
Taixia WangDepartment of Medical Ultrasound, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai 200072, China.
Qiuxia PengDepartment of Orthopedics and Central Laboratory, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610072, China.
Kun ZhangDepartment of Orthopedics and Central Laboratory, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan 610072, China.ORCID https://orcid.org/0000-0002-6971-1164
Dan WangDepartment of Ultrasound, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200071, China.
Shihao XuDepartment of Ultrasound Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sepsis, a systemic inflammatory response syndrome, causes severe immune dysfunction and is associated with high mortality because of the lack of effective clinical interventions. To address the pathogenesis of sepsis, such as bacterial infection and the exacerbation of inflammation and oxidative stress, an acidity-activated polylysine (PLL)-based copolymer self-assembly (PPDD) was developed. This material was synthesized by conjugating polyethylene glycol-modified PLL (PEG-PLL) with 2,7-dichlorofluorescein diacetate (DCFH-DA). PPDD, with its PLL-derived antibacterial and antioxidant properties, can scavenge reactive oxygen species (ROS), mitigate inflammation and eliminate bacteria. These combined actions help alleviate the symptoms of sepsis and improve survival rates.

Indexed as

acidity-activated structural transformationself-assemblysepsissepsis monitoringsepsis pathogenesis targeting

Identifiers

PMID41159133
PMCPMC12556073

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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.