Evidence map›Paper›PMID 41413029›Full record

ArticleNature communications2025

Large-area and soft magnetoelastic sensing for normal and shear stress distribution toward pressure injury prevention.

Zijie Liu, Yibin Chen, Chuxuan Guo, Yuchao Liu, Yongbo Zhang, Yuxuan Bai, Bei Li, Chunjin Zhang, Zhuo Li, Hong Chen and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

11 authors.

Zijie LiuInstitute of Medical Equipment Science and Engineering, State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Yibin ChenInstitute of Medical Equipment Science and Engineering, State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Chuxuan GuoInstitute of Medical Equipment Science and Engineering, State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Yuchao LiuInstitute of Medical Equipment Science and Engineering, State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.ORCID http://orcid.org/0009-0006-6575-6572
Yongbo ZhangInstitute of Medical Equipment Science and Engineering, State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Yuxuan BaiInstitute of Medical Equipment Science and Engineering, State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Bei LiNursing Department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Chunjin ZhangNursing Department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Zhuo LiCollege of Smart Materials and Future Energy, Fudan University, Shanghai, China.ORCID http://orcid.org/0000-0003-1067-4191
Hong ChenNursing Department, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China. hopn1213@tjh.tjmu.edu.cn.
Jiajie GuoInstitute of Medical Equipment Science and Engineering, State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China. jiajie.guo@hust.edu.cn.ORCID http://orcid.org/0000-0002-7740-6792

Funding

National Natural Science Foundation of China (National Science Foundation of China) 52027806National Natural Science Foundation of China (National Science Foundation of China) 52188102National Natural Science Foundation of China (National Science Foundation of China) 92248304
6 · The paper itself

Abstract

Pressure injuries (PIs) are prevalent clinical complications arising from excessive normal and shear stresses, severely impacting patients' lives and increasing healthcare burdens. Current prevention strategies are limited by subjective nature of nursing assessments and inadequate monitoring systems with insufficient durability, limited sensing area, and poor shear sensitivity. Here, we develop a soft, durable, large-area tri-axial stress sensing array optimized via magnetoelastic model. Thin-film encapsulation with interfacial lubrication minimizes strain in large-area multi-layer assembly, while hybrid stiffness architecture reduces interface stresses to 24% of fracture stress. This design is durable (exceeding 100 hours) and cost-effective ($100/unit). Crucially, it enabled quantitative positioning optimization to reduce peak stress by 5.7 kPa (66.8% reduction compared to empirical placements). This is the first tri-axial stress sensing system to offer full-area coverage for PIs prevention and the first to quantitatively optimize skin-interface stress, offering a practical solution to improve surgical safety and postoperative care.

Indexed as

Pressure UlcerStress, MechanicalElasticityHumansShear Strength

Identifiers

PMID41413029
PMCPMC12800317

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.