Interpretable Machine Learning Insights into Adhesion and Modulus of Biomedical HA-Dopamine Hydrogels.

Yuze Zhang, Yabei Xu, Yimin Shi, Daxin Liang

Abstract read

In one paragraph

Article in Gels (Basel, Switzerland), 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

4 authors.

Yuze ZhangKey Laboratory of Bio-Based Material Science and Technology, Ministry of Education, Northeast Forestry University, Harbin 150040, China.

Yabei XuKey Laboratory of Bio-Based Material Science and Technology, Ministry of Education, Northeast Forestry University, Harbin 150040, China.

Yimin ShiKey Laboratory of Science and Engineering for the Multi-Modal Prevention and Control of Major Chronic Diseases, Ministry of Industry and Information Technology, Zhengzhou Research Institute of Harbin Institute of Technology, Zhengzhou 450000, China.

Daxin LiangKey Laboratory of Bio-Based Material Science and Technology, Ministry of Education, Northeast Forestry University, Harbin 150040, China.ORCID 0000-0002-3220-0555

Funding

Natural Science Foundation of Heilongjiang Province ZD2024E002

6 · The paper itself

Abstract

Hyaluronic acid-dopamine (HA-Dopa) hydrogels have emerged as promising adhesive biomaterials for biomedical applications. However, the complex dependencies between formulation parameters and hydrogel performance pose challenges for rational material design. In this study, an interpretable machine learning framework was developed to investigate the structure-property relationships of HA-Dopa hydrogels. A dataset comprising 228 data points was collected from 37 peer-reviewed publications, representing heterogeneous experimental conditions across different research groups, and gradient boosting regression models were established to predict adhesion strength and elastic modulus, achieving test R

Indexed as

biomedical materialshyaluronic acidmachine learning

Identifiers

PMID41892530

DOI10.3390/gels12030206

PMCPMC13025816

What OpenQuestion holds

Textmetadata

LicenceCC BY

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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.