Evidence map›Paper›PMID 41560827›Full record

ArticleMaterials today. Bio2026

Machine learning guided stimuli-responsive catheter for directional drug delivery and dynamic biliary state recognition.

Zhiwei Jiang, Song Wang, Qian Xiang, Ying Wang, Songchao Fu, Huibiao Deng, Qing He, Yue Wang, Zheng Mao, Cihui Liu and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Materials today. Bio, 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

12 authors.

Zhiwei JiangDigestive Endoscopic Center, Department of Gastroenterology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China.
Song WangDigestive Endoscopic Center, Department of Gastroenterology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China.
Qian XiangDepartment of Nursing, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ying WangCenter for Future Optoelectronic Functional Materials, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing, 210046, China.
Songchao FuCenter for Future Optoelectronic Functional Materials, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing, 210046, China.
Huibiao DengDepartment of Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 650 New Songjiang Rd, Songjiang, Shanghai, 201600, China.
Qing HeCenter for Future Optoelectronic Functional Materials, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing, 210046, China.
Yue WangCenter for Future Optoelectronic Functional Materials, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing, 210046, China.
Zheng MaoCenter for Future Optoelectronic Functional Materials, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing, 210046, China.
Cihui LiuCenter for Future Optoelectronic Functional Materials, School of Computer and Electronic Information/School of Artificial Intelligence, Nanjing Normal University, Nanjing, 210046, China.
Hui DengDepartment of Geriatrics, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, 210046, China.
Xinjian WanDigestive Endoscopic Center, Department of Gastroenterology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200233, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Precise drug delivery in the biliary tract remains challenging due to the dynamic physiological environment and lack of control in existing systems. Here we report a thermo- and pH-responsive semi-permeable catheter with unidirectional drug transport and integrated with machine learning-based environmental state recognition. Addressing the critical challenges of low local drug delivery efficiency and the difficulty of systems adapting to dynamic physiological environments in biliary tract diseases, the catheter adapts its swelling behavior and drug permeability in response to changes in temperature and pH. To achieve precise state recognition, real-time electrical signal data is classified using supervised and unsupervised learning algorithms. We simulated six distinct biliary states and achieved over 95 % accuracy in state recognition using a Random Forest model with Gini-based feature selection. The directional wall design ensured asymmetric diffusion and localized drug release. The research findings demonstrate a system capable of sensing and learning from environmental stimuli, laying the foundation for adaptive biliary tract treatment.

Indexed as

Biliary tract therapyDual-response cathetersMachine learningStimulus-responsive hydrogelsUnidirectional drug delivery

Identifiers

PMID41560827
PMCPMC12813357

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.