Evidence map›Paper›PMID 42469867›Full record

ReviewJournal of nanobiotechnology2026

Transforming stroke care: the promise of feedback-guided thrombolytic nanomedicine.

Yizi Wang, Tuan Wang, Xiaohan Qu, Liang Guo, Yun Wang, Shasha Yu, Haishan Zhang

Abstract readReview
In one paragraph

Review in Journal of nanobiotechnology, 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.

Yizi Wang *Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
Tuan Wang *Department of Anesthesiology, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, Liaoning province, China.
Xiaohan Qu *Department of Thoracic Surgery, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, China.
Liang GuoDepartment of Emergency, The First Hospital of China Medical University, Shenyang, China.
Yun WangDepartment of Anesthesiology, The First Hospital of China Medical University, No. 155 Nanjing North Street, Shenyang, Liaoning province, China. wyida_1021@126.com.
Shasha YuDepartment of Cardiology, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, China. ysscmu1h@163.com.
Haishan ZhangDepartment of Cardiology, The First Hospital of China Medical University, Shenyang, 110001, Liaoning, China. zhanghaishan99@sohu.com.ORCID https://orcid.org/0009-0004-7264-1066

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ischemic stroke and thrombotic vascular occlusion remain leading causes of mortality and disability worldwide, yet current therapies operate largely in an open-loop paradigm, delivering thrombolytics or mechanical intervention without adaptive feedback on clot state or treatment response. This review reframes thrombolysis as a dynamic, controllable biological process and introduces a systems-engineering perspective built around the Sense → Target → Lyse → Report framework for closed-loop nanomedicine. We synthesize advances across nanotechnology, thrombosis biology, and bioresponsive materials to examine how next-generation nanosystems can detect clot-specific biochemical and biomechanical cues, localize to thrombi under physiological flow, actuate controlled lytic activity, and provide real-time reporting of therapeutic progress. We first characterize the thrombus as a heterogeneous, evolving immunothrombotic structure. It comprises platelet-rich shells, red blood cell (RBC)-dense cores, and microdomains enriched in neutrophil extracellular traps (NETs), each presenting distinct molecular and mechanical signatures that can serve as sensing handles. We then analyze emerging stimulus-responsive nanoplatforms activated by thrombin, reactive oxygen species (ROS), shear, or pH, alongside biomimetic and flow-optimized targeting strategies designed to overcome washout and penetration barriers. Particular emphasis is placed on theranostic systems that integrate imaging and therapy, laying the foundation for feedback-guided intervention. To organize this rapidly evolving field, we propose Closed-Loop Readiness Levels (CLRL) as a translational framework that classifies thrombolytic technologies along a five-tier scale. The scale runs from open-loop systems with no feedback (CLRL-0) to fully autonomous, self-regulating nanosystems that adapt therapy in real time and terminate it once reperfusion is achieved (CLRL-4). Across experimental models, closed-loop concepts show promise in improving spatial precision, reducing systemic exposure, and adapting lytic intensity to clot resistance. However, key barriers remain, including hemodynamic complexity, protein corona effects, sensor specificity, and integration of reporting with autonomous control. Intermediate levels capture the progressive integration of stimulus-triggered activation, thrombus targeting, therapeutic actuation, and reporting before full autonomous control is reached. By unifying disparate advances under a control-systems paradigm, this review positions closed-loop thrombolysis as a transformative direction in stroke therapy, with the potential to shift treatment from static dosing toward responsive, intelligent, and patient-specific vascular intervention.

Indexed as

Fibrinolytic AgentsNanomedicineStrokeThrombolytic TherapyAnimalsHumansNanoparticlesThrombosisFibrinolytic AgentsDrug delivery systemsNanoparticlesStroke, IschemicTheranostic nanomedicineThrombolytic therapyThrombosis

Identifiers

PMID42469867
PMCPMC13492018

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.