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Record W4408246507 · doi:10.1002/adma.202417327

Cardiac Organoid Model Inspired Micro‐Robot Smart Patch to Treat Myocardial Infarction

2025· article· en· W4408246507 on OpenAlexaff
Fangfang Wang, Zilu Xu, Feiyang Zheng, Dongcheng Yang, Kaige Xu, Ming Ke, Xiaofeng Zhou, Xiaohan Zhang, Ju Tan, Yong Liu, Yansha Hao, Malcolm Xing, Chuhong Zhu

Bibliographic record

VenueAdvanced Materials · 2025
Typearticle
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversity of Manitoba
FundersNational Natural Science Foundation of China
KeywordsOrganoidMyocardial infarctionMicrovesiclesCardiologyBiomedical engineeringInternal medicineMaterials scienceMedicineBiologyNeurosciencemicroRNA

Abstract

fetched live from OpenAlex

Post-myocardial infarction (MI), the rapid decrease in pH triggers myocardial cell acidosis, which compromises the therapeutic efficacy of exosomes in MI. The groundbreaking research in the human cardiac organoid MI model suggests that exosomes, when paired with pH adjustment, dramatically reduce cardiomyocyte mortality while maintaining their proliferative potential, underscoring the importance of pH regulation in myocardial preservation. Micro-robot mounted micro-needle (MN) patch is thus proposed, targeting MI-acidic microenvironmet, to deliver exosomes into deep injured tissue. Upon injection, the patch base releases VEGF-laden nanoparticles adhering to the infarcted myocardium. The smart patch is found not only 3D reconstructs the vascular network in MI regions but also effectively saves cardiomyocytes in rats. Furthermore, the minimally invasive delivery of MN patches are also verified to hearts of rabbits and pigs via thoracoscopic surgery underscores. These findings suggest that precise regulation of the microenvironment is a key to improving treatment outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.270
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2025
Admission routes1
Has abstractyes

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