Evidence map›Paper›PMID 42273289›Full record

ReviewFrontiers in bioengineering and biotechnology2026

Artificial intelligence-driven design and optimization of mitochondria-targeted nanocarriers for chronic heart failure.

Zhengyi Zhang, Xiujuan Zhou, Tao Wei, Luanluan Meng, Kunlan Long

Abstract readReview
In one paragraph

Review in Frontiers in bioengineering and biotechnology, 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

5 authors.

Zhengyi ZhangSchool of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Xiujuan ZhouDepartment of Critical Care Medicine, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Tao WeiDepartment of Critical Care Medicine, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Luanluan MengDepartment of Critical Care Medicine, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.
Kunlan LongDepartment of Critical Care Medicine, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Chronic heart failure (CHF) poses a substantial global public health challenge, yet contemporary therapeutic strategies remain predominantly confined to hemodynamic compensation without reversing the fundamental myocardial bioenergetic failure. Mitochondrial dysfunction represents the core pathological driver of disease progression; however, conventional pharmacological agents encounter substantial barriers in traversing systemic biological obstacles and the mitochondrial double-membrane system to achieve precise organelle targeting. This review systematically delineates advances in the convergence of artificial intelligence (AI) and nanotechnology for mitochondria-targeted therapy in CHF. We first dissect the pathological mechanisms encompassing mitochondrial dynamics dysregulation, energetic collapse, and quality control failure. We subsequently elucidate how AI-engineered nanocarriers hierarchically navigate multiple physiological barriers from administration routes to mitochondrial membranes, with particular emphasis on machine learning-optimized antioxidant nanoformulations, biomimetic membrane encapsulation technologies, and synthetic mitochondria constructs. This review aims to catalyze multi-omics-guided personalized mitochondria-targeted repair strategies, facilitating a paradigm shift from hemodynamic compensation to precision etiological treatment.

Indexed as

artificial intelligencechronic heart failuremitochondriamitochondria-targetednanomedicinereview

Identifiers

PMID42273289
PMCPMC13246410

What OpenQuestion holds

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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.