ArticleScience advances2026
Artificial intelligence-enabled "inherited noninvasive intracellular recording" for prolonged monitoring of cardiac action potentials.
Suhang Liu, Xingyuan Xu, Yijing Cai, Chuanjie Yao, Zhengjie Liu, Lisheng Hou, Minghao Li, Xiaotong Li, Yan Li, Guanbin Li and 8 more
Abstract read
In one paragraphArticle in Science advances, 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 itWhat 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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
18 authors.
Suhang LiuState Key Laboratory of Optoelectronic Materials and Technologies; Guangdong Province Key Laboratory of Display Material and Technology; and School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China.ORCID 0009-0002-0900-6479 Xingyuan XuState Key Laboratory of Optoelectronic Materials and Technologies; Guangdong Province Key Laboratory of Display Material and Technology; and School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China.ORCID 0009-0002-0052-7307 Yijing CaiState Key Laboratory of Optoelectronic Materials and Technologies; Guangdong Province Key Laboratory of Display Material and Technology; and School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China.
Chuanjie YaoState Key Laboratory of Optoelectronic Materials and Technologies; Guangdong Province Key Laboratory of Display Material and Technology; and School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China.
Zhengjie LiuState Key Laboratory of Optoelectronic Materials and Technologies; Guangdong Province Key Laboratory of Display Material and Technology; and School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China.
Lisheng HouState Key Laboratory of Optoelectronic Materials and Technologies; Guangdong Province Key Laboratory of Display Material and Technology; and School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China.ORCID 0009-0007-7607-1023 Minghao LiState Key Laboratory of Optoelectronic Materials and Technologies; Guangdong Province Key Laboratory of Display Material and Technology; and School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China.ORCID 0009-0003-0155-3125 Xiaotong LiState Key Laboratory of Optoelectronic Materials and Technologies; Guangdong Province Key Laboratory of Display Material and Technology; and School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China.ORCID 0009-0004-0106-605X Yan LiDepartment of Cardiology, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China.
Guanbin LiSchool of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China.
Mingqiang LiLaboratory of Biomaterials and Translational Medicine, Center for Nanomedicine, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China.ORCID 0000-0002-5178-4138 Shuang HuangState Key Laboratory of Optoelectronic Materials and Technologies; Guangdong Province Key Laboratory of Display Material and Technology; and School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China.ORCID 0009-0001-3483-7656 Xinshuo HuangState Key Laboratory of Optoelectronic Materials and Technologies; Guangdong Province Key Laboratory of Display Material and Technology; and School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China.
Xi ChenShanghai Namin Core Technology Co. Ltd., Shanghai 201210, China.
Hui-Jiuan ChenState Key Laboratory of Optoelectronic Materials and Technologies; Guangdong Province Key Laboratory of Display Material and Technology; and School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China.
Xi XieState Key Laboratory of Optoelectronic Materials and Technologies; Guangdong Province Key Laboratory of Display Material and Technology; and School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou 510006, China.ORCID 0000-0001-7406-8444 Funding
No grant is acknowledged in the PubMed record.
6 · The paper itselfAbstract
Intracellular action potential (AP) recording that allows long-term monitoring is challenging because permanent membrane penetration is impossible due to cell death or resealing of perforated cell membrane. Herein, an "inherited noninvasive intracellular recording" methodology was proposed, which was based on the fusion of artificial intelligence (AI) with microelectrode array (MEA)-electroporation system (AI-MEA-EP) to enable prolonged monitoring of intracellular APs in cardiomyocytes. It used MEA-electroporation (MEA-EP) for minimally invasive collection of intracellular signals transiently (~1 minute), as well as noninvasive recording of extracellular signals in long term. The recorded extracellular APs were converted into corresponding intracellular APs by a convolutional neural network-long short-term memory-based AI model enhanced by model self-calibration. The intracellular APs detected by the AI-MEA-EP exhibited high consistency with those physically obtained through MEA-EP. It was demonstrated to monitor cardiac intracellular AP under drug treatments and glucose challenging during >5 consecutive days. This method offers a unique solution to achieve prolonged recording of intracellular signals for advancing cardiac research.
Indexed as
Action PotentialsArtificial IntelligenceMyocytes, CardiacAnimalsConvolutional Neural NetworksIntelligent SystemsMicroelectrodes
Identifiers
PMID41544156
PMCPMC12810580
What OpenQuestion holds
Textmetadata
LicenceCC BY-NC
Read underepoch 390