Conduction velocity analysis enhanced by neural network denoising reveals a predictive relationship between NADH dynamics and arrhythmogenic remodelingOA
Background:Metabolic stress is one of the main causes of irreversible damage to cardiac tissue.Repeated episodes can lead to cumulative damage throughout life,increasing the risk of arrhythmias and sudden cardiac death.While nicotinamide adenine dinucleotide(reduced form)(NADH)fluorescence and its recovery after photobleaching reflect metabolic state and mitochondrial enzyme activity,current studies focus on molecular mechanisms,neglecting the predictive potential of NADH dynamics for long-term outcomes.Methods:We hypothesized that NADH photobleaching dynamics during stress could forecast pro-arrhythmic tissue remodeling.We propose that evaluating metabolic stress requires assessing the mitochondrial system’s capacity to maintain redox balance alongside molecular markers.Utilizing human induced pluripotent stem cell-derived cardiomyocyte(hiPSC-CM)monolayers,we conducted unobstructed optical monitoring of action potential(AP)conductance and NADH photobleaching before,during(0-4 h),and after(1 week)pharmacologically induced metabolic stress.Results:To assess pro-arrhythmogenic remodeling,we developed an algorithm for conduction velocity analysis from AP space-time plots,enhanced by neural network-based noise reduction.The metric derived from NADH photobleaching dynamics during stress strongly predicted subsequent(1 week)conduction velocity impairment(R^(2)=0.925,P<0.01),consistent across metabolic stress types.Conclusion:By combining advanced optical mapping with new data processing tools,we establish and validate an experimental framework that links metabolic injury to subsequent electrophysiological remodeling.Our main finding is that NADH dynamics provide a functional readout of metabolic state with significant potential for predicting cardiac tissue remodeling and optimizing cardioprotective strategies.
Mikhail Mikhailovich Slotvitsky;Mikhail Stanislavovich Medvedev;Georgii Sergeevich Pashintsev;Valeryia Siarhejeuna Kachan;Vadim Alexandrovich Gryaznov;Anastasia Pavlovna Sinitsyna;Valeriya Alexandrovna Tsvelaya;Konstantin Igorevich Agladze
Information Technologies,Mechanics and Optics University,St.Petersburg 197101,Russia Laboratory of Experimental and Cellular Medicine,Moscow Center for Advanced Studies,Moscow 123592,Russia Molecular cellular diagnostics laboratory,M.F.Vladimirsky Moscow Regional Research Clinical Institute,Moscow 129110,Russia.Information Technologies,Mechanics and Optics University,St.Petersburg 197101,Russia Laboratory of Experimental and Cellular Medicine,Moscow Center for Advanced Studies,Moscow 123592,RussiaInformation Technologies,Mechanics and Optics University,St.Petersburg 197101,Russia Laboratory of Experimental and Cellular Medicine,Moscow Center for Advanced Studies,Moscow 123592,RussiaInformation Technologies,Mechanics and Optics University,St.Petersburg 197101,Russia Laboratory of Experimental and Cellular Medicine,Moscow Center for Advanced Studies,Moscow 123592,RussiaLaboratory of Experimental and Cellular Medicine,Moscow Center for Advanced Studies,Moscow 123592,RussiaLaboratory of Experimental and Cellular Medicine,Moscow Center for Advanced Studies,Moscow 123592,RussiaInformation Technologies,Mechanics and Optics University,St.Petersburg 197101,Russia Laboratory of Experimental and Cellular Medicine,Moscow Center for Advanced Studies,Moscow 123592,Russia Molecular cellular diagnostics laboratory,M.F.Vladimirsky Moscow Regional Research Clinical Institute,Moscow 129110,Russia.Laboratory of Experimental and Cellular Medicine,Moscow Center for Advanced Studies,Moscow 123592,Russia Molecular cellular diagnostics laboratory,M.F.Vladimirsky Moscow Regional Research Clinical Institute,Moscow 129110,Russia.
医药卫生
optical mappinghiPSC-CMsneural networksmetabolic stress
《Biomedical Engineering Communications》 2026 (4)
P.43-51,9
supported by the Russian Science Foundation(Research Grant#24-21-00162)for Mikhail Mikhailovich Slotvitsky,Mikhail Stanislavovich Medvedev,Georgii Sergeevich Pashintsev,and Valeryia Siarhejeuna Kachan。
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