Dynamic and Label-Free Sensing of Cardiomyocyte Responses to Nanosized Vesicles for Cardiac Oxidative Stress Injury Therapy

Three-Dimensional-Printed Flexible Nanosilver Electrode Array for Parallel and Robust Intracellular Electrophysiological Recording

Cardiac electrophysiology, particularly intracellular action potential (iAP) recordings, is vital for advancing the understanding and treatment of cardiovascular diseases. In this work, we present a 3D-printed flexible nanosilver electrode array (FlexNEA) that enables simple and efficient circuit fabrication within several minutes using a multimaterial electric-field-driven (EFD) micro-jet 3D printing strategy and achieves over 99% success rates in intracellular access through electroporation. The NEA with flexible property creates an enhanced cell-electrode coupling, with the cardiomyocyte membrane wrapping tightly around the nanosilver electrode, leading to superior signal quality in contrast to the conventional planar electrodes. The 3D-printed FlexNEA enables stable, high-fidelity intracellular recordings by multiple consecutive biosafe electroporations over a short or long period of time. Moreover, the platform exhibits a powerful drug screening function by accurately detecting drug-induced iAP alterations, providing a precise and quantitative assessment of ion-channel drug effects. In summary, the 3D-printed FlexNEA device and integrated biosensing-regulating platform present a significant advance in the high-fidelity intracellular recording technology of cardiac electrophysiology. The platform advances the development of low-cost, biocompatible NEA systems for preclinical research in the cardiology and pharmacology fields.

High-Efficiency ICG Molecular Vibration Therapy for Bradyarrhythmia Using Cardiomyocyte-Based Biosensing

Bradyarrhythmia is a major cause of cardiovascular disease morbidity and mortality. Currently, medication and/or surgery are the conventional clinical therapeutic strategies for bradyarrhythmia, whereas drug side effects, invasive surgery, or potential complications limit their extensive application. Therefore, the development of alternative therapies for bradyarrhythmia is urgently needed. Herein, we propose a universal and efficient drug-mimicking strategy to treat bradyarrhythmia, which relies on the photothermal properties of near-infrared-triggered indocyanine green (ICG). An in situ integrated cell-based biosensing-regulating platform was developed to assess treatment efficacy by dynamically analyzing the cardiomyocyte electrophysiology activities. These findings indicate that the thermal vibration of ICG can efficiently enhance the electrophysiology of cardiomyocytes with bradyarrhythmia and maintain a rhythmic state for a long time, which is superior to that of Au nanorod plasmonic localized heating. Moreover, qualitative investigations confirmed that thermal stimulation is a pivotal factor in enhancing cardiomyocyte electrophysiological activity during photothermal treatment. This study provides a noninvasive drug-mimicking treatment strategy for bradyarrhythmia and establishes a reliable cell-based biosensing-regulating platform for electrophysiological assessment and drug screening, contributing to the further development of bradyarrhythmia therapies.

Monitoring of inflammatory preterm responses via myometrial cell based multimodal electrophysiological and optical biosensing platform

Preterm birth (PTB) remains a leading cause of neonatal morbidity and mortality, with inflammation-induced PTB posing a significant challenge due to its complex pathophysiology. To address this, we developed an in vitro platform utilizing hTERT-immortalized human myometrial (hTERT-HM) cells integrated with a multielectrode array (MEA) biosensing system and optical calcium imaging. Compared to primary uterine myometrial cells, hTERT-HM cells exhibit superior reproducibility, high scalability, and convenient manipulation, facilitating the consistent and large-scale investigations. This advanced system facilitates simultaneous real-time monitoring of electrophysiological activity and intracellular calcium transient, providing detailed insights into uterine cell behavior during inflammatory PTB. Our study revealed that oxytocin (OT) induces regular contractions in hTERT-HM cells, and the synergistic effect of OT and lipopolysaccharide (LPS) disrupts electrophysiological patterns and calcium signaling, closely mimicking the pathophysiology of inflammation-induced PTB. Meanwhile, magnesium sulfate is validated to effectively suppress OT-induced calcium release and mitigate LPS-triggered irregular electrophysiological signals. By integrating advanced biosensing technologies and advantages of hTERT-HM cells, this platform offers a reliable, reproducible model to investigate the mechanisms of inflammation-driven PTB and further develop targeted therapeutic interventions.

Dynamic and quantitative assessment of quercetin for cardiac oxidative stress injury prevention using sensitive cardiomyocyte based biosensing

Myocardial infarction is a leading cause of morbidity and mortality associated with cardiovascular diseases worldwide. Although novel medications and treatments greatly alleviate patient suffering, challenges related to prognostic limit the recovery of cardiac function. Currently, treatment with monomeric compounds displays promise in prognostic interventions for cardiac diseases. However, there is a lack of dynamic and quantitative assessment of cardiomyocyte response to these drugs. Herein, an integrated biosensing platform with a microelectrode array was constructed for label-free, non-invasive, long-term, and real-time recording of cardiomyocyte electrophysiological signals. By analyzing the signals of cardiomyocytes before and after treatment, we established the safe concentration of quercetin in cardiomyocytes and identified its long-term cardiotoxicity. Moreover, quercetin also demonstrated significant protective effects on cardiomyocytes in a H2O2-induced oxidative stress injury model. This study provides a trustworthy platform to evaluate the effects of monomeric compounds on cardiomyocytes, and offers a novel approach for drug screening and efficacy testing in cardiovascular diseases.

Energy stress induced cardiac autophagy detection via a chronic and dynamic cardiomyocytes-based biosensing platform

Hypoglycemia is a common complication which occurs during the treatment of diabetes, closely associated with cardiovascular events. A sudden decrease in blood glucose increases the risk of arrhythmia, which can lead to sudden cardiac death. This event is usually accompanied by abnormal electrophysiological activities in cardiomyocytes. However, traditional models do not efficiently reflect real-time cardiomyocyte electrophysiological changes under various glucose deprivation conditions in a large-scale and high-throughput manner. Therefore, we need to develop a new biosensing platform to aid in related scientific research. In this study, a cardiomyocyte-based biosensor was developed for real-time, noninvasive monitoring of the electrophysiological responses of cardiomyocytes under different glucose concentrations. The findings show that low-glucose conditions result in abnormal electrophysiology in cardiomyocytes, but autophagy enables cells to survive this adversity. Inhibition of autophagy exacerbates electrophysiological abnormalities, and long-term glucose starvation causes irreversible damage to cardiomyocytes. The proposed chronic and dynamic cardiomyocyte-based biosensing platform provides a new tool for understanding the effects of hypoglycemia on the in vitro cardiomyocyte-based heart model, revealing that autophagy has the potential to be an alternative treatment for diabetes and hypoglycemia.

A Wearable Integrated Microneedle Electrode Patch for Exercise Management in Diabetes

Exercise is one of the preferred management strategies for diabetic patients, but the exercise mode including type, intensity, and duration time is quite different for each patient because of individual differences. Inadequate exercise has no effect on the blood glucose control, while overexercise may cause serious side effects, such as hypoglycemia and loss of blood glucose control. In this work, we report a closed-loop feedback mode for exercise management in diabetes. A minimally invasive, biocompatible microneedle electrode patch was fabricated and used for continuously monitoring the glucose in the interstitial fluid. Further, in conjunction with using a wireless electrochemical device, the glucose signals can be analyzed to output the potency of exercise and give advice on exercise management. A custom exercise given by this closed-loop feedback mode can reduce the used dose of insulin and avoid side effect during and after exercise. We believe that this work can provide a novel comprehensive guidance for diabetic patients.

A dual-mode, image-enhanced, miniaturized microscopy system for incubator-compatible monitoring of live cells

Imaging live cells under stable culture conditions is essential to investigate cell physiological activities and proliferation. To achieve this goal, typically, a specialized incubation chamber that creates desired culture conditions needs to be incorporated into a microscopy system to perform cell monitoring. However, such imaging systems are generally large and costly, hampering their wide applications. Recent advances in the field of miniaturized microscopy systems have enabled incubator cell monitoring, providing a hospitable environment for live cells. Although these systems are more cost-effective, they are usually limited in imaging modalities and spatial temporal resolution. Here, we present a dual-mode, image-enhanced, miniaturized microscopy system (termed MiniCube) for direct monitoring of live cells inside incubators. MiniCube enables both bright field imaging and fluorescence imaging with single-cell spatial resolution and sub-second temporal resolution. Moreover, this system can also perform cell monitoring inside the incubator with tunable time scales ranging from a few seconds to days. Meanwhile, automatic cell segmentation and image enhancement are realized by the proposed data analysis pipeline of this system, and the signal-to-noise ratio (SNR) of acquired data is significantly improved using a deep learning based image denoising algorithm. Image data can be acquired with 5 times lower light exposure while maintaining comparable SNR. The versatility of this miniaturized microscopy system lends itself to various applications in biology studies, providing a practical platform and method for studying live cell dynamics within the incubator.

Exosomes based strategies for cardiovascular diseases: Opportunities and challenges

Exosomes, as nanoscale extracellular vesicles (EVs), are secreted by all types of cells to facilitate intercellular communication in living organisms. After being taken up by neighboring or distant cells, exosomes can alter the expression levels of target genes in recipient cells and thereby affect their pathophysiological outcomes depending on payloads encapsulated therein. The functions and mechanisms of exosomes in cardiovascular diseases have attracted much attention in recent years and are thought to have cardioprotective and regenerative potential. This review summarizes the biogenesis and molecular contents of exosomes and details the roles played by exosomes released from various cells in the progression and recovery of cardiovascular disease. The review also discusses the current status of traditional exosomes in cardiovascular tissue engineering and regenerative medicine, pointing out several limitations in their application. It emphasizes that some of the existing emerging industrial or bioengineering technologies are promising to compensate for these shortcomings, and the combined application of exosomes and biomaterials provides an opportunity for mutual enhancement of their performance. The integration of exosome-based cell-free diagnostic and therapeutic options will contribute to the further development of cardiovascular regenerative medicine.

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.