Debkalpa Goswami

Cancer progression is driven by coordinated alterations in signalling networks that regulate proliferation, plasticity, metabolism and therapeutic response. Although genetic and epigenetic mechanisms are well characterised, there is an increasing body of evidence that suggests bioelectric signalling constitutes an additional integrative regulatory layer in tumour biology. In diverse experimental systems, malignant cells consistently exhibit depolarised transmembrane potentials ( V _m ), which correlate with proliferation, stemness, invasion and therapy resistance, suggesting depolarisation as a conserved bioelectric hallmark of malignancy. This shifts the central question from whether cancer can be considered a bioelectric disease to the extent to which bioelectric signalling constitutes a relevant organising dimension of tumour biology. However, a quantitative, translationally actionable framework for membrane potential in cancer is lacking. Existing studies and reviews have largely focused on individual ion channels, specific tumour contexts, or conceptual aspects of bioelectricity without systematically establishing V _m as a cross‐tumour, systems‐level state variable. Here, we summarise approximately 15 years of experimental and translational research to evaluate the extent to which V _m functions as an integrative regulatory dimension of malignancy. Here, we define a state variable as a measurable, dynamically tuneable parameter that integrates multiple regulatory inputs and predicts system‐level cellular behaviour. At the same time, we identify key limitations in the current evidence base, including limited quantitative comparability across tumour types, incomplete mechanistic integration across regulatory layers, insufficient resolution of tumour heterogeneity and a lack of standardisation for clinical translation. Based on this review, we introduce a quantitative framework and a structured translational roadmap for incorporating bioelectric state control into precision oncology. This establishes membrane potential as not only a supplementary biomarker, but also a functional pharmacodynamic indicator and an actionable control variable for state‐guided therapeutic intervention.

Background: There is growing interest in using biosignals from wearable devices to assess anxiety disorders. Among these, electrocardiography is the most widely used due to its ability to monitor cardiovascular activity. Other signals, such as respiratory, electrodermal activity, and photoplethysmography, also show promise. This review aims to evaluate how these signals, individually and in combination, have been used for anxiety detection. Methods: We systematically reviewed 26 studies published between 2014 and 2024 that used wearable devices to collect signals for anxiety detection. Extracted information included study design, signal types, features, classification methods, and accuracy outcomes. Pooled accuracies were calculated to compare single-signal and multi-signal approaches. Results: Here we show that approaches combining multiple signals outperform those using a single signal, with a pooled accuracy of 81.94% compared to 76.85%. Electrocardiography was the most reliable individual signal, with a pooled accuracy of 80.34% across 12 studies. However, the limited number of single-sensor studies and methodological variability limit conclusions about the superiority of any one modality. The most common features included mean heart rate and heart rate variability for electrocardiography, the mean inspiratory-to-expiratory time ratio for respiratory signals, mean skin conductance for electrodermal activity, and the mean heart rate for photoplethysmography. Support vector machine was the predominant classifier. Conclusions: This review underscores the clinical potential of wearable devices for anxiety detection, emphasizing the value of multimodal approaches. Future research should focus on refining algorithms, expanding sample sizes, and exploring diverse contexts to improve the accuracy and generalizability of these methods.

Advanced imaging of the heart, including cardiovascular magnetic resonance imaging (CMR), has revolutionized the diagnosis and prognosis for cardiovascular disease ^1-3 . For the past 40 years, CMR has primarily relied on the acquisition of numerous breath-held 2D images resulting in complex scanner operation, patient discomfort, long scan durations, and cumbersome image interpretation ^4,5 . These limitations constrain CMR use to major academic hospital systems and severely limit patient access to CMR, which makes up < 1% of total cardiovascular imaging despite being represented in two thirds of all AHA/ACC guidelines ^6,7 . By leveraging advanced multidimensional physics and artificial intelligence, we overcome these challenges by developing a 30-minute end-to-end automated CMR exam (AutoCMR) that delivers 4D anatomical, functional, and tissue characterization of the whole heart in a single click without breath-holds. AutoCMR was rigorously validated in three cohorts: preclinical large animals, patients scanned in an academic hospital setting with over 40 years of CMR experience, and patients scanned in a community health center with no prior CMR experience. While providing simplified CMR acquisition and automated analysis, we demonstrated that AutoCMR was not significantly different than conventional CMR in imaging biomarkers and human interpretation. With its 4D whole thoracic coverage, we further showcased that AutoCMR can enable next generation patient analytics including personalized digital twins, 3D printing, virtual reality, and automated clinical structured summaries. Due to its inherent scalability, we anticipate AutoCMR will promote the democratization of CMR, increasing patient access for all including underserved health communities, while enabling powerful downstream cutting-edge technologies aimed at personalized medicine.

Mechanical metamaterials represent a promising class of materials characterized by unconventional mechanical properties derived from their engineered architectures. In the realm of bioengineering, these materials offer unique opportunities for applications spanning in vitro models, wearable devices, and implantable biomedical technologies. This review discusses recent advancements and applications of mechanical metamaterials in bioengineering contexts. Mechanical metamaterials, tailored to mimic specific mechanical properties of biological tissues, enhance the fidelity and relevance of in vitro models for disease modeling and therapy testing. Integration of these materials into wearable devices enables the creation of comfortable and adaptive interfaces with the human body. Utilization of mechanical metamaterials in implantable devices promotes tissue regeneration, supports biomechanical functions, and minimizes host immune responses. Key design strategies and material selection criteria critical for optimizing the performance and biocompatibility of these metamaterials are elucidated. Representative case studies demonstrating recent applications in benchtop phantoms and scaffolds (in vitro platforms); footwear, architectured fabrics, and epidermal sensors (wearables); and implantable cardiovascular, gastrointestinal, and orthopedic devices, and multifunctional patches are highlighted. Finally, the challenges and future directions in the field are discussed, emphasizing the potential for mechanical metamaterials to transform bioengineering research by enabling novel functionalities and improving outcomes across diverse use cases.

Inferior vena cava (IVC) filters are vital in preventing pulmonary embolism (PE) by trapping large blood clots, especially in patients unsuitable for anticoagulation. In this study, the accuracy of two common simplifying assumptions in numerical studies of IVC filters—the rigid wall assumption and the laminar flow model—is examined, contrasting them with more realistic hyperelastic wall and turbulent flow models. Using fluid–structure interaction (FSI) and computational fluid dynamics (CFD) techniques, the investigation focuses on three hemodynamic parameters: time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), and relative residence time (RRT). Simulations are conducted with varying sizes of clots captured in the filter. The findings show that, in regions of high wall shear stress, the rigid wall model predicted higher TAWSS values, suggesting an increased disease risk compared to the hyperelastic model. However, the laminar and turbulent flow models did not show significant differences in TAWSS predictions. Conversely, in areas of low wall shear stress, the rigid wall model indicated lower OSI and RRT, hinting at a reduced risk compared to the hyperelastic model, with this discrepancy being more evident with larger clots. While the predictions for OSI and TAWSS were closely aligned for both laminar and turbulent flows, divergences in RRT predictions became apparent, especially in scenarios with very large clots.

Aortic stenosis (AS) affects about 1.5 million people in the United States and is associated with a 5-year survival rate of 20% if untreated. In these patients, aortic valve replacement is performed to restore adequate hemodynamics and alleviate symptoms. The development of next-generation prosthetic aortic valves seeks to provide enhanced hemodynamic performance, durability, and long-term safety, emphasizing the need for high-fidelity testing platforms for these devices. We propose a soft robotic model that recapitulates patient-specific hemodynamics of AS and secondary ventricular remodeling which we validated against clinical data. The model leverages 3D-printed replicas of each patient’s cardiac anatomy and patient-specific soft robotic sleeves to recreate the patients’ hemodynamics. An aortic sleeve allows mimicry of AS lesions due to degenerative or congenital disease, whereas a left ventricular sleeve recapitulates loss of ventricular compliance and diastolic dysfunction (DD) associated with AS. Through a combination of echocardiographic and catheterization techniques, this system is shown to recreate clinical metrics of AS with greater controllability compared with methods based on image-guided aortic root reconstruction and parameters of cardiac function that rigid systems fail to mimic physiologically. Last, we leverage this model to evaluate the hemodynamic benefit of transcatheter aortic valves in a subset of patients with diverse anatomies, etiologies, and disease states. Through the development of a high-fidelity model of AS and DD, this work demonstrates the use of soft robotics to recreate cardiovascular disease, with potential applications in device development, procedural planning, and outcome prediction in industrial and clinical settings.