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Background: In cardiovascular engineering, the recent introduction of soft robotic technologies sheds new light on the future of implantable cardiac devices, enabling the replication of complex bioinspired architectures and motions. To support human heart function, assistive devices and total artificial hearts have been developed. However, the system's functionality, hemocompatibility, and overall implantability are still open challenges.
Methods: Here, the design of a soft robotic artificial cardiac wall is presented: the action of a bioinspired myocardium of pneumatic McKibben actuators in a double helix is coupled with an engineered passive and deformable endocardial layer made of silicone. The correlation between the helix angle of the actuators and the ejection fraction of the artificial cardiac wall was preliminarily studied with a simplified analytical model. A FEM model was introduced to represent the complex deformation of the endocardial layer during the actuation of the cardiac wall.
Results: Experimental tests report an ejection fraction of 68%, i.e., 77.2 ± 0.4 mL against 90 mmHg, satisfying the minimum physiological requirements and, therefore, proving the concept's functionality.
Conclusions: The conceived device paves the way for a new generation of innovative approaches where engineered bioinspiration might be the key to future artificial cardiac pumps that could support or even substitute the human failing heart.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12269355 | PMC |
http://dx.doi.org/10.1111/aor.14978 | DOI Listing |
J Interv Card Electrophysiol
September 2025
School of Medicine and Health, Department of Clinical Medicine-Clinical Department for Cardiology, University Medical Centre, Technical University of Munich, Munich, Germany.
JACC Cardiovasc Imaging
September 2025
Division of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist DeBakey Heart and Vascular Center, Houston, Texas, USA.
Background: Coronary computed tomography angiography (CTA)-derived plaque burden is associated with the risk of cardiovascular events and is expected to be used in clinical practice. Understanding the normative values of computed tomography-based quantitative plaque volume in the general population is clinically important for determining patient management.
Objectives: This study aimed to investigate the distribution of plaque volume in the general population and to develop nomograms using MiHEART (Miami Heart Study) at Baptist Health South Florida, a large community-based cohort study.
Cardiovasc Res
September 2025
Department of Advanced Medical and Surgical Sciences, University of Campania 'Luigi Vanvitelli', Piazza Miraglia, 2, Naples 80138, Italy.
JAMIA Open
October 2025
Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, United States.
Objectives: Unstructured data, such as procedure notes, contain valuable medical information that is frequently underutilized due to the labor-intensive nature of data extraction. This study aims to develop a generative artificial intelligence (GenAI) pipeline using an open-source Large Language Model (LLM) with built-in guardrails and a retry mechanism to extract data from unstructured right heart catheterization (RHC) notes while minimizing errors, including hallucinations.
Materials And Methods: A total of 220 RHC notes were randomly selected for pipeline development and 200 for validation from the Pulmonary Vascular Disease Registry.
Gastro Hep Adv
July 2025
Vatche and Tamar Manoukian Division of Digestive Diseases, David Geffen School of Medicine at UCLA, Los Angeles, California.
Background And Aims: Colonoscopy is the gold standard screening modality for colorectal cancer; however, it is operator-dependent and reliant on exam quality. Incorporating artificial intelligence (AI) into colonoscopy may improve adenoma detection and clinical outcomes, but this is a sociotechnical challenge that requires effective human-AI teaming incorporating provider attitudes.
Methods: We conducted a systematic review of studies evaluating attitudes and perspectives of providers toward AI-assisted colonoscopy.