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Background: An inward force is experienced by the guide catheter during device retrieval resulting in potential risk of deep engagement into the ostio-proximal coronary segment. This undesired movement can result in coronary injury. There is no systematic data or reports of techniques to prevent such inadvertent guide movement during difficult retrieval of devices.
Methods: In 25 patients undergoing percutaneous coronary intervention, where the conventional methods of guide stabilization failed to prevent deep engagement of guide catheter during device retrieval we used 'floating aortic wire' technique and reattempted retrieval. The primary endpoint was the successful retrieval of the device without deep engagement of the guide.
Results: Successful retrieval was seen without deep engagement of guide in 23(92 %) patients. Left anterior descending(n = 15, 60 %) artery was the most common coronary artery. The XB guide(n = 14, 70 %) was the most commonly used guide for left coronary intervention while Judgkins right and Amplatz left were used most commonly for right coronary intervention. Stent balloon(n = 15, 60 %) was the most common device which required using floating aortic wire for retrieval. Other devices were jailed wire(n = 5,20 %), non-compliant balloon(n = 4,16 %) and cutting balloon(n = 1,4 %). Intravascular ultrasound did not show any guide related vessel injury(dissection or intramural hematoma) in any cases. The floating aortic wire failed to prevent deep engagement in two patients because of longer segment of jailed wire and long stent balloon in distal right coronary artery.
Conclusion: Floating aortic wire assisted retrieval of coronary devices is a simple, reliable and safe technique that prevents deep guide engagement during difficult retrieval.
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http://dx.doi.org/10.1016/j.ihj.2025.08.005 | DOI Listing |
Anat Sci Educ
September 2025
Human Anatomy, Vita-Salute San Raffaele University, Milan, Italy.
As emerging technologies reshape both the body and how we represent it, anatomical education stands at a threshold. Virtual dissection tools, AI-generated images, and immersive platforms are redefining how students learn anatomy, while real-world bodies are becoming hybridized through implants, neural interfaces, and bioengineered components. This Viewpoint explores what it means to teach human anatomy when the body is no longer entirely natural, and the image is no longer entirely real.
View Article and Find Full Text PDFRisk Anal
September 2025
Edward J. Bloustein School, Rutgers University, New Brunswick, New Jersey, USA.
This AI-assisted review article offers a dual review: a book review of Living with Risk in the Late Roman World by Cam Grey, and a critical review of the current potential of large language models (LLMs), specifically ChatGPT's DeepResearch mode, to assist in thoughtful and scholarly book reviewing within risk science. Grey's book presents an innovative reconstruction of how communities in the late Roman Empire perceived and adapted to chronic environmental and societal risks, emphasizing spatial variability, cultural interpretation, and the normalization of uncertainty. Drawing on commentary from a human reviewer and a parallel AI-assisted analysis, we compare the distinct strengths and limitations of each approach.
View Article and Find Full Text PDFIndian Heart J
September 2025
Department of Medicine, Fortis Hospital, Kangra, India.
Background: An inward force is experienced by the guide catheter during device retrieval resulting in potential risk of deep engagement into the ostio-proximal coronary segment. This undesired movement can result in coronary injury. There is no systematic data or reports of techniques to prevent such inadvertent guide movement during difficult retrieval of devices.
View Article and Find Full Text PDFDisabil Rehabil Assist Technol
September 2025
School of Foreign Languages, Ningbo University of Technology, Ningbo, China.
The speech and language rehabilitation are essential to people who have disorders of communication that may occur due to the condition of neurological disorder, developmental delays, or bodily disabilities. With the advent of deep learning, we introduce an improved multimodal rehabilitation pipeline that incorporates audio, video, and text information in order to provide patient-tailored therapy that adapts to the patient. The technique uses a cross-attention fusion multimodal hierarchical transformer architectural model that allows it to jointly design speech acoustics as well as the facial dynamics, lip articulation, and linguistic context.
View Article and Find Full Text PDFExpert Rev Anticancer Ther
September 2025
AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan.
Introduction: Deep learning (DL) is transforming cancer research by enabling data-driven drug discovery. However, its clinical translation, particularly in endometrial cancer (EC), faces significant challenges.
Areas Covered: This review discusses recent DL applications across drug discovery stages in EC, including target identification, virtual screening, and de novo drug design.