Background: The morphology and extensity of the stenotic lesion is crucial as well as the obstruction ratio. It is well known that the complexity of lesions has a direct impact on endovascular treatment (PTCA/stent); however, the arrangement of the lesions is underestimated and not well studied.
Aim: We sought to evaluate the haemodynamic effects of different stenotic lesion models and arrangements in vitro.
Methods: Vascular circulation was simulated in vitro. Oxygenator, tubing set, polytetrahidroflouroethylene synthetic graft, pressure and flow rate, sensors were used to build the simulation model. Measurements of isolated short, isolated long, identical stenotic tandem short, identical stenotic tandem long, sub-critical long, and critical short lesion combinations were performed and haemodynamic parameters were recorded.
Results: Tandem lesions were more likely to result in critical stenosis comparing single lesions with the same obstruction ratio. This difference became more significant as the obstruction ratio was raised. Tandem long lesions also resulted in more critical stenosis than tandem short lesions. It can be claimed that tandem lesions can result in more flow restriction with reference to single lesions with the same stenotic ratio. Contrary to expectations, tandem short lesions were found to be more stenotic compared with the same degree long individual lesions.
Conclusions: It is effortless to give the decision for simple, discrete and individual lesions, while the ideal decision for long and complicated lesions may remain unclear. Even if these "grey zone" lesions are considered non-critical while investigating them one by one, it must be kept in mind that the overall stenotic effect of these lesions may lead to more haemodynamic impairment.
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http://dx.doi.org/10.5603/KP.a2017.0163 | DOI Listing |
Sci Rep
December 2024
Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Republic of Korea.
This study aimed to investigate alterations in a multilayer network combining structural and functional layers in patients with end-stage kidney disease (ESKD) compared with healthy controls. In all, 38 ESKD patients and 43 healthy participants were prospectively enrolled. They exhibited normal brain magnetic resonance imaging (MRI) without any structural lesions.
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December 2024
Department of Neuroscience and Padova Neuroscience Center, Università di Padova, Padova, Italy.
Can focal brain lesions, such as those caused by stroke, disrupt critical brain dynamics? What biological mechanisms drive its recovery? In a recent study, we showed that focal lesions generate a sub-critical state that recovers over time in parallel with behavior (Rocha et al., Nat. Commun.
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December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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December 2024
Medical Image Analysis, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Immune checkpoint inhibitor (ICI) treatment has proven successful for advanced melanoma, but is associated with potentially severe toxicity and high costs. Accurate biomarkers for response are lacking. The present work is the first to investigate the value of deep learning on CT imaging of metastatic lesions for predicting ICI treatment outcomes in advanced melanoma.
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December 2024
Institute of Informatics, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland.
Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-consuming and error-prone. Automatic detection and segmentation can assist radiologists in these tasks. This work explores the automated detection and segmentation of brain metastases (BMs) in longitudinal MRIs.
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