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Objective: The aim of this article is to determine the predictive value of the leukocyte-glycemic index in the long-term evolution of diabetic patients with peripheral arterial disease following endovascular treatment.

Abdominal aortic aneurysm (AAA) represents the increase of the diameter of the aorta by more than 50% in the absence of surgical or endovascular intervention. The risk of rupture and, therefore, mortality is increased significantly in AAA. The role of the mechanical characteristics of the AAA wall is poorly studied.

In vascular reconstruction in arterial trauma, ruptured abdominal aortic aneurysm or ruptured aneurysmal arteriovenous fistula, the challenge no longer lies in the surgical procedure itself, but rather the prevention of intimal hyperplasia, thrombosis and aneurysm formation, in parallel with extending as long as possible the patency of the grafts.

The current paper presents an examination of the emerging role of deep learning-based simulation software in enhancing preprocedural planning for intracranial aneurysm treatment using flow diverters. Intracranial aneurysms pose significant risk due to their potential rupture leading to life-threatening subarachnoid hemorrhage. Innovative endovascular treatment options like flow diverters, which redirect blood flow and promote healing, are gaining attention. The role of simulation software in optimizing these procedures is becoming increasingly crucial.

Introduction: Severe ischemia occurring in the lower limbs represents the advanced stage of peripheral artery disease (PAD). Atherosclerosis and inflammatory markers have been intensively studied to identify prognostic tools with a role in the evolution of patients with PAD. The aim of this study is to demonstrate the predictive value of systemic inflammatory markers such as the neutrophil-to-lymphocyte ratio (NLR) and the systemic inflammation index (SII) in the prognosis of patients diagnosed with critical leg ischemia (CLI) undergoing infrainguinal surgical revascularization with remote endarterectomy.

Background In determining mechanical characteristics, the accuracy of the thickness of the specimens can influence the biomechanical behavior, especially in the case of human tissues, where there is an important variability. This study aims to compare the accuracy of two routine measuring instruments, i.e., the digital vernier caliper and the digital thickness gauge, when measuring the thickness of venous specimens multiple times. Methodology In this study, we used 12 tubular vena cava specimens obtained from common breed pigs aged 18-24 weeks at the time of sacrifice from a local slaughterhouse. The measurements were performed using a digital vernier caliper (Multicomp PRO MP012475) for the first four protocols and a digital thickness gauge (Mitutoyo 547-500S) for the fifth protocol. In the first protocol, three measurements were taken on the same side, and their average was recorded as the sample thickness. The second protocol involved taking measurements on two opposite sides, and the average of these measurements was recorded as the sample thickness. In the third protocol, the thickness of each side was measured at its midpoint, and the average of the four measurements was recorded as the sample thickness. In the last protocol using a digital vernier caliper, the thickness of the vernier specimens was calculated as the average of the measurements taken at each corner of the square sample. Finally, for the fifth protocol, three consecutive measurements were taken using the digital thickness gauge, and their average was recorded as the final thickness of the sample. Results In the first protocol, we observed lower values during the first measurement in comparison to the second (0.409 ± 0.063 vs. 0.536 ± 0.064, p < 0.0001) and the third (0.409 ± 0.063 vs. 0.528 ± 0.055, p = 0.0001). Moreover, with the second protocol, we observed lower values during the first two measurements in comparison to the third measurement (p = 0.0279 and p = 0.0054). Regarding protocols three and four, we recorded higher values for the second and third measurements than the first one, with higher values for the third measurement than the second one. In the fifth protocol, there were no significant statistical differences between the three consecutive measurements (p = 0.953, p = 0.742, and p = 0.897). Further, we examined the variations in sample thickness determined using each of the protocols proposed for the digital vernier caliper, as well as the values obtained with the digital thickness gauge protocol. As a result, during the first and second measurements, we observed lower thickness values for the venous wall samples using the first four protocols compared to the fifth protocol (for all p < 0.05). However, no differences were noted between the five protocols during the third measurement. Conclusions The digital thickness gauge Mitutoyo 547-500S provided superior accuracy with no difference between three successive measurements of venous wall thickness, regardless of the examiner's experience. Accurately determining the thickness of venous specimens is crucial for calculating the tissue's biomechanical properties., Competing Interests: Human subjects: All authors have confirmed that this study did not involve human participants or tissue. Animal subjects: Committee of Ethics in Scientific Research of the George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, Romania Issued protocol number 2653/15.12.2023. Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work., (Copyright © 2024, Ion et al.)