Carotid endarterectomy compared with carotid artery stenting for extracranial carotid artery stenosis: a retrospective single-centre study

Aim: One of the main risk factors for an ischemic stroke is significant carotid artery stenosis, and extracranial severe carotid artery stenosis accounts for 20% of ischemic strokes. Prior to the development of carotid artery stenting (CAS), the only effective and reliable treatment for carotid artery stenosis was carotid endarterectomy (CEA). This study compares the results of CAS and CEA in patients with significant carotid artery stenosis. Methods: Between 2018 and 2022, hospital records of all patients who underwent carotid artery revascularization at the institution were retrospectively analyzed. Patients were divided into two groups depending on whether CEA or CAS was performed for carotid revascularization. Propensity score matching was performed to reduce bias by equating the baseline clinical characteristics of the groups. To compare 30-day, 1-year, and long-term outcomes, rates of transient ischemic attack (TIA), myocardial infarction, stroke, all-cause mortality, and composite endpoints were analyzed. Results: After PSM, 76 patients each in the CEA and CAS groups were compared. The mean age was 69.80 years ± 11.35 years and 121 (80%) were male. The patients were followed up for a mean of 33 months ± 6 months. The incidence of TIA in the perioperative period [9 (12%) vs. 4 (5%); P < 0.05], TIA and composite endpoint at 1-year period [11 (15%) vs. 2 (3%); P < 0.05 and 27 (36%) vs. 16 (21%); P < 0.05, respectively] were significantly higher in the CAS group than in the CEA group. No difference was observed between the groups in the long-term. Conclusions: There was no noticeable difference between the CEA and CAS groups in the examination of cases with severe carotid artery stenosis in terms of 1-month, and 1-year results (apart from TIA and composite endpoints), or long-term outcomes. Extracranial carotid artery stenosis can be treated safely and effectively also by CAS

Authoritarian Neoliberalism and Democratic Backsliding in Turkey: Beyond the Narratives of Progress

Unpacking the core themes that are discussed in this collection, this article both offers a research agenda to re-analyse Turkey’s ‘authoritarian turn’ and mounts a methodological challenge to the conceptual frameworks that reinforce a strict analytical separation between the ‘economic’ and the ‘political’ factors. The paper problematises the temporal break in scholarly analyses of the AKP period and rejects the argument that the party’s methods of governance have shifted from an earlier ‘democratic’ model – defined by ‘hegemony’ – to an emergent ‘authoritarian’ one. In contrast, by retracing the mechanisms of the state-led reproduction of neoliberalism since 2003, the paper demonstrates that the party’s earlier ‘hegemonic’ activities were also shaped by authoritarian tendencies which manifested at various levels of governance

Deep Bingham networks: dealing with uncertainty and ambiguity in pose estimation

In this work, we introduce Deep Bingham Networks (DBN), a generic framework that can naturally handle pose-related uncertainties and ambiguities arising in almost all real life applications concerning 3D data. While existing works strive to find a single solution to the pose estimation problem, we make peace with the ambiguities causing high uncertainty around which solutions to identify as the best. Instead, we report a family of poses which capture the nature of the solution space. DBN extends the state of the art direct pose regression networks by (i) a multi-hypotheses prediction head which can yield different distribution modes; and (ii) novel loss functions that benefit from Bingham distributions on rotations. This way, DBN can work both in unambiguous cases providing uncertainty information, and in ambiguous scenes where an uncertainty per mode is desired. On a technical front, our network regresses continuous Bingham mixture models and is applicable to both 2D data such as images and to 3D data such as point clouds. We proposed new training strategies so as to avoid mode or posterior collapse during training and to improve numerical stability. Our methods are thoroughly tested on two different applications exploiting two different modalities: (i) 6D camera relocalization from images; and (ii) object pose estimation from 3D point clouds, demonstrating decent advantages over the state of the art. For the former we contributed our own dataset composed of five indoor scenes where it is unavoidable to capture images corresponding to views that are hard to uniquely identify. For the latter we achieve the top results especially for symmetric objects of ModelNet dataset (Wu et al., in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1912–1920, 2015). The code and dataset accompanying this paper is provided under https://multimodal3dvision.github.io