Search Results (4)

Background/Objectives: The aim of this study was to investigate the effects of cranio-cervical artery stenosis (CAS) and cerebral infarction (CI) on the function of the glymphatic system (GS). Methods: Hospitalised patients with CI and/or CAS were enrolled, along with a control group. A total of 111 participants (62.68 ± 9.85 years; 37% female) were enrolled in this study. GS function was assessed using the diffusion tensor imaging analysis along with the perivascular space (DTI-ALPS) method. The influencing factors and the individual and combined effects of CI and CAS on the DTI-ALPS index were analysed. Results: Age (p = 0.024), CI (p < 0.001), and CAS (p = 0.001) were independent predictors of a lower DTI-ALPS index. There were statistically significant differences in the DTI-ALPS index among the four groups (CI, CAS, CI + CAS, control) (F(3, 107) = 91.4, p < 0.0001). The DTI-ALPS index was lower in the CI, CAS, and CI + CAS groups compared with the control group (p < 0.0001); in the CI group compared with the CAS group (p < 0.0001); and in the CI + CAS group compared with the CI group (p < 0.05). CI and CAS were found to have a significant interaction effect on the DTI-ALPS index (F(1, 107) = 6.43, p = 0.013). Conclusions: Aging, CAS, and CI independently impair GS function, with CI having a stronger effect. All three are independent predictors of GS dysfunction. Patients with CAS experience more significant GS dysfunction after suffering CI than patients without CAS. CI and CAS have a synergistic effect on GS impairment. Full article

Objectives: Carotid geometry and cardiovascular risk factors play a significant role in the development of carotid atherosclerotic plaques. This study aimed to investigate the correlation between carotid plaque formation and carotid artery geometry characteristics. Methods: A retrospective cross-sectional analysis was performed on 1227 patients, categorized into a normal group (n = 685) and carotid plaque groups causing either mild stenosis (<50% stenosis based on NASCET criteria, n = 385) or moderate-to-severe stenosis (>50%, n = 232). The left and right carotid were evaluated individually for each group. Patient data, including cardiovascular risk factors and laboratory test results, were collected. Carotid geometric measurements were obtained from 3D models reconstructed from cranio-cervical computed tomography angiography (CTA) using semi-automated software (MIMICS). The geometric variables analyzed included the vascular diameter and sectional area of the common carotid artery (CCA), internal carotid artery (ICA), external carotid artery (ECA), and carotid artery bifurcation (CAB), as well as the carotid bifurcation angles and carotid tortuosity. Results: Compared to the normal group, in both the right and left carotid arteries, patients with carotid plaques exhibited a significantly higher age (p < 0.001) and a greater prevalence of hypertension (p < 0.001) and diabetes mellitus (p < 0.001). Additionally, they demonstrated a larger CCA and a smaller carotid bifurcation dimension (p < 0.05). In the analysis of the left carotid artery, patients with carotid plaques also had a significantly smaller ICA dimension (p < 0.05) than the normal group. Conclusions: This study found that patients with carotid plaques were older and had a higher prevalence of hypertension and diabetes, larger CCAs, and smaller carotid bifurcations. The plaque-positive left ICA was significantly smaller than that of the plaque-negative group, suggesting a side-specific vulnerability. These findings highlight the role of carotid geometry in plaque formation and its potential clinical implications for personalized risk assessment and targeted interventions. Full article

This study introduces a machine learning (ML) approach to diagnosing carotid artery diseases, including stenosis, aneurysm, and dissection, by leveraging craniocervical computed tomography angiography (CTA) data. A meticulously curated, balanced dataset of 122 patient cases was used, ensuring reproducibility and data quality, and this is publicly accessible at (insert dataset location). The proposed method integrates a super learner model which combines adaptive boosting, gradient boosting, and random forests algorithms, achieving an accuracy of $90\%$. To enhance model robustness and generalization, techniques such as k-fold cross-validation, bootstrapping, data augmentation, and the synthetic minority oversampling technique (SMOTE) were applied, expanding the dataset to 1000 instances and significantly improving performance for minority classes like aneurysm and dissection. The results highlight the pivotal role of blood vessel structural analysis in diagnosing carotid artery diseases and demonstrate the superior performance of the super learner model in comparison with state-of-the-art (SOTA) methods in terms of both accuracy and robustness. This manuscript outlines the methodology, compares the results with state-of-the-art approaches, and provides insights for future research directions in applying machine learning to medical diagnostics. Full article

Background. The aim of our study was to evaluate the incidence of early postoperative cognitive decline (POCD) and determine perioperative risk factors as well as the impact of asymptomatic cerebral vascular lesion on the development of neurocognitive complications.
Materials and methods. A total of 127 consecutive adult patients undergoing on-pump coronary artery bypass grafting were studied. Neuropsychological testing was performed the day before surgery and 7–9 days after operation. Stepwise logistic regression analysis determined independent predictors of POCD.
Results. The incidence of postoperative cognitive decline was 46% (n=59). Patients in the POCD group were older (P=0.04) and had an increased prevalence of asymptomatic carotid artery stenosis (P=0.0001). POCD was associated with longer time in surgery (P=0.018), inotropic support intraoperativelly (P=0.02) and during postoperative period (P=0.008). Patients in the POCD group had an increased incidence of postoperative bleeding (P=0.037), delirium (P=0.016) and stayed in hospital for a longer period (P=0.007). Age of more than 65 years (OR, 2.7), asymptomatic carotid artery stenosis of more than 50% (OR, 26.89), duration of surgery of more than 4 hours (OR, 4.08), postoperative mechanical ventilation of more than 6 hours (OR, 3.33), and stay in an intensive care unit for more than 3 days (OR, 3.38) were significant independent predictors of cognitive decline.
Conclusions. Increased age, preoperative prevalence of craniocervical atherosclerotic lesions, longer time in surgery, longer stay in an intensive care unit and mechanical ventilation time were found to be the risk factors for developing postoperative cognitive decline. Full article