Benefit of selective shunt use during carotid endarterectomy under regional anesthesia.

OBJECTIVES: Carotid cross-clamping during endarterectomy exposes the patient to intraoperative neurological deficits due to embolism or cerebral hypoperfusion. To prevent further cerebrovascular incidents, resorting to shunt is frequently recommended. However, since this method is also considered a stroke risk factor, the use is still controversial. This study aims to shed some light on the best approach regarding the use of shunt in symptomatic cerebral malperfusion after carotid artery cross-clamping. METHODS: From January 2012 to January 2018, 79 patients from a tertiary referral hospital who underwent carotid endarterectomy with regional anesthesia for carotid artery stenosis and manifested post-clamping neurologic deficits were prospectively gathered. Shunt use was left to the decision of the surgeon and performed in 31.6% (25) of the patients. Demographics, comorbidities, imaging tests, and clinical/intraoperative features were evaluated. For data assessment, univariate analysis was performed. RESULTS: Regarding 30-day stroke, 30-day postoperative complications (stroke, surgical hematoma, hyperperfusion syndrome), and cranial nerve injury, no significant differences were found (P = 0.301, P = 0.460, and P = 0.301, respectively) between resource to shunt and non-shunt. Clamping and surgery times were significantly higher in the shunt group (P < 0.001 and P = 0.0001, respectively). CONCLUSIONS: Selective-shunting did not demonstrate superiority for patients who developed focal deficits regarding stroke or other postoperative complications. However, due to the limitations of this study, the benefit of shunting cannot be excluded. Further randomized trials are recommended for precise results on this matter with current sparse clinical evidence.

Computational analysis of the SARS-CoV-2 and other viruses based on the Kolmogorov's complexity and Shannon's information theories.

This paper tackles the information of 133 RNA viruses available in public databases under the light of several mathematical and computational tools. First, the formal concepts of distance metrics, Kolmogorov complexity and Shannon information are recalled. Second, the computational tools available presently for tackling and visualizing patterns embedded in datasets, such as the hierarchical clustering and the multidimensional scaling, are discussed. The synergies of the common application of the mathematical and computational resources are then used for exploring the RNA data, cross-evaluating the normalized compression distance, entropy and Jensen-Shannon divergence, versus representations in two and three dimensions. The results of these different perspectives give extra light in what concerns the relations between the distinct RNA viruses.