Background and Purpose—Acute stroke is associated with impairment of cardiac autonomic balance and increased incidence of arrhythmias. These abnormalities appear more relevant in the case of involvement of the right insula in the infarct area. The aim of this study was to assess the impact of right-sided insular damage, cardiac autonomic derangement, and arrhythmias on clinical outcome after acute ischemic stroke.Methods—Holter monitoring for 24 hours was performed in 208 consecutive patients with first-ever acute ischemic stroke. Time- and frequency-domain measures of heart rate variability and arrhythmias were considered in all cases. All patients were followed for a 12-month period after the initial event.Results—During the 12-month follow-up period, 48 patients died (1-year probability of death, 0.23; 95% CI, 0.17 to 0.30). Multivariate analysis demonstrated that age (hazard ratio [HR], 1.06; 95% CI, 1.01 to 1.10;P=0.0087), stroke severity on admission (HR, 1.25; 95% CI, 1.13 to 1.39;P=0.0001), presence of right-sided insular damage (HR, 2.01; 95% CI, 1.13 to 1.39;P=0.0187), as well as lower values of the SD of all normal-to-normal RR intervals (HR, 3.32; 95% CI, 1.67 to 6.24;P=0.002), and presence of nonsustained ventricular tachycardia during Holter monitoring (HR, 2.99; 95% CI, 1.58 to 5.67;P=0.0007) were independent predictors of 1-year mortality.Conclusions—The integration of traditional risk stratifiers with autonomic and arrhythmic markers, and the careful search for right-sided insular involvement, may represent an effective approach for identification of stroke patients at risk for early mortality.

Systemic inflammation might cause neuronal damage and sustain neurodegenerative diseases and behavior impairment, with the participation of pro-inflammatory cytokines, like tumor necrosis factor (TNF)-α and interleukin (IL)-18. However, the potential contribution of these cytokines to behavioral impairment in the long-term period has not been fully investigated.

Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer’s disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.

Acute ischemic stroke, the most frequent cause of permanent disability in adults worldwide, results from transient or permanent reduction in regional cerebral blood flow and involves oxidative stress and inflammation. Despite the success of experimental animal models of stroke in identifying anti-inflammatory/neuroprotective compounds, translation of these putative neuroprotectants to human clinical trials has failed to produce a positive outcome. Tissue injury and stress activate endogenous mechanisms which function to restore homeostatic balance and prevent further damage by upregulating the synthesis of lipid signaling molecules, including N-palmitoylethanolamine (PEA or palmitoylethanolamide). PEA exerts neuroprotection and reduces inflammatory secondary events associated with brain ischemia reperfusion injury (middle cerebral artery occlusion (MCAo)). Here, we examined the neuroprotective potential of a co-ultramicronized composite containing PEA and the antioxidant flavonoid luteolin (10:1 by mass), nominated co-ultraPEALut. The study consisted of two arms. In the first, rats subjected to MCAo and treated with co-ultraPEALut post-ischemia showed reduced edema and brain infract volume, improved neurobehavioral functions, and reduced expression of pro-inflammatory markers and astrocyte markers. In the second arm, a cohort of 250 stroke patients undergoing neurorehabilitation on either an inpatient or outpatient basis were treated for 60 days with a pharmaceutical preparation of co-ultraPEALut (Glialia®). At baseline and after 30 days of treatment, all patients underwent a battery of evaluations to assess neurological status, impairment of cognitive abilities, the degree of spasticity, pain, and independence in daily living activities. All indices showed statistically significant gains at study end. Despite its observational nature, this represents the first description of co-ultraPEALut administration to human stroke patients and clinical improvement not otherwise expected from spontaneous recovery. Further, controlled trials are warranted to confirm the utility of co-ultraPEALut to improve clinical outcome in human stroke.