We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission. The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3-months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p-value < 2.2e−16). Conditions required for a parametric test do not hold, and we performed a paired Wilcoxon Test assuming the null hypothesis that all the groups have the same performance. The null hypothesis was rejected with a value < 2.2e−16, so there are statistical differences between IS and ICH groups. In conclusion, machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity.

Background: Recent preclinical studies have shown that regulatory T cells (Treg) play a key role in the immune response after ischemic stroke (IS). However, the role of Treg in human acute IS has been poorly investigated. Our aim was to study the relationship between circulating Treg and outcome in human IS patients.Methods: A total of 204 IS patients and 22 control subjects were recruited. The main study variable was good functional outcome at 3 months (modified Rankin scale ≤2) considering infarct volume, Early Neurological Deterioration (END) and risk of infections as secondary variables. The percentage of circulating Treg was measured at admission, 48, 72 h and at day 7 after stroke onset.Results: Circulating Treg levels were higher in IS patients compared to control subjects. Treg at 48 h were independently associated with good functional outcome (OR, 3.5; CI: 1.9–7.8) after adjusting by confounding factors. Patients with lower Treg at 48 h showed higher frequency of END and risk of infections. In addition, a negative correlation was found between circulating Treg at 48 h (r = − 0.414) and 72 h (r = − 0.418) and infarct volume.Conclusions: These findings suggest that Treg may participate in the recovery of IS patients. Therefore, Treg may be considered a potential therapeutic target in acute ischemic stroke.

Magnetic resonance imaging (MRI) is one of the most powerful non‐invasive imaging modalities used in clinics due to its great spatial resolution and excellent soft‐tissue contrast, though still less sensitive than other techniques such as the nuclear imaging modalities. This lack of sensitivity can be improved with the use of contrast agents based on nanomaterials. In recent years, researchers have focused on the development of magnetic nanoparticles, given their role as enhancers of the contrast signal based on the magnetic resonance. Manganese ferrite nanoparticles stand out, given their high magnetic susceptibility and magnetic soft nature. Herein, 10 nm MnFe₂O₄ nanoparticles, functionalized with the natural antioxidant vitamin E (VitE‐MFO) are encapsulated into simple, biodegradable and non‐toxic nanoemulsions (NEs), by a reproducible one‐step method obtaining stable 150 nm‐sized magnetic nanoemulsions (VitE‐MFO‐NEs). After encapsulation, the superparamagnetic properties of VitE‐MFO are maintained and MR imaging studies reveal an extremely high transverse relaxivity for VitE‐MFO‐NEs (652.9 × 10⁻³ m⁻¹ s⁻¹), twofold higher than VitE‐MFO value. Moreover, VitE‐MFO‐NEs show great in vivo biocompatibility and good signal in in vivo and ex vivo MRI, which indicates their great potential for biomedical imaging enhancing the negative MR contrast and significantly improving the sensitivity of MRI.

Ischemic stroke is a leading cause of death and disability worldwide. Following an ischemic insult, cells undergo endoplasmic reticulum (ER) stress, which increases the ER’s protein-folding and degradative capacities and blocks the global synthesis of proteins by phosphorylating the eukaryotic translation initiation factor 2-alpha (eIF2α). Phosphorylation of eIF2α is directly related to the dynamics of stress granules (SGs), which are membraneless organelles composed of RNA-binding proteins and mRNA. SGs play a critical role in mRNA metabolism and translational control. Other translation factors are also linked to cellular pathways, including SG dynamics following a stroke. Because the formation of SGs is closely connected to mRNA translation, it is interesting to study the relationship between SG dynamics and cellular outcome in cases of ischemic damage. Therefore, in this review, we focus on the role of SG dynamics during cerebral ischemia.

Studying the impact of demographic changes and progress in the management of stroke patients is necessary in order to organize care structures for the coming years. Consequently, we analyzed the prognostic trends of patients admitted to the Stroke Unit of a tertiary hospital in the last ten years. The University Clinical Hospital of Santiago de Compostela is the referral hospital for stroke in a catchment area that accounts for 16.5% of the population of Galicia. Data from patients admitted to the Stroke Unit were registered prospectively. A multinomial logistic regression was performed to determine the influence of new trends in demographic factors and in the management of patients with acute stroke. For the expected trend of progression, a 2008–2011 and 2012–2017 time series model was made by selecting the most appropriate model. In the last 10 years, the age of stroke onset has only increased in women (from 74.4 ± 2.2 years in 2008 to 78.8 ± 2.1 years in 2017; p = 0.037), and the same happens with the severity of neurological symptoms (ischemic stroke (IS), p < 0.0001; from 14 [10, 19] in 2008 to 19 [15, 26] in 2017), with a higher percentage of cardioembolic strokes (40.7% vs. 32.2% of cardioembolic strokes in women vs. men, p < 0.0001). In a multiple linear regression model, hospital improvement was mainly associated with the use of reperfusion treatment (B 53.11, CI 95% 49.87, 56.36, p < 0.0001). A differentiated multinomial logistic regression analysis conducted for the whole sample with ischemic strokes in the two time periods (2008–2011 and 2012–2017) showed no differences in the influence of factors associated with higher morbidity and mortality. The modeling of time series showed a distinct falling trend in mortality, with a slight increase in good outcome as well as morbidity in both ischemic and hemorrhagic stroke. Our results showed that mortality decreased in the entire sample; however, although outcome at discharge improved in ischemic stroke, severe disability also increased in these patients. Importantly, this tendency towards increased morbidity seems to be confirmed for the coming years.

Anti-inflammatory therapies reduce major adverse cardiovascular events (MACE) in coronary artery disease but remain unproven after stroke. Establishing the subtype-specific association between inflammatory markers and recurrence risk is essential for optimal selection of patients in randomized trials (RCTs) of anti-inflammatory therapies for secondary stroke prevention. Using individual participant data (IPD) identified from a systematic review, we analyzed the association between high-sensitivity C-reactive protein, interleukin-6 (IL-6), and vascular recurrence after ischemic stroke or transient ischemic attack. The prespecified coprimary end points were (1) any recurrent MACE (first major coronary event, recurrent stroke, or vascular death) and (2) any recurrent stroke (ischemic, hemorrhagic, or unspecified) after sample measurement. Analyses were performed stratified by stroke mechanism, per quarter and per biomarker unit increase after log_e transformation. We then did study-level meta-analysis with comparable published studies not providing IPD. Preferred Reporting Items for Systematic Review and Meta-Analyses IPD guidelines were followed. IPD was obtained from 10 studies (8,420 patients). After adjustment for vascular risk factors and statins/antithrombotic therapy, IL-6 was associated with recurrent MACE in stroke caused by large artery atherosclerosis (LAA) (risk ratio [RR] 2.30, 95% CI 1.21-4.36, p = 0.01), stroke of undetermined cause (UND) (RR 1.78, 1.19-2.66, p = 0.005), and small vessel occlusion (SVO) (RR 1.71, 0.99-2.96, p = 0.053) (quarter 4 [Q4] vs quarter 1 [Q1]). No association was observed for stroke due to cardioembolism or other determined cause. Similar results were seen for recurrent stroke and when analyzed per log_e unit increase for MACE (LAA, RR 1.26 [1.06-1.50], p = 0.009; SVO, RR 1.22 [1.01-1.47], p = 0.04; UND, RR 1.18 [1.04-1.34], p = 0.01). High-sensitivity CRP was associated with recurrent MACE in UND stroke only (Q4 vs Q1 RR 1.45 [1.04-2.03], p = 0.03). Findings were consistent on study-level meta-analysis of the IPD results with 2 other comparable studies (20,136 patients). Our data provide new evidence for the selection of patients in future RCTs of anti-inflammatory therapy in stroke due to large artery atherosclerosis, small vessel occlusion, and undetermined etiology according to inflammatory marker profile.