The efficient generation of striatal neurons from human embryonic stem cells (hESCs) and induced pluripotent stem cells (hiPSCs) is fundamental for realising their promise in disease modelling, pharmaceutical drug screening and cell therapy for Huntington9s disease. GABAergic medium-sized spiny neurons (MSNs) are the principal projection neurons of the striatum and specifically degenerate in the early phase of Huntington9s disease. Here we report that activin A induces lateral ganglionic eminence (LGE) characteristics in nascent neural progenitors derived from hESCs and hiPSCs in a sonic hedgehog-independent manner. Correct specification of striatal phenotype was further demonstrated by the induction of the striatal transcription factors CTIP2, GSX2 and FOXP2. Crucially, these human LGE progenitors readily differentiate into postmitotic neurons expressing the striatal projection neuron signature marker DARPP32, both in culture and following transplantation in the adult striatum in a rat model of Huntington9s disease. Activin-induced neurons also exhibit appropriate striatal-like electrophysiology in vitro. Together, our findings demonstrate a novel route for efficient differentiation of GABAergic striatal MSNs from human pluripotent stem cells.

Objective: To discover, by using metabolomics, novel candidate biomarkers for stroke recurrence (SR) with a higher prediction power than present ones. Methods: Metabolomic analysis was performed by liquid chromatography coupled to mass spectrometry in plasma samples from an initial cohort of 131 TIA patients recruited <24 hours after the onset of symptoms. Pattern analysis and metabolomic profiling, performed by multivariate statistics, disclosed specific SR and large-artery atherosclerosis (LAA) biomarkers. The use of these methods in an independent cohort (162 subjects) confirmed the results obtained in the first cohort. Results: Metabolomics analyses could predict SR using pattern recognition methods. Low concentrations of a specific lysophosphatidylcholine (LysoPC[16:0]) were significantly associated with SR. Moreover, LysoPC(20:4) also arose as a potential SR biomarker, increasing the prediction power of age, blood pressure, clinical features, duration of symptoms, and diabetes scale (ABCD2) and LAA. Individuals who present early (<3 months) recurrence have a specific metabolomic pattern, differing from non-SR and late SR subjects. Finally, a potential LAA biomarker, LysoPC(22:6), was also described. Conclusions: The use of metabolomics in SR biomarker research improves the predictive power of conventional predictors such as ABCD2 and LAA. Moreover, pattern recognition methods allow us to discriminate not only SR patients but also early and late SR cases.

Understanding the mechanisms underlying neural progenitor differentiation and neuronal fate specification is critical for the use of embryonic stem cells (ESCs) for regenerative medicine. Cortical interneurons are of particular interest for cell transplantation; however, only a limited subset of these neurons can be generated from ESCs. Here we uncover a pivotal role for Activin in regulating the differentiation and identity of telencephalic neural precursors derived from mouse and human ESCs. We show that Activin directly inhibits the mitogenic sonic hedgehog pathway in a Gli3-dependent manner while enhancing retinoic acid signalling, the pro-neurogenic pathway. In addition, we demonstrate that Activin provides telencephalic neural precursors with positional cues that specifically promote the acquisition of a calretinin interneuron fate by controlling the expression of genes that regulate cortical interneuron identity. This work demonstrates a novel means for regulating neuronal differentiation and specification of subtype identity.

Background: Chronic kidney disease (CKD) is an independent risk factor for atherosclerotic disease. We hypothesized that CKD promotes a proatherogenic lipid profile modifying lipoprotein composition and particle number. Methods: Cross-sectional study in 395 non-diabetic individuals (209 CKD patients and 186 controls) without statin therapy. Conventional lipid determinations were combined with advanced lipoprotein profiling by nuclear magnetic resonance, and their discrimination ability was assessed by machine learning. Results: CKD patients showed an increase of very-low-density (VLDL) particles and a reduction of LDL particle size. Cholesterol and triglyceride content of VLDLs and intermediate-density (IDL) particles increased. However, low-density (LDL) and high-density (HDL) lipoproteins gained triglycerides and lost cholesterol. Total-Cholesterol, HDL-Cholesterol, LDL-Cholesterol, non-HDL-Cholesterol and Proprotein convertase subtilsin-kexin type (PCSK9) were negatively associated with CKD stages, whereas triglycerides, lipoprotein(a), remnant cholesterol, and the PCSK9/LDL-Cholesterol ratio were positively associated. PCSK9 was positively associated with total-Cholesterol, LDL-Cholesterol, LDL-triglycerides, LDL particle number, IDL-Cholesterol and remnant cholesterol. Machine learning analysis by random forest revealed that new parameters have a higher discrimination ability to classify patients into the CKD group, compared to traditional parameters alone: area under the curve (95% CI), .789 (.711, .853) vs .687 (.611, .755), respectively. Conclusions: non-diabetic CKD patients have a hidden proatherogenic lipoprotein profile.

Objective: Recent studies have demonstrated that there is a decrease in the risk of subsequent stroke after transient ischemic attack (TIA) when urgent care (UC) is administered. However, no meta-analysis has been developed with contemporaneous TIA studies. We perform a systematic review and a meta-analysis to establish the risk of early stroke recurrence (SR) considering data from studies that offered UC to TIA patients. Methods: We searched for studies, without language restriction, from January 2007 to January 2015 according to PRISMA guidelines. We included studies with TIA patients who underwent UC and reported the proportion of SR at 90 days. We excluded studies that were centered on less than 100 patients and cohorts including both stroke and TIA, if stroke risk after TIA was not described. For its relevance, we included the TIAregistry.org study published in 2016. We performed both fixed and random effects meta-analyses to determine SR and assess sources of heterogeneity. Results: From 4,103 identified citations, we selected 15 papers that included 14,889 patients. There was great variation in terms of the number of patients included in each study, ranging from 115 to 4,160. Seven studies were TIA clinic based. The mean age and the percentage of men were similar among studies, ranging from 62.4 to 73.1 years and 45.1-62%, respectively. The reported risk of stroke ranged from 0 to 1.46% 2 days after TIA (9 studies included), 0-2.55% 7 days after TIA (11 studies included), 1.91-2.85% 30 days after TIA (4 studies included), and 0.62-4.76% 90 days after TIA (all studies included). The pooled stroke risk was 3.42% (95% CI 3.14-3.74) at 90 days, 2.78% (95% CI 2.47-3.12) at 30 days, 2.06% (95% CI 1.83-2.33) at 7 days and 1.36% (95% CI 1.15-1.59) at 2 days. Although we did not find statistically significant heterogeneity in SR among studies, those with a higher proportion of patients with motor weakness had a significantly higher risk of SR. No statistically significant association was observed between TIA clinic management and SR. Conclusion: The pooled early SR is lower than in previous meta-analyses and homogeneous for all studies with an urgent assessment and management strategy regardless of vascular risk factors and clinical characteristics. Therefore, the best setting for TIA management can be individualized for each center.