The ABCD(3)-I score had a higher predictive value than the ABCD(2) score for assessing the risk of early stroke after transient ischemic attack in a Chinese population.

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Background: The combined index of hemoglobin, albumin, lymphocyte, and platelet (HALP) is considered a novel score to reflect systemic inflammation and nutritional status. This study aimed to investigate the association between HALP score and poor outcome in patients with acute ischemic stroke (AIS).Methods: Consecutive AIS patients within 24 h after onset were prospectively enrolled. Poor outcome was a combination of a new stroke event (ischemic and hemorrhagic) and all-cause death within 90 days and 1 year. The association between HALP score and poor outcome was analyzed using Cox proportional hazards.Results: A total of 1,337 patients were included. Overall, 60 (4.5%) and 118 (8.8%) patients experienced poor outcome within 90 days and 1 year, respectively. Patients in the highest tertile of HALP score had a lower risk of poor outcome within 90 days and 1 year (hazard ratio: 0.25 and 0.42; 95% confidence intervals: 0.11–0.57 and 0.25–0.69, P for trend <0.01 for all) compared with those in the lowest tertile after adjusting relevant confounding factors. Adding HALP score to the conventional risk factors improved prediction of poor outcome in patients with AIS within 90 days and 1 year (net reclassification index, 48.38 and 28.95%; integrated discrimination improvement, 1.51 and 1.51%; P < 0.05 for all).Conclusions: Increased HALP score was associated with a decreased risk of recurrent stroke and death within 90 days and 1 year after stroke onset, suggesting that HALP score may serve as a powerful indicator for AIS.

Aim: Monocyte-to-high-density lipoprotein ratio (MHR) recently emerged as an inflammatory marker and has been reported to be a novel prognostic indicator of cardiovascular diseases. However, the relationship between MHR and prognosis of acute ischemic stroke (AIS) remains unclear. Methods: Consecutive AIS patients were prospectively identified from January 2015 to December 2017. Functional outcome was evaluated by the modified Rankin Scale (mRS). Poor outcome was defined as of mRS 3–6. Multivariate logistic regression analysis was conducted to evaluate the relationship between MHR and poor outcome. Results: A total of 1090 AIS patients within 24 hours of the onset of symptoms were recruited. MHR was higher in poor outcome group compared to that in good outcome group [0.53 (0.37–0.69) vs. 0.48 (0.33–0.60), P = 0.007]. Multivariate logistic regression analysis indicated that higher MHR level was independently associated with the poor outcome at 3 months (OR 2.58, 95% CI, 1.21–5.51, P = 0.015), especially the stroke subtype of large artery atherosclerosis (OR 2.52, 95% CI, 1.03–6.19, P = 0.034). Receiver operating curve (ROC) analysis showed that the area under the ROC curves for MHR was 0.67 and the best predictive cutoff value of MHR was 0.51, with a sensitivity of 62.3% and a specificity of 66.5%. Conclusions: MHR may be a significant and independent predictor of poor functional outcome in patients with AIS.

Existing techniques have many limitations in the diagnosis and classification of ischemic stroke (IS). Considering this, we used metabolomics to screen for potential biomarkers of IS and its subtypes and to explore the underlying related pathophysiological mechanisms. Serum samples from 99 patients with acute ischemic stroke (AIS) [the AIS subtypes included 49 patients with large artery atherosclerosis (LAA) and 50 patients with small artery occlusion (SAO)] and 50 matched healthy controls (HCs) were analyzed by non-targeted metabolomics based on liquid chromatography–mass spectrometry. A multivariate statistical analysis was performed to identify potential biomarkers. There were 18 significantly different metabolites, such as oleic acid, linoleic acid, arachidonic acid, L-glutamine, L-arginine, and L-proline, between patients with AIS and HCs. These different metabolites are closely related to many metabolic pathways, such as fatty acid metabolism and amino acid metabolism. There were also differences in metabolic profiling between the LAA and SAO groups. There were eight different metabolites, including L-pipecolic acid, 1-Methylhistidine, PE, LysoPE, and LysoPC, which affected glycerophospholipid metabolism, glycosylphosphatidylinositol-anchor biosynthesis, histidine metabolism, and lysine degradation. Our study effectively identified the metabolic profiles of IS and its subtypes. The different metabolites between LAA and SAO may be potential biomarkers in the context of clinical diagnosis. These results highlight the potential of metabolomics to reveal new pathways for IS subtypes and provide a new avenue to explore the pathophysiological mechanisms underlying IS and its subtypes.

As an essential variable in linking water, carbon, and energy cycles, evapotranspiration (ET) is difficult to measure. Remote sensing, reanalysis, and land surface model-based ET products offer comprehensive alternatives at different spatio-temporal intervals, but their performance varies. In this study, we selected four popular ET global products: The Global Land Evaporation Amsterdam Model version 3.0a (GLEAM3.0a), the Modern Era Retrospective-Analysis for Research and Applications-Land (MERRA-Land) project, the Global Land Data Assimilation System version 2.0 with the Noah model (GLDAS2.0-Noah) and the EartH2Observe ensemble (EartH2Observe-En). Then, we comprehensively evaluated the performance of these products over China using a stratification method, six validation criteria, and high-quality eddy covariance (EC) measurements at 12 sites. The aim of this research was to provide important quantitative information to improve and apply the ET models and to inform choices about the appropriate ET product for specific applications. Results showed that, within one stratification, the performance of each ET product based on a certain criterion differed among classifications of this stratification. Furthermore, the optimal ET (OET) among these products was identified by comparing the magnitudes of each criterion. Results suggested that, given a criterion (a stratification classification), the OETs varied among stratification classifications (the selected six criteria). In short, no product consistently performed best, according to the selected validation criterion. Thus, multi-source ET datasets should be employed in future studies to enhance confidence in ET-related conclusions.

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