First-Cycle Oxidative Generation of Lithium Nucleation Sites Stabilizes Lithium-Metal Electrodes

Although lithium‐metal electrodes have very high capacities, their use as negative electrodes in batteries is associated with stability and safety problems due to formation of dendrites, mossy as well as dead lithium. These problems generally result from the difficulty to ensure that the deposition and stripping of lithium occur homogeneously on the entire electrode surface. As a result, the lithium‐metal electrode is gradually transformed into a thick, porous, and poorly performing electrode. It is therefore essential to develop approaches that facilitate the attainment of homogeneous (i.e., 2D) lithium nucleation and growth. It is also important to note that if the lithium electrode is oxidized on the first half‐cycle, the formed oxidation pits will control the subsequent lithium deposition step. Herein, it is shown that the performance of lithium‐metal electrodes can be straightforwardly improved by introducing a short (e.g., 1 s long) potentiostatic pulse so that the first oxidation step takes place more homogeneously on the lithium surface. This surface activation step gives rise to a large number of preferential lithium nucleation sites facilitating the subsequent attainment of a uniform lithium deposition step. The experimental results indicate that this straightforward pulse approach can significantly increase the lifetime of lithium‐metal electrodes

On the Capacity Losses Seen for Optimized Nano‐Si Composite Electrodes in Li‐Metal Half‐Cells

While the use of silicon‐based electrodes can increase the capacity of Li‐ion batteries considerably, their application is associated with significant capacity losses. In this work, the influences of solid electrolyte interphase (SEI) formation, volume expansion, and lithium trapping are evaluated for two different electrochemical cycling schemes using lithium‐metal half‐cells containing silicon nanoparticle–based composite electrodes. Lithium trapping, caused by incomplete delithiation, is demonstrated to be the main reason for the capacity loss while SEI formation and dissolution affect the accumulated capacity loss due to a decreased coulombic efficiency. The capacity losses can be explained by the increasing lithium concentration in the electrode causing a decreasing lithiation potential and the lithiation cut‐off limit being reached faster. A lithium‐to‐silicon atomic ratio of 3.28 is found for a silicon electrode after 650 cycles using 1200 mAhg−1 capacity limited cycling. The results further show that the lithiation step is the capacity‐limiting step and that the capacity losses can be minimized by increasing the efficiency of the delithiation step via the inclusion of constant voltage delithiation steps. Lithium trapping due to incomplete delithiation consequently constitutes a very important capacity loss phenomenon for silicon composite electrodes

Risk score to predict gastrointestinal bleeding after acute ischemic stroke

BackgroundGastrointestinal bleeding (GIB) is a common and often serious complication after stroke. Although several risk factors for post-stroke GIB have been identified, no reliable or validated scoring system is currently available to predict GIB after acute stroke in routine clinical practice or clinical trials. In the present study, we aimed to develop and validate a risk model (acute ischemic stroke associated gastrointestinal bleeding score, the AIS-GIB score) to predict in-hospital GIB after acute ischemic stroke.MethodsThe AIS-GIB score was developed from data in the China National Stroke Registry (CNSR). Eligible patients in the CNSR were randomly divided into derivation (60%) and internal validation (40%) cohorts. External validation was performed using data from the prospective Chinese Intracranial Atherosclerosis Study (CICAS). Independent predictors of in-hospital GIB were obtained using multivariable logistic regression in the derivation cohort, and β-coefficients were used to generate point scoring system for the AIS-GIB. The area under the receiver operating characteristic curve (AUROC) and the Hosmer-Lemeshow goodness-of-fit test were used to assess model discrimination and calibration, respectively.ResultsA total of 8,820, 5,882, and 2,938 patients were enrolled in the derivation, internal validation and external validation cohorts. The overall in-hospital GIB after AIS was 2.6%, 2.3%, and 1.5% in the derivation, internal, and external validation cohort, respectively. An 18-point AIS-GIB score was developed from the set of independent predictors of GIB including age, gender, history of hypertension, hepatic cirrhosis, peptic ulcer or previous GIB, pre-stroke dependence, admission National Institutes of Health stroke scale score, Glasgow Coma Scale score and stroke subtype (Oxfordshire). The AIS-GIB score showed good discrimination in the derivation (0.79; 95% CI, 0.764-0.825), internal (0.78; 95% CI, 0.74-0.82) and external (0.76; 95% CI, 0.71-0.82) validation cohorts. The AIS-GIB score was well calibrated in the derivation (P = 0.42), internal (P = 0.45) and external (P = 0.86) validation cohorts.ConclusionThe AIS-GIB score is a valid clinical grading scale to predict in-hospital GIB after AIS. Further studies on the effect of the AIS-GIB score on reducing GIB and improving outcome after AIS are warranted

Risk score to predict gastrointestinal bleeding after acute ischemic stroke

© 2014 Ji et al.; licensee BioMed Central Ltd. Background: Gastrointestinal bleeding (GIB) is a common and often serious complication after stroke. Although several risk factors for post-stroke GIB have been identified, no reliable or validated scoring system is currently available to predict GIB after acute stroke in routine clinical practice or clinical trials. In the present study, we aimed to develop and validate a risk model (acute ischemic stroke associated gastrointestinal bleeding score, the AIS-GIB score) to predict in-hospital GIB after acute ischemic stroke.Methods: The AIS-GIB score was developed from data in the China National Stroke Registry (CNSR). Eligible patients in the CNSR were randomly divided into derivation (60%) and internal validation (40%) cohorts. External validation was performed using data from the prospective Chinese Intracranial Atherosclerosis Study (CICAS). Independent predictors of in-hospital GIB were obtained using multivariable logistic regression in the derivation cohort, and β-coefficients were used to generate point scoring system for the AIS-GIB. The area under the receiver operating characteristic curve (AUROC) and the Hosmer-Lemeshow goodness-of-fit test were used to assess model discrimination and calibration, respectively.Results: A total of 8,820, 5,882, and 2,938 patients were enrolled in the derivation, internal validation and external validation cohorts. The overall in-hospital GIB after AIS was 2.6%, 2.3%, and 1.5% in the derivation, internal, and external validation cohort, respectively. An 18-point AIS-GIB score was developed from the set of independent predictors of GIB including age, gender, history of hypertension, hepatic cirrhosis, peptic ulcer or previous GIB, pre-stroke dependence, admission National Institutes of Health stroke scale score, Glasgow Coma Scale score and stroke subtype (Oxfordshire). The AIS-GIB score showed good discrimination in the derivation (0.79; 95% CI, 0.764-0.825), internal (0.78; 95% CI, 0.74-0.82) and external (0.76; 95% CI, 0.71-0.82) validation cohorts. The AIS-GIB score was well calibrated in the derivation (P = 0.42), internal (P = 0.45) and external (P = 0.86) validation cohorts.Conclusion: The AIS-GIB score is a valid clinical grading scale to predict in-hospital GIB after AIS. Further studies on the effect of the AIS-GIB score on reducing GIB and improving outcome after AIS are warranted.Link_to_subscribed_fulltex

A novel risk score to predict 1-year functional outcome after intracerebral hemorrhage and comparison with existing scores

Introduction: Spontaneous intracerebral hemorrhage (ICH) is one of leading causes of mortality and morbidity worldwide. Several predictive models have been developed for ICH; however, none of them have been consistently used in routine clinical practice or clinical research. In the study, we aimed to develop and validate a risk score for predicting 1-year functional outcome after ICH (ICH Functional Outcome Score, ICH-FOS). Furthermore, we compared discrimination of the ICH-FOS and 8 existing ICH scores with regard to 30-day, 3-month, 6-month, and 1-year functional outcome and mortality after ICH.Methods: The ICH-FOS was developed based on the China National Stroke Registry, in which eligible patients were randomly divided into derivation (60%) and validation (40%) cohorts. Poor functional outcome was defined as modified Rankin Scale score (mRS) ≥3 at 1 year after ICH. Multivariable logistic regression was performed to determine independent predictors, and β-coefficients were used to generate scoring system of the ICH-FOS. The area under the receiver operating characteristic curve (AUROC) and Hosmer-Lemeshow goodness-of-fit test were used to assess model discrimination and calibration.Results: The overall 1-year poor functional outcome (mRS ≥ 3) was 46.7% and 44.9% in the derivation (n = 1,953) and validation (n = 1,302) cohorts, respectively. A 16-point ICH-FOS was developed from the set of independent predictors of 1-year poor functional outcome after ICH including age (P < 0.001), admission National Institutes of Health Stroke Scale score (P < 0.001), Glasgow Coma Scale score (P < 0.001), blood glucose (P = 0.002), ICH location (P < 0.001), hematoma volume (P < 0.001), and intraventricular extension (P < 0.001). The ICH-FOS showed good discrimination (AUROC) in the derivation (0.836, 95% CI: 0.819-0.854) and validation (0.830, 95% CI: 0.808-0.852) cohorts. The ICH-FOS was well calibrated (Hosmer-Lemeshow test) in the derivation (P = 0.42) and validation (P = 0.39) cohort. When compared to 8 prior ICH scores, the ICH-FOS showed significantly better discrimination with regard to 1-year functional outcome and mortality after ICH (all P < 0.0001). Meanwhile, the ICH-FOS also demonstrated either comparable or significantly better discrimination for poor functional outcome and mortality at 30-day, 3-month, and 6-month after ICH.Conclusion: The ICH-FOS is a valid clinical grading scale for 1-year functional outcome after ICH. Further validation of the ICH-FOS in different populations is needed. © 2013 Ji et al.; licensee BioMed Central Ltd.Link_to_subscribed_fulltex

Web-based tool for dynamic functional outcome after acute ischemic stroke and comparison with existing models

BackgroundAcute ischemic stroke (AIS) is one of the leading causes of death and adult disability worldwide. In the present study, we aimed to develop a web-based risk model for predicting dynamic functional status at discharge, 3-month, 6-month, and 1-year after acute ischemic stroke (Dynamic Functional Status after Acute Ischemic Stroke, DFS-AIS).MethodsThe DFS-AIS was developed based on the China National Stroke Registry (CNSR), in which eligible patients were randomly divided into derivation (60%) and validation (40%) cohorts. Good functional outcome was defined as modified Rankin Scale (mRS) score ≤ 2 at discharge, 3-month, 6-month, and 1-year after AIS, respectively. Independent predictors of each outcome measure were obtained using multivariable logistic regression. The area under the receiver operating characteristic curve (AUROC) and plot of observed and predicted risk were used to assess model discrimination and calibration.ResultsA total of 12,026 patients were included and the median age was 67 (interquartile range: 57–75). The proportion of patients with good functional outcome at discharge, 3-month, 6-month, and 1-year after AIS was 67.9%, 66.5%, 66.9% and 66.9%, respectively. Age, gender, medical history of diabetes mellitus, stroke or transient ischemic attack, current smoking and atrial fibrillation, pre-stroke dependence, pre-stroke statins using, admission National Institutes of Health Stroke Scale score, admission blood glucose were identified as independent predictors of functional outcome at different time points after AIS. The DFS-AIS was developed from sets of predictors of mRS ≤ 2 at different time points following AIS. The DFS-AIS demonstrated good discrimination in the derivation and validation cohorts (AUROC range: 0.837-0.845). Plots of observed versus predicted likelihood showed excellent calibration in the derivation and validation cohorts (all r = 0.99, P < 0.001). When compared to 8 existing models, the DFS-AIS showed significantly better discrimination for good functional outcome and mortality at discharge, 3-month, 6-month, and 1-year after AIS (all P < 0.0001).ConclusionThe DFS-AIS is a valid risk model to predict functional outcome at discharge, 3-month, 6-month, and 1-year after AIS.Electronic supplementary materialThe online version of this article (doi:10.1186/s12883-014-0214-z) contains supplementary material, which is available to authorized users

Expansion of Pathogenic Cardiac Macrophages in Immune Checkpoint Inhibitor Myocarditis.

BACKGROUND: Immune checkpoint inhibitors (ICIs), antibodies targeting PD-1 (programmed cell death protein 1)/PD-L1 (programmed death-ligand 1) or CTLA4 (cytotoxic T-lymphocyte-associated protein 4), have revolutionized cancer management but are associated with devastating immune-related adverse events including myocarditis. The main risk factor for ICI myocarditis is the use of combination PD-1 and CTLA4 inhibition. ICI myocarditis is often fulminant and is pathologically characterized by myocardial infiltration of T lymphocytes and macrophages. Although much has been learned about the role of T-cells in ICI myocarditis, little is understood about the identity, transcriptional diversity, and functions of infiltrating macrophages. METHODS: We used an established murine ICI myocarditis model (Ctla4+/-Pdcd1-/- mice) to explore the cardiac immune landscape using single-cell RNA-sequencing, immunostaining, flow cytometry, in situ RNA hybridization, molecular imaging, and antibody neutralization studies. RESULTS: We observed marked increases in CCR2 (C-C chemokine receptor type 2)+ monocyte-derived macrophages and CD8+ T-cells in this model. The macrophage compartment was heterogeneous and displayed marked enrichment in an inflammatory CCR2+ subpopulation highly expressing Cxcl9 (chemokine [C-X-C motif] ligand 9), Cxcl10 (chemokine [C-X-C motif] ligand 10), Gbp2b (interferon-induced guanylate-binding protein 2b), and Fcgr4 (Fc receptor, IgG, low affinity IV) that originated from CCR2+ monocytes. It is important that a similar macrophage population expressing CXCL9, CXCL10, and CD16α (human homologue of mouse FcgR4) was expanded in patients with ICI myocarditis. In silico prediction of cell-cell communication suggested interactions between T-cells and Cxcl9+Cxcl10+ macrophages via IFN-γ (interferon gamma) and CXCR3 (CXC chemokine receptor 3) signaling pathways. Depleting CD8+ T-cells or macrophages and blockade of IFN-γ signaling blunted the expansion of Cxcl9+Cxcl10+ macrophages in the heart and attenuated myocarditis, suggesting that this interaction was necessary for disease pathogenesis. CONCLUSIONS: These data demonstrate that ICI myocarditis is associated with the expansion of a specific population of IFN-γ-induced inflammatory macrophages and suggest the possibility that IFN-γ blockade may be considered as a treatment option for this devastating condition