Background: Post-stroke cognitive impairment (PSCI) significantly affects stroke survivors' quality of life and rehabilitation. A risk model identifying cognitive decline at admission would help to improve early detection and management of post-stroke patients.Objective: To develop a new clinical risk score for ischemic stroke survivors in predicting 6-12 months PSCI. Methods: We prospectively enrolled 179 patients diagnosed with acute ischemic stroke within a 7-day onset. Data were analyzed based on baseline demographics, clinical risk factors, and radiological parameters. Logistic regression and area under the receiver operating curve (AUROC) were used to evaluate model efficiency. Results: One hundred forty-five subjects completed a 6-12-month follow-up visit, and 77 patients (53.1%) were diagnosed with PSCI. Age ( = 0.065, OR = 1.067, 95% CI = 1.016-1.120), years of education ( = -0.346, OR = 0.707, 95% CI = 0.607-0.824), periventricular hyperintensity grading ( = 1.253, OR = 3.501, 95% CI = 1.652-7.417), diabetes mellitus ( = 1.762, OR = 5.825, 95% CI = 2.068-16.412), and the number of acute nonlacunar infarcts ( = 0.569, OR = 1.766, 95% CI = 1.243-2.510) were independently associated with 6-12 month PSCI, constituting a model with optimal predictive efficiency (AUC = 0.884, 95% CI = 0.832-0.935). Conclusions: The optimized risk model was effective in screening stroke survivors at high risk of developing 6-12 months PSCI in a simple and pragmatic way. It could be a potential tool to identify patients with a high risk of PSCI at an early stage in clinical practice after further independent external cohort validation.
Background With an increasing number of motor vehicle crashes, there is an urgent need in emergency departments (EDs) to assess patients with multiple trauma quickly, easily, and reliably. Trauma severity can range from a minor to major threats to life or bodily function. In-hospital mortality and trauma severity prediction in such cases is crucial in the ED for the management of multiple trauma and improvement of the outcome of these patients. Previous studies have examined the performance of Modified Early Warning Score (MEWS) or Circulation, Respiration, Abdomen, Motor, and Speech (CRAMS) score based solely on mortality prediction or injury severity prediction. However, to the best of our knowledge, the performances of both scoring systems on in-hospital mortality and trauma severity prediction have not been compared previously. This retrospective study evaluated the value of MEWS and CRAMS score to predict in-hospital mortality and trauma severity in patients presenting to the ED with multiple traumatic injuries. Methods All study subjects were multiple trauma patients. Medical data of 1,127 patients were analyzed between January 2014 and April 2018. The MEWS and CRAMS score were calculated, and logistic regression and receiver operating characteristic curve analysis were conducted to investigate their performances regarding in-hospital mortality and trauma severity prediction. Results For in-hospital mortality prediction, the areas under the receiver operating characteristic curve (AUROCs) for MEWS and CRAMS score were 0.90 and 0.91, respectively, indicating that both of them were good in-hospital mortality predictors. Further, our study indicated that the CRAMS score performed better in trauma severity prediction, with an AUROC value of 0.84, which was higher than that of MEWS (AUROC = 0.77). For trauma severity prediction, the optimal cut-off value for MEWS was 2, while that of the CRAMS score was 8. Conclusions We found that both MEWS and CRAMS score can be used as predictors for trauma severity and in-hospital mortality for multiple trauma patients, but that CRAMS score was superior to MEWS for trauma severity prediction. CRAMS score should be prioritized in the prediction of trauma severity due to its excellence as a multiple trauma triage tool and potential contribution to rapid emergency rescue decisions.
Microglia play an important role in neurodegenerative disease [i.e., Parkinson’s disease (PD), Alzheimer’s disease (AD), and amyotrophic lateral sclerosis (ALS)]. These diseases share some similar pathological changes and several microglia-associated processes, including immune response, neuroinflammation, phagocytosis, elimination of synapses et al. Microglia in the central nervous system (CNS) has been described as having both destructive and protective effects in neurological disorders. Besides, considerable evidence also indicates that microglia play a significant role in neurogenesis, neuronal cell death, and synaptic interactions. The communication between microglia and neurons is of vital role in regulating complex functions which are key to appropriate the activity of the brain. Accumulating studies have also demonstrated that exosomes with sizes ranging from 40–100 nm, released by microglia, could serve as key mediators in intercellular signaling. These exosomes, identified in terms of cellular origin in many kinds of biological fluids, exert their effects by delivering specific cargos such as proteins, microRNAs (miRNAs), and mRNAs. It was shown that microglial exosomes could transport to and be uptake by neurons, which may either be beneficial or instead, detrimental to CNS diseases. The focus of this review is to summarize the involvement of microglial exosomes in critical pathologies associated with neurodegenerative disease and how they contribute to these disorders, including PD, AD, and ALS. We also review the application of microglia exosomes as potential biomarkers in monitoring disease progression, as well as focusing on their roles as drug delivery vehicles in treating neurodegenerative disorders.
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