Optimal Delay Time of CT Perfusion for Predicting Cerebral Parenchymal Hematoma After Intra-Arterial tPA Treatment

Background and Purpose: Cerebral hemorrhage is a serious potential complication of stroke revascularization, especially in patients receiving intra-arterial tissue-type plasminogen activator (tPA) therapy. We investigated the optimal pre-intervention delay time (DT) of computed tomography perfusion (CTP) measurement to predict cerebral parenchymal hematoma (PH) in acute ischemic stroke (AIS) patients after intra-arterial tissue plasminogen activator (tPA) treatment.Methods: The study population consisted of a series of patients with AIS who received intra-arterial tPA treatment and had CTP and follow-up computed tomography/magnetic resonance imaging (CT/MRI) to identify hemorrhagic transformation. The association of increasing DT thresholds (>2, >4, >6, >8, and >10 s) with PH was examined using receiver operating characteristic (ROC) analysis and logistic regression.Results: Of 94 patients, 23 developed PH on follow-up imaging. Receiver operating characteristic analysis revealed that the greatest area under the curve for predicting PH occurred at DT > 4 s (area under the curve, 0.66). At this threshold of > 4 s, DT lesion volume ≥ 30.85 mL optimally predicted PH with 70% sensitivity and 59% specificity. DT > 4 s volume was independently predictive of PH in a multivariate logistic regression model (P < 0.05).Conclusions: DT > 4 s was the parameter most strongly associated with PH. The volume of moderate, not severe, hypo-perfusion on DT is more strongly associated and may allow better prediction of PH after intra-arterial tPA thrombolysis

Prognostication of chronic disorders of consciousness using brain functional networks and clinical characteristics

Disorders of consciousness are a heterogeneous mixture of different diseases or injuries. Although some indicators and models have been proposed for prognostication, any single method when used alone carries a high risk of false prediction. This study aimed to develop a multidomain prognostic model that combines resting state functional MRI with three clinical characteristics to predict one year outcomes at the single-subject level. The model discriminated between patients who would later recover consciousness and those who would not with an accuracy of around 90% on three datasets from two medical centers. It was also able to identify the prognostic importance of different predictors, including brain functions and clinical characteristics. To our knowledge, this is the first implementation reported of a multidomain prognostic model based on resting state functional MRI and clinical characteristics in chronic disorders of consciousness. We therefore suggest that this novel prognostic model is accurate, robust, and interpretable.Comment: Although some prognostic indicators and models have been proposed for disorders of consciousness, each single method when used alone carries risks of false prediction. Song et al. report that a model combining resting state functional MRI with clinical characteristics provided accurate, robust, and interpretable prognostications. 52 pages, 1 table, 7 figure

Could Arterial Spin Labeling Distinguish Patients in Minimally Conscious State from Patients in Vegetative State?

PurposeDiagnostic error is common among patients with vegetative state (VS) and minimally conscious state (MCS). The purpose of this article is to use three-dimensional pseudo-continuous arterial spin labeling (pcASL) to compare cerebral blood flow (CBF) patterns in patients in MCS with those in VS.MethodsPatients meeting MCS and VS criteria were identified. Two post-labeling delay (PLD) time pcASL on 3.0-Tesla magnetic resonance imaging scanner system were performed with patients in the resting awake state. After registration to T1WI structure imaging, multiple brain regions of interest of ASL CBF map were automatically separated. The average CBF value of every brain region was calculated and compared between the MCS and VS groups with t-tests.ResultsFifteen patients with VS were identified, with ages ranging from 33 to 71 years. Eight patients who met the MCS criteria ranged in age from 23 to 61 years. Compared with VS, the regional CBF for MCS had a pattern of significantly increased CBF in the regions including the putamen, anterior cingulate gyrus, and medial frontal cortex. A left-lateralized pattern was observed to differentiate MCS from VS. CBF with PLD 2.5 s could find more regions of pattern differentiating MCS from VS than with PLD 1.5 s, except for the pallidum.ConclusionMCS might be differentiated from VS by different ranges of regional CBF as measured by ASL. Multi-PLD ASL may serve as an adjunct method to separate MCS from VS and assess functional reserve in patients recovering from severe brain injuries

C19orf66 interrupts Zika virus replication by inducing lysosomal degradation of viral NS3.

The rapidly emerging human health crisis associated with the Zika virus (ZIKV) epidemic and its link to severe complications highlights the growing need to identify the mechanisms by which ZIKV accesses hosts. Interferon response protects host cells against viral infection, while the cellular factors that mediate this defense are the products of interferon-stimulated genes (ISGs). Although hundreds of ISGs have been identified, only a few have been characterized for their antiviral potential, target specificity and mechanisms of action. In this work, we focused our investigation on the possible antiviral effect of a novel ISG, C19orf66 in response to ZIKV infection and the associated mechanisms. We found that ZIKV infection could induce C19orf66 expression in ZIKV-permissive cells, and such an overexpression of C19orf66 remarkably suppressed ZIKV replication. Conversely, the depletion of C19orf66 led to a significant increase in viral replication. Furthermore, C19orf66 was found to interact and co-localize with ZIKV nonstructural protein 3 (NS3), thus inducing NS3 degradation via a lysosome-dependent pathway. Taken together, this study identified C19orf66 as a novel ISG that exerts antiviral effects against ZIKV by specifically degrading a viral nonstructural protein. These findings uncovered an intriguing mechanism of C19orf66 that targeting NS3 protein of ZIKV, providing clues for understanding the actions of innate immunity, and affording the possible availability of new drug targets that can be used for therapeutic intervention

Linearity, Bias, and Precision of Hepatic Proton Density Fat Fraction Measurements by Using MR Imaging: A Meta-Analysis.

Purpose To determine the linearity, bias, and precision of hepatic proton density fat fraction (PDFF) measurements by using magnetic resonance (MR) imaging across different field strengths, imager manufacturers, and reconstruction methods. Materials and Methods This meta-analysis was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A systematic literature search identified studies that evaluated the linearity and/or bias of hepatic PDFF measurements by using MR imaging (hereafter, MR imaging-PDFF) against PDFF measurements by using colocalized MR spectroscopy (hereafter, MR spectroscopy-PDFF) or the precision of MR imaging-PDFF. The quality of each study was evaluated by using the Quality Assessment of Studies of Diagnostic Accuracy 2 tool. De-identified original data sets from the selected studies were pooled. Linearity was evaluated by using linear regression between MR imaging-PDFF and MR spectroscopy-PDFF measurements. Bias, defined as the mean difference between MR imaging-PDFF and MR spectroscopy-PDFF measurements, was evaluated by using Bland-Altman analysis. Precision, defined as the agreement between repeated MR imaging-PDFF measurements, was evaluated by using a linear mixed-effects model, with field strength, imager manufacturer, reconstruction method, and region of interest as random effects. Results Twenty-three studies (1679 participants) were selected for linearity and bias analyses and 11 studies (425 participants) were selected for precision analyses. MR imaging-PDFF was linear with MR spectroscopy-PDFF (R2 = 0.96). Regression slope (0.97; P < .001) and mean Bland-Altman bias (-0.13%; 95% limits of agreement: -3.95%, 3.40%) indicated minimal underestimation by using MR imaging-PDFF. MR imaging-PDFF was precise at the region-of-interest level, with repeatability and reproducibility coefficients of 2.99% and 4.12%, respectively. Field strength, imager manufacturer, and reconstruction method each had minimal effects on reproducibility. Conclusion MR imaging-PDFF has excellent linearity, bias, and precision across different field strengths, imager manufacturers, and reconstruction methods. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 2, 2017