Stroke is a worldwide medical emergency and an important issue in stroke research is looking for the early pathophysiological markers which can predict the severity of brain injury. Decreased cerebral blood flow (CBF) has been serving as the most important indicator of ischemic stroke. Particular attention is paid to study the spatio-temporal CBF changes immediately after the onset of stroke in a rat intraluminal filament middle cerebral artery occlusion (MCAO) model and investigation of its correlation with brain infarct volume after 24 h. We implement an on-line laser speckle imaging (LSI) system, which could provide real time high spatio-temporal resolution CBF information before, during, and immediately after the rat MCAO surgery. We found a significant correlation between the affected area with 50% CBF reduction (CBF50) at the first minute after occlusion with the infarct volume. To the best of our knowledge, this is the earliest CBF marker for infarct volume prediction. Based on such a CBF-infarct volume correlation, LSI may be used as a real time guidance for improving the consistency of intraluminal filament MCAO model since the depth of filament insertion could be adjusted promptly and those unsuccessful models could be excluded in the earliest stage.
Intraluminal middle cerebral artery occlusion (MCAO) model in rats has been widely used to mimic human ischemic stroke and serves as an indispensable tool in the stroke research field. One limitation of this model is its high variability in infarct volume. The cerebral blood flow (CBF) information after cerebrovascular occlusion may reflect the availability of collateral circulation, which serves as a key factor for brain infarct volume. Laser speckle contrast imaging (LSCI) is a valuable tool for full-field imaging of CBF with high spatial and temporal resolution. In this paper, we investigated the spatio-temporal changes of CBF in rat MCAO stroke model using our self-developed real-time LSCI system. CBF images of adult male Sprague Dawley rats (n=13) were recorded before surgery, during first 1.5 hours after surgery, and 24 hours after stroke. We compared the CBF changes of different functional vessels during this period. In the ipsilateral hemisphere, CBF of veins and arteries both decreased as expected, while CBF of veins increased after occlusion in the contralateral hemisphere. Moreover, we found a linear correlation between early-stage CBF after occlusion and brain infarct volume, which can be utilized for surgery guidance to improve the uniformity of rat MCAO stroke models.
BackgroundStroke is the second leading cause of death worldwide and a major cause of long-term neurological disability, imposing an enormous financial burden on families and society. This study aimed to identify the predictors in stroke patients and construct a nomogram prediction model based on these predictors.MethodsThis retrospective study included 11,435 participants aged >20 years who were selected from the NHANES 2011–2018. Randomly selected subjects (n = 8531; 75%) and the remaining subjects comprised the development and validation groups, respectively. The least absolute shrinkage and selection operator (LASSO) binomial and logistic regression models were used to select the optimal predictive variables. The stroke probability was calculated using a predictor-based nomogram. Nomogram performance was assessed by the area under the receiver operating characteristic curve (AUC) and the calibration curve with 1000 bootstrap resample validations. Decision curve analysis (DCA) was performed to evaluate the clinical utility of the nomogram.ResultsAccording to the minimum criteria of non-zero coefficients of Lasso and logistic regression screening, older age, lower education level, lower family income, hypertension, depression status, diabetes, heavy smoking, heavy drinking, trouble sleeping, congestive heart failure (CHF), coronary heart disease (CHD), angina pectoris and myocardial infarction were independently associated with a higher stroke risk. A nomogram model for stroke patient risk was established based on these predictors. The AUC (C statistic) of the nomogram was 0.843 (95% CI: 0.8186–0.8430) in the development group and 0.826 (95% CI: 0.7811, 0.8716) in the validation group. The calibration curves after 1000 bootstraps displayed a good fit between the actual and predicted probabilities in both the development and validation groups. DCA showed that the model in the development and validation groups had a net benefit when the risk thresholds were 0–0.2 and 0–0.25, respectively.DiscussionThis study effectively established a nomogram including demographic characteristics, vascular risk factors, emotional factors and lifestyle behaviors to predict stroke risk. This nomogram is helpful for screening high-risk stroke individuals and could assist physicians in making better treatment decisions to reduce stroke occurrence.
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