SEMIPARAMETRIC BIVARIATE HIERARCHICAL STATE SPACE MODEL WITH APPLICATION TO HORMONE CIRCADIAN RELATIONSHIP.

The adrenocorticotropic hormone and cortisol play critical roles in stress regulation and the sleep-wake cycle. Most research has been focused on how the two hormones regulate each other in terms of short-term pulses. Few studies have been conducted on the circadian relationship between the two hormones and how it differs between normal and abnormal groups. The circadian patterns are difficult to model as parametric functions. Directly extending univariate functional mixed effects models would result in a large dimensional problem and a challenging nonparametric inference. In this article, we propose a semi-parametric bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with nonparametric population-average and subject-specific components. The bivariate relationship is constructed by concatenating two latent independent subject-specific random functions specified by a design matrix, leading to a parametric inference on the correlation. We propose a computationally efficient state-space EM algorithm for estimation and inference. We apply the proposed method to a study of chronic fatigue syndrome and fibromyalgia and discover an erratic regulation pattern in the patient group in contrast to a circadian regulation pattern conforming to the day-night cycle in the control group.

Selection of number of clusters and warping penalty in clustering functional electrocardiogram.

Clustering functional data aims to identify unique functional patterns in the entire domain, but this can be challenging due to phase variability that distorts the observed patterns. Curve registration can be used to remove this variability, but determining the appropriate level of warping flexibility can be complicated. Curve registration also requires a target to which a functional object is aligned, typically the cross-sectional mean of functional objects within the same cluster. However, this mean is unknown prior to clustering. Furthermore, there is a trade-off between flexible warping and the number of resulting clusters. Removing more phase variability through curve registration can lead to fewer remaining variations in the functional data, resulting in a smaller number of clusters. Thus, the optimal number of clusters and warping flexibility cannot be uniquely identified. We propose to use external information to solve the identification issue. We define a cross validated Kullback-Leibler information criterion to select the number of clusters and the warping penalty. The criterion is derived from the predictive classification likelihood considering the joint distribution of both the functional data and external variable and penalizes the uncertainty in the cluster membership. We evaluate our method through simulation and apply it to electrocardiographic data collected in the Chronic Renal Insufficiency Cohort study. We identify two distinct clusters of electrocardiogram (ECG) profiles, with the second cluster exhibiting ST segment depression, an indication of cardiac ischemia, compared to the normal ECG profiles in the first cluster.

Dynamic hierarchical state space forecasting.

In this paper, we aim to both borrow information from existing units and incorporate the target unit's history data in time series forecasting. We consider a situation when we have time series data from multiple units that share similar patterns when aligned in terms of an internal time. The internal time is defined as an index according to evolving features of interest. When mapped back to the calendar time, these time series can span different time intervals that can include the future calendar time of the targeted unit, over which we can borrow the information from other units in forecasting the targeted unit. We first build a hierarchical state space model for the multiple time series data in terms of the internal time, where the shared components capture the similarities among different units while allowing for unit-specific deviations. A conditional state space model is then constructed to incorporate the information of existing units as the prior information in forecasting the targeted unit. By running the Kalman filtering based on the conditional state space model on the targeted unit, we incorporate both the information from the other units and the history of the targeted unit. The forecasts are then transformed from internal time back into calendar time for ease of interpretation. A simulation study is conducted to evaluate the finite sample performance. Forecasting state-level new COVID-19 cases in United States is used for illustration.

Lateralized brunt of sleep deprivation on white matter injury in a rat model of Alzheimer's disease.

Sleep disturbance is a recognized risk factor for Alzheimer's disease (AD), but the underlying micro-pathological evidence remains limited. To bridge this gap, we established an amyloid-ß oligomers (AßO)-induced rat model of AD and subjected it to intermittent sleep deprivation (SD). Diffusion tensor imaging (DTI) and transmission electron microscopy were employed to assess white matter (WM) integrity and ultrastructural changes in myelin sheaths. Our findings demonstrated that SD exacerbated AßO-induced cognitive decline. Furthermore, we found SD aggravated AßO-induced asymmetrical impairments in WM, presenting with reductions in tract integrity observed in commissural fibers and association fasciculi, particularly the right anterior commissure, right corpus callosum, and left cingulum. Ultrastructural changes in myelin sheaths within the hippocampus and corpus callosum further confirmed a lateralized effect. Moreover, SD worsened AßO-induced lateralized disruption of the brain structural network, with impairments in critical nodes of the left hemisphere strongly correlated with cognitive dysfunction. This work represents the first identification of a lateralized impact of SD on the mesoscopic network and cognitive deficits in an AD rat model. These findings could deepen our understanding of the complex interplay between sleep disturbance and AD pathology, providing valuable insights into the early progression of the disease, as well as the development of neuroimaging biomarkers for screening early AD patients with self-reported sleep disturbances. Enhanced understanding of these mechanisms may pave the way for targeted interventions to alleviate cognitive decline and improve the quality of life for individuals at risk of or affected by AD.

Predicting SARS-CoV-2 infection among hemodialysis patients using multimodal data.

Background: The coronavirus disease 2019 (COVID-19) pandemic has created more devastation among dialysis patients than among the general population. Patient-level prediction models for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are crucial for the early identification of patients to prevent and mitigate outbreaks within dialysis clinics. As the COVID-19 pandemic evolves, it is unclear whether or not previously built prediction models are still sufficiently effective. Methods: We developed a machine learning (XGBoost) model to predict during the incubation period a SARS-CoV-2 infection that is subsequently diagnosed after 3 or more days. We used data from multiple sources, including demographic, clinical, treatment, laboratory, and vaccination information from a national network of hemodialysis clinics, socioeconomic information from the Census Bureau, and county-level COVID-19 infection and mortality information from state and local health agencies. We created prediction models and evaluated their performances on a rolling basis to investigate the evolution of prediction power and risk factors. Result: From April 2020 to August 2020, our machine learning model achieved an area under the receiver operating characteristic curve (AUROC) of 0.75, an improvement of over 0.07 from a previously developed machine learning model published by Kidney360 in 2021. As the pandemic evolved, the prediction performance deteriorated and fluctuated more, with the lowest AUROC of 0.6 in December 2021 and January 2022. Over the whole study period, that is, from April 2020 to February 2022, fixing the false-positive rate at 20%, our model was able to detect 40% of the positive patients. We found that features derived from local infection information reported by the Centers for Disease Control and Prevention (CDC) were the most important predictors, and vaccination status was a useful predictor as well. Whether or not a patient lives in a nursing home was an effective predictor before vaccination, but became less predictive after vaccination. Conclusion: As found in our study, the dynamics of the prediction model are frequently changing as the pandemic evolves. County-level infection information and vaccination information are crucial for the success of early COVID-19 prediction models. Our results show that the proposed model can effectively identify SARS-CoV-2 infections during the incubation period. Prospective studies are warranted to explore the application of such prediction models in daily clinical practice.

Time-to-Event Analysis with Unknown Time Origins via Longitudinal Biomarker Registration.

In observational studies, the time origin of interest for time-to-event analysis is often unknown, such as the time of disease onset. Existing approaches to estimating the time origins are commonly built on extrapolating a parametric longitudinal model, which rely on rigid assumptions that can lead to biased inferences. In this paper, we introduce a flexible semiparametric curve registration model. It assumes the longitudinal trajectories follow a flexible common shape function with person-specific disease progression pattern characterized by a random curve registration function, which is further used to model the unknown time origin as a random start time. This random time is used as a link to jointly model the longitudinal and survival data where the unknown time origins are integrated out in the joint likelihood function, which facilitates unbiased and consistent estimation. Since the disease progression pattern naturally predicts time-to-event, we further propose a new functional survival model using the registration function as a predictor of the time-to-event. The asymptotic consistency and semiparametric efficiency of the proposed models are proved. Simulation studies and two real data applications demonstrate the effectiveness of this new approach.

Sleep deprivation aggravated amyloid ß oligomers-induced damage to the cerebellum of rats: Evidence from magnetic resonance imaging.

For quite a long time, researches on Alzheimer's disease (AD) primarily focused on the cortex and hippocampus, while the cerebellum has been ignored because of its abnormalities considered to appear in the late stage of AD. In recent years, increasing evidence suggest that the cerebellar pathological changes possibly occur in the preclinical phase of AD, which is also associated with sleep disorder. Sleep disturbance is a high risk factor of AD. However, the changes and roles of cerebellum has rarely been reported under conditions of AD accompanied with sleep disorders. In this study, using an amyloid-ß oligomers (AßO)-induced rat model of AD subjected to sleep deprivation, combining with a 7.0 T animals structural magnetic resonance imaging (MRI), we assessed structural changes of cerebellum in MRI. Our results showed that sleep deprivation combined with AßO led to an increased FA value in the anterior lobe of cerebellum, decreased ADC value in the cerebellar lobes and cerebellar nuclei, and increased cerebellum volume. Besides that, sleep deprivation exacerbated the damage of AßO to the cerebellar structural network. This study demonstrated that sleep deprivation could aggravate the damage to cerebellum induced by AßO. The present findings provide supporting evidence for the involvement of cerebellum in the early pathology of AD and sleep loss. Our data would contribute to advancing the understanding of the mysterious role of cerebellum in AD and sleep disorders, as well as would be helpful for developing non-invasive MRI biomarkers for screening early AD patients with self-reported sleep disturbances.

Predicting age at Alzheimer's dementia onset with the cognitive clock.

INTRODUCTION: Intervention of Alzheimer's dementia hinges on early diagnosis and advanced planning. This work utilizes the cognitive clock, a novel indicator of brain health, to develop a dementia prediction model that can be easily applied in broad settings. METHODS: Data came from over 3000 community-dwelling older adults. Cognitive age was estimated by aligning Mini-Mental State Examination (MMSE) scores to a clock that represents the typical cognitive aging profile. We identified a mean cognitive age at Alzheimer's dementia onset and predicted the corresponding chronological age at person-specific level. RESULTS: The mean chronological age at baseline was 78 years. A total of 881 (28%) participants developed Alzheimer's dementia. The mean cognitive age at onset was 91 years. The predicted chronological age at onset had a mean (standard deviation) of 87.6 (6.7) years. The model's prediction accuracy was supported by multiple testing statistics. DISCUSSION: Our model offers an easy-to-use tool for predicting person-specific age at Alzheimer's dementia onset.

Dynamic logistic state space prediction model for clinical decision making.

Prediction modeling for clinical decision making is of great importance and needed to be updated frequently with the changes of patient population and clinical practice. Existing methods are either done in an ad hoc fashion, such as model recalibration or focus on studying the relationship between predictors and outcome and less so for the purpose of prediction. In this article, we propose a dynamic logistic state space model to continuously update the parameters whenever new information becomes available. The proposed model allows for both time-varying and time-invariant coefficients. The varying coefficients are modeled using smoothing splines to account for their smooth trends over time. The smoothing parameters are objectively chosen by maximum likelihood. The model is updated using batch data accumulated at prespecified time intervals, which allows for better approximation of the underlying binomial density function. In the simulation, we show that the new model has significantly higher prediction accuracy compared to existing methods. We apply the method to predict 1 year survival after lung transplantation using the United Network for Organ Sharing data.

Modified Brier score for evaluating prediction accuracy for binary outcomes.

The Brier score has been a popular measure of prediction accuracy for binary outcomes. However, it is not straightforward to interpret the Brier score for a prediction model since its value depends on the outcome prevalence. We decompose the Brier score into two components, the mean squares between the estimated and true underlying binary probabilities, and the variance of the binary outcome that is not reflective of the model performance. We then propose to modify the Brier score by removing the variance of the binary outcome, estimated via a general sliding window approach. We show that the new proposed measure is more sensitive for comparing different models through simulation. A standardized performance improvement measure is also proposed based on the new criterion to quantify the improvement of prediction performance. We apply the new measures to the data from the Breast Cancer Surveillance Consortium and compare the performance of predicting breast cancer risk using the models with and without its most important predictor.

MRI evaluation of cerebral metabolic rate of oxygen (CMRO2) in obstructive sleep apnea.

Patients with obstructive sleep apnea (OSA) are at elevated risk of developing systemic vascular disease and cognitive dysfunction. Here, cerebral oxygen metabolism was assessed in patients with OSA by means of a magnetic resonance-based method involving simultaneous measurements of cerebral blood flow rate and venous oxygen saturation in the superior sagittal sinus for a period of 10 minutes at an effective temporal resolution of 1.3 seconds before, during, and after repeated 24-second breath-holds mimicking spontaneous apneas, yielding, along with pulse oximetry-derived arterial saturation, whole-brain CMRO2 via Fick's Principle. Enrolled subjects were classified based on their apnea-hypopnea indices into OSA (N = 31) and non-sleep apnea reference subjects (NSA = 21), and further compared with young healthy subjects (YH, N = 10). OSA and NSA subjects were matched for age and body mass index. CMRO2 was lower in OSA than in the YH group during normal breathing (105.6 ± 14.1 versus 123.7 ± 22.8 µmol O2/min/100g, P = 0.01). Further, the fractional change in CMRO2 in response to a breath-hold challenge was larger in OSA than in the YH group (15.2 ± 9.2 versus 8.5 ± 3.4%, P = 0.04). However, there was no significant difference in CMRO2 between OSA and NSA subjects. The data suggest altered brain oxygen metabolism in OSA and possibly in NSA as well.

Functional mixed effects clustering with application to longitudinal urologic chronic pelvic pain syndrome symptom data.

By clustering patients with the urologic chronic pelvic pain syndromes (UCPPS) into homogeneous subgroups and associating these subgroups with baseline covariates and other clinical outcomes, we provide opportunities to investigate different potential elements of pathogenesis, which may also guide us in selection of appropriate therapeutic targets. Motivated by the longitudinal urologic symptom data with extensive subject heterogeneity and differential variability of trajectories, we propose a functional clustering procedure where each subgroup is modeled by a functional mixed effects model, and the posterior probability is used to iteratively classify each subject into different subgroups. The classification takes into account both group-average trajectories and between-subject variabilities. We develop an equivalent state-space model for efficient computation. We also propose a cross-validation based Kullback-Leibler information criterion to choose the optimal number of subgroups. The performance of the proposed method is assessed through a simulation study. We apply our methods to longitudinal bi-weekly measures of a primary urological urinary symptoms score from a UCPPS longitudinal cohort study, and identify four subgroups ranging from moderate decline, mild decline, stable and mild increasing. The resulting clusters are also associated with the one-year changes in several clinically important outcomes, and are also related to several clinically relevant baseline predictors, such as sleep disturbance score, physical quality of life and painful urgency.

Non-invasive diffuse optical neuromonitoring during cardiopulmonary resuscitation predicts return of spontaneous circulation.

Neurologic injury is a leading cause of morbidity and mortality following pediatric cardiac arrest. In this study, we assess the feasibility of quantitative, non-invasive, frequency-domain diffuse optical spectroscopy (FD-DOS) neuromonitoring during cardiopulmonary resuscitation (CPR), and its predictive utility for return of spontaneous circulation (ROSC) in an established pediatric swine model of cardiac arrest. Cerebral tissue optical properties, oxy- and deoxy-hemoglobin concentration ([HbO2], [Hb]), oxygen saturation (StO2) and total hemoglobin concentration (THC) were measured by a FD-DOS probe placed on the forehead in 1-month-old swine (8-11 kg; n = 52) during seven minutes of asphyxiation followed by twenty minutes of CPR. ROSC prediction and time-dependent performance of prediction throughout early CPR (< 10 min), were assessed by the weighted Youden index (Jw, w = 0.1) with tenfold cross-validation. FD-DOS CPR data was successfully acquired in 48/52 animals; 37/48 achieved ROSC. Changes in scattering coefficient (785 nm), [HbO2], StO2 and THC from baseline were significantly different in ROSC versus No-ROSC subjects (p < 0.01) after 10 min of CPR. Change in [HbO2] of + 1.3 µmol/L from 1-min of CPR achieved the highest weighted Youden index (0.96) for ROSC prediction. We demonstrate feasibility of quantitative, non-invasive FD-DOS neuromonitoring, and stable, specific, early ROSC prediction from the third minute of CPR.

Route Choices and Adolescent-Adult Connections in Mitigating Exposure to Environmental Risk Factors During Daily Activities.

While adolescent-adult connections have been shown to be protective against violence perpetration and victimization, mechanisms through which these connections confer protection from violence are poorly understood. We assessed whether adolescent-adult connections protected youth in lower resource urban neighborhoods from exposure to environmental risk factors for violence during daily activities. We overlaid on the city landscape minute-by-minute activity paths from 274 randomly sampled predominantly African American male youth, ages 10 to 24, enrolled in a population-based study of daily activities in Philadelphia, PA, to calculate environmental exposures and to compare exposures along actual versus shortest potential travel routes. Adolescent-adult connections were defined using brief survey questions and detailed family genograms. Analyses demonstrated that youth's selected travel routes resulted in significantly lower exposure to several types of crime, including vandalism, narcotics arrests, and disorderly conduct, than would have occurred on shortest potential routes. On average, youth with adolescent-adult connections spent less time outdoors than youth without connections, although these differences did not reach statistical significance (p = .06). There were no significant differences in environmental risk factors encountered by youth with versus without adolescent-adult connections. Future mixed-methods research combining qualitative and geographic information systems (GIS) approaches should investigate which factors shape travel decisions during daily activities to guide multimodal violence prevention interventions.

Acute e-cig inhalation impacts vascular health: a study in smoking naïve subjects.

This study was designed to investigate the acute effects of nonnicotinized e-cigarette (e-cig) aerosol inhalation in nonsmokers both in terms of blood-based markers of inflammation and oxidative stress and evaluate their association with hemodynamic-metabolic MRI parameters quantifying peripheral vascular reactivity, cerebrovascular reactivity, and aortic stiffness. Thirty-one healthy nonsmokers were subjected to two blood draws and two identical MRI protocols, each one before and after a standardized e-cig vaping session. After vaping, the serum levels of C-reactive protein, soluble intercellular adhesion molecule, and the danger signal machinery high-mobility group box 1 (HMGB1) and its downstream effector and the NLR family pyrin domain containing 3 (NLRP3) inflammasome (as monitored by its adaptor protein ASC) increased significantly relative to the respective baseline (prevaping) values. Moreover, nitric oxide metabolites and reactive oxygen species production decreased and increased, respectively. These observations were paralleled by impaired peripheral vascular reactivity (with reduced flow-mediated dilation and attenuated hyperemic response after a cuff-occlusion test) and metabolic alterations expressed by decreased venous oxygen saturation, postvaping. The current results suggest propagation of inflammation signaling via activation of the danger signaling axis (HMGB1-NLRP3). The findings indicate that a single episode of vaping has adverse impacts on vascular inflammation and function.NEW & NOTWORTHY Endothelial cell signaling and blood biomarkers were found to correlate with functional vascular changes in a single episode e-cigarettes inhalation in healthy adults. This is indicative of the potential of e-cigarettes (even when inhaled acutely) to lead of vascular dysfunction.

Reactive oxygen species explicit dosimetry to predict tumor growth for benzoporphyrin derivative-mediated vascular photodynamic therapy.

Photodynamic therapy (PDT) is a well-established treatment modality for cancer and other malignant diseases; however, quantities such as light fluence and PDT dose do not fully account for all of the dynamic interactions between the key components involved. In particular, fluence rate (ϕ) effects, which impact the photochemical oxygen consumption rate, are not accounted for. In this preclinical study, reacted reactive oxygen species ([ROS]rx) was investigated as a dosimetric quantity for PDT outcome. The ability of [ROS]rx to predict the cure index (CI) of tumor growth, CI = 1 - k / kctr, where k and kctr are the growth rate of tumor under PDT study and the control tumor without PDT, respectively, for benzoporphyrin derivative (BPD)-mediated PDT, was examined. Mice bearing radiation-induced fibrosarcoma (RIF) tumors were treated with different in-air fluences (Φ = 22.5 to 166.7 J / cm2) and in-air fluence rates (ϕair = 75 to 250 mW / cm2) with a BPD dose of 1 mg / kg and a drug-light interval (DLI) of 15 min. Treatment was delivered with a collimated laser beam of 1-cm-diameter at 690 nm. Explicit measurements of in-air light fluence rate, tissue oxygen concentration, and BPD concentration were used to calculate for [ROS]rx. Light fluence rate at 3-mm depth (ϕ3 mm), determined based on Monte-Carlo simulations, was used in the calculation of [ROS]rx at the base of tumor. CI was used as an endpoint for three dose metrics: light fluence, PDT dose, and [ROS]rx. PDT dose was defined as the product of the time-integral of photosensitizer concentration and ϕ3 mm. Preliminary studies show that [ROS]rx best correlates with CI and is an effective dosimetric quantity that can predict treatment outcome. The threshold dose for [ROS]rx for vascular BPD-mediated PDT using DLI of 15 min is determined to be 0.26 mM and is about 3.8 times smaller than the corresponding value for conventional BPD-mediated PDT using DLI of 3 h.

MRI evaluation of cerebrovascular reactivity in obstructive sleep apnea.

Obstructive sleep apnea (OSA) is characterized by intermittent obstruction of the airways during sleep. Cerebrovascular reactivity (CVR) is an index of cerebral vessels' ability to respond to a vasoactive stimulus, such as increased CO2. We hypothesized that OSA alters CVR, expressed as a breath-hold index (BHI) defined as the rate of change in CBF or BOLD signal during a controlled breath-hold stimulus mimicking spontaneous apneas by being both hypercapnic and hypoxic. In 37 OSA and 23 matched non sleep apnea (NSA) subjects, we obtained high temporal resolution CBF and BOLD MRI data before, during, and between five consecutive BH stimuli of 24 s, each averaged to yield a single BHI value. Greater BHI was observed in OSA relative to NSA as derived from whole-brain CBF (78.6 ± 29.6 vs. 60.0 ± 20.0 mL/min2/100 g, P = 0.010) as well as from flow velocity in the superior sagittal sinus (0.48 ± 0.18 vs. 0.36 ± 0.10 cm/s2, P = 0.014). Similarly, BOLD-based BHI was greater in OSA in whole brain (0.19 ± 0.08 vs. 0.15 ± 0.03%/s, P = 0.009), gray matter (0.22 ± 0.09 vs. 0.17 ± 0.03%/s, P = 0.011), and white matter (0.14 ± 0.06 vs. 0.10 ± 0.02%/s, P = 0.010). The greater CVR is not currently understood but may represent a compensatory mechanism of the brain to maintain oxygen supply during intermittent apneas.

Prospective cohort study examining the use of regional anesthesia for early pain management after combat-related extremity injury.

BACKGROUND: No studies have examined the long-term benefits of regional anesthesia (RA) for pain management after combat-related injury. The objective of this prospective cohort study was to examine the relationship between RA administration and patient-reported pain-related outcomes among Operation Enduring Freedom (OEF) and Operation Iraqi Freedom (OIF) service members sustaining a combat-related extremity injury. METHODS: Between 2007 and 2013, n=358 American military personnel injured in OEF/OIF were enrolled at two military treatment facilities. Individuals were followed for up to 2 years after injury. Cohorts were defined based on whether participants were administered RA within 7 days after sustaining a combat-related injury, or not. Linear mixed effects models examined the association between RA and average pain intensity. Secondary outcomes included pain relief, pain interference, neuropathic pain symptoms, treatment outcomes related to pain management, and mental health symptoms. RESULTS: Receiving early RA was associated with improved average pain over the first 6 months after injury (ß=-0.57; p=0.012) adjusting for injury severity and length of stay at the primary treatment facility. This difference was observed up to 24 months after injury (ß=-0.36; p=0.046). Individuals receiving early RA reported greater pain relief, improved neuropathic pain intensity, and higher satisfaction with pain outcomes; however, by 24 months, mean scores did not significantly differ between cohorts. CONCLUSION: Findings indicate that when administered soon after traumatic injury, RA is a valuable pain management intervention. Future longitudinal studies investigating the timely delivery of RA for optimal pain management in civilian trauma settings are needed. TRIAL REGISTRATION NUMBER: NCT00431847.

Cerebral Blood Flow Response During Bolus Normal Saline Infusion After Ischemic Stroke.

GOALS: We quantified cerebral blood flow response to a 500 cc bolus of 0.9%% normal saline (NS) within 96 hours of acute ischemic stroke (AIS) using diffuse correlation spectroscopy (DCS). MATERIALS AND METHODS: Subjects with AIS in the anterior, middle, or posterior cerebral artery territory were enrolled within 96 hours of symptom onset. DCS measured relative cerebral blood flow (rCBF) in the bilateral frontal lobes for 15 minutes at rest (baseline), during a 30-minute infusion of 500 cc NS (bolus), and for 15 minutes after completion (post-bolus). Mean rCBF for each time period was calculated for individual subjects and median rCBF for the population was compared between time periods. Linear regression was used to evaluate for associations between rCBF and clinical features. RESULTS: Among 57 subjects, median rCBF (IQR) increased relative to baseline in the ipsilesional hemisphere by 17% (-2.0%, 43.1%), P< 0.001, and in the contralesional hemisphere by 13.3% (-4.3%, 36.0%), P < .004. No significant associations were found between ipsilesional changes in rCBF and age, race, infarct size, infarct location, presence of large vessel stenosis, NIH stroke scale, or symptom duration. CONCLUSION: A 500 cc bolus of .9% NS produced a measurable increase in rCBF in both the affected and nonaffected hemispheres. Clinical features did not predict rCBF response.

Acute Effects of Electronic Cigarette Aerosol Inhalation on Vascular Function Detected at Quantitative MRI.

Background Previous studies showed that nicotinized electronic cigarettes (hereafter, e-cigarettes) elicit systemic oxidative stress and inflammation. However, the effect of the aerosol alone on endothelial function is not fully understood. Purpose To quantify surrogate markers of endothelial function in nonsmokers after inhalation of aerosol from nicotine-free e-cigarettes. Materials and Methods In this prospective study (from May to September 2018), nonsmokers underwent 3.0-T MRI before and after inhaling nicotine-free e-cigarette aerosol. Peripheral vascular reactivity to cuff-induced ischemia was quantified by temporally resolving blood flow velocity and oxygenation (SvO2) in superficial femoral artery and vein, respectively, along with artery luminal flow-mediated dilation. Precuff occlusion, resistivity index, baseline blood flow velocity, and SvO2 were evaluated. During reactive hyperemia, blood flow velocity yielded peak velocity, time to peak, and acceleration rate (hyperemic index); SvO2 yielded washout time of oxygen-depleted blood, rate of resaturation, and maximum SvO2 increase (overshoot). Cerebrovascular reactivity was assessed in the superior sagittal sinus, evaluating the breath-hold index. Central arterial stiffness was measured via aortic pulse wave velocity. Differences before versus after e-cigarette vaping were tested with Hotelling T2 test. Results Thirty-one healthy never-smokers (mean age, 24.3 years ± 4.3; 14 women) were evaluated. After e-cigarette vaping, resistivity index was higher (0.03 of 1.30 [2.3%]; P < .05), luminal flow-mediated dilation severely blunted (-3.2% of 9.4% [-34%]; P < .001), along with reduced peak velocity (-9.9 of 56.6 cm/sec [-17.5%]; P < .001), hyperemic index (-3.9 of 15.1 cm/sec2 [-25.8%]; P < .001), and delayed time to peak (2.1 of 7.1 sec [29.6%]; P = .005); baseline SvO2 was lower (-13 of 65 %HbO2 [-20%]; P < .001) and overshoot higher (10 of 19 %HbO2 [52.6%]; P < .001); and aortic pulse wave velocity marginally increased (0.19 of 6.05 m/sec [3%]; P = .05). Remaining parameters did not change after aerosol inhalation. Conclusion Inhaling nicotine-free electronic cigarette aerosol transiently impacted endothelial function in healthy nonsmokers. Further studies are needed to address the potentially adverse long-term effects on vascular health. © RSNA, 2019 Online supplemental material is available for this article.