Biomarkers.

Background: Post-COVID cognitive dysfunctions, impacting attention, memory, and learning, might be linked to inflammation-induced blood-brain barrier (BBB) impairment. This study explores post-COVID BBB permeability changes using a non-contrast water-exchange based MRI and their associations with blood Alzheimer's biomarkers.

Method: Sixty-seven participants were classified based on COVID (COV) and cognitive (COG) statuses into three groups: COV+/COG- (n=34), COV+/COG+ (n=23), and COV- (n=10) for comparisons (COV+: Laboratory-verified SARS-CoV-2 infection; COV-: No history of SARS-CoV-2 infection and negative SARS-CoV-2 nucleocapsid antibody test.

Biomarkers.

Background: Cognitive impairment is a major symptom among patients with post-acute sequelae of COVID-19; the underlying pathogenesis is unknown. This impairment may be associated with changes in the level of plasma biomarkers of neurodegeneration and neuroinflammation.

Method: Plasma samples were collected from COVID-19 patients (Covpos, median 724 days from index SARS-CoV-2 infection), and from non-COVID-19 controls (Covneg; no history of SARS-CoV-2 infection and negative SARS-CoV-2 nucleocapsid antibody).

Biomarkers.

Background: A decline in gait has been associated with an escalated risk of cognitive decline and changes in Alzheimer's disease (AD) biomarkers, thus offering prognostic insight. However, the utility of gait analysis in preclinical stages of AD is unclear, and prior studies have primarily used qualitative or gross measures of gait. Furthermore, gait analysis has predominantly been performed in cohorts of non-Hispanic Whites.

Biomarkers.

Background: Cognitive impairment is one of the most frequently reported post-acute sequelae of COVID-19, yet the pathophysiology underpinning this symptom remains unknown. We aimed to explore the correlation of blood markers of inflammation, BBB disruption and neurodegeneration with MRI volume measurements in COVID-19 patients with and without cognitive impairment, and among patients with no prior history of COVID-19.

Method: We conducted a prospective study of COVID-19 patients (COV+; laboratory verified SARS-CoV-2 infection) and non-COVID-19 controls (COV-; no history of SARS-CoV-2 infection and negative SARS-CoV-2 nucleocapsid antibody).

Biomarkers.

Background: Plasma biomarkers of Alzheimer's disease (AD) are less invasive and have lower costs than cerebrospinal fluid (CSF) biomarkers. Blood biomarkers are potential instruments for diagnosis, prognosis, and monitoring of disease progression. We assessed the diagnostic potential of several plasma biomarkers for the detection of early stages of AD.

Alzheimer's Imaging Consortium.

Background: Cognitive impairment is one of the most frequently reported post-acute sequelae of COVID-19, yet the pathophysiology underpinning this symptom remains unknown. We aimed to explore the correlation of blood markers of inflammation, BBB disruption and neurodegeneration with MRI volume measurements in COVID-19 patients with and without cognitive impairment, and among patients with no prior history of COVID-19.

Method: We conducted a prospective study of COVID-19 patients (COV+; laboratory verified SARS-CoV-2 infection) and non-COVID-19 controls (COV-; no history of SARS-CoV-2 infection and negative SARS-CoV-2 nucleocapsid antibody).

Alzheimer's Imaging Consortium.

Background: Post-COVID cognitive dysfunctions, impacting attention, memory, and learning, might be linked to inflammation-induced blood-brain barrier (BBB) impairment. This study explores post-COVID BBB permeability changes using a non-contrast water-exchange based MRI and their associations with blood Alzheimer's biomarkers.

Method: Sixty-seven participants were classified based on COVID (COV) and cognitive (COG) statuses into three groups: COV+/COG- (n=34), COV+/COG+ (n=23), and COV- (n=10) for comparisons (COV+: Laboratory-verified SARS-CoV-2 infection; COV-: No history of SARS-CoV-2 infection and negative SARS-CoV-2 nucleocapsid antibody test.

Information Challenges Associated With Accessing and Sharing of Patient Information in Disasters: A Qualitative Analysis.

As disasters increase in frequency and severity, so too does the health impact on affected populations. Disasters exacerbate the already challenging health information-sharing landscape. A reduced capacity to access and share patient information may have negative impacts on providers' ability to care for patients individually and to address disaster health outcomes at the population level.

Integrable Atomtronic Interferometry.

High sensitivity quantum interferometry requires more than just access to entangled states. It is achieved through the deep understanding of quantum correlations in a system. Integrable models offer the framework to develop this understanding.

Adverse childhood experiences predict reaction to multiple sclerosis diagnosis.

Objective: At the time of multiple sclerosis (MS) diagnosis, identifying those at risk for poorer health-related quality of life and emotional well-being can be a critical consideration for treatment planning. This study aimed to test whether adverse childhood experiences predict MS patients' health-related quality of life and emotional functioning at time of diagnosis and initial course of disease.

Methods: We recruited patients at the time of new MS diagnosis to complete self-report surveys at baseline and a one-year follow-up.

Applying an Innovative Model of Disaster Resilience at the Neighborhood Level : The COPEWELL New York City Experience.

Community resilience is a community's ability to maintain functioning (ie, delivery of services) during and after a disaster event. The Composite of Post-Event Well-Being (COPEWELL) is a system dynamics model of community resilience that predicts a community's disaster-specific functioning over time. We explored COPEWELL's usefulness as a practice-based tool for understanding community resilience and to engage partners in identifying resilience-strengthening strategies.

The COPEWELL Rubric: A Self-Assessment Toolkit to Strengthen Community Resilience to Disasters.

Measurement is a community endeavor that can enhance the ability to anticipate, withstand, and recover from a disaster, as well as foster learning and adaptation. This project's purpose was to develop a self-assessment toolkit-manifesting a bottom-up, participatory approach-that enables people to envision community resilience as a concrete, desirable, and obtainable goal; organize a cross-sector effort to evaluate and enhance factors that influence resilience; and spur adoption of interventions that, in a disaster, would lessen impacts, preserve community functioning, and prompt a more rapid recovery. In 2016-2018, we engaged in a process of literature review, instrument development, stakeholder engagement, and local field-testing, to produce a self-assessment toolkit (or "rubric") built on the Composite of Post-Event Well-being (COPEWELL) model that predicts post-disaster community functioning and resilience.

COPEWELL: A Conceptual Framework and System Dynamics Model for Predicting Community Functioning and Resilience After Disasters.

Objective: Policy-makers and practitioners have a need to assess community resilience in disasters. Prior efforts conflated resilience with community functioning, combined resistance and recovery (the components of resilience), and relied on a static model for what is inherently a dynamic process. We sought to develop linked conceptual and computational models of community functioning and resilience after a disaster.

Use of Sub-Ensembles and Multi-Template Observers to Evaluate Detection Task Performance for Data That are Not Multivariate Normal.

The Hotelling Observer (HO) is widely used to evaluate image quality in medical imaging. However, applying it to data that are not multivariate-normally (MVN) distributed is not optimal. In this paper, we apply two multi-template linear observer strategies to handle such data.

Collimator optimization in myocardial perfusion SPECT using the ideal observer and realistic background variability for lesion detection and joint detection and localization tasks.

In SPECT imaging, collimators are a major factor limiting image quality and largely determine the noise and resolution of SPECT images. In this paper, we seek the collimator with the optimal tradeoff between image noise and resolution with respect to performance on two tasks related to myocardial perfusion SPECT: perfusion defect detection and joint detection and localization. We used the Ideal Observer (IO) operating on realistic background-known-statistically (BKS) and signal-known-exactly (SKE) data.

Collimator optimization and collimator-detector response compensation in myocardial perfusion SPECT using the ideal observer with and without model mismatch and an anthropomorphic model observer.

The collimator is the primary factor that determines the spatial resolution and noise tradeoff in myocardial perfusion SPECT images. In this paper, the goal was to find the collimator that optimizes the image quality in terms of a perfusion defect detection task. Since the optimal collimator could depend on the level of approximation of the collimator-detector response (CDR) compensation modeled in reconstruction, we performed this optimization for the cases of modeling the full CDR (including geometric, septal penetration and septal scatter responses), the geometric CDR, or no model of the CDR.

Optimization and comparison of simultaneous and separate acquisition protocols for dual isotope myocardial perfusion SPECT.

Dual-isotope simultaneous-acquisition (DISA) rest-stress myocardial perfusion SPECT (MPS) protocols offer a number of advantages over separate acquisition. However, crosstalk contamination due to scatter in the patient and interactions in the collimator degrade image quality. Compensation can reduce the effects of crosstalk, but does not entirely eliminate image degradations.

Optimization of energy window and evaluation of scatter compensation methods in myocardial perfusion SPECT using the ideal observer with and without model mismatch and an anthropomorphic model observer.

We used the ideal observer (IO) and IO with model mismatch (IO-MM) applied in the projection domain and an anthropomorphic channelized Hotelling observer (CHO) applied to reconstructed images to optimize the acquisition energy window width and to evaluate various scatter compensation methods in the context of a myocardial perfusion single-photon emission computed tomography (SPECT) defect detection task. The IO has perfect knowledge of the image formation process and thus reflects the performance with perfect compensation for image-degrading factors. Thus, using the IO to optimize imaging systems could lead to suboptimal parameters compared with those optimized for humans interpreting SPECT images reconstructed with imperfect or no compensation.

Estimation of dynamic time activity curves from dynamic cardiac SPECT imaging.

Whole-heart coronary flow reserve (CFR) may be useful as an early predictor of cardiovascular disease or heart failure. Here we propose a simple method to extract the time-activity curve, an essential component needed for estimating the CFR, for a small number of compartments in the body, such as normal myocardium, blood pool, and ischemic myocardial regions, from SPECT data acquired with conventional cameras using slow rotation. We evaluated the method using a realistic simulation of (99m)Tc-teboroxime imaging.

Building a national model of public mental health preparedness and community resilience: validation of a dual-intervention, systems-based approach.

Objective: Working within a series of partnerships among an academic health center, local health departments (LHDs), and faith-based organizations (FBOs), we validated companion interventions to address community mental health planning and response challenges in public health emergency preparedness.

Methods: We implemented the project within the framework of an enhanced logic model and employed a multi-cohort, pre-test/post-test design to assess the outcomes of 1-day workshops in psychological first aid (PFA) and guided preparedness planning (GPP). The workshops were delivered to urban and rural communities in eastern and midwestern regions of the United States.

Investigating and improving pedestrian safety in an urban environment.

Background: Prompted by a series of fatal and nonfatal pedestrian-vehicle collisions, university leadership from one urban institution collaborated with its academic injury research center to investigate traffic-related hazards facing pedestrians.

Methods: This descriptive epidemiologic study used multiple data collection strategies to determine the burden of pedestrian injury in the target area. Data were collected in 2011 through a review of university crash reports from campus police; a systematic environmental audit and direct observations using a validated instrument and trained raters; and focus groups with faculty, students, and staff.

Translating legal research on mental and behavioral health during emergencies for the public health workforce.

Translation strategies are critical for sharing research with public health practitioners. To disseminate our analyses of legal issues that arise relative to mental and behavioral health during emergencies, we created 10 brief translational tools for members of the public health workforce. In consultation with an interdisciplinary project advisory group (PAG), we identified each tool's topic and format.

An academic-government-faith partnership to build disaster mental health preparedness and community resilience.

Objectives: Faculty and affiliates of the Johns Hopkins Preparedness and Emergency Response Research Center partnered with local health departments and faith-based organizations to develop a dual-intervention model of capacity-building for public mental health preparedness and community resilience. Project objectives included (1) determining the feasibility of the tri-partite collaborative concept; (2) designing, delivering, and evaluating psychological first aid (PFA) training and guided preparedness planning (GPP); and (3) documenting preliminary evidence of the sustainability and impact of the model.

Methods: We evaluated intervention effectiveness by analyzing pre- and post-training changes in participant responses on knowledge-acquisition tests administered to three urban and four rural community cohorts.