946 research outputs found

    Monoclonal Antibody Identification of Subpopulations of Cerebral Cortical Neurons Affected in Alzheimer disease

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    Neuronal degeneration is one of the hallmarks of Alzheimer disease (AD). Given the paucity of molecular markers available for the identification of neuronal subtypes, the specificity of neuronal loss within the cerebral cortex has been difficult to evaluate. With a panel of four monoclonal antibodies (mAbs) applied to central nervous system tissues from AD patients, we have immunocytochemically identified a population of vulnerable cortical neurons; a subpopulation of pyramidal neurons is recognized by mAbs 3F12 and 44.1 in the hippocampus and neocortex, and clusters of multipolar neurons in the entorhinal cortex reactive with mAb 44.1 show selective degeneration. Closely adjacent stellate-like neurons in these regions, identified by mAb 6A2, show striking preservation in AD. The neurons recognized by mAbs 3F12 and 44.1, to the best of our knowledge, do not comprise a single known neurotransmitter system. mAb 3A4 identifies a phosphorylated antigen that is undetectable in normal brain but accumulates early in the course of AD in somas of vulnerable neurons. Antigen 3A4 is distinct from material reactive with thioflavin S or antibody generated against paired helical filaments. Initially, antigen 3A4 is localized to neurons in the entorhinal cortex and subiculum, later in the association neocortex, and, ultimately in cases of long duration, in primary sensory cortical regions. mAb 3F12 recognizes multiple bands on immunoblots of homogenates of normal and Ad cortical tissues, whereas mAb 3A4 does not bind to immunoblots containing neurofilament proteins or brain homogenates from AD patients. Ultrastructurally, antigen 3A4 is localized to paired-helical filaments. Using these mAbs, further molecular characterization of the affected cortical neurons is now possible

    Palliative Care Consultations in Nursing Homes and Reductions in Acute Care Use and Potentially Burdensome End-of-Life Transitions

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    To evaluate how receipt and timing of nursing home (NH) palliative care consults (primarily by nurse practitioners with palliative care expertise) is associated with end-of-life care transitions and acute care us

    Palliative Care Consultations in Nursing Homes and End-of-Life Hospitalizations

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    While specialty palliative care in hospital and outpatient settings is associated with lower acute care use, its impact in U.S. nursing homes (NHs) is unknown

    Telehealth and Mobile Health Applied To IntegratedBehavioral Care: OpportunitiesFor Progress In New Hampshire

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    This paper is an accompanying document to a webinar delivered on May 16, 2017, for the New Hampshire Citizens Health Initiative (Initiative). As integrated behavioral health efforts in New Hampshire gain traction, clinicians, administrators, payers, and policy makers are looking for additional efficiencies in delivering high quality healthcare. Telehealth and mobile health (mHealth) have the opportunity to help achieve this while delivering a robust, empowered patient experience. The promise of video-based technology was first made in 1964 as Bell Telephone shared its Picturephone¼ with the world. This was the first device with audio and video delivered in an integrated technology platform. Fast-forward to today with Skype, FaceTime, and webinar tools being ubiquitous in our personal and business lives, but often slow to be adopted in the delivery of medicine. Combining technology-savvy consumers with New Hampshire’s high rate of electronic health record (EHR) technology adoption, a fairly robust telecommunications infrastructure, and a predominately rural setting, there is strong foundation for telehealth and mHealth expansion in New Hampshire’s integrated health continuum

    Integrating Behavioral Health & Primary Care in New Hampshire: A Path Forward to Sustainable Practice & Payment Transformation

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    New Hampshire residents face challenges with behavioral and physical health conditions and the interplay between them. National studies show the costs and the burden of illness from behavioral health conditions and co-occurring chronic health conditions that are not adequately treated in either primary care or behavioral health settings. Bringing primary health and behavioral health care together in integrated care settings can improve outcomes for both behavioral and physical health conditions. Primary care integrated behavioral health works in conjunction with specialty behavioral health providers, expanding capacity, improving access, and jointly managing the care of patients with higher levels of acuity In its work to improve the health of NH residents and create effective and cost-effective systems of care, the NH Citizens Health Initiative (Initiative) created the NH Behavioral Health Integration Learning Collaborative (BHI Learning Collaborative) in November of 2015, as a project of its Accountable Care Learning Network (NHACLN). Bringing together more than 60 organizations, including providers of all types and sizes, all of the state’s community mental health centers, all of the major private and public insurers, and government and other stakeholders, the BHI Learning Collaborative built on earlier work of a NHACLN Workgroup focused on improving care for depression and co-occurring chronic illness. The BHI Learning Collaborative design is based on the core NHACLN philosophy of “shared data and shared learning” and the importance of transparency and open conversation across all stakeholder groups. The first year of the BHI Learning Collaborative programming included shared learning on evidence-based practice for integrated behavioral health in primary care, shared data from the NH Comprehensive Healthcare Information System (NHCHIS), and work to develop sustainable payment models to replace inadequate Fee-for-Service (FFS) revenues. Provider members joined either a Project Implementation Track working on quality improvement projects to improve their levels of integration or a Listen and Learn Track for those just learning about Behavioral Health Integration (BHI). Providers in the Project Implementation Track completed a self-assessment of levels of BHI in their practice settings and committed to submit EHR-based clinical process and outcomes data to track performance on specified measures. All providers received access to unblinded NHACLN Primary Care and Behavioral Health attributed claims data from the NHCHIS for provider organizations in the NH BHI Learning Collaborative. Following up on prior work focused on developing a sustainable model for integrating care for depression and co-occurring chronic illness in primary care settings, the BHI Learning Collaborative engaged consulting experts and participants in understanding challenges in Health Information Technology and Exchange (HIT/HIE), privacy and confidentiality, and workforce adequacy. The BHI Learning Collaborative identified a sustainable payment model for integrated care of depression in primary care. In the process of vetting the payment model, the BHI Learning Collaborative also identified and explored challenges in payment for Substance Use Disorder Screening, Brief Intervention and Referral to Treatment (SBIRT). New Hampshire’s residents will benefit from a health care system where primary care and behavioral health are integrated to support the care of the whole person. New Hampshire’s current opiate epidemic accentuates the need for better screening for behavioral health issues, prevention, and treatment referral integrated into primary care. New Hampshire providers and payers are poised to move towards greater integration of behavioral health and primary care and the Initiative looks forward to continuing to support progress in supporting a path to sustainable integrated behavioral and primary care

    Does the process map influence the outcome of quality improvement work? A comparison of a sequential flow diagram and a hierarchical task analysis diagram

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    Background: Many quality and safety improvement methods in healthcare rely on a complete and accurate map of the process. Process mapping in healthcare is often achieved using a sequential flow diagram, but there is little guidance available in the literature about the most effective type of process map to use. Moreover there is evidence that the organisation of information in an external representation affects reasoning and decision making. This exploratory study examined whether the type of process map - sequential or hierarchical - affects healthcare practitioners' judgments.Methods: A sequential and a hierarchical process map of a community-based anti coagulation clinic were produced based on data obtained from interviews, talk-throughs, attendance at a training session and examination of protocols and policies. Clinic practitioners were asked to specify the parts of the process that they judged to contain quality and safety concerns. The process maps were then shown to them in counter-balanced order and they were asked to circle on the diagrams the parts of the process where they had the greatest quality and safety concerns. A structured interview was then conducted, in which they were asked about various aspects of the diagrams.Results: Quality and safety concerns cited by practitioners differed depending on whether they were or were not looking at a process map, and whether they were looking at a sequential diagram or a hierarchical diagram. More concerns were identified using the hierarchical diagram compared with the sequential diagram and more concerns were identified in relation to clinical work than administrative work. Participants' preference for the sequential or hierarchical diagram depended on the context in which they would be using it. The difficulties of determining the boundaries for the analysis and the granularity required were highlighted.Conclusions: The results indicated that the layout of a process map does influence perceptions of quality and safety problems in a process. In quality improvement work it is important to carefully consider the type of process map to be used and to consider using more than one map to ensure that different aspects of the process are captured

    Seasonal prediction of bottom temperature on the Northeast U.S. Continental Shelf

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    © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Chen, Z., Kwon, Y.-O., Chen, K., Fratantoni, P., Gawarkiewicz, G., Joyce, T. M., Miller, T. J., Nye, J. A., Saba, V. S., & Stock, B. C. Seasonal prediction of bottom temperature on the Northeast U.S. Continental Shelf. Journal of Geophysical Research: Oceans, 126(5), (2021): e2021JC017187, https://doi.org/10.1029/2021JC017187.The Northeast U.S. shelf (NES) is an oceanographically dynamic marine ecosystem and supports some of the most valuable demersal fisheries in the world. A reliable prediction of NES environmental variables, particularly ocean bottom temperature, could lead to a significant improvement in demersal fisheries management. However, the current generation of climate model-based seasonal-to-interannual predictions exhibits limited prediction skill in this continental shelf environment. Here, we have developed a hierarchy of statistical seasonal predictions for NES bottom temperatures using an eddy-resolving ocean reanalysis data set. A simple, damped local persistence prediction model produces significant skill for lead times up to ∌5 months in the Mid-Atlantic Bight and up to ∌10 months in the Gulf of Maine, although the prediction skill varies notably by season. Considering temperature from a nearby or upstream (i.e., more poleward) region as an additional predictor generally improves prediction skill, presumably as a result of advective processes. Large-scale atmospheric and oceanic indices, such as Gulf Stream path indices (GSIs) and the North Atlantic Oscillation Index, are also tested as predictors for NES bottom temperatures. Only the GSI constructed from temperature observed at 200 m depth significantly improves the prediction skill relative to local persistence. However, the prediction skill from this GSI is not larger than that gained using models incorporating nearby or upstream shelf/slope temperatures. Based on these results, a simplified statistical model has been developed, which can be tailored to fisheries management for the NES.This work was supported by NOAA's Climate Program OfïŹce's Modeling, Analysis, Predictions, and Projections (MAPP) Program (NA17OAR4310111, NA19OAR4320074), and Climate Program Office's Climate Variability and Predictability (CVP) Program (NA20OAR4310482). We acknowledge our participation in MAPP's Marine Prediction Task Force
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