CXCR6 orchestrates brain CD8+ T cell residency and limits mouse Alzheimer's disease pathology.

Neurodegenerative diseases, including Alzheimer's disease (AD), are characterized by innate immune-mediated inflammation, but functional and mechanistic effects of the adaptive immune system remain unclear. Here we identify brain-resident CD8+ T cells that coexpress CXCR6 and PD-1 and are in proximity to plaque-associated microglia in human and mouse AD brains. We also establish that CD8+ T cells restrict AD pathologies, including ß-amyloid deposition and cognitive decline. Ligand-receptor interaction analysis identifies CXCL16-CXCR6 intercellular communication between microglia and CD8+ T cells. Further, Cxcr6 deficiency impairs accumulation, tissue residency programming and clonal expansion of brain PD-1+CD8+ T cells. Ablation of Cxcr6 or CD8+ T cells ultimately increases proinflammatory cytokine production from microglia, with CXCR6 orchestrating brain CD8+ T cell-microglia colocalization. Collectively, our study reveals protective roles for brain CD8+ T cells and CXCR6 in mouse AD pathogenesis and highlights that microenvironment-specific, intercellular communication orchestrates tissue homeostasis and protection from neuroinflammation.

ARAF protein kinase activates RAS by antagonizing its binding to RASGAP NF1.

RAF protein kinases are effectors of the GTP-bound form of small guanosine triphosphatase RAS and function by phosphorylating MEK. We showed here that the expression of ARAF activated RAS in a kinase-independent manner. Binding of ARAF to RAS displaced the GTPase-activating protein NF1 and antagonized NF1-mediated inhibition of RAS. This reduced ERK-dependent inhibition of RAS and increased RAS-GTP. By this mechanism, ARAF regulated the duration and consequences of RTK-induced RAS activation and supported the RAS output of RTK-dependent tumor cells. In human lung cancers with EGFR mutation, amplification of ARAF was associated with acquired resistance to EGFR inhibitors, which was overcome by combining EGFR inhibitors with an inhibitor of the protein tyrosine phosphatase SHP2 to enhance inhibition of nucleotide exchange and RAS activation.

Meta-Analysis of the Mechanisms Underlying COVID-19 Modulation of Parkinson's Disease.

Coronavirus disease-19 (COVID-19) is caused by the infection of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The virus enters host cells through receptor-mediated endocytosis of angiotensin-converting enzyme-2 (ACE2), leading to systemic inflammation, also known as a "cytokine storm", and neuroinflammation. COVID-19's upstream regulator, interferon-gamma (IFNG), is downregulated upon the infection of SARS-CoV-2, which leads to the downregulation of ACE2. The neuroinflammation signaling pathway (NISP) can lead to neurodegenerative diseases, such as Parkinson's disease (PD), which is characterized by the formation of Lewy bodies made primarily of the α-synuclein protein encoded by the synuclein alpha (SNCA) gene. We hypothesize that COVID-19 may modulate PD progression through neuroinflammation induced by cytokine storms. This study aimed to elucidate the possible mechanisms and signaling pathways involved in COVID-19-triggered pathology associated with neurodegenerative diseases like PD. This study presents the analysis of the pathways involved in the downregulation of ACE2 following SARS-CoV-2 infection and its effect on PD progression. Through QIAGEN's Ingenuity Pathway Analysis (IPA), the study identified the NISP as a top-five canonical pathway/signaling pathway and SNCA as a top-five upstream regulator. Core Analysis was also conducted on the associated molecules between COVID-19 and SNCA to construct a network connectivity map. The Molecule Activity Predictor tool was used to simulate the infection of SARS-CoV-2 by downregulating IFNG, which leads to the predicted activation of SNCA, and subsequently PD, through a dataset of intermediary molecules. Downstream effect analysis was further used to quantify the downregulation of ACE2 on SNCA activation.

Consolidation of primary care physicians and its impact on healthcare utilization.

We use administrative data from Medicare to document the massive consolidation of primary care physicians over the last decade and its impact on patient healthcare utilization. We first document that primary care organizations have consolidated all over the United States between 2008 and 2014. We then show that regions that experienced greater consolidation are associated with greater decline in overall healthcare spending. Finally, in our primary exercise, we exploit transitions of patients across organizations that are driven by changes in the organizational affiliations of their primary care physicians to study the impact of organizational size on overall spending. Our preferred specification suggests that patients switching from small to large physician organizations reduce their overall healthcare spending by 16%, and that this reduction is primarily driven by a 13% reduction in primary care visits and 0.09 (21%) fewer inpatient admissions per year.

Evolution-Guided Structural and Functional Analyses of the HERC Family Reveal an Ancient Marine Origin and Determinants of Antiviral Activity.

In humans, homologous to the E6-AP carboxyl terminus (HECT) and regulator of chromosome condensation 1 (RCC1)-like domain-containing protein 5 (HERC5) is an interferon-induced protein that inhibits replication of evolutionarily diverse viruses, including human immunodeficiency virus type 1 (HIV-1). To better understand the origin, evolution, and function of HERC5, we performed phylogenetic, structural, and functional analyses of the entire human small-HERC family, which includes HERC3, HERC4, HERC5, and HERC6. We demonstrated that the HERC family emerged >595 million years ago and has undergone gene duplication and gene loss events throughout its evolution. The structural topology of the RCC1-like domain and HECT domains from all HERC paralogs is highly conserved among evolutionarily diverse vertebrates despite low sequence homology. Functional analyses showed that the human small HERCs exhibit different degrees of antiviral activity toward HIV-1 and that HERC5 provides the strongest inhibition. Notably, coelacanth HERC5 inhibited simian immunodeficiency virus (SIV), but not HIV-1, particle production, suggesting that the antiviral activity of HERC5 emerged over 413 million years ago and exhibits species- and virus-specific restriction. In addition, we showed that both HERC5 and HERC6 are evolving under strong positive selection, particularly blade 1 of the RCC1-like domain, which we showed is a key determinant of antiviral activity. These studies provide insight into the origin, evolution, and biological importance of the human restriction factor HERC5 and the other HERC family members.IMPORTANCE Intrinsic immunity plays an important role as the first line of defense against viruses. Studying the origins, evolution, and functions of proteins responsible for effecting this defense will provide key information about virus-host relationships that can be exploited for future drug development. We showed that HERC5 is one such antiviral protein that belongs to an evolutionarily conserved family of HERCs with an ancient marine origin. Not all vertebrates possess all HERC members, suggesting that different HERCs emerged at different times during evolution to provide the host with a survival advantage. Consistent with this, two of the more recently emerged HERC members, HERC5 and HERC6, displayed strong signatures of having been involved in an ancient evolutionary battle with viruses. Our findings provide new insights into the evolutionary origin and function of the HERC family in vertebrate evolution, identifying HERC5 and possibly HERC6 as important effectors of intrinsic immunity in vertebrates.

Statistical Inference on Health Disparity Indices for Complex Surveys.

The National Cancer Institute developed the Health Disparities Calculator (HD*Calc) to facilitate research on health disparities. HD*Calc calculates multiple measures of health disparities using data collected from population-based disease surveillance systems, such as cancer registries. In this paper, we extend the use of HD*Calc to complex survey data by developing plug-in point estimators and Taylor linearization variance estimators that consider complex designs: stratification, multistage clustering, and differential weighting. Our simulation indicates that the plug-in estimators are approximately unbiased and the Taylor linearization variance estimators are accurate. Using 2011-2016 data from the National Health and Nutrition Examination Survey, we demonstrate the use of these estimators in evaluating socioeconomic disparities in the prevalence of child and adolescent (ages 2-18 years) obesity in the United States. Statistical software has been developed for ease of disparity analyses using complex survey data.

Genetic variation, simplicity, and evolutionary constraints for function-valued traits.

Understanding the patterns of genetic variation and constraint for continuous reaction norms, growth trajectories, and other function-valued traits is challenging. We describe and illustrate a recent analytical method, simple basis analysis (SBA), that uses the genetic variance-covariance (G) matrix to identify "simple" directions of genetic variation and genetic constraints that have straightforward biological interpretations. We discuss the parallels between the eigenvectors (principal components) identified by principal components analysis (PCA) and the simple basis (SB) vectors identified by SBA. We apply these methods to estimated G matrices obtained from 10 studies of thermal performance curves and growth curves. Our results suggest that variation in overall size across all ages represented most of the genetic variance in growth curves. In contrast, variation in overall performance across all temperatures represented less than one-third of the genetic variance in thermal performance curves in all cases, and genetic trade-offs between performance at higher versus lower temperatures were often important. The analyses also identify potential genetic constraints on patterns of early and later growth in growth curves. We suggest that SBA can be a useful complement or alternative to PCA for identifying biologically interpretable directions of genetic variation and constraint in function-valued traits.

Socioeconomic status and access to mental health care: The case of psychiatric medications for children in Ontario Canada.

We examine differences in the prescribing of psychiatric medications to lower-income and higher-income children in the Canadian province of Ontario using rich administrative data that includes diagnosis codes and physician identifiers. Our most striking finding is that conditional on diagnosis and medical history, low-income children are more likely to be prescribed antipsychotics and benzodiazepines than higher-income children who see the same doctors. These are drugs with potentially dangerous side effects that ideally should be prescribed to children only under narrowly proscribed circumstances. Lower-income children are also less likely to be prescribed SSRIs, the first-line treatment for depression and anxiety conditional on diagnosis. Hence, socioeconomic differences in the prescribing of psychotropic medications to children persist even in the context of universal public health insurance and universal drug coverage.

Mental health treatment and the role of tele-mental health at the veterans health administration during the COVID-19 pandemic.

To quantify overall trends in patients treated for mental health disorders and adverse events, including via tele-mental health (TMH) and psychopharmacology during pandemic-related health care transformation. Longitudinal observational study including veterans receiving mental health treatment at a Veterans Health Administration (VHA) facility from January 1, 2017 to June 16, 2020. Observed and expected patient care for on-going and new treatment of depression, posttraumatic stress, substance use disorder, severe mental illness diagnoses, overdose, and suicide attempts, and psychotropic prescriptions for antidepressant, antipsychotic, benzodiazepine, opioid, and mood stabilizing medications are depicted. Percent change between actual and expected counts in the early months of the COVID pandemic (March 18-May 5, 2020) are computed. Decreases in counts of patients receiving mental health treatment early in the pandemic ranged from 7% to 20% for on-going treatment, and 28% to 37% for new treatment. TMH rapidly expanded across VHA, becoming the primary means by which encounters were delivered. Counts of patients receiving on-going care for suicide attempts were stable, and for overdoses, decreased by 17%. Counts of patients initiating care for suicide attempts and overdoses decreased by 30% and 38%, respectively. Weekly prescriptions and medication on-hand for psychotropics ranged from a 2% decrease to a 4% increase. New patient prescribing decreased 21%-50%. VHA and other large healthcare systems will need to expand outreach and continue to develop TMH services to maintain care continuity and initiate care for existing and new patients during COVID-19 and future large-scale outbreaks, epidemics, and disasters. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Aided recall of The Real Cost e-cigarette prevention advertisements among a nationally representative sample of adolescents.

E-cigarette use among youth remains a significant public health concern. In 2018, The Real Cost campaign began disseminating messages about the harms of vaping, primarily using digital media. We sought to determine the prevalence of aided recall of The Real Cost e-cigarette prevention ads and identify potential differences by participant characteristics. Participants were a nationally representative sample of adolescents living in United States (US) households recruited by the National Opinion Research Center (NORC) at the University of Chicago's AmeriSpeak panel in September and October of 2020. A total of 623 adolescents completed the survey. Analyses were weighted to represent the distribution of youth in the US, and effect sizes for individual characteristics were estimated using an adjusted marginalized two-part model. Seventy-one percent of adolescents recalled at least one of the five The Real Cost e-cigarette prevention ads, with individual ad recall ranging from a low of 38.8% (for Magic) to a high of 50.1% (for Narrative). Adjusted estimates of aided recall identified significantly higher recall among Black adolescents and those that used social media at medium or high frequencies (p < 0.05). Results support ongoing efforts by the FDA to reach youth with e-cigarette prevention messages using primarily digital media.

Selection-Bias-Corrected Visualization via Dynamic Reweighting.

The collection and visual analysis of large-scale data from complex systems, such as electronic health records or clickstream data, has become increasingly common across a wide range of industries. This type of retrospective visual analysis, however, is prone to a variety of selection bias effects, especially for high-dimensional data where only a subset of dimensions is visualized at any given time. The risk of selection bias is even higher when analysts dynamically apply filters or perform grouping operations during ad hoc analyses. These bias effects threaten the validity and generalizability of insights discovered during visual analysis as the basis for decision making. Past work has focused on bias transparency, helping users understand when selection bias may have occurred. However, countering the effects of selection bias via bias mitigation is typically left for the user to accomplish as a separate process. Dynamic reweighting (DR) is a novel computational approach to selection bias mitigation that helps users craft bias-corrected visualizations. This paper describes the DR workflow, introduces key DR visualization designs, and presents statistical methods that support the DR process. Use cases from the medical domain, as well as findings from domain expert user interviews, are also reported.

Prescribing of Opioid Analgesics and Buprenorphine for Opioid Use Disorder During the COVID-19 Pandemic.

Importance: The COVID-19 pandemic disrupted medical care, impacting prescribing of opioid analgesics and buprenorphine for opioid use disorder. Understanding these patterns can help address barriers to care. Objective: To evaluate how prescribing of opioid analgesics and buprenorphine for opioid use disorder changed throughout the COVID-19 pandemic among both new and existing patients. Design, Setting, and Participants: In this cross-sectional study, use of opioid analgesics and buprenorphine for opioid use disorder from March 18 to September 1, 2020, was projected using a national database of retail prescriptions from January 1, 2018, to March 3, 2020. Actual prescribing was compared with projected levels for all, existing, and new patients. Exposures: The data include prescriptions to patients independent of insurance status or type and cover 90% of retail prescriptions, 70% of mail-order prescriptions, and 70% of nursing home prescriptions. Main Outcomes and Measures: Prescriptions for opioid analgesics and buprenorphine for opioid use disorder. Outcomes included total number of prescriptions, total morphine milligram equivalents, mean morphine milligram equivalents per prescription, mean dispensed units per prescription, and number of patients filling prescriptions. Results: A total of 452 691 261 prescriptions for opioid analgesics and buprenorphine for opioid use disorder were analyzed for 90 420 353 patients (50 921 535 female patients [56%]; mean [SD] age, 49 [20] years). From March 18 to May 19, 2020, 1877 million total morphine milligram equivalents of opioid analgesics were prescribed weekly vs 1843 million projected, a ratio of 102% (95% prediction interval [PI], 94%-111%; P = .71). The weekly number of opioid-naive patients receiving opioids was 370 051 vs 564 929 projected, or 66% of projected (95% PI, 63%-68%; P < .001). Prescribing of buprenorphine was as projected for existing patients, while the number of new patients receiving buprenorphine weekly was 9865 vs 12 008 projected, or 82% (95% PI, 76%-88%; P < .001). From May 20 to September 1, 2020, opioid prescribing for new patients returned to 100% of projected (95% PI, 96%-104%; P = .95), while the number of new patients receiving buprenorphine weekly was 10 436 vs 11 613 projected, or 90% (95% PI, 83%-97%; P = .009). Conclusions and Relevance: In this cross-sectional study, existing patients receiving opioid analgesics and buprenorphine for opioid use disorder generally maintained access to these medications during the COVID-19 pandemic. Opioid prescriptions for opioid-naive patients decreased briefly and then rebounded, while initiation of buprenorphine remained at a low rate through August 2020. Reductions in treatment entry may be associated with increased overdose deaths.

Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data.

The collection of large, complex datasets has become common across a wide variety of domains. Visual analytics tools increasingly play a key role in exploring and answering complex questions about these large datasets. However, many visualizations are not designed to concurrently visualize the large number of dimensions present in complex datasets (e.g. tens of thousands of distinct codes in an electronic health record system). This fact, combined with the ability of many visual analytics systems to enable rapid, ad-hoc specification of groups, or cohorts, of individuals based on a small subset of visualized dimensions, leads to the possibility of introducing selection bias-when the user creates a cohort based on a specified set of dimensions, differences across many other unseen dimensions may also be introduced. These unintended side effects may result in the cohort no longer being representative of the larger population intended to be studied, which can negatively affect the validity of subsequent analyses. We present techniques for selection bias tracking and visualization that can be incorporated into high-dimensional exploratory visual analytics systems, with a focus on medical data with existing data hierarchies. These techniques include: (1) tree-based cohort provenance and visualization, including a user-specified baseline cohort that all other cohorts are compared against, and visual encoding of cohort "drift", which indicates where selection bias may have occurred, and (2) a set of visualizations, including a novel icicle-plot based visualization, to compare in detail the per-dimension differences between the baseline and a user-specified focus cohort. These techniques are integrated into a medical temporal event sequence visual analytics tool. We present example use cases and report findings from domain expert user interviews.

Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation.

Temporal event data are collected across a broad range of domains, and a variety of visual analytics techniques have been developed to empower analysts working with this form of data. These techniques generally display aggregate statistics computed over sets of event sequences that share common patterns. Such techniques are often hindered, however, by the high-dimensionality of many real-world event sequence datasets which can prevent effective aggregation. A common coping strategy for this challenge is to group event types together prior to visualization, as a pre-process, so that each group can be represented within an analysis as a single event type. However, computing these event groupings as a pre-process also places significant constraints on the analysis. This paper presents a new visual analytics approach for dynamic hierarchical dimension aggregation. The approach leverages a predefined hierarchy of dimensions to computationally quantify the informativeness, with respect to a measure of interest, of alternative levels of grouping within the hierarchy at runtime. This information is then interactively visualized, enabling users to dynamically explore the hierarchy to select the most appropriate level of grouping to use at any individual step within an analysis. Key contributions include an algorithm for interactively determining the most informative set of event groupings for a specific analysis context, and a scented scatter-plus-focus visualization design with an optimization-based layout algorithm that supports interactive hierarchical exploration of alternative event type groupings. We apply these techniques to high-dimensional event sequence data from the medical domain and report findings from domain expert interviews.

Proteomic analysis of transitional cell carcinoma-like variant of tubo-ovarian high-grade serous carcinoma.

The current World Health Organization classification does not distinguish transitional cell carcinoma of the ovary (TCC) from conventional tubo-ovarian high-grade serous carcinoma (HGSC), despite evidence suggesting improved prognosis for patients with TCC; instead, it is considered a morphologic variant of HGSC. The immunohistochemical (IHC) markers applied to date do not distinguish between TCC and HGSC. Therefore, we sought to compare the proteomic profiles of TCC and conventional HGSC to identify proteins enriched in TCC. Prognostic biomarkers in HGSC have proven to be elusive, and our aim was to identify biomarkers of TCC as a way of reliably and reproducibly identifying patients with a favorable prognosis and better response to chemotherapy compared with those with conventional HGSC. Quantitative global proteome analysis was performed on archival material of 12 cases of TCC and 16 cases of HGSC using SP3 (single-pot, solid phase-enhanced, sample preparation)-Clinical Tissue Proteomics, a recently described protocol for full-proteome analysis from formalin-fixed paraffin-embedded tissues. We identified 430 proteins that were significantly enriched in TCC over HGSC. Unsupervised co-clustering perfectly distinguished TCC from HGSC based on protein expression. Pathway analysis showed that proteins associated with cell death, necrosis, and apoptosis were highly expressed in TCCs, whereas proteins associated with DNA homologous recombination, cell mitosis, proliferation and survival, and cell cycle progression pathways had reduced expression. From the proteomic analysis, three potential biomarkers for TCC were identified, claudin-4 (CLDN4), ubiquitin carboxyl-terminal esterase L1 (UCHL1), and minichromosome maintenance protein 7 (MCM7), and tested by IHC analysis on tissue microarrays. In agreement with the proteomic analysis, IHC expression of those proteins was stronger in TCC than in HGSC (p < 0.0001). Using global proteomic analysis, we are able to distinguish TCC from conventional HGSC. Follow-up studies will be necessary to confirm that these molecular and morphologic differences are clinically significant.

What to expect when you are expecting: Are health care consumers forward-looking?

A fundamental question in health insurance markets is how do health care consumers dynamically optimize their medical utilization under non-linear insurance contracts? Our paper tests the neoclassical prediction that a fully forward-looking agent only responds to their expected end-of-year price. Our unique identification strategy studies families during the year of childbirth who will likely satisfy their annual deductible, thereby knowing their expected end-of-year price. We find that during the year of a childbirth, fathers increase medical spending by 11% per month after their deductible is satisfied, rejecting the null of fully forward-looking consumers. This behavior cannot be explained by fathers increasing utilization in response to the childbirth itself. Furthermore, this myopia translates to a 21-24% decrease in total annual medical spending, relative to the counterfactual of fully forward-looking behavior, and is concentrated in elective procedures; we find no response in low value or urgent care. Our findings suggest the need for modeling non-linear incentives while accounting for myopic behavior when studying the medical utilization responses to health insurance.

Augmented Reality with Virtual Cerebral Aneurysms: A Feasibility Study.

OBJECTIVE: Advanced magnetic resonance imaging (MRI) acquisition methods such as 4D phase contrast magnetic resonance imaging (4D pcMRI) have found widespread applications in assessing hemodynamics in cerebral aneurysms. One of the goals is the identification of a hemodynamic scenario predictive of aneurysm rupture. Because of the technical complexity the 4D pcMRI technology, challenges exist in extracting and communicating the wealth of information contained in the MRI image data, in particular, to a multidisciplinary team of health professionals and basic scientists. METHODS: Here we introduce a new resource in the form of a commercially available optical see-through head-mounted display, which allows augmenting the real world by computer-generated virtual objects. RESULTS: An image processing algorithm was presented for integrating 3D surface models derived from 3D rotational angiography data, 3D time-of-flight MRI data, and 4D pcMRI data into the augmented reality environment. This new approach was successfully evaluated on blood flow data acquired before and after flow diverter treatment in 6 patients harboring a cerebral aneurysm. CONCLUSIONS: Augmented reality may become an additional means for visualizing complex medical data, in particular complex flows as illustrated here.

Subarachnoid hemorrhage - Induced block of cerebrospinal fluid flow: Role of brain coagulation factor III (tissue factor).

Subarachnoid hemorrhage (SAH) in 95% of cases results in long-term disabilities due to brain damage, pathogenesis of which remains uncertain. Hindrance of cerebrospinal fluid (CSF) circulation along glymphatic pathways is a possible mechanism interrupting drainage of damaging substances from subarachnoid space and parenchyma. We explored changes in CSF circulation at different time following SAH and possible role of brain tissue factor (TF). Fluorescent solute and fluorescent microspheres injected into cisterna magna were used to track CSF flow in mice. SAH induced by perforation of circle of Willis interrupted CSF flow for up to 30 days. Block of CSF flow did not correlate with the size of hemorrhage. Following SAH, fibrin deposits were observed on the brain surface including areas without visible blood. Block of astroglia-associated TF by intracerebroventricular administration of specific antibodies increased size of hemorrhage, decreased fibrin deposition and facilitated spread of fluorophores in sham/naïve animals. We conclude that brain TF plays an important role in localization of hemorrhage and also regulates CSF flow under normal conditions. Targeting of the TF system will allow developing of new therapeutic approaches to the treatment of SAH and pathologies related to CSF flow such as hydrocephalus.

Association between an arachnoid cyst and intracranial aneurysms misdiagnosed as a cystic tumor with a mural nodule. Case report and review of the literature.

The authors report on a 63-year-old man initially admitted to an outside hospital for altered mental status and respiratory distress. A head computed tomography scan disclosed a right frontal cystic mass, suspected to be a neoplasm. An open biopsy was performed at an outside institution, and on visualization of the cyst, an aneurysm was found incidentally. Postoperatively, an angiogram and magnetic resonance image confirmed the presence of a distal right M1 segment aneurysm. The patient was transferred to our institution where, in addition to the middle cerebral artery lesion, a right anterior choroidal artery aneurysm was found intraoperatively; the necks of both aneurysms were clipped successfully. A review of the literature revealed 14 additional cases of intracranial aneurysms associated with arachnoid cysts. Data in the present report highlight the importance of considering an intracystic aneurysm in the differential diagnosis when reviewing cases that involve a cystic mass with a mural nodule. The authors provide a comprehensive summary of documented cases of aneurysms associated with arachnoid cysts. In addition, they include a discussion of prevailing thoughts on the origin and evolution of arachnoid cysts.