103 research outputs found

    Late onset of development of natural anti-nonGal antibodies in infant humans and baboons:implications for xenotransplantation in infants

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    If an ABO-incompatible heart is transplanted into an infant before natural antibodies have developed to the specific donor carbohydrate A/B antigen(s), then B-cell tolerance to the donor A/B antigen is achieved, and these antibodies never develop. Anti-carbohydrate antibodies play a role in the rejection of wild type (WT) and alpha1,3-galactosyltransferase gene-knockout (GT-KO) pig xenografts. We investigated development of these antibodies in infant baboons and humans. Serum samples from infant baboons (n = 42) and humans (n = 42) were tested by flow cytometry for immunoglobulin M and immunoglobulin G binding to peripheral blood mononuclear cells from WT and GT-KO pigs, and for complement-dependent cytotoxicity. The presence of anti-blood group antibodies was tested in baboon serum. In infant baboons and humans, cytotoxic anti-Galalpha1,3Gal antibodies develop during the first 3 months, and steadily increase with age, whereas cytotoxic anti-nonGal antibodies are either absent or minimal in the majority of cases throughout the first year of life. Anti-blood group antibodies were not detected before 16 weeks of age. Our data suggest GT-KO pig organ/cell transplants could be carried out in early infancy in the absence of preformed cytotoxic anti-nonGalalpha1,3Gal antibodies.</p

    Interoception and Mental Health: A Roadmap

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    Interoception refers to the process by which the nervous system senses, interprets, and integrates signals originating from within the body, providing a moment-by-moment mapping of the body’s internal landscape across conscious and unconscious levels. Interoceptive signaling has been considered a component process of reflexes, urges, feelings, drives, adaptive responses, and cognitive and emotional experiences, highlighting its contributions to the maintenance of homeostatic functioning, body regulation, and survival. Dysfunction of interoception is increasingly recognized as an important component of different mental health conditions, including anxiety disorders, mood disorders, eating disorders, addictive disorders, and somatic symptom disorders. However, a number of conceptual and methodological challenges have made it difficult for interoceptive constructs to be broadly applied in mental health research and treatment settings. In November 2016, the Laureate Institute for Brain Research organized the first Interoception Summit, a gathering of interoception experts from around the world, with the goal of accelerating progress in understanding the role of interoception in mental health. The discussions at the meeting were organized around four themes: interoceptive assessment, interoceptive integration, interoceptive psychopathology, and the generation of a roadmap that could serve as a guide for future endeavors. This review article presents an overview of the emerging consensus generated by the meeting

    Suppression of Methylation-Mediated Transcriptional Gene Silencing by βC1-SAHH Protein Interaction during Geminivirus-Betasatellite Infection

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    DNA methylation is a fundamental epigenetic modification that regulates gene expression and represses endogenous transposons and invading DNA viruses. As a counter-defense, the geminiviruses encode proteins that inhibit methylation and transcriptional gene silencing (TGS). Some geminiviruses have acquired a betasatellite called DNA β. This study presents evidence that suppression of methylation-mediated TGS by the sole betasatellite-encoded protein, βC1, is crucial to the association of Tomato yellow leaf curl China virus (TYLCCNV) with its betasatellite (TYLCCNB). We show that TYLCCNB complements Beet curly top virus (BCTV) L2- mutants deficient for methylation inhibition and TGS suppression, and that cytosine methylation levels in BCTV and TYLCCNV genomes, as well as the host genome, are substantially reduced by TYLCCNB or βC1 expression. We also demonstrate that while TYLCCNB or βC1 expression can reverse TGS, TYLCCNV by itself is ineffective. Thus its AC2/AL2 protein, known to have suppression activity in other geminiviruses, is likely a natural mutant in this respect. A yeast two-hybrid screen of candidate proteins, followed by bimolecular fluorescence complementation analysis, revealed that βC1 interacts with S-adenosyl homocysteine hydrolase (SAHH), a methyl cycle enzyme required for TGS. We further demonstrate that βC1 protein inhibits SAHH activity in vitro. That βC1 and other geminivirus proteins target the methyl cycle suggests that limiting its product, S-adenosyl methionine, may be a common viral strategy for methylation interference. We propose that inhibition of methylation and TGS by βC1 stabilizes geminivirus/betasatellite complexes

    Reduced fire severity offers near-term buffer to climate-driven declines in conifer resilience across the western United States

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    Increasing fire severity and warmer, drier postfire conditions are making forests in the western United States (West) vulnerable to ecological transformation. Yet, the relative importance of and interactions between these drivers of forest change remain unresolved, particularly over upcoming decades. Here, we assess how the interactive impacts of changing climate and wildfire activity influenced conifer regeneration after 334 wildfires, using a dataset of postfire conifer regeneration from 10,230 field plots. Our findings highlight declining regeneration capacity across the West over the past four decades for the eight dominant conifer species studied. Postfire regeneration is sensitive to high-severity fire, which limits seed availability, and postfire climate, which influences seedling establishment. In the near-term, projected differences in recruitment probability between low- and high-severity fire scenarios were larger than projected climate change impacts for most species, suggesting that reductions in fire severity, and resultant impacts on seed availability, could partially offset expected climate-driven declines in postfire regeneration. Across 40 to 42% of the study area, we project postfire conifer regeneration to be likely following low-severity but not high-severity fire under future climate scenarios (2031 to 2050). However, increasingly warm, dry climate conditions are projected to eventually outweigh the influence of fire severity and seed availability. The percent of the study area considered unlikely to experience conifer regeneration, regardless of fire severity, increased from 5% in 1981 to 2000 to 26 to 31% by mid-century, highlighting a limited time window over which management actions that reduce fire severity may effectively support postfire conifer regeneration. © 2023 the Author(s)

    Do physician outcome judgments and judgment biases contribute to inappropriate use of treatments? Study protocol

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    <p>Abstract</p> <p>Background</p> <p>There are many examples of physicians using treatments inappropriately, despite clear evidence about the circumstances under which the benefits of such treatments outweigh their harms. When such over- or under- use of treatments occurs for common diseases, the burden to the healthcare system and risks to patients can be substantial. We propose that a major contributor to inappropriate treatment may be how clinicians judge the likelihood of important treatment outcomes, and how these judgments influence their treatment decisions. The current study will examine the role of judged outcome probabilities and other cognitive factors in the context of two clinical treatment decisions: 1) prescription of antibiotics for sore throat, where we hypothesize overestimation of benefit and underestimation of harm leads to over-prescription of antibiotics; and 2) initiation of anticoagulation for patients with atrial fibrillation (AF), where we hypothesize that underestimation of benefit and overestimation of harm leads to under-prescription of warfarin.</p> <p>Methods</p> <p>For each of the two conditions, we will administer surveys of two types (Type 1 and Type 2) to different samples of Canadian physicians. The primary goal of the Type 1 survey is to assess physicians' perceived outcome probabilities (both good and bad outcomes) for the target treatment. Type 1 surveys will assess judged outcome probabilities in the context of a representative patient, and include questions about how physicians currently treat such cases, the recollection of rare or vivid outcomes, as well as practice and demographic details. The primary goal of the Type 2 surveys is to measure the specific factors that drive individual clinical judgments and treatment decisions, using a 'clinical judgment analysis' or 'lens modeling' approach. This survey will manipulate eight clinical variables across a series of sixteen realistic case vignettes. Based on the survey responses, we will be able to identify which variables have the greatest effect on physician judgments, and whether judgments are affected by inappropriate cues or incorrect weighting of appropriate cues. We will send antibiotics surveys to family physicians (300 per survey), and warfarin surveys to both family physicians and internal medicine specialists (300 per group per survey), for a total of 1,800 physicians. Each Type 1 survey will be two to four pages in length and take about fifteen minutes to complete, while each Type 2 survey will be eight to ten pages in length and take about thirty minutes to complete.</p> <p>Discussion</p> <p>This work will provide insight into the extent to which clinicians' judgments about the likelihood of important treatment outcomes explain inappropriate treatment decisions. This work will also provide information necessary for the development of an individualized feedback tool designed to improve treatment decisions. The techniques developed here have the potential to be applicable to a wide range of clinical areas where inappropriate utilization stems from biased judgments.</p

    Bandwidth is Political: Reachability in the Public Internet

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    A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants.

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    This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/ng.3448Advanced age-related macular degeneration (AMD) is the leading cause of blindness in the elderly, with limited therapeutic options. Here we report on a study of >12 million variants, including 163,714 directly genotyped, mostly rare, protein-altering variants. Analyzing 16,144 patients and 17,832 controls, we identify 52 independently associated common and rare variants (P < 5 × 10(-8)) distributed across 34 loci. Although wet and dry AMD subtypes exhibit predominantly shared genetics, we identify the first genetic association signal specific to wet AMD, near MMP9 (difference P value = 4.1 × 10(-10)). Very rare coding variants (frequency <0.1%) in CFH, CFI and TIMP3 suggest causal roles for these genes, as does a splice variant in SLC16A8. Our results support the hypothesis that rare coding variants can pinpoint causal genes within known genetic loci and illustrate that applying the approach systematically to detect new loci requires extremely large sample sizes.We thank all participants of all the studies included for enabling this research by their participation in these studies. Computer resources for this project have been provided by the high-performance computing centers of the University of Michigan and the University of Regensburg. Group-specific acknowledgments can be found in the Supplementary Note. The Center for Inherited Diseases Research (CIDR) Program contract number is HHSN268201200008I. This and the main consortium work were predominantly funded by 1X01HG006934-01 to G.R.A. and R01 EY022310 to J.L.H

    Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

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    Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations
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