bluuebunny/crossref_metadata_2025_split huggingface dataset:
Health indicators as measures of individual health status, perceived importance, and their associated factors—an observational study
Authors: Jing Xia , Zhou Yuchun , Sokoya Temiloluwa , Diaz Sebastian , Law Timothy , Himawan Lina , Lekey Francisca , Shi Lu , Griffin Sarah , Gimbel Ronald W. 
Published: September 2022

AbstractBackgroundSelf-rated health status, a subjective measure, is used broadly to describe an individual’s overall health status. Our long-term goal is to create a more objective, comprehensive, and accurate measure of individual health status. We selected 29 health indicators and prioritized them by conducting online surveys. Thirteen of these 29 indicators received relatively more consistent ratings across 3 samples.ObjectivesTo explore the main and interaction effects of 4 demographic factors as independent variables (age, gender, professional group, and educational level) in the importance ratings of the 13 health indicators.MethodsWe conducted a 4-way multivariate analysis of variance (MANOVA) with post-hoc testing to examine the effects of independent variables on all 13 dependent variables. Descriptive statistics and bivariate correlation analysis were also conducted.DesignCross-sectional study.SettingAn online survey (≥ 18 years).Participants791 participants in the USA.Results13 health indicators were significantly correlated with each other. Age correlated with most of the health indicators (8 of 13). The MANOVA modeling results indicated that gender, age, and education levels significantly affected the combination of the 13 health indicators. There was a significant interaction effect by age and professional group on 5 health indicators.ConclusionsAge is critical in rating the 13 health indicators. Among all the statistically significant main effects of demographic factors, the effect sizes descend regarding age, gender, educational level, and interaction between age and professional group. These results can provide a foundation for further studies to explore behavioral interventions for individual subgroups.Article SummaryStrengths and limitations of this studyThe work establishes the interactions and effects between demographic data (age, gender, education, and professional group) and the perceived importance of 13 health indicators via MANOVA analysisThe interactions and effects of demographic data on the importance ratings of the 13 health indicatorscan guide future study designs for behavioral interventionsDeep analysis of the demographic variables and their effects on and interactions with the rating results are helpful for thoroughly understanding the perspectivesThe study is an observational study despite with relatively large sample size and a robust analysisThe data are not racially representative
Ambient Air Pollution and Subclinical Cardiovascular Disease Measured by Magnetic Resonance Imaging in the Canadian Alliance for Healthy Hearts and Minds Study
Authors: Azab Sandi M , Schulze Karleen M , Brook Jeffrey R , Doiron Dany , Smith Eric E , Moody Alan R , Desai Dipika , Brauer Michael , Friedrich Matthias G , Bangdiwala Shrikant I , Zeraatkar Dena , Lee Douglas , Anand Sonia S , de Souza Russell J 
Published: September 2022

AbstractBackgroundLong-term exposure to air pollution, even at levels below regulatory standards, has been associated with higher risk of cardiovascular-related mortality. Less is known about the association of air pollution and initial development of CVD in low-exposure settings in generally healthy human populations.ObjectiveIn the Canadian Alliance for Healthy Hearts and Minds Cohort Study (CAHHM), we aimed to investigate the association between low-level exposure to key air pollutants and subclinical carotid atherosclerosis in adults without known clinical CVD. To date, the association of ambient air pollution and atherosclerosis measured by magnetic resonance imaging (MRI) has not been studied.MethodsWe studied 6,645 Canadian adults recruited between 2014-2018 from the provinces of British Columbia, Alberta, Ontario, Quebec, and Nova Scotia, for whom average long-term exposures to nitrogen dioxide (NO2), ozone (O3), and fine particulate matter (PM2.5) were estimated for five years prior to the start of CAHHM recruitment, and who underwent MRI to assess carotid vessel wall volume (CWV). Linear mixed models were used to quantify associations between each air pollutant and CWV adjusting for individual-level and community-level risk factors for CVD. Secondary analyses included region-specific stratification and modeling the effect of one pollutant on CWV within low, medium, and high levels of a second pollutant to test for interactions.ResultsHigher PM2.5 was nominally associated with lower CWV (quintile 5: 893.3 mm3, quintile 1: 908.8 mm3; p-trend =0.05), but this was not robust in region-stratified analysis. Higher NO2 was associated with lower CWV (quintile 5: 889.5 mm3, quintile 1: 918.6 mm3; p-trend <.0001). Higher O3 was associated with higher CWV (quintile 5: 925.4 mm3, quintile 1: 899.7 mm3; p-trend =0.02). NO2 emerged as a consistent effect modifier of both PM2.5 and O3.ConclusionIn a cohort of generally healthy adults living in Canada, a country with relatively low levels of air pollution, exposure to NO2 was negatively associated, and O3 was positively associated with CWV as a measure of subclinical atherosclerosis by MRI, while associations to PM2.5 were inconsistent. The reasons for these associations warrant further study.
Prenatal exposure to persistent organic pollutants and changes in infant growth and childhood growth trajectories
Authors: Cai Anran , Portengen Lützen , Govarts Eva , Martin Laura Rodriguez , Schoeters Greet , Legler Juliette , Vermeulen Roel , Lenters Virissa , Remy Sylvie 
Published: September 2022

AbstractBackgroundChildren are born with a burden of persistent organic pollutants (POPs) which may have endocrine disrupting properties and have been postulated to contribute to the rise in childhood obesity. The current evidence is equivocal, which may be because many studies investigate the effects at one time point during childhood. We assessed associations between prenatal exposure to POPs and growth during infancy and childhood.MethodsWe used data from two Belgian cohorts with cord blood measurements of five organochlorines [(dichlorodiphenyldichloroethylene (p,p’-DDE), hexachlorobenzene (HCB), polychlorinated biphenyls (PCB-138, -150, -180)] (N = 1,418) and two perfluoroalkyl substances [perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS)] (N = 346). We assessed infant growth, defined as body mass index (BMI) z-score change between birth and 2 years, and childhood growth, characterized as BMI trajectory from birth to 8 years. To evaluate associations between POP exposures and infant growth, we applied a multi-pollutant approach, using penalized elastic net regression with stability selection, controlling for covariates. To evaluate associations with childhood growth, we used single-pollutant linear mixed models with random effects for child individual, parametrized using a natural cubic spline formulation.ResultsPrenatal exposures to p,p’-DDE and PCB-153 were selected in elastic net models for infant growth analysis, but the selections were unstable. No clear association between any of the exposures and longer-term childhood growth trajectories was observed. We did not find evidence of effect modification by child sex.ConclusionOur results suggest that prenatal exposure to PCB-153 and p,p’-DDE may affect infant growth in the first two years, with little evidence of more persistent effects.
GraMDTA: Multimodal Graph Neural Networks for Predicting Drug-Target Associations
Authors: Yella Jaswanth K. , Ghandikota Sudhir K. , Jegga Anil G. 
Published: September 2022

AbstractFinding novel drug-target associations is vital for drug discovery. However, screening millions of small molecules for a select target protein is challenging. Several computational approaches have been developed in the past using Machine learning methods for computational drug-target association (DTA) prediction predominantly use structural data of drugs and proteins. Some of these approaches use knowledge graph networks and link prediction. To the best of our knowledge there have been no approaches that use both structural learning that offers molecular-based representations and knowledge graph-based learning which offers interaction-based representations for DTA discovery. Based on the premise that multimodal sources of information acting complimentarily could improve the robustness of DTA predictions, we developed GraMDTA, a multimodal graph neural network that learns both structural and knowledge graph representations utilizing multi-head attention to fuse the multimodal representations. We compare GraMDTA with other computational approaches for DTA prediction to demonstrate the power of multimodal fusion for discovery of DTA.
Coenzyme A governs proinflammatory macrophage metabolism
Authors: Timblin Greg. A. , Tharp Kevin. M. , ten Hoeve Johanna , Kantner Daniel S. , Baydemir Ilayda , Noel Eric A. , Khantwal Chandra , Singh Pankaj K. , Farahzad Joshua N. , Domínguez-Andrés Jorge , Vance Russell E. , Snyder Nathaniel W. , Weaver Valerie M. 
Published: September 2022

AbstractToll-like receptor (TLR)-dependent macrophage responses rely on acute increases in oxidative mitochondrial glucose metabolism that epigenetically support rapid proinflammatory transcriptional programming via histone acetylation. Subsequent suppression of oxidative metabolism restrains this metabolic-epigenetic support of proinflammatory gene transcription to enforce tolerance, an immunosuppressed state of innate immune memory. Identifying biology that promotes or counters these metabolic-epigenetic changes will inform therapeutic approaches to influence proinflammatory, antimicrobial, and immunosuppressed myeloid cellular states. Here, we demonstrate that Coenzyme A (CoA) is a “metabolic adjuvant”, as supplying exogenous CoA to macrophages both enhances the magnitude of TLR-driven proinflammatory and antimicrobial responses, and reverse tolerance, via promotion of oxidative metabolism. Extracellular CoA, which we isotopically trace to show its direct uptake by macrophages, works synergistically with tonic TLR signaling, which we demonstrate is a critical regulator of nutrient uptake, metabolism, histone acetylation, and gene expression in macrophages. Together, TLR signaling and exogenous CoA promote mitochondrial glucose oxidation, acetyl-CoA production, and TLR target gene-specific histone acetylation, enhancing metabolic-epigenetic support of proinflammatory transcriptional programming. Exogenous CoA unlocks tumor-associated macrophage (TAM)-dependent TLR agonist anti-tumor activity in anin vivobreast cancer model, and promotes macrophage restriction of the intracellular bacterial pathogenLegionella pneumophila in vitrovia anIrg1-dependent antimicrobial state of CoA-augmented itaconate biosynthesis. Our findings demonstrate direct acquisition of intact extracellular CoA, and the ability of this exogenously supplemented metabolic cofactor to augment a key oxidative metabolic-epigenetic pathway supporting proinflammatory and antimicrobial macrophage phenotypes. This may inform host-targeted metabolic adjuvant therapies to reverse myeloid immunosuppression.
Exercise performance is not improved in mice with skeletal muscle deletion of natriuretic peptide clearance receptor
Authors: Jia Brigitte , Hasse Alexander , Shi Fubiao , Collins Sheila 
Published: September 2022

AbstractNatriuretic peptides (NP), including atrial, brain, and C-type natriuretic peptides (ANP, BNP, and CNP), play essential roles in regulating blood pressure, cardiovascular homeostasis, and systemic metabolism. One of the major metabolic effects of NP is manifested by their capacity to stimulate lipolysis and the thermogenesis gene program in adipocytes, however, their metabolic effect on skeletal muscle is much less appreciated. There are three NP receptors (NPR): NPRA, NPRB, and NPRC, and all three NPR genes are expressed in C2C12 myocytes. Treatment with either ANP, BNP, or CNP evokes the cGMP signaling process in C2C12 myocytes. We then generated a genetic model with Nprc gene deletion in skeletal muscle and tested whether enhancing NP signaling by preventing its clearance in skeletal muscle would improve exercise performance in mice. Under sedentary conditions, Nprc skeletal muscle knockout (MKO) mice showed comparable exercise performance to their floxed littermates in terms of maximal running velocity and total endurance running time. Eight weeks of voluntary running-wheel training in a young cohort increased exercise performance, but no significant differences were observed in MKO compared with control mice. Furthermore, 6-weeks of treadmill training in a relatively aged cohort also increased exercise performance compared with their baseline but did not result in an improvement in MKO mice compared with the controls. In summary, our study suggests that NP signaling is potentially important in myocytes but its function in skeletal muscle in vivo needs to be further studied in alternative physiological conditions or with new genetic mouse models.
A Comprehensive Enumeration of the Human Proteostasis Network. 1. Components of Translation, Protein Folding, and Organelle-Specific Systems
Authors:   The Proteostasis Consortium,   Overall coordination, Elsasser Suzanne , Elia Lisa P. , Morimoto Richard I. , Powers Evan T. ,   Harvard Medical School group (analysis), Finley Daniel ,   University of California, San Francisco and Gladstone Institutes group I (chaperones, analysis), Mockler Eric , Lima Leandro , Finkbeiner Steve ,   University of California, San Francisco group II (chaperones, analysis), Gestwicki Jason E. ,   Northwestern University group (chaperones, analysis), Stoeger Thomas , Cao Kedi ,   The Scripps Research Institute group (chaperones, endoplasmic reticulum proteostasis, mitochondrial proteostasis, analysis), Garza Dan , Kelly Jeffery W. ,   Stanford University group (chaperones, translation, mitochondrial proteostasis), Collier Miranda , Rainbolt T. Kelly , Taguwa Shuhei , Chou Ching-Chieh , Aviner Ranen , Barbosa Natália , Morales-Polanco Fabián , Masto Vincent B. , Frydman Judith 
Published: September 2022

AbstractThe condition of having a healthy, functional proteome is known as protein homeostasis, or proteostasis. Establishing and maintaining proteostasis is the province of the proteostasis network, approximately 2,500 genes that regulate protein synthesis, folding, localization, and degradation. The proteostasis network is a fundamental entity in biology with direct relevance to many diseases of protein conformation. However, it is not well defined or annotated, which hinders its functional characterization in health and disease. In this series of manuscripts, we aim to operationally define the human proteostasis network by providing a comprehensive, annotated list of its components. Here, we provide a curated list of 959 unique genes that comprise the protein synthesis machinery, chaperones, folding enzymes, systems for trafficking proteins into and out of organelles, and organelle-specific degradation systems. In subsequent manuscripts, we will delineate the human autophagy-lysosome pathway, the ubiquitin-proteasome system, and the proteostasis networks of model organisms.
PIP2 and Ca2+ regulation of TMEM16A currents in excised inside-out patches
Authors: Tembo Maiwase , Lara-Santos Crystal , Rosenbaum Joel C. , Carlson Anne E. 
Published: September 2022

AbstractThe Ca2+ activated Cl− channel formed by transmembrane member 16A (TMEM16A) is broadly expressed and regulates diverse processes. In addition to Ca2+, TMEM16A channels require the acidic phospholipid phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) to open. Like other channels regulated by PI(4,5)P2, TMEM16A-conducted currents recorded in excised patches slowly decay overtime. Here we assessed how intracellular Ca2+ alters the rate of this current rundown, using the channels endogenously expressed in oocytes from the African clawed frog, Xenopus laevis. We found that in excised, inside-out patches, the concentration of applied Ca2+ alters the rate of rundown, with high Ca2+ concentrations speeding rundown by activating membrane associated phospholipase C (PLC). Together, these results clarify our understanding of how Ca2+ regulates both TMEM16A directly, and targets PLC to regulate the membrane PI(4,5)P2 content.
Neuronal C-Reactive Protein Mediates Neuropathic Pain by Activating Nociceptive FcγRI-Coupled Signaling
Authors: Liu Fan , Su Si , Zhang Li , Fang Yehong , Cui Huan , Sun Jianru , Xie Yikuan , Ma Chao 
Published: September 2022

AbstractBackgroundNeuropathic pain is difficult to treat in clinical practice, and the underlying mechanisms are insufficiently elucidated. Previous studies have demonstrated that Fcγ receptor I (FcγRI) is expressed in the neurons of the dorsal root ganglion (DRG) and may be involved in chronic pain.MethodsChronic constriction injury (CCI) was used to induce neuropathic pain in rats. Primary neuron-specificFcgr1conditional knockout (CKO) rats were established by crossing rats carrying aFcgr1loxP+/+with thePirtCRE+line. Behavioral and molecular studies were conducted to evaluate the differences between wild-type and CKO rats after CCI.ResultsWe first revealed that CCI activated neuronal FcγRI-related signaling in the DRG. CCI-induced neuropathic pain was alleviated in CKO rats. C-reactive protein (CRP) was increased in the DRG after nerve injury. Intraganglionic injection or overexpression of the recombinant CRP protein in the DRG evoked pain accompanied and activated neuronal FcγRI. CRP-evoked pain was significantly reduced in CKO rats. Furthermore, microinjection of native IgG into the DRG alleviated neuropathic pain and the activation of neuronal FcγRI-related signaling.ConclusionsOur results indicate that the activation of neuronal CRP/FcγRI-related signaling plays an important role in the development of pain in CCI. Our findings may provide novel insights into the neuroimmune responses after peripheral nerve injury and might suggest potential therapeutic targets for neuropathic pain.
Single-cell multi-cohort dissection of the schizophrenia transcriptome
Authors: Ruzicka W. Brad , Mohammadi Shahin , Fullard John F. , Davila-Velderrain Jose , Subburaju Sivan , Tso Daniel Reed , Hourihan Makayla , Jiang Shan , Lee Hao-Chih , Bendl Jaroslav ,   PsychENCODE Consortium, Voloudakis Georgios , Haroutunian Vahram , Hoffman Gabriel E. , Roussos Panos , Kellis Manolis 
Published: September 2022

AbstractSchizophrenia is a prevalent mental illness with a high societal burden, complex pathophysiology, and diverse genetic and environmental etiology. Its complexity, polygenicity, and heterogeneity have hindered mechanistic elucidation and the search for new therapeutics. We present a single-cell dissection of schizophrenia-associated transcriptomic changes in the human prefrontal cortex across two independent cohorts, one deeply profiling 48 subjects (361,996 cells), and the other broadly profiling 92 subjects (106,761 cells). We identified 25 cell types that we used to produce a high-resolution atlas of schizophrenia-altered genes and pathways. Excitatory neurons were the most affected cell group, with transcriptional changes converging on neurodevelopment and synapse-related molecular pathways. Differentially expressed gene sets implicate a coherently expressed module of trans-acting regulatory factors involved in neurodevelopment and genetically associated with schizophrenia risk. Transcriptional alterations significantly overlapped with known genetic risk factors, suggesting convergence of rare and common genomic variants on reproducible neuronal population specific alterations in schizophrenia. The severity of transcriptional pathology segregated two populations of schizophrenia subjects in a manner consistent with the expression of specific transcriptional patterns marked by genes involved in synaptic function and chromatin dynamics. Our results provide a high-resolution single cell atlas linking transcriptomic changes within specific cell populations to etiological genetic risk factors, contextualizing established knowledge within the cytoarchitecture of the human cortex and facilitating mechanistic understanding of schizophrenia pathophysiology and heterogeneity.
ACQuA: Arrhythmia Classification with Quasi-Attractors
Authors: Rudman William , Merullo Jack , Mercurio Laura , Eickhoff Carsten 
Published: September 2022

AbstractIn recent years, deep learning has redefined algorithms for detecting cardiac abnormalities. However, many state of the art algorithms still rely on calculating handcrafted features from a given heart signal that are then fed into shallow 1D convolutional networks or transformer architectures. We propose ACQuA (Anomaly Classification with Quasi Attractors), a task agnostic algorithm that can be used in a wide variety of cardiac settings, from classifying cardiac arrhythmias from ECG signals to detecting heart murmurs from PCG signals. Using theorems from dynamical analysis and topological data analysis, we create informative attractor images that 1) are human distinguishable and 2) can be used to train small, off the shelf deep neural networks for anomaly classification. In the George B. Moody 2022 Challenge, we receive an official score of 0.433 (263/305) for murmur classification and a score of 12616 (208/305) for outcome classification. Additionally, we evaluate our model on the CinC 2017 Challenge data that tasks practitioners to classify cardiac arrhythmias from ECG signals. On the CinC 2017 Challenge data, we improve upon the winning F1 scores by approximately 14% on the hidden validation data.
Federal Funding and Citation Metrics of Biomedical Research in the USA
Authors: Ioannidis John P.A. , Hozo Iztok , Djulbegovic Benjamin 
Published: September 2022

ABSTRACTBoth citation and funding metrics converge in shaping current perceptions of academic success. We aimed to evaluate what proportion of the most-cited USA-based biomedical scientists are funded by biomedical federal agencies and whether funded scientists are more cited than not funded ones. We linked a Scopus-based database on top-cited researchers (n=75,316 USA-based) and the NIH RePORTER database of 33 biomedical federal agencies (n=204,603 grant records) with matching based on name and institution. The 40,887 USA-based top-cited scientists who were allocated to any of 69 scientific subfields highly related to biomedicine were considered in the main analysis. The proportion of USA-based top-cited biomedical scientists (based on career-long citation impact) who had received any federal funding from biomedical research agencies was 63% for any funding (1996-2022), 21% for recent funding (2015-2022), and 14% for current funding (2021-2022). Respective proportions were 65%, 31%, and 21%, when top-cited scientists based on recent single year impact were considered. There was large variability across scientific subfields. No subfield had more than 31% of its top-cited USA-based scientists (career-long impact) currently funded. Funded top-cited researchers were overall more cited than non-funded top-cited scientists, e.g. mean (median) 14,420 (8983) versus 8,445 (4613) (p<0.001) and a substantial difference remained (, after adjusting for subfield and years since first publication. Differences were more prominent in some specific biomedical subfields. Overall, biomedical federal funding has offered support to approximately two-thirds of the top-cited biomedical scientists at some point during the last quarter century, but only a small minority of top-cited scientists have current federal biomedical funding. The large unevenness across subfields needs to be addressed with ways that improve equity, efficiency, excellence, and translational potential.