bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gut permeability and cognitive decline: A pilot investigation in the Northern Manhattan Study
Authors: Rundek Tatjana , Roy Sabita , Hornig Mady , Cheung Ying Kuen , Gardener Hannah , DeRosa Janet , Levin Bonnie , Wright Clinton B. , Brutto Victor A. Del , Elkind Mitchell SV , Sacco Ralph L. 
Published: September 2020

BackgroundGut microbiota may impact cognitive function and decline, though data is limited. This pilot study examines the associations between gut dysbiosis products, plasma lipopolysaccharide (LPS) and soluble CD14 (sCD14), with cognitive decline and immune molecule activation among 40 participants in the longitudinal population-based Northern Manhattan Study.MethodsWe selected stroke- and dementia-free participants at baseline with high activation levels of core components of the immune signaling pathways underlying microbiota metabolite-cognitive associations (IL-1, IL-17, TNF). Participants were followed with up to three complete neuropsychological assessments.ResultsElevated sCD14 was associated with high levels of IL-1 (p<0.05), whereas in samples where only IL-17 and TNF were increased, LPS and sCD14 levels were not elevated. LPS was associated with decline in global cognitive performance over 2-3 assessments (adjusted β=-0.023 per SD per year, 95%CI:-0.036, −0.010). The association between sCD14 and cognitive decline was marginal (adjusted β=-0.018 per SD per year, 95%CI:-0.040, 0.004).ConclusionsThese preliminary data support the hypothesis that gut dysbiosis leads to systemic and neuro-inflammation, and subsequently cognitive decline. Further large targeted and untargeted gut microbiota-derived metabolomic studies are needed.
Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19
Authors: Li Matthew D. , Arun Nishanth T. , Aggarwal Mehak , Gupta Sharut , Singh Praveer , Little Brent P. , Mendoza Dexter P. , Corradi Gustavo C.A. , Takahashi Marcelo S. , Ferraciolli Suely F. , Succi Marc D. , Lang Min , Bizzo Bernardo C. , Dayan Ittai , Kitamura Felipe C. , Kalpathy-Cramer Jayashree 
Published: September 2020

ABSTRACTPurposeTo improve and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations.Materials and MethodsA published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from four test sets, including 3 from the United States (patients hospitalized at an academic medical center (N=154), patients hospitalized at a community hospital (N=113), and outpatients (N=108)) and 1 from Brazil (patients at an academic medical center emergency department (N=303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson r). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results.ResultsTuning the deep learning model with outpatient data improved model performance in two United States hospitalized patient datasets (r=0.88 and r=0.90, compared to baseline r=0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (r=0.86 and r=0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets.ConclusionsPerformance of a deep learning-based model that extracts a COVID-19 severity score on CXRs improved using training data from a different patient cohort (outpatient versus hospitalized) and generalized across multiple populations.
COVID19 epidemic modelling and the effect of public health interventions in India-SEIQHRF model
Authors: Sengupta Pooja , Ganguli Bhaswati , Chatterjee Aditya , Roy Sugata Sen , Chatterjee Moumita 
Published: September 2020

AbstractA dynamic epidemic modeling, based on real time data, of COVID19 has been attempted for India and few selected Indian states. Various scenarios of intervention strategies to contain the spread of the disease are explored.
KIM-1/TIM-1 is a Receptor for SARS-CoV-2 in Lung and Kidney
Authors: Mori Yutaro , Fink Corby , Ichimura Takaharu , Sako Keisuke , Mori Makiko , Lee Nathan N. , Aschauer Philipp , Padmanabha Das Krishna M. , Hong SoonGweon , Song Minsun , Padera Robert F. , Weins Astrid , Lee Luke P. , Nasr Mahmoud L. , Dekaban Gregory A. , Dikeakos Jimmy D. , Bonventre Joseph V. 
Published: September 2020

AbstractSARS-CoV-2 precipitates respiratory distress by infection of airway epithelial cells and is often accompanied by acute kidney injury. We report that Kidney Injury Molecule-1/T cell immunoglobulin mucin domain 1 (KIM-1/TIM-1) is expressed in lung and kidney epithelial cells in COVID-19 patients and is a receptor for SARS-CoV-2. Human and mouse lung and kidney epithelial cells express KIM-1 and endocytose nanoparticles displaying the SARS-CoV-2 spike protein (virosomes). Uptake was inhibited by anti-KIM-1 antibodies and TW-37, a newly discovered inhibitor of KIM-1-mediated endocytosis. Enhanced KIM-1 expression by human kidney tubuloids increased uptake of virosomes. KIM-1 binds to the SARS-CoV-2 Spike protein in vitro. KIM-1 expressing cells, not expressing angiotensin-converting enzyme 2 (ACE2), are permissive to SARS-CoV-2 infection. Thus, KIM-1 is an alternative receptor to ACE2 for SARS-CoV-2. KIM-1 targeted therapeutics may prevent and/or treat COVID-19.
Involvement of mutant and wild-type CYSLTR2 in the development and progression of uveal nevi and melanoma
Authors: Nell Rogier J. , Menger Nino V. , Versluis Mieke , Luyten Gregorius P.M. , Verdijk Robert M. , Madigan Michele C. , Jager Martine J. , van der Velden Pieter A. 
Published: September 2020

AbstractBackgroundActivating Gαq signalling mutations are considered an early event in the development of uveal melanoma. Whereas most tumours harbour a mutation in GNAQ or GNA11, CYSLTR2 (encoding G-protein coupled receptor CysLT2R) forms a rare alternative. The role of wild-type CysLT2R in uveal melanoma remains unknown.MethodsWe performed a digital PCR-based molecular analysis of benign choroidal nevi and primary uveal melanomas. Publicly available bulk and single cell sequencing data were mined to further study wild-type and mutant CYSLTR2.Results1/16 nevi and 2/120 melanomas carried the CYSLTR2 mutation. The mutation was subclonal in the nevus, while being clonal in both melanomas. In the melanomas, secondary, subclonal CYSLTR2 alterations shifted the allelic balance towards the mutant. The resulting genetic heterogeneity was confirmed in distinct areas of both tumours. At the RNA level, further silencing of wild-type and preferential expression of mutant CYSLTR2 was identified, which was also observed in 2/3 CYSLTR2 mutant melanomas from the TCGA cohort. In CYSLTR2 wild-type melanomas, high expression of CYSLTR2 originated from melanoma cells and correlated to tumour inflammation.ConclusionsOur findings suggest that CYSLTR2 is involved in both early and late development of uveal melanoma. Whereas the CYSLTR2 p.L129Q mutation is likely to be the initiating oncogenic event, various mechanisms further increase the mutant allele abundance during tumour progression. This makes mutant CysLT2R an attractive therapeutic target in uveal melanoma. In GNAQ, GNA11 and PLCB4 mutant melanomas, expression of wild-type CYSLTR2 possibly facilitates an interaction with immune cells in the microenvironment and may also have therapeutic potential.
The enactment of compassionate leadership by nursing and midwifery managers: Results from an international online survey
Authors: Papadopoulos Irena , Wright Steve , Lazzarino Runa , Koulouglioti Christina , Aagard Magdeline , Akman Özlem , Alpers Lise-Merete , Apostolara Paraskevi , Araneda B. Julieta , Biglete-Pangilinan Sylvia , Regev Orit Eldar , González-Gil Maria Teresa , Kouta Christiana , Krepinska Radka , Lesińska-Sawicka Małgorzata , Liskova Miroslava , Lopez-Diaz Lucero , Malliarou Maria , Martín-García Ángel , Muñoz S. Mara , Nagórska Małgorzata , Ngunyulu Roinah Nkhensani , Nissim Sara , Nortvedt Line , Oconer-Rubiano Florinda , Oter-Quintana Cristina , öztürk Candan , Papp Katalin , Piratoba-Hernandez Blanca , Rousou Elena , Tolentino-Diaz Maria Ymelda , Tothova Valerie , Zorba Akile 
Published: September 2020

ABSTRACTAimTo explore the views of an international sample of nursing and midwifery managers concerning attributes that they associate with compassionate management.MethodA cross-sectional online survey. Using a snowballing sampling method, 1,217 responses were collected from nursing and midwifery managers in 17 countries. A total of complete 933 responses to a question related to which actions and behaviours indicated that a manager was exercising compassionate leadership were analysed for this paper. Firstly, content analysis of the responses was conducted, and secondly a relative distribution of the identified themes for the overall sample and for each participating country was calculated.ResultsSix main themes were identified describing the attributes of a compassionate leader: 1) Virtuous Support, 2) Communication, 3) Personal Virtues of the Manager, 4) Participatory Communication, 5) Growth/Flourishing/ Nurturing, and 6) Team Cohesion. The first three themes mentioned above collectively accounted for 63% of the responses, and can therefore be considered to be the most important characteristics of compassionate management behaviour.ConclusionThe key indicators of compassionate management in nursing and midwifery which were identified emphasise approachability, active and sensitive listening, sympathetic responses to staff members’ difficulties (especially concerning child- and other caring responsibilities), active support of and advocacy for the staff team, and active problem solving and conflict resolution. While there were differences between the countries’ views on compassionate healthcare management, some themes were widely represented among different countries’ responses, which suggest key indicators of compassionate management that apply across cultures.
Integrative Genomics Analysis Reveals a Novel 21q22.11 Locus Contributing to Susceptibility of COVID-19
Authors: Ma Yunlong , Huang Yukuan , Zhao Sen , Yao Yinghao , Zhang Yaru , Qu Jia , Wu Nan , Su Jianzhong 
Published: September 2020

AbstractThe systematic identification of host genetic risk factors is essential for the understanding and treatment of COVID-19. By performing a meta-analysis of two independent genome-wide association (GWAS) summary datasets (N = 680,128), a novel locus at 21q22.11 was identified to be associated with COVID-19 infection (rs9976829 in IFNAR2 and upstream of IL10RB, OR = 1.16, 95% CI = 1.09 - 1.23, P = 2.57×10−6). The rs9976829 represents a strong splicing quantitative trait locus (sQTL) for both IFNAR2 and IL10RB genes, especially in lung tissue (P 1.8×10−24). Gene-based association analysis also found IFNAR2 was significantly associated with COVID-19 infection (P = 2.58×10−7). Integrative genomics analysis of combining GWAS with eQTL data showed the expression variations of IFNAR2 and IL10RB have prominent effects on COVID-19 in various types of tissues, especially in lung tissue. The majority of IFNAR2-expressing cells were dendritic cells (40%) and plasmacytoid dendritic cells (38.5%), and IL10RB-expressing cells were mainly nonclassical monocytes (29.6%). IFNAR2 and IL10RB are targeted by several interferons-related drugs. Together, our results uncover 21q22.11 as a novel susceptibility locus for COVID-19, in which individuals with G alleles of rs9976829 have a higher probability of COVID-19 susceptibility than those with non-G alleles.
Clinical conditions and their impact on utility of genetic scores for prediction of acute coronary syndrome
Authors: Lee Jiwoo , Kiiskinen Tuomo , Mars Nina , Jukarainen Sakari ,   FinnGen Project, Ingelsson Erik , Neale Benjamin , Ripatti Samuli , Natarajan Pradeep , Ganna Andrea 
Published: September 2020

AbstractAimsEarly prediction of acute coronary syndrome (ACS) is a major goal for prevention of coronary heart disease (CHD). Genetic information has been proposed to improve prediction beyond well-established clinical risk factors. While polygenic scores (PS) can capture an individual’s genetic risk for ACS, its prediction performance may vary in the context of diverse correlated clinical conditions. Here, we aimed to test whether clinical conditions impact the association between PS and ACS.Methods and ResultsWe explored the association between 405 clinical conditions diagnosed before baseline and 9,080 incident cases of ACS in 387,832 individuals from the UK Biobank. We identified 80 conventional (e.g., stable angina pectoris (SAP), type 2 diabetes mellitus) and unconventional (e.g., diaphragmatic hernia, inguinal hernia) associations with ACS. Results were replicated in 6,430 incident cases of ACS in 177,876 individuals from FinnGen. The association between PS and ACS was consistent in individuals with and without most clinical conditions. However, a diagnosis of SAP yielded a differential association between PS and ACS. PS was associated with a significantly reduced (interaction p-value=2.87×10−8) risk for ACS in individuals with SAP (HR=1.163 [95% CI: 1.082–1.251]) compared to individuals without SAP (HR=1.531 [95% CI: 1.497–1.565]). These findings were replicated in FinnGen (interaction p-value=1.38×10−6).ConclusionIn summary, while most clinical conditions did not impact utility of PS for prediction of ACS, we found that PS was substantially less predictive of ACS in individuals with prevalent stable CHD. PS for ACS may be more appropriate for asymptomatic individuals than symptomatic individuals with clinical suspicion for CHD.
Accuracy of self-reported HIV testing history and awareness of HIV-positive status among people living with HIV in four Sub-Saharan African countries
Authors: Xia Yiqing , Milwid Rachael M , Godin Arnaud , Boily Marie-Claude , Johnson Leigh F , Marsh Kimberly , Eaton Jeffrey W , Maheu-Giroux Mathieu 
Published: September 2020

AbstractBackgroundIn many countries in Sub-Saharan Africa, self-reported HIV testing history and awareness of HIV-positive status from household surveys are used to estimate the percentage of people living with HIV (PLHIV) who know their HIV status. Despite widespread use, there is limited empirical information on the sensitivity of those self-reports, which can be affected by non-disclosure.MethodsBayesian latent class models were used to estimate the sensitivity of self-reported HIV testing history and awareness of HIV-positive status in four Population-based HIV Impact Assessment surveys in Eswatini, Malawi, Tanzania, and Zambia. Antiretroviral (ARV) metabolites biomarkers were used to identify persons on treatment who did not accurately report their status. For those without ARV biomarkers, the pooled estimate of non-disclosure among untreated persons was 1.48 higher than those on treatment.ResultsAmong PLHIV, the sensitivity of self-reported HIV testing history ranged 96% to 99% across surveys. Sensitivity of self-reported awareness of HIV status varied from 91% to 97%. Non-disclosure was generally higher among men and those aged 15-24 years. Adjustments for imperfect sensitivity did not substantially influence estimates of of PLHIV ever tested (difference <4%) but the proportion of PLHIV aware of their HIV-positive status was higher than the unadjusted proportion (difference <8%).ConclusionsSelf-reported HIV testing histories in four Eastern and Southern African countries are generally robust although adjustment for non-disclosure increases estimated awareness of status. These findings can contribute to further refinements in methods for monitoring progress along the HIV testing and treatment cascade.
Environmental and climatic impact on the infection and mortality of SARS-CoV-2 in Peru
Authors: Samillan Victor J. , Flores Diana , Rojas Eduardo , Zutta Brian R. 
Published: September 2020

ABSTRACTThe role of the environment and climate in the transmission and case-fatality rates of SARS-CoV-2 is still being investigated. Elevation and air quality are believed to be significant factors in the current development of the pandemic, but the influence of additional environmental factors remain unclear.In this study, we explored the relationship between the cumulative number of infections and mortality cases with climate (temperature, precipitation, solar radiation, water vapor pressure, wind), environmental data (elevation, NDVI, PM2.5 and NO2 concentration), and population density in Peru. Using the data from confirmed cases of infection from 1287 districts and confirmed cases of mortality in 479 districts, we used Spearman’s correlations to assess the correlation between environmental and climatic factors with cumulative infection cases, cumulative mortality and case-fatality rate. We also explored district cases by the ecozones of coast, sierra, high montane forest and lowland rainforest.Multiple linear regression models indicate elevation, mean solar radiation, air quality, population density and green cover are influential factors in the distribution of infection and mortality of SARS-CoV-2 in Peru. Case-fatality rate was weakly associated with elevation. Our results also strongly suggest that exposure to poor air quality is a significant factor in the mortality of individuals with SARS-CoV-2 below the age of 30.We conclude that environmental and climatic factors do play a significant role in the transmission and case-fatality rates in Peru, however further study is required to see if these relationships are maintained over time.
Whole-genome analysis of Malawian Plasmodium falciparum isolates identifies potential targets of allele-specific immunity to clinical malaria
Authors: Shah Zalak , Naung Myo T. , Moser Kara A. , Adams Matthew , Buchwald Andrea G. , Dwivedi Ankit , Ouattara Amed , Seydel Karl B , Mathanga Don P. , Barry Alyssa E. , Serre David , Laufer Miriam K. , Silva Joana C. , Takala-Harrison Shannon 
Published: September 2020

AbstractIndividuals acquire immunity to clinical malaria after repeated Plasmodium falciparum infections. This immunity to disease is thought to reflect the acquisition of a repertoire of responses to multiple alleles in diverse parasite antigens. In previous studies, we identified polymorphic sites within individual antigens that are associated with parasite immune evasion by examining antigen allele dynamics in individuals followed longitudinally. Here we expand this approach by analyzing genome-wide polymorphisms using whole genome sequence data from 140 parasite isolates representing malaria cases from a longitudinal study in Malawi and identify 25 genes that encode likely targets of naturally acquired immunity and that should be further characterized for their potential as vaccine candidates.
Emergence of Canonical Functional Networks from the Structural Connectome
Authors: Xie Xihe , Damasceno Pablo F. , Cai Chang , Nagarajan Srikantan , Raj Ashish 
Published: September 2020

AbstractHow do functional brain networks emerge from the underlying wiring of the brain? We examine how resting-state functional activation patterns emerge from the underlying connectivity and length of white matter fibers that constitute its “structural connectome”. By introducing realistic signal transmission delays along fiber projections, we obtain a complex-valued graph Laplacian matrix that depends on two parameters: coupling strength and oscillation frequency. This complex Laplacian admits a complex-valued eigen-basis in the frequency domain that is highly tunable and capable of reproducing the spatial patterns of canonical functional networks without requiring any detailed neural activity modeling. Specific canonical functional networks can be predicted using linear superposition of small subsets of complex eigenmodes. Using a novel parameter inference procedure we show that the complex Laplacian outperforms the real-valued Laplacian in predicting functional networks. The complex Laplacian eigenmodes therefore constitute a tunable yet parsimonious substrate on which a rich repertoire of realistic functional patterns can emerge. Although brain activity is governed by highly complex nonlinear processes and dense connections, our work suggests that simple extensions of linear models to the complex domain effectively approximate rich macroscopic spatial patterns observable on BOLD fMRI.
Stoichiometric modeling of artificial string chemistries
Authors: Moyer Devlin , Pacheco Alan R. , Bernstein David B. , Segrè Daniel 
Published: September 2020

AbstractUncovering the general principles that govern the architecture of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries,in silicorepresentations of chemical reaction networks arising from a defined set of mathematical rules, can help address this challenge by enabling the exploration of alternative chemical universes and the possible metabolic networks that could emerge within them. Here we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We study string chemistries using tools borrowed from the field of stoichiometric constraint-based modeling of organismal metabolic networks, through a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET). In addition to exploring the complexity and connectivity properties of different string chemistries, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental molecules within the string chemistry framework. We found that the identities of the metabolites in the biomass reaction wield much more influence over the structure of the minimal metabolic networks than the identities of the nutrient metabolites — a notion that could help us better understand the rise and evolution of biochemical organization. Our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of microbial communities.