bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gestational age and infection are greater predictors of placental histopathology than maternal prepregnancy BMI
Authors: Duffley Eleanor , Grynspan David , Scott Hailey , Lafrenière Anthea , de Andrade Cherley Borba Vieira , Bloise Enrrico , Connor Kristin L 
Published: December 2021

AbstractThe placenta undergoes morphological and functional adaptions to adverse exposures during pregnancy. The effects of suboptimal maternal body mass index (BMI), preterm birth, and infection on placental histopathological phenotypes remain unclear, despite the association between these conditions and poor offspring outcomes. We hypothesized that suboptimal maternal prepregnancy BMI and preterm birth (with and without infection) would associate with altered placental maturity and morphometry, and that altered placental maturity would associate with poor birth outcomes. Clinical data and human placentae were collected from 96 pregnancies where mothers were underweight, normal weight, overweight, or obese, without other major complications. Placental histopathological characteristics were scored with an anatomical pathologist. Associations between maternal BMI, placental pathology (immaturity and hypermaturity), placental morphometry, and infant outcomes were investigated at term and preterm, with and without infection. Fetal vascular endothelium volumetric proportion was decreased, whereas syncytial knot volumetric proportion was increased, in placentae from preterm pregnancies with chorioamnionitis compared to term placentae. At term and preterm, pregnancies with overweight and obesity had a high percentage increase in proportion of immature placentae compared to normal weight. Placental maturity did not associate with infant birth outcomes. We observed placental hypermaturity and altered placental morphometry among preterm pregnancies with chorioamnionitis, suggestive of altered placental development, which may inform about pregnancies susceptible to preterm birth and infection. Our data increase our understanding of how common metabolic exposures and preterm birth, in the absence of other comorbidities or perinatal events, potentially contribute to poor pregnancy outcomes and the programming of offspring development.
A high-resolution flux-matrix model describes the spread of diseases in a spatial network and the effect of mitigation strategies
Authors: Le Treut Guillaume , Huber Greg , Kamb Mason , Kawagoe Kyle , McGeever Aaron , Miller Jonathan , Pnini Reuven , Veytsman Boris , Yllanes David 
Published: December 2021

AbstractPropagation of an epidemic across a spatial network of communities is described by a variant of the SIR model accompanied by an intercommunity infectivity matrix. This matrix is estimated from fluxes between communities, obtained from cell-phone tracking data recorded in the USA between March 2020 and February 2021. We apply this model to the SARS-CoV-2 pandemic by fitting just one global parameter representing the frequency of interaction between individuals. We find that the predicted infections agree reasonably well with the reported cases. We clearly see the effect of “shelter-in-place” policies introduced at the onset of the pandemic. Interestingly, a model with uniform transmission rates produces similar results, suggesting that the epidemic transmission was deeply influenced by air travel. We then study the effect of alternative mitigation policies, in particular restricting long-range travel. We find that this policy is successful in decreasing the epidemic size and slowing down the spread, but less effective than the shelter-in-place policy. This policy can result in a pulled wave of infections. We express its velocity and characterize the shape of the traveling front as a function of the epidemiological parameters. Finally, we discuss a policy of selectively constraining travel based on an edge-betweenness criterion.
Understanding the impact of the COVID-19 pandemic on a socially deprived UK coastal town: a preliminary exploratory analysis of health and socioeconomic data
Authors: French Maddy , Spencer Mark , Walker Mike , Patel Afzal , Clarke Neil , Hughes Ross , Taylor Collette , Orwin Margaret , Elliott Alicia , Worsley Karl , Casson Julie , Milan Stephen , Bowen Mark , Sedda Luigi 
Published: December 2021

AbstractIntroductionIn addition to the direct impact of COVID-19 infections on health and mortality, a growing body of literature indicates there are wide-ranging indirect impacts of the COVID-19 pandemic and associated public health measures on population health and wellbeing. Exploring these indirect impacts in the context of a socially deprived UK coastal town will help identify priority areas to focus COVID-19 recovery efforts on.MethodsData on primary care diagnosis, hospital admissions, and several socioeconomic outcomes between 2016 and Spring 2021 in the UK town of Fleetwood were collected and analysed in an exploratory analysis looking at pre- and post-COVID-19 patterns in health and social outcomes. Weekly and monthly trends were plotted by time and differences between periods examined using Chi-squared and t-tests.ResultsInitial falls in hospital admissions and diagnoses of conditions in primary care in March 2020 were followed by sustained changes to health service activity for specific diagnostic and demographic groups, including for chronic kidney disease and young people. Increases in the number of people receiving Universal Credit and children eligible for free school meals appear to be greater for those in the least deprived areas of the town.DiscussionThese exploratory findings provide initial evidence of the sustained impact of the pandemic across several health and social outcomes. Examining these trends in multivariate analyses will further test these associations and establish the strength of the medium term impact of the pandemic on the population of this coastal town. Advanced modelling of this data is ongoing and will be published shortly.
The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia
Authors: Chulián Salvador , Stolz Bernadette J. , Martínez-Rubio Álvaro , Goñi Cristina Blázquez , Rodríguez Gutiérrez Juan F. , Velázquez Teresa Caballero , Quintana Águeda Molinos , Orellana Manuel Ramírez , Robleda Ana Castillo , Soler José Luis Fuster , Puras Alfredo Minguela , Martínez Sánchez María V. , Rosa María , Pérez-García Víctor M. , Byrne Helen M. 
Published: December 2021

AbstractAlthough children and adolescents with acute lymphoblastic leukaemia (ALL) have high survival rates, approximately 15-20% of patients relapse. Risk of relapse is routinely estimated at diagnosis by biological factors, including flow cytometry data. This high-dimensional data is typically manually assessed by projecting it onto a subset of biomarkers. Cell density and “empty spaces” in 2D projections of the data, i.e. regions devoid of cells, are then used for qualitative assessment. Here, we use topological data analysis (TDA), which quantifies shapes, including empty spaces, in data, to analyse pre-treatment ALL datasets with known patient outcomes. We combine these fully unsupervised analyses with Machine Learning (ML) to identify significant shape characteristics and demonstrate that they accurately predict risk of relapse, particularly for patients previously classified as ‘low risk’. We independently confirm the predictive power of CD10, CD20, CD38, and CD45 as biomarkers for ALL diagnosis. Based on our analyses, we propose three increasingly detailed prognostic pipelines for analysing flow cytometry data from ALL patients depending on technical and technological availability: 1. Visual inspection of specific biological features in biparametric projections of the data; 2. Computation of quantitative topological descriptors of such projections; 3. A combined analysis, using TDA and ML, in the four-parameter space defined by CD10, CD20, CD38 and CD45. Our analyses readily extend to other haematological malignancies.Author summaryAcute lymphoblastic leukaemia (ALL) is a blood cancer which affects predominantly children and adolescents. Therapy typically fails in approximately 20 % of patients, who suffer from relapse. To determine disease status, clinicians assess cell types, their interactions, as well as deviations from normal behaviour. Flow cytometry (FC) is a method that quantifies the intensity of specific cell markers, giving rise to high-dimensional data. This routinely collected information is then reduced to obtain human-interpretable visualisation for prognosis. Topological Data Analysis (TDA) is a field of mathematics that studies shapes in data, considering isolated data islands and empty spaces between them. We showcase how to use TDA to extract shape characteristics in FC data of relapsing patients. We propose three pipelines, of increasing methodological complexity, to aid clinical decisions for risk stratification in ALL. In combination with Machine Learning, TDA enables high-accuracy predictions of relapse to be made at the time of diagnosis.
Smoking, race, ancestry and prospective abstinence
Authors: Bergen Andrew W , Ervin Carolyn M , McMahan Christopher S , Baurley James W , Javitz Harold S , Hall Sharon 
Published: December 2021

AbstractBackgroundFactors influencing cessation include biopsychosocial characteristics, treatments and responses to treatment. The first cessation trial designed to assess cessation disparities between African American and White cigarette smokers demonstrated that socioeconomic, treatment, psychosocial and smoking characteristics explained cessation disparities. Ongoing translational efforts in precision cessation treatment grounded in genetically informed biomarkers have identified cessation differences by genotype, metabolism, ancestry and treatment.MethodsIn planned analyses, we evaluated six smoking-related measures, demographic and socioeconomic covariates, and prospective abstinence (7-day point prevalence at 12 weeks with bupropion, nicotine replacement and counseling treatments). We assessed concurrent and predictive validity in two covariate models differing by inclusion of Office of Management and Budget (OMB) race/ethnicity or genomic ancestry.ResultsWe studied Pharmacogenetic Study participants (N=456, mean age 49.5 years, 41.5% female, 7.4% African American, 9.4% Multiracial, 6.5% Other, and 6.7% Hispanic). Cigarettes per day (OR=0.95, P<.001), Fagerström score (OR=0.89, P<=.014), Time-To-First-Cigarette (OR=0.75, P<=.005) and predicted urinary nicotine metabolite ratio (OR=0.57, P<=.039) were associated with abstinence. OMB African American race (ORs from 0.31 and 0.35, p-values<=.007) and African genomic ancestry (ORs from 0.21 and 0.26, p-values<=.004) were associated in all abstinence models.ConclusionsFour smoking-related measures exhibited association with abstinence, including predicted nicotine metabolism based on a novel genomic model. African genomic ancestry was independently associated with reduced abstinence. Treatment research that includes social, psychological, treatment and biological factors is needed to reduce cessation disparities.ImplicationsThis is the first application of a statistical learning model of the urinary nicotine metabolite ratio to cessation. Results are concordant with biochemical and genetic models of the plasma nicotine metabolite ratio in multiethnic samples.The urinary ratio exhibits the largest standardized effect size of four smoking-related measures associated with cessation (time-to-first cigarette, total Fagerström score and cigarettes per day were the others).The social construct of African American race and genomic African ancestry are significant covariates in all cessation models.Results point to social and biological mechanisms requiring investigation in larger samples to understand and reduce cessation health disparities.
The use of a Cohort Size Shrinkage Index (CSSI) to quantify regional famine intensity during the Chinese famine of 1959-1961
Authors: Liu Chunyu , Li Chihua , Xu Hongwei , Zhou Zhenwei , Lumey L.H. 
Published: December 2021

ABSTRACTThe Cohort Size Shrinkage Index (CSSI) is the most widely used intensity measure in studies of the Chinese famine of 1959-1961. We examined requirements to construct a valid CSSI measure: reliable information on birth cohort size; a stable cohort size trend outside the famine years; and limited migrations. We used census data to examine the cohort size trend and concentrated on the time window of 1950-1970 to exclude events with a large impact on cohort size trends other than the famine. We observed a significant difference in cohort size trends comparing pre-famine and post-famine births. Compared to pre-famine based CSSI (p-CSSI), pre- & post-famine based CSSI (pp-CSSI) tends to overestimate famine intensity at higher mean CSSI levels and underestimate intensity at lower mean levels. In Sichuan province, we demonstrated a less pronounced dose-response relation between famine intensity and tuberculosis outcomes using p-CSSI as compared to pp-CSSI. These observations demonstrates that the CSSI is not a robust measure as had been assumed previously. We recommend the use of p-CSSI and encourage researchers to re-examine their results of Chinese famine studies.
Assessing the contribution of rare-to-common protein-coding variants to circulating metabolic biomarker levels via 412,394 UK Biobank exome sequences
Authors: Nag Abhishek , Middleton Lawrence , Dhindsa Ryan S. , Vitsios Dimitrios , Wigmore Eleanor , Allman Erik L. , Reznichenko Anna , Carss Keren , Smith Katherine R. , Wang Quanli , Challis Benjamin , Paul Dirk S. , Harper Andrew R. , Petrovski Slavé 
Published: December 2021

AbstractGenome-wide association studies have established the contribution of common and low frequency variants to metabolic biomarkers in the UK Biobank (UKB); however, the role of rare variants remains to be assessed systematically. We evaluated rare coding variants for 198 metabolic biomarkers, including metabolites assayed by Nightingale Health, using exome sequencing in participants from four genetically diverse ancestries in the UKB (N=412,394). Gene-level collapsing analysis – that evaluated a range of genetic architectures – identified a total of 1,303 significant relationships between genes and metabolic biomarkers (p<1×10−8), encompassing 207 distinct genes. These include associations between rare non-synonymous variants inGIGYF1and glucose and lipid biomarkers,SYT7and creatinine, and others, which may provide insights into novel disease biology. Comparing to a previous microarray-based genotyping study in the same cohort, we observed that 40% of gene-biomarker relationships identified in the collapsing analysis were novel. Finally, we applied Gene-SCOUT, a novel tool that utilises the gene-biomarker association statistics from the collapsing analysis to identify genes having similar biomarker fingerprints and thus expand our understanding of gene networks.
Early Computational Detection of Potential High Risk SARS-CoV-2 Variants
Authors: Beguir Karim , Skwark Marcin J. , Fu Yunguan , Pierrot Thomas , Carranza Nicolas Lopez , Laterre Alexandre , Kadri Ibtissem , Korched Abir , Lowegard Anna U. , Lui Bonny Gaby , Sänger Bianca , Liu Yunpeng , Poran Asaf , Muik Alexander , Sahin Ugur 
Published: December 2021

AbstractThe ongoing COVID-19 pandemic is leading to the discovery of hundreds of novel SARS-CoV-2 variants on a daily basis. While most variants do not impact the course of the pandemic, some variants pose a significantly increased risk when the acquired mutations allow better evasion of antibody neutralisation in previously infected or vaccinated subjects or increased transmissibility. Early detection of such high risk variants (HRVs) is paramount for the proper management of the pandemic. However, experimental assays to determine immune evasion and transmissibility characteristics of new variants are resource-intensive and time-consuming, potentially leading to delays in appropriate responses by decision makers. Here we present a novelin silicoapproach combining spike (S) protein structure modelling and large protein transformer language models on S protein sequences to accurately rank SARS-CoV-2 variants for immune escape and fitness potential. These metrics can be combined into an automated Early Warning System (EWS) capable of evaluating new variants in minutes and risk-monitoring variant lineages in near real-time. The system accurately pinpoints the putatively dangerous variants by selecting on average less than 0.3% of the novel variants each week. With only the S protein nucleotide sequence as input, the EWS detects HRVs earlier and with better precision than baseline metrics such as the growth metric (which requires real-world observations) or random sampling. Notably, Omicron BA.1 was flagged by the EWS on the day its sequence was made available. Additionally, our immune escape and fitness metrics were experimentally validated usingin vitropseudovirus-based virus neutralisation test (pVNT) assays and binding assays. The EWS flagged as potentially dangerous all 16 variants (Alpha-Omicron BA.1/2/4/5) designated by the World Health Organisation (WHO) with an average lead time of more than one and a half months ahead of them being designated as such.One-Sentence SummaryA COVID-19 Early Warning System combining structural modelling with machine learning to detect and monitor high risk SARS-CoV-2 variants, identifying all 16 WHO designated variants on average more than one and a half months in advance by selecting on average less than 0.3% of the weekly novel variants.
PyLiger: Scalable single-cell multi-omic data integration in Python
Authors: Lu Lu , Welch Joshua D. 
Published: December 2021

AbstractMotivationLIGER is a widely-used R package for single-cell multi-omic data integration. However, many users prefer to analyze their single-cell datasets in Python, which offers an attractive syntax and highly-optimized scientific computing libraries for increased efficiency.ResultsWe developed PyLiger, a Python package for integrating single-cell multi-omic datasets. PyLiger offers faster performance than the previous R implementation (2-5× speedup), interoperability with AnnData format, flexible on-disk or in-memory analysis capability, and new functionality for gene ontology enrichment analysis. The on-disk capability enables analysis of arbitrarily large single-cell datasets using fixed memory.AvailabilityPyLiger is available on Github at https://github.com/welch-lab/pyliger and on the Python Package Index.Contactwelchjd@umich.eduSupplementary informationSupplementary data are available at Bioinformatics online.
Clinical grade ACE2 effectively inhibits SARS-CoV-2 Omicron infections
Authors: Monteil Vanessa , Stephanie Devignot , Klingström Jonas , Thålin Charlotte , Kellner Max J. , Christ Wanda , Havervall Sebastian , Mereiter Stefan , Knapp Sylvia , Montserrat Nuria , Braunsfeld Benedict , Kozieradzki Ivona , Ali Omar Hasan , Hagelkruys Astrid , Stadlmann Johannes , Oostenbrink Chris , Wirnsberger Gerald , Penninger Josef M. , Mirazimi Ali 
Published: December 2021

AbstractThe recent emergence of the SARS-CoV-2 variant Omicron has caused considerable concern due to reduced vaccine efficacy and escape from neutralizing antibody therapeutics. Omicron is spreading rapidly around the globe and is suspected to account for most new COVID-19 cases in several countries, though the severity of Omicron-mediated disease is still under debate. It is therefore paramount to identify therapeutic strategies that inhibit the Omicron SARS-CoV-2 variant. Here we report using 3D structural modelling that Spike of Omicron can still associate with human ACE2. Sera collected after the second mRNA-vaccination did not exhibit a protective effect against Omicron while strongly neutralizing infection of VeroE6 cells with the reference Wuhan strain, confirming recent data by other groups on limited vaccine and convalescent sera neutralization efficacy against Omicron. Importantly, clinical grade recombinant human soluble ACE2, a drug candidate currently in clinical development, potently neutralized Omicron infection of VeroE6 cells with markedly enhanced potency when compared to reference SARS-CoV-2 isolates. These data show that SARS-CoV-2 variant Omicron can be readily inhibited by soluble ACE2, providing proof of principle of a viable and effective therapeutic approach against Omicron infections.
Expanded ACE2 dependencies of diverse SARS-like coronavirus receptor binding domains
Authors: Roelle Sarah M. , Shukla Nidhi , Pham Anh T. , Bruchez Anna M. , Matreyek Kenneth A. 
Published: December 2021

AbstractViral spillover from animal reservoirs can trigger public health crises and cripple the world economy. Knowing which viruses are primed for zoonotic transmission can focus surveillance efforts and mitigation strategies for future pandemics. Successful engagement of receptor protein orthologs is necessary during cross-species transmission. The clade 1 sarbecoviruses including SARS-CoV and SARS-CoV-2 enter cells via engagement of ACE2, while the receptor for clade 2 and clade 3 remains largely uncharacterized. We developed a mixed cell pseudotyped virus infection assay to determine whether various clade 2 and 3 sarbecovirus spike proteins can enter HEK 293T cells expressing human orRhinolophushorseshoe bat ACE2 proteins. The receptor binding domains from BtKY72 and Khosta-2 used human ACE2 for entry, while BtKY72 and Khosta-1 exhibited widespread use of diverse rhinolophid ACE2s. A lysine at ACE2 position 31 appeared to be a major determinant of the inability of these RBDs to use a certain ACE2 sequence. The ACE2 protein fromR. alcyoneengaged all known clade 3 and clade 1 receptor binding domains. We observed little use ofRhinolophusACE2 orthologs by the clade 2 viruses, supporting the likely use of a separate, unknown receptor. Our results suggest that clade 3 sarbecoviruses from Africa and Europe useRhinolophusACE2 for entry, and their spike proteins appear primed to contribute to zoonosis under the right conditions.
Pandemic-related decline in injuries related to women wearing high-heeled shoes: Analysis of U.S. data for 2016-2020
Authors: Cohen Philip N. 
Published: December 2021

AbstractBackgroundWearing high-heeled shoes is associated with injury risk. During the COVID-19 pandemic, changes in work and social behavior may have reduced women’s use of such footwear.MethodsThis study assessed the trend in high-heel related injuries among U.S. women, using 2016-2020 data from the U.S. Consumer Product Safety Commission’s National Electronic Injury Surveillance System (NEISS).ResultsIn 2020 there were an estimated 6,290 high-heel related emergency department visits involving women ages 15-69, down from 16,000 per year in 2016-2019. The 2020 decline began after the start of the COVID-19 shutdowns on March 15. There was no significant change in the percentage of fractures or hospital admissions.ConclusionsThe COVID-19 pandemic was associated with a decline in reported injuries related to high-heeled shoes among US women. If this resulted from fewer women wearing such shoes, and such habits influence future behavior, the result may be fewer injuries in the future.