bluuebunny/crossref_metadata_2025_split huggingface dataset:
Mono- and bi-allelic effects of coding variants on disease in 176,899 Finns
Authors: Heyne H. O. , Karjalainen J. , Karczewski K. J. , Lemmelä S. M. , Zhou W. , FinnGen  , Havulinna A. S. , Kurki M. , Rehm H. L. , Palotie A. , Daly M. J. 
Published: November 2021

AbstractIdentifying Mendelian diseases with recessive inheritance is challenging as the majority of cases are caused by compound heterozygous genotypes which require sequencing data in families to definitively identify. Bottleneck events, such as in the Finnish population, enrich specific homozygous variants to higher frequencies and thus facilitate identification of disease associations through easily recognized homozygous genotypes. Here, we study homozygous and heterozygous effects of 82,516 coding variants on 2,444 disease endpoints using nationwide electronic health record (EHR) data of 176,899 Finns. We find known and novel associations to homozygous genotypes across a broad spectrum of phenotypes such as retinal dystrophy, adult-onset cataract and female infertility (13/20 of which would have been missed by the traditional additive GWAS model). With these results, and supporting simulations, we demonstrate the added benefit of homozygous scans in GWAS. We further use these results to explore inheritance patterns of known Mendelian variants. We find many Mendelian variants whose inheritance cannot be adequately described with the traditional definition of dominant or recessive. In particular, we find disease risk in heterozygous carriers of variants known to cause disease with recessive inheritance, as well as for variants labeled benign in ClinVar. Our results demonstrate how biobank efforts, particularly in founder populations, can broaden our understanding of the impact of genetic variants.
Evaluating Digital Device Technology in Alzheimer’s Disease via Artificial Intelligence
Authors: Sood Meemansa , Aborageh Mohamed , Domingo-Fernández Daniel , Harms Robbert , Lordick Thomas , Birkenbihl Colin , Owens Andrew P , Coello Neva , Narayan Vaibhav A. , Aarsland Dag , Bügler Maximilian , Fröhlich Holger ,   for the Alzheimer’s Disease Neuroimaging Initiative,   RADAR-AD Consortium
Published: November 2021

AbstractThe use of digital technologies may help to diagnose Alzheimer’s Disease (AD) at the pre-symptomatic stage. However, before implementation into clinical practice, digital measures (DMs) need to be evaluated for their diagnostic benefit compared to established questionnaire-based assessments, such as the Mini-Mental State Examination (MMSE) for cognition and Functional Activity Questionnaire (FAQ) for daily functioning. Moreover, the quantitative and qualitative relationship of DMs to these well understood scores needs to be clarified to aid interpretation. In this work we analyzed data from 148 subjects, 58 cognitively normal and 90 at different stages of the disease, which had performed a smartphone based virtual reality game to assess cognitive function. In addition, we used clinical data from Alzheimer’s Disease Neuroimaging Initiative (ADNI). We employed an Artificial Intelligence (AI) based approach to elucidate the relationship of DMs to questionnaire-based cognition and functional activity scores. In addition, we used Machine Learning (ML) and statistical methods to assess the diagnostic benefit of DMs compared to questionnaire-based scores. We found non-trivial relationships between DMs, MMSE, and FAQ which can be visualized as a complex network. DMs, in particular those reflecting scores of individual tasks in the virtual reality game, showed a better ability to discriminate between different stages of the disease than questionnaire-based methods. Our results indicate that DMs have the potential to act as a crucial measure in the early diagnosis and staging of AD.
cfTrack: Exome-wide mutation analysis of cell-free DNA to simultaneously monitor the full spectrum of cancer treatment outcomes: MRD, recurrence, and evolution
Authors: Li Shuo , Zeng Weihua , Ni Xiaohui , Zhou Yonggang , Stackpole Mary L. , Noor Zorawar S. , Yuan Zuyang , Neal Adam , Memarzadeh Sanaz , Garon Edward B. , Dubinett Steven M. , Li Wenyuan , Zhou Xianghong Jasmine 
Published: November 2021

AbstractPurposeCell-free DNA (cfDNA) offers a non-invasive approach to monitor cancer. Here we develop a method using whole-exome sequencing (WES) of cfDNA for simultaneously monitoring the full spectrum of cancer treatment outcomes, including MRD, recurrence, evolution, and second primary cancer.Experimental DesignThree simulation datasets were generated from 26 cancer patients to benchmark the detection performance of MRD/recurrence and second primary cancers. For further validation, cfDNA samples (n=76) from cancer patients (n=35) with six different cancer types were used for validating the performance of cancer monitoring during various treatments.ResultsWe present a cfDNA-based cancer monitoring method, named cfTrack. Taking advantage of the broad genome coverage of WES data, cfTrack can sensitively detect MRD and cancer recurrence by integrating signals across the known clonal tumor mutations of a patient. In addition, cfTrack detects tumor evolution and second primary cancers by de novo identifying emerging tumor mutations. A series of machine learning and statistical denoising techniques are applied to enhance the detection power. On the simulation data, cfTrack achieved an average AUC of 99% on the validation dataset and 100% on the independent dataset in detecting recurrence in samples with tumor fraction ≥0.05%. In addition, cfTrack yielded an average AUC of 88% in detecting second primary cancers in samples with tumor fraction ≥0.2%. On real data, cfTrack accurately monitors tumor evolution during treatment, which cannot be accomplished by previous methods.ConclusionOur results demonstrated that cfTrack can sensitively and specifically monitor the full spectrum of cancer treatment outcomes using exome-wide mutation analysis of cfDNA.Translational RelevanceContinuous cancer monitoring is clinically necessary for cancer patients to detect minimal residual disease (MRD), recurrence, and progression, allowing for early intervention and therapy adjustment. Cell-free DNA (cfDNA) in blood has become an appealing option due to its non-invasiveness. Until now, cfDNA-based cancer monitoring methods have been focused on deep sequencing at a few known mutations, which are however insufficient when tumors evolve or new tumors emerge. We present the method, cfTrack, which for the first time uses whole-exome sequencing (WES) of cfDNA to track the full range of cancer treatment outcomes, including MRD, recurrence, evolution, and second primary cancer. We demonstrate that, even with very low tumor fractions, cfTrack achieves sensitive and specific monitoring of tumor MRD/recurrence/evolution based on both simulation data and a cohort of cancer patients. These findings demonstrate the clinical utility of cfTrack.
Quantifying changes in vaccine coverage in mainstream media as a result of COVID-19 outbreak
Authors: Christensen Bente , Laydon Daniel J , Chelkowski Tadeusz , Jemielniak Dariusz , Vollmer Michaela , Bhatt Samir , Krawczyk Konrad 
Published: November 2021

AbstractBackgroundAchieving vaccine-derived herd immunity depends on public acceptance of vaccination, which in turn relies on people’s understanding of its risks and benefits. The fundamental objective of public health messaging on vaccines is therefore the clear and concise communication of often complex information, and increasingly the countering of misinformation. The primary outlet shaping societal understanding is the mainstream online news media. There was widespread media coverage of the multiple vaccines that were rapidly developed in response to COVID-19. We studied vaccine coverage on the front pages of mainstream online news, using text-mining analysis to quantify the amount of information and sentiment polarization of vaccine coverage delivered to readers.MethodsWe analyzed 28 million articles from 172 major news sources, across 11 countries between July 2015 and April 2021. We employed keyword-based frequency analysis to estimate the proportion of coverage given to vaccines in our dataset. We performed topic detection using BERTopic and Named Entity Recognition to identify the leading subjects and actors mentioned in the context of vaccines. We used the Vader Python module to perform sentiment polarization quantification of all our English-language articles.ResultsWe find that the proportion of headlines mentioning vaccines on the front pages of international major news sites increased from 0.1% to 3.8% with the outbreak of COVID-19. The absolute number of negatively polarized articles increased from a total of 6,698 before the COVID-19 outbreak 2015-2019 compared to 28,552 in 2020-2021. Overall, however, before the COVID-19 pandemic, vaccine coverage was slightly negatively polarized (57% negative) whereas with the outbreak, the coverage was primarily positively polarized (38% negative).ConclusionsBecause of COVID-19, vaccines have risen from a marginal topic to a widely discussed topic on the front pages of major news outlets. Despite a perceived rise in hesitancy, the mainstream online media, i.e. the primary information source to most individuals, has been strongly positive compared to pre-pandemic vaccine news, which was mainly negative. However, the pandemic was accompanied with an order of magnitude increase in vaccine news volume that due to pre-pandemic low frequency sampling bias may contribute to a perceived negative sentiment. These results highlight the important interactions between the volume of news and overall polarisation. To the best of our knowledge, our work is the first systematic text mining study of vaccines in the context of COVID-19.
Plasma Soluble Tumor Necrosis Factor Receptor Concentrations and Clinical Events after Hospitalization: Findings from ASSESS-AKI and ARID studies
Authors: Coca Steven G. , Vasquez-Rios George , Mansour Sherry G. , Moledina Dennis G. , Thiessen-Philbrook Heather , Wurfel Mark M. , Himmelfarb Jonathan , Siew Eddie , Garg Amit X. , Hsu Chi-yuan , Liu Kathleen D. , Kimmel Paul L. , Chinchilli Vernon M. , Kaufman James S. , Wilson Michelle , Banks Rosamonde E , Packington Rebecca , McCole Eibhlin , Kurth Mary Jo , Richardson Ciaran , Go Alan S. , Selby Nicholas M , Parikh Chirag R. 
Published: November 2021

AbstractBackgroundThe role of plasma soluble tumor necrosis factor receptor (sTNFR)1 and sTNFR2 in the prognosis of clinical events after hospitalization with or without acute kidney injury (AKI) is unknown.MethodsWe measured sTNFR1 and sTNFR2 obtained 3 months post-discharge using samples from Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) and AKI Risk in Derby (ARID) that enrolled patients with and without AKI. The associations between biomarkers with longitudinal kidney disease incidence and progression, heart failure and death were evaluated. Analyses were adjusted for demographics and key covariates at the 3-month visit.ResultsAmong 1474 participants with plasma biomarker measurements, 19% developed kidney disease progression, 14% had later heart failure, and 21% died over a median follow-up of 4.4 years. For the kidney outcome, the adjusted HRs per doubling in concentration were 2.9 (2.2-3.9) for sTNFR1 and 1.9 (1.5-2.5) for sTNFR2. AKI during the index hospitalization did not modify the association between biomarkers and kidney events. For heart failure, the adjusted HRs per doubling in concentration were 1.9 (1.4-2.5) for sTNFR1 and 1.5 (1.2-2.0) for sTNFR2. For mortality, the adjusted HRs were 3.3 (2.5-4.3) for sTNFR1 and 2.5 (2.0-3.1) for sTNFR2. The findings in ARID were qualitatively similar for the magnitude of association between biomarkers and outcomes.ConclusionPlasma sTNFR1 and sTNFR2 measured 3 months after discharge were independently associated with clinical events, regardless of AKI status during the index admission. sTNFR1 and sTNFR2 may assist with the risk stratification of patients during follow-up.Significance StatementSoluble tumor necrosis factor receptor 1 (sTNFR1) and sTNFR2 associate with kidney outcomes in patients with chronic kidney disease with and without diabetes mellitus. However, their role in the post-hospitalization stage is unknown. High sTNFR1 and sTNFR2 obtained 3 months after discharge associate with kidney events, heart failure hospitalizations, and death among patients who did and did not have acute kidney injury (AKI). Furthermore, sTNFRs provide discriminative value at the time of predicting kidney events. These findings were demonstrated in two large independent prospective cohorts. sTNFR1 and sTNFR2 may detect patients at risk of future adverse events even when patients do not meet the clinical criteria for AKI or exhibit biochemical abnormalities.
Polygenic risk prediction and SNCA haplotype analysis in a Latino Parkinson’s disease cohort
Authors: Loesch Douglas , Horimoto Andrea R. V. R. , Sarihan Elif Irem , Inca-Martinez Miguel , Mason Emily , Cornejo-Olivas Mario , Torres Luis , Mazzetti Pilar , Cosentino Carlos , Sarapura-Castro Elison , Rivera-Valdivia Andrea , Medina Angel C. , Dieguez Elena , Raggio Victor , Lescano Andres , Tumas Vitor , Borges Vanderci , Ferraz Henrique B. , Rieder Carlos R. , Schumacher-Schuh Artur , Santos-Lobato Bruno L. , Velez-Pardo Carlos , Jimenez-Del-Rio Marlene , Lopera Francisco , Moreno Sonia , Chana-Cuevas Pedro , Fernandez William , Arboleda Gonzalo , Arboleda Humberto , Arboleda-Bustos Carlos E. , Yearout Dora , Zabetian Cyrus P. ,   International Parkinson Disease Genomics Consortium (IPDGC), Thornton Timothy A. , Mata Ignacio F. , O’Connor Timothy D. 
Published: November 2021

AbstractBackgroundLarge-scale Parkinson’s disease (PD) genome-wide association studies (GWAS) and meta-analyses have, until recently, only been conducted on subjects with European-ancestry. Consequently, polygenic risk scores (PRS) constructed using PD GWAS data are likely to be less predictive when applied to non-European cohorts.MethodsUsing GWAS data from Nalls et al. 2019, we constructed a PD PRS for a Latino PD cohort (LARGE-PD) and tested it for association with PD status. We validated the PRS performance through testing the PD PRS in an independent cohort of Latino PD patients and by repeating the PRS analysis in LARGE-PD with the addition of 440 external Peruvian controls. To explore the global distribution of PD PRS, we utilized 1000 Genomes Project (1KGP) and Peruvian Genome Project (PGP) data to estimate PD risk allele frequencies. We also tested SNCA haplotypes for association with PD risk using logistic regression in LARGE-PD and a European-ancestry PD cohort from the International Parkinson Disease Genomics Consortium (IPDGC).ResultsThe GWAS-significant PD PRS had an area under the receiver-operator curve (AUC) of 0.668 (95% CI: 0.640-0.695) and explained 2.2% of the phenotypic variance on the liability scale in LARGE-PD. The inclusion of external Peruvian data as controls mitigated this result, dropping the AUC 0.632 (95% CI: 0.607-0.657). In 1KGP Latinos, we found the PD PRS to exhibit a bias by ancestry. At the SNCA locus, haplotypes differ by ancestry. Ancestry-specific SNCA haplotypes were associated with PD status in both LARGE-PD and the IPDGC cohort (p-value < 0.05). Apart from rs356182, these haplotypes share as little as 14% of their variants.ConclusionThe PD PRS has potential for PD risk prediction in Latinos, but variability caused by admixture patterns and bias in the PD PRS calculated using only European-ancestry data limits its utility. The inclusion of diverse subjects can help elucidate PD risk loci and improve risk prediction in non-European cohorts. In the case of the SNCA locus, by leveraging a Latino cohort, we provide orthogonal evidence for rs356182 causality.
Ultrafast RNA extraction-free SARS-CoV-2 detection by direct RT-PCR using a rapid thermal cycling approach
Authors: Struijk Robin , van den Ouden Anton , McNally Brian , de Groot Theun , Mulder Bert , de Vos Gert 
Published: November 2021

AbstractThe surging COVID19 pandemic has underlined the need for quick, sensitive, and high-throughput SARS-CoV-2 detection assays. Although many different methods to detect SARS-CoV-2 particles in clinical material have been developed, none of these assays are successful in combining all three of the above characteristics into a single, easy-to-use method that is suitable for large-scale use. Here we report the development of a direct RT-PCR SARS-CoV-2 detection method that can reliably detect minute quantities of SARS-CoV-2 gRNA in nasopharyngeal swab samples as well as the presence of human genomic DNA. An extraction-less validation protocol was carried out to determine performance characteristics of the assay in both synthetic SARS-CoV-2 RNA as well as clinical specimens. Feasibility of the assay and analytical sensitivity was first determined by testing a dilution series of synthetic SARS-CoV-2 RNA in two different solvents (water and AMIES VTM), revealing a high degree of linearity and robustness in fluorescence readouts. Following analytical performance using synthetic RNA, the limit of detection was determined at equal to or less than 1 SARS-CoV-2 copy/ul of sample in a commercially available sample panel that contains surrogate clinical samples with varying SARS-CoV-2 viral load. Lastly, we benchmarked our method against a reference qPCR method by testing 87 nasopharyngeal swab samples. The direct endpoint ultra-fast RT-PCR method exhibited a positive percent agreement score of 98.5% and a negative percent agreement score of 100% as compared to the reference method, while RT-PCR cycling was completed in 27 minutes/sample as opposed to 60 minutes/sample in the reference qPCR method. In summary, we describe a rapid direct RT-PCR method to detect SARS-CoV-2 material in clinical specimens which can be completed in significantly less time as compared to conventional RT-PCR methods, making it an attractive option for large-scale SARS-CoV-2 screening applications.
Cerebellar and Prefrontal Structures Associated with Executive Functioning in Pediatric Patients with Congenital Heart Defects
Authors: Badaly Daryaneh , Beers Sue R. , Ceschin Rafael , Lee Vincent K. , Sulaiman Shahida , Zahner Alexandria , Wallace Julia , Berdaa-Sahel Aurélia , Burns Cheryl , Lo Cecilia W. , Panigrahy Ashok 
Published: November 2021

AbstractObjectiveChildren, adolescents, and young adults with congenital heart defects (CHD) often display cognitive and behavioral manifestations of executive dysfunction. We consider the prefrontal and cerebellar brain structures as mechanisms for executive dysfunction among those with CHD.Method55 participants with CHD (M age = 13.93) and 95 healthy controls (M age = 13.13) completed magnetic resonance imaging (MRI) of the brain, from which we extracted volumetric data on prefrontal and cerebellar regions. Participants also completed neuropsychological tests of executive functioning; their parents completed behavioral ratings of their executive functions.ResultsCompared to healthy controls, those with CHD had smaller cerebellums and lateral, medial, and orbital prefrontal regions, they performed more poorly on tests of working memory, inhibitory control, and mental flexibility, and their parents rated them as having poorer executive functions across several indices. Across both groups, there were significant correlations for cerebellar and/or prefrontal volumes with cognitive assessments of working memory, mental flexibility, and inhibitory control and with behavioral ratings of working memory, task initiation, and emotional control. Greater prefrontal volumes were associated with better working memory, among those with larger cerebellums (with group differences based on the measure and the prefrontal region). Greater prefrontal volumes were related to better emotional regulation only among participants with CHD with smaller cerebellar volumes, and with poorer inhibition and emotional regulation only among healthy controls with larger cerebellar volumes.ConclusionThe cerebellum modulates the relationships between prefrontal regions and executive functioning differently for pediatric patients with CHD versus health controls.
Projecting vaccine demand and impact for emerging zoonotic pathogens
Authors: Lerch A , ten Bosch QA , L’Azou Jackson M , Bettis AA , Bernuzzi M , Murphy GAV , Tran QM , Huber JH , Siraj AS , Bron GM , Elliott M , Hartlage CS , Koh K , Strimbu K , Walters M , Perkins TA , Moore SM 
Published: November 2021

AbstractDespite large outbreaks in humans seeming improbable for a number of zoonotic pathogens, several pose a concern due to their epidemiological characteristics and evolutionary potential. To enable effective responses to these pathogens in the event that they undergo future emergence, the Coalition for Epidemic Preparedness Innovations is advancing the development of vaccines for several pathogens prioritized by the World Health Organization. A major challenge in this pursuit is anticipating demand for a vaccine stockpile to support outbreak response. We developed a modeling framework for outbreak response for emerging zoonoses under three reactive vaccination strategies. Annual vaccine regimen requirements for a population-wide strategy ranged from >670,000 (95% prediction interval: 0-3,630,000) regimens for Lassa virus to 1,190,000 (95% PrI: 0-8,480,000) regimens for Rift Valley fever virus, while the regimens required for ring vaccination or targeting healthcare workers (HCWs) were several orders of magnitude lower (between 1/25 and 1/700) than those required by a population-wide strategy. For each pathogen and vaccination strategy, reactive vaccination typically prevented fewer than 10% of cases, because of their presently low R0 values. Targeting HCWs had a higher per-regimen impact than population-wide vaccination. Our framework provides a flexible methodology for estimating vaccine stockpile needs and the geographic distribution of demand under a range of outbreak response scenarios.
Attractor Landscapes as a Model Selection Criterion in Data Poor Environments
Authors: Lyman Cole A. , Richman Spencer , Morris Matthew C. , Cao Hongbao , Scerri Antony , Cheadle Chris , Broderick Gordon 
Published: November 2021

AbstractModeling of systems for which data is limited often leads to underdetermined model identification problems, where multiple candidate models are equally adherent to data. In such situations additional optimality criteria are useful in model selection apart from the conventional minimization of error and model complexity. This work presents the attractor landscape as a domain for novel model selection criteria, where the number and location of attractors impact desirability. A set of candidate models describing immune response dynamics to SARS-CoV infection is used as an example for model selection based on features of the attractor landscape. Using this selection criteria, the initial set of 18 models is ranked and reduced to 7 models that have a composite objective value with a p-value < 0.05. Additionally, the impact of pharmacologically induced remolding of the attractor landscape is presented.
Fascicles Split or Merge Every ~560 Microns Within the Human Cervical Vagus Nerve
Authors: Upadhye Aniruddha R. , Kolluru Chaitanya , Druschel Lindsey , Lababidi Luna Al , Ahmad Sami S. , Menendez Dhariyat M. , Buyukcelik Ozge N. , Settell Megan L. , Blanz Stephan L. , Jenkins Michael W. , Wilson David L. , Zhang Jing , Tatsuoka Curtis , Grill Warren M. , Pelot Nicole A. , Ludwig Kip A. , Gustafson Kenneth J. , Shoffstall Andrew J. 
Published: November 2021

1AbstractVagus nerve stimulation (VNS) is FDA approved for stroke rehabilitation, epilepsy, and depression; however, the vagus functional anatomy underlying the implant is poorly understood. We used microCT to quantify fascicular structure and neuroanatomy within human cervical vagus nerves. Fascicles split or merged every ~560 μm (17.8 ± 6.1 events/cm). The high degree of fascicular splitting and merging in humans may explain the clinical heterogeneity in patient responses.