bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative performance of multiplex salivary and commercially available serologic assays to detect SARS-CoV-2 IgG and neutralization titers
Authors: Heaney Christopher D. , Pisanic Nora , Randad Pranay R. , Kruczynski Kate , Howard Tyrone , Zhu Xianming , Littlefield Kirsten , Patel Eshan U. , Shrestha Ruchee , Laeyendecker Oliver , Shoham Shmuel , Sullivan David , Gebo Kelly , Hanley Daniel , Redd Andrew D. , Quinn Thomas C. , Casadevall Arturo , Zenilman Jonathan M. , Pekosz Andrew , Bloch Evan M. , Tobian Aaron A. R. 
Published: February 2021

ABSTRACTOral fluid (hereafter saliva) offers a non-invasive sampling method for the detection of SARS-CoV-2 antibodies. However, data comparing performance of salivary tests against commercially-available serologic and neutralizing antibody (nAb) assays are lacking. This study compared the performance of a multiplex salivary SARS-CoV-2 IgG assay targeting antibodies to nucleocapsid (N), receptor binding domain (RBD) and spike (S) antigens to three commercially-available SARS-CoV-2 serology enzyme immunoassays (EIAs) (Ortho Vitros, Euroimmun, and BioRad) and nAb. Paired saliva and plasma samples were collected from 101 eligible COVID-19 convalescent plasma (CCP) donors >14 days since PCR+ confirmed diagnosis. Concordance was evaluated using positive (PPA) and negative (NPA) percent agreement, overall percent agreement (PA), and Cohen’s kappa coefficient. The range between salivary and plasma EIAs for SARS-CoV-2-specific N was PPA: 54.4-92.1% and NPA: 69.2-91.7%, for RBD was PPA: 89.9-100% and NPA: 50.0-84.6%, and for S was PPA: 50.6-96.6% and NPA: 50.0-100%. Compared to a plasma nAb assay, the multiplex salivary assay PPA ranged from 62.3% (N) and 98.6% (RBD) and NPA ranged from 18.8% (RBD) to 96.9% (S). Combinations of N, RBD, and S and a summary algorithmic index of all three (N/RBD/S) in saliva produced ranges of PPA: 87.6-98.9% and NPA: 50-91.7% with the three EIAs and ranges of PPA: 88.4-98.6% and NPA: 21.9-34.4% with the nAb assay. A multiplex salivary SARS-CoV-2 IgG assay demonstrated comparable performance to three commercially-available plasma EIAs and a nAb assay, and may be a viable alternative to assist in screening CCP donors and monitoring population-based seroprevalence and vaccine antibody response.
Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14
Authors: Liu Jian , Wu Tianqi , Guo Zhiye , Hou Jie , Cheng Jianlin 
Published: February 2021

AbstractSubstantial progresses in protein structure prediction have been made by utilizing deep-learning and residue-residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system in the three main aspects: (1) a new deep-learning based protein inter-residue distance predictor (DeepDist) to improve template-free (ab initio) tertiary structure prediction, (2) an enhanced template-based tertiary structure prediction method, and (3) distance-based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked 7thout of 146 predictors in protein tertiary structure prediction and ranked 3rdout of 136 predictors in inter-domain structure prediction. The results of MULTICOM demonstrate that the template-free modeling based on deep learning and residue-residue distance prediction can predict the correct topology for almost all template-based modeling targets and a majority of hard targets (template-free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. The performance of template-free tertiary structure prediction largely depends on the accuracy of distance predictions that is closely related to the quality of multiple sequence alignments. The structural model quality assessment works reasonably well on targets for which a sufficient number of good models can be predicted, but may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed.
12-lead Electrocardiogram in Hospitalized COVID 19 Patients
Authors: Shokr Mohamed , Chehab Omar , Ajam Mustafa , Singh Manmohan , Ashraf Said , Dawdy John , Pahuja Mohit , Reddy Vivek , Subahi Ahmed , Alraies M. Chadi , Afonso Luis , Lieberman Randy 
Published: February 2021

AbstractCOVID-19 pandemic resulted in considerable morbidity and mortality. We analyzed 345 Electrocardiograms of 100 COVID-19 patients admitted to our tertiary care center in Detroit, during the initial month of Covid-19. Findings were correlated with mortality, cardiac injury and inflammatory markers. Our cohort included 61% males and 77% African Americans. The median age and BMI were 66 years (57-74) and 31 kg/m2 (26.1-39), respectively. We observed atrial arrhythmias in 29% of the patients (17% new onset), First degree heart block in 12%, ST-T segment changes in 17%, S1Q3T3 pattern in 19%, premature ventricular complexes in 23%, premature atrial complexes in 13%, Q waves in 27%, T wave inversion in 42% of the cases. While presence of premature atrial complexes or left atrial abnormality correlated with mortality (P = 0.02 & 0.03, respectively), other findings did not show significant correlation in this small cohort of patients.
Vaccination strategies for minimizing loss of life in Covid-19 in a Europe lacking vaccines
Authors: Hunziker Patrick 
Published: February 2021

AbstractAim and BackgroundWe aimed at identifying vaccination strategies that minimize loss of life in the Covid-19 pandemic in a Europe lacking vaccines. Covid-19 mainly kills the elderly, but the pandemic is driven by social contacts that are more frequent in the young. Vaccines elicit stronger immune responses in younger persons. As vaccine production is a bottleneck, many countries have adopted a strategy of first vaccinating the elderly and vulnerable, while postponing vaccination of the young.MethodsBased on published age-stratified immunogenicity data of the Moderna mRNA-1273 vaccine, we compared the established “one dose fits all” approach with tailored strategies by epidemic modeling: The known differential immunogenicity of vaccine doses in different age groups is exploited to vaccinate the elderly at full dose, while the young receive a reduced dose, increasing the number of individuals receiving the vaccine early. A modeling approach at European Union scale with population structure, Covid-19 case and death rates according to Europe in late January 2021 is used.ResultsWhen the elderly were vaccinated preferentially, the pandemic initially continued essentially unchecked, as it was dominantly driven by social contacts in other age groups. Tailored strategies, including regular dosing in the elderly but reduced dose vaccination in the young, multiplied early vaccination counts, and even with some loss in protection degree for the individual person, the protective effect towards stopping the pandemic and protecting lives was enhanced, even for the elderly. In the European Union, pandemic duration (threshold >100’000 cases/day) was shortened from 53 to 18-24 days; cumulative death count over 100 days was reduced by >30’000. Data suggest that the findings may be relevant to both, the Moderna and the Pfizer-BioNTech mRNA vaccines.ConclusionProtecting the vulnerable, minimizing overall deaths and stopping the pandemic in Europe is best achieved by an adaptive vaccination strategy using an age-tailored vaccine dose.
SFARI Genes and where to find them; classification modelling to identify genes associated with Autism Spectrum Disorder from RNA-seq data
Authors: Navarro Magdalena , Simpson T Ian 
Published: February 2021

AbstractMotivationAutism spectrum disorder (ASD) has a strong, yet heterogeneous, genetic component. Among the various methods that are being developed to help reveal the underlying molecular aetiology of the disease, one that is gaining popularity is the combination of gene expression and clinical genetic data. For ASD, the SFARI-gene database comprises lists of curated genes in which presumed causative mutations have been identified in patients. In order to predict novel candidate SFARI-genes we built classification models combining differential gene expression data for ASD patients and unaffected individuals with a gene’s status in the SFARI-gene list.ResultsSFARI-genes were not found to be significantly associated with differential gene expression patterns, nor were they enriched in gene co-expression network modules that had a strong correlation with ASD diagnosis. However, network analysis and machine learning models that incorporate information from the whole gene co-expression network were able to predict novel candidate genes that share features of existing SFARI genes and have support for roles in ASD in the literature. We found a statistically significant bias related to the absolute level of gene expression for existing SFARI genes and their scores. It is essential that this bias be taken into account when studies interpret ASD gene expression data at gene, module and whole-network levels.AvailabilitySource code is available from GitHub (https://doi.org/10.5281/zenodo.4463693) and the accompanying data from The University of Edinburgh DataStore (https://doi.org/10.7488/ds/2980)Contactian.simpson@ed.ac.uk
Nephrotoxicity of the BRAF-kinase inhibitor Vemurafenib is driven by off-target Ferrochelatase inhibition
Authors: Bai Yuntao , Kim Ji Young , Jayne Laura A. , Gandhi Megha , Huang Kevin M. , Silvaroli Josie A. , Sander Veronika , Prosek Jason , Jhaveri Kenar D. , Baker Sharyn D. , Sparreboom Alex , Bajwa Amandeep , Pabla Navjot Singh 
Published: February 2021

AbstractA multitude of disease and therapy related factors drive the frequent development of renal disorders in cancer patients. Along with chemotherapy, the newer targeted therapeutics can also cause renal dysfunction through on and off-target mechanisms. Interestingly, among the small-molecule inhibitors approved for the treatment of cancers that harbor BRAF-kinase activating mutations, vemurafenib can trigger tubular damage and acute kidney injury (AKI). To investigate the underlying mechanisms, here, we have developed cell culture and mouse models of vemurafenib nephrotoxicity. Our studies show that at clinically relevant concentrations vemurafenib induces cell-death in transformed and primary murine and human renal tubular epithelial cells (RTEC). In mice, two weeks of daily vemurafenib treatment causes moderate AKI with histopathological characteristics of RTEC injury. Importantly, RTEC-specific BRAF gene deletion did not influence renal function under normal conditions or alter the severity of vemurafenib-associated renal impairment. Instead, we found that inhibition of ferrochelatase (FECH), an enzyme involved in heme biosynthesis contributes to vemurafenib nephrotoxicity. FECH overexpression protected RTECs and conversely FECH knockdown increased the sensitivity to vemurafenib nephrotoxicity. Collectively, these studies suggest that vemurafenib-associated RTEC dysfunction and nephrotoxicity is BRAF-independent and caused in part by off-target FECH inhibition.Translational StatementBRAF is the most frequently mutated protein kinase and a critical oncogenic driver in human cancers. In melanoma and other cancers with BRAF activating mutations, BRAF targeted small-molecule therapeutics such as vemurafenib, and dabrafenib have shown remarkable clinical benefits. However, recent clinical studies have shown that a significant number of patients that receive vemurafenib develop AKI through mechanisms that remain unknown. The present study describes the development of novel experimental models of vemurafenib nephrotoxicity and reveals the underlying off-target mechanisms that contribute to renal injury.
How certain are we? Development of an ensemble based framework for assessing astronaut cancer risks from space radiation
Authors: Simonsen Lisa C. , Slaba Tony C. 
Published: February 2021

I.AbstractA new approach to NASA space radiation risk modeling has successfully extended the current NASA probabilistic cancer risk model to an ensemble framework able to consider sub-model parameter uncertainty as well as model-form uncertainty associated with differing theoretical or empirical formalisms. Ensemble methodologies are already widely used in weather prediction, modeling of infectious disease outbreaks, and certain terrestrial radiation protection applications to better understand how uncertainty may influence risk decision-making. Applying ensemble methodologies to space radiation risk projections offers the potential to efficiently incorporate emerging research results, allow for the incorporation of future models, improve uncertainty quantification for underlying sub-models, and reduce the impact of subjective bias on risk projections. Moreover, risk forecasting across an ensemble of multiple predictive models can provide stakeholders additional information on risk acceptance if current health/medical standards cannot be met for future space exploration missions, such as human missions to Mars. In this work, ensemble risk projections implementing multiple sub-models of radiation quality, dose and dose-rate effectiveness factors, excess risk, and latency as ensemble members are presented. Initial consensus methods for ensemble model weights and correlations to account for individual model bias are discussed. In these analyses, the ensemble forecast compares well to results from NASA’s current operational cancer risk projection model used to assess permissible mission durations for astronauts. However, a large range of projected risk values are obtained at the upper 95thconfidence level where models must extrapolate beyond available biological data sets. Closer agreement is seen at the median + one sigma due to the inherent similarities in available models. Identification of potential new models, epidemiological data, and methods for statistical correlation between predictive ensemble members are discussed. Alternate ways of communicating risk and acceptable uncertainty with respect to NASA’s current permissible exposure limits are explored.
Robust, sensitive, and quantitative single-cell proteomics based on ion mobility filtering
Authors: Woo Jongmin , Clair Geremy C. , Williams Sarah M. , Feng Song , Tsai Chia-Feng , Moore Ronald J. , Chrisler William B. , Smith Richard D. , Kelly Ryan T. , Pasa-Tolic Ljiljana , Ansong Charles , Zhu Ying 
Published: February 2021

AbstractUnbiased single-cell proteomics (scProteomics) promises to advance our understanding of cell functions within complex biological systems. However, a major challenge for current methods is their ability to identify and provide accurate quantitative information for low abundance proteins. Herein, we describe an ion mobility-enhanced mass spectrometry acquisition and peptide identification method, TIFF (Transferring Identification based on FAIMS Filtering), designed to improve the sensitivity and accuracy of label-free scProteomics. TIFF significantly extends the ion accumulation times for peptide ions by filtering out singly charged background ions. The peptide identities are then assigned by a 3-dimensional MS1 feature matching approach (retention time, accurate mass, and FAIMS compensation voltage). The TIFF method enabled unbiased proteome analysis to a depth of >1,700 proteins in single HeLa cells with >1,100 proteins consistently quantified. As a demonstration, we applied the TIFF method to obtain temporal proteome profiles of >150 single murine macrophage cells during a lipopolysaccharide stimulation experiment and identified time-dependent proteome profiles.
MYCcontrols metastatic heterogeneity in pancreatic cancer
Authors: Maddipati Ravikanth , Norgard Robert J. , Baslan Timour , Rathi Komal S. , Zhang Amy , Raman Pichai , Pitarresi Jason R. , Wengyn Maximilian D. , Yamazoe Taiji , Li Jinyang , Balli David , LaRiviere Michael J. , Folkert Ian W. , Millstein Ian D. , Bermeo Jonathan , Carpenter Erica L. , Lowe Scott , Iacobuzio-Donahue Christine , Notta Faiyaz , Stanger Ben Z. 
Published: February 2021

AbstractThe degree of metastatic disease varies widely amongst cancer patients and impacts clinical outcomes. However, the biological and functional differences that drive the extent of metastasis are poorly understood. We analyzed primary tumors and paired metastases using a multi-fluorescent lineage-labeled mouse model of pancreatic ductal adenocarcinoma (PDAC) – a tumor type where most patients present with metastases. Genomic and transcriptomic analysis revealed an association between metastatic burden and gene amplification or transcriptional upregulation ofMYCand its downstream targets. Functional experiments showed that MYC promotes metastasis by recruiting tumor associated macrophages (TAMs), leading to greater bloodstream intravasation. Consistent with these findings, metastatic progression in human PDAC was associated with activation of MYC signaling pathways and enrichment for MYC amplifications specifically in metastatic patients. Collectively, these results implicate MYC activity as a major determinant of metastatic burden in advanced PDAC.
Performance of Heart Failure Clinical Prediction Models: A Systematic External Validation Study
Authors: Upshaw Jenica N. , Nelson Jason , Koethe Benjamin , Park Jinny G. , McGinnes Hannah , Wessler Benjamin S. , Konstam Marvin A. , Udelson James E. , Van Calster Ben , van Klaveren David , Steyerberg Ewout , Kent David M. 
Published: February 2021

BackgroundMost heart failure (HF) clinical prediction models (CPMs) have not been externally validated.MethodsWe performed a systematic review to identify CPMs predicting outcomes in HF, stratified by acute and chronic HF CPMs. External validations were performed using individual patient data from 8 large HF trials (1 acute, 7 chronic). CPM discrimination (c-statistic, % relative change in c-statistic), calibration (calibration slope, Harrell’s E, E90), and net benefit were evaluated for each CPM with and without recalibration.ResultsOf 135 HF CPMs screened, 24 (18%) were compatible with the population, predictors and outcomes to the trials and 42 external validations were performed (14 acute HF, 28 chronic HF). The median derivation c-statistic of acute HF CPMs was 0.76 (IQR, 0.75, 0.8), validation c-statistic was 0.67 (0.65, 0.68) and model-based c-statistic was 0.68 (0.66, 0.76), Hence, most of the apparent decrement in model performance was due to narrower case-mix in the validation cohort compared with the development cohort. The median derivation c-statistic for chronic HF CPMs was 0.76 (0.74, 0.8), validation c-statistic 0.61 (0.6, 0.63) and model-based c-statistic 0.68 (0.62, 0.71), suggesting that the decrement in model performance was only partially due to case-mix heterogeneity. Calibration was generally poor - median E (standardized by outcome rate) was 0.5 (0.4, 2.2) for acute HF CPMs and 0.5 (0.3, 0.7) for chronic HF CPMs. Updating the intercept alone led to a significant improvement in calibration in acute HF CPMs, but not in chronic HF CPMs. Net benefit analysis showed potential for harm in using CPMs when the decision threshold was not near the overall outcome rate but this improved with model recalibration.ConclusionsOnly a small minority of published CPMs contained variables and outcomes that were compatible with the clinical trial datasets. For acute HF CPMs, discrimination is largely preserved after adjusting for case-mix; however, the risk of net harm is substantial without model recalibration for both acute and chronic HF CPMs.
Postload glucose spike but not fasting glucose determines prognosis after myocardial infarction in patients without known or newly diagnosed diabetes
Authors: Chattopadhyay Sudipta , George Anish , John Joseph , Sathyapalan Thozhukat 
Published: February 2021

AbstractBackgroundThe effect of postload glucose spikes (PGS), the difference between 2 hour post‐load plasma glucose (2hPLPG) and fasting plasma glucose (FPG), on post–myocardial infarction (post‐MI) prognosis in nondiabetic patients is unexplored.MethodsThis is a retrospective cohort analysis of 847 nondiabetic post‐MI survivors who underwent a predischarge oral glucose tolerance test (median PGS: 2.4 mmol/L). Patients were divided into the unmatched groups 1 and 2 (PGS ≤ and > 2.4 mmol/L) and the propensity score‐matched groups 1M and 2M (355 pairs assembled from the overall cohort), and these groups were compared. Major adverse cardiac events (MACE: death and nonfatal reinfarction) were recorded during follow‐up (median: 3.4 years). Event‐free survival was compared by the Kaplan‐Meier method. Multivariate Cox proportional hazards regression determined the predictors of MACE. C‐statistics (change in area under the curve, δAUC), continuous net reclassification improvement (NRI>0), and integrated discrimination improvement (IDI) were used to compare models.ResultsThe number of MACE was higher in groups 2 (27.3% vs 14.2%, P < .001) and 2M (24.5% vs 15.5%, P < .001). Event‐free survival was worse in groups 2 (hazard ratio [HR] 2.01; 95% CI, 1.49‐2.71; P < .001) and 2M (HR 1.63; 95% CI, 1.17‐2.27; P = .004). PGS independently predicted MACE‐free survival in the whole (HR 1.16; 95% CI, 1.06‐1.26; P = .002) and matched cohort (HR 1.12; 95% CI, 1.02‐1.24; P = .021). PGS, but not FPG or 2hPPG, improved the predictive performance of the base model (δAUC 0.013, P = .046), with greater improvement seen when PGS was added and compared to 2hPPG (δAUC 0.005, P = .034; NRI>0 0.2107, P = .013; IDI 0.0042, P = .046).ConclusionPGS is a better predictor of post‐MI prognosis than 2hPPG in nondiabetic patients.
Oviposition behaviour and electrophysiological responses of Alabama argillacea (Hübner, 1823) (Lepidoptera: Erebidae) to essential oils and chemical compounds
Authors: Santos Andrezo A , Wanderley‐Teixeira Valéria , Navarro Daniela M A F , Dutra Kamilla A , Cruz Glaucilane S , Teixeira Álvaro A C , Oliveira José V , Milet‐Pinheiro Paulo 
Published: February 2021

AbstractAlabama argillacea (Lepidoptera: Erebidae) is a cotton pest. It is considered the most important defoliator species. It is commonly controlled using non‐selective synthetic insecticides. However, they cause adverse effects to the environment. Thus, there is a need for more sustainable forms of management. In this case, essential oils may be a promising alternative. They have proven effects against countless agricultural pests. However, there is still few information on the effects of essential oils against A. argillacea. The present work aims to evaluate the deterrent activity of essential oils of Mentha spicata, Litsea cubeba, Melaleuca alternifolia and Juniperus virginiana on the oviposition of A. argillacea and identify which compounds of these oils are responsible for this action. Oviposition bioassays were performed to test oils. Gas chromatography coupled with electroantennography (GC‐EAD) was performed to identify constituents detected by A. argillacea. In oviposition bioassays, only the oil of M. spicata showed activity against A. argillacea. GC‐EAD analyses of this oil revealed that three compounds elicited visible antennal responses, namely, limonene, carvone and (E)‐β‐caryophyllene. Bioassays testing these three compounds showed that only limonene elicited oviposition deterrence against A. argillacea. Hence, the findings suggest that the essential oil of M. spicata and, more specially, limonene may contribute to a more sustainable management of this important pest of cotton.