bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of opinion dynamics on the public health damage inflicted by COVID-19 in the presence of societal heterogeneities
Authors: Ali Rex N. , Sarkar Saswati 
Published: March 2023

AbstractCertain behavioral practices such as wearing surgical masks, observing social distancing, and accepting vaccines impede the spread of COVID-19 and contain the severity of symptoms in the infected individuals. Opinions regarding whether to observe such behavioral practices evolve over time through interactions via networks that overlap with but are not identical to the physical interaction networks over which the disease progresses. This necessitates the joint study of COVID-19 evolution and opinion dynamics. We develop a mathematical model that can be easily adapted to a wide range of behavioral practices and captures in a computationally tractable manner the joint evolution of the disease and relevant opinions in populations of large sizes. Populations of large sizes are typically heterogeneous in that they comprise individuals of different age groups, genders, races, and underlying health conditions. Such groups have different propensities to imbibe severe forms of the disease, different physical contact, and social interaction patterns and rates. These lead to different disease and opinion dynamics in them. Our model is able to capture such diversities. Computations using our model reveal that opinion dynamics have a strong impact on fatality and hospitalization counts and the number of man-days lost due to symptoms both in the regular form of the disease and the extended forms, more commonly known as long COVID. We show that opinion dynamics in certain groups have a disproportionate impact on the overall public health attributes because they have high physical interaction rates, even when they have the lowest propensity to imbibe severe forms of the disease. This identifies a social vulnerability that mal-actors can utilize to inflict heavy public health damages through opinion campaigns targeting specific segments. Once such vulnerabilities are identified, which we accomplish, adequate precautions may be designed to enhance resilience to such targeted attacks and better protect public health.
Transient loss and recovery of oral chemesthesis, taste and smell with COVID-19: a small case-control series
Authors: Weir Elisabeth M. , Exten Cara , Gerkin Richard C. , Munger Steven D. , Hayes John E. 
Published: March 2023

AbstractAnosmia is common with respiratory virus infections, but loss of taste or chemesthesis is rare. Reports of true taste loss with COVID-19 were viewed skeptically until confirmed by multiple studies. Nasal menthol thresholds are elevated in some with prior COVID-19 infections, but data on oral chemesthesis are lacking. Many patients recover quickly, but precise timing and synchrony of recovery are unclear. Here, we collected broad sensory measures over 28 days, recruiting adults (18-45 years) who were COVID-19 positive or recently exposed (close contacts per U.S. CDC criteria at the time of the study) in the first half of 2021. Participants received nose clips, red commercial jellybeans (Sour Cherry and Cinnamon), and scratch-n-sniff cards (ScentCheckPro). Among COVID-19 cases who entered the study on or before Day 10 of infection, Gaussian Process Regression showed odor identification and odor intensity (two distinct measures of function) each declined relative to controls (close contacts who never developed COVID-19), but effects were larger for intensity than identification. To assess changes during early onset, we identified four COVID-19 cases who enrolled on or prior to Day 1 of their illness – this allowed for visualization of baseline ratings, loss, and recovery of function over time. Four controls were matched for age, gender, and race. Variables included sourness and sweetness (Sour Cherry jellybeans), oral burn (Cinnamon jellybeans), mean orthonasal intensity of four odors (ScentCheckPro), and perceived nasal blockage. Data were plotted over 28 days, creating panel plots for the eight cases and controls. Controls exhibited stable ratings over time. By contrast, COVID-19 cases showed sharp deviations over time. No single pattern of taste loss or recovery was apparent, implying different taste qualities might recover at different rates. Oral burn was transiently reduced for some before recovering quickly, suggesting acute loss may be missed in data collected after acute illness ends. Changes in odor intensity or odor identification were not explained by nasal blockage. Collectively, intensive daily testing shows orthonasal smell, oral chemesthesis and taste were each altered by acute COVID-19 infection, and this disruption was dyssynchronous for different modalities, with variable loss and recovery rates across modalities and individuals.
Optimized ITS/CITS models for intervention evaluation considering the nonlinear impact of covariates
Authors: Zhang Xiangliang , Yin Rong , Pan Yan , Zhong Wenfang , Kong Di , Chen Wen 
Published: March 2023

AbstractThere is a lack of approaches to evaluate the effectiveness of interventions when there are nonlinear impacts of covariates to the outcome series.Based on the classic framework of ITS/CITS segmented regression, while considering autocorrelation of time series, we adopted a nonlinear dynamic modeling strategy (Hammerstein) to measure the nonlinear effects of covariates, and proposed four optimized models: ITS-A, CITS-A, ITS-HA, and CITS-HA. To compare the accuracy and precision in estimating the long-term impact of an intervention between the optimized and classic segmented models, we constructed a sequence generator to simulate the outcome series with actual characteristics. The relative error with respect to the true value was the accuracy indicator, and the width of the 95% CI and the truth value coverage rate of the corresponding 95% CI are the precision indicator for model assessments.The relative error of impact evaluation in the four optimized models was 4.49 percentage points lower than that in the classic models, specifically ITS-A (14.34%) and ITS-HA (21.47%) relative to ITS (26.66%), CITS-A (16.57%), and CITS-HA (17.94%) relative to CITS (21.59%). The width of the 95% CI of point estimate of long-term impacts in the optimized models was 0.1261, which was expanded by 58.71% compared with 0.0875 for the classic model. However, the optimized models covered the true value in all test scenarios, whereas the coverage rates of the classic ITS and CITS models were 73.33% and 83.33%, respectively.The optimized models are useful tools as they can assess the long-term impact of interventions with additional considerations for the nonlinear effects of covariates and allow for modeling of time-series autocorrelation and lag of intervention effects.
A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics
Authors: Zhang Zixuan , Jung Junghyun , Kim Artem , Suboc Noah , Gazal Steven , Mancuso Nicholas 
Published: March 2023

AbstractGenome-wide association studies (GWAS) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes, while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N=420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (P=2.58E-10), and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest novel shared etiologies between rheumatoid arthritis and periodontal condition, in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWAS.
Interactive association between gut microbiota and thyroid cancer: a Mendelian randomization and systematic review
Authors: Hou Tianzhichao , Wang Qi , Dai Huajie , Hou Yanan , Zheng Jie , Wang Tiange , Lin Hong , Wang Shuangyuan , Li Mian , Zhao Zhiyun , Chen Yuhong , Xu Yu , Lu Jieli , Liu Ruixin , Ning Guang , Wang Weiqing , Xu Min , Bi Yufang 
Published: March 2023

AbstractContextThe association between gut microbiota and thyroid cancer remains controversial.ObjectiveWe aimed to systematically investigate the interactive causal relationships between the abundance and metabolism pathways of gut microbiota, and thyroid cancer.MethodsWe leveraged the genome wide association studies for the abundance of 211 microbiota taxa from the MiBioGen study (N=18,340); 205 microbiota metabolism pathways from the Dutch Microbiome Project (N=7738); and thyroid cancer from the largest meta-analysis of Global Biobank Meta-analysis Initiative (N cases=6699 and N participants=1,620,354). We performed a bidirectional Mendelian randomization (MR) to investigate the causality from microbiota taxa, metabolism pathways to thyroid cancer, and vice versa. We did a systematic review of the previous observational studies and compared MR results with observational findings.ResultsEight taxa and twelve metabolism pathways had causal effects on thyroid cancer, whereRuminococcaceaeUCG004genus (P=0.001),Streptococcaceae family(P=0.016),Olsenellagenus (P=0.029), ketogluconate metabolism pathway (P=0.003), pentose phosphate pathway (P=0.016), and L-arginine degradation II in AST pathway (P=0.0007) were supported by sensitivity analyses. Conversely, thyroid cancer had causal effects on three taxa and two metabolism pathways, whereHoldemanellagenus (P=0.015) was supported by sensitivity analyses. TheProteobacteriaphylum,Streptococcaceaefamily,Ruminococcus2genus, andHoldemanellagenus were significantly associated with thyroid cancer in both systematic review and MR, while other 121 significant taxa in observational results were not supported by MR.DiscussionsThese findings implicated the potential role of host-microbiota crosstalk in thyroid cancer, while the discrepancy among observational studies called for further investigations.
Assessment and Improvement of Elixhauser Comorbidity Index for Predicting In-hospital Mortality in Heart Transplant Patients
Authors: Li Renxi 
Published: March 2023

AbstractBackgroundHeart transplant (HT) has a high in-hospital mortality of around 5%. Risk prediction in-hospital mortality can be informative for transplant candidacy and post-HT prognosis. Elixhauser Comorbidity Index (ECI) is an ICD diagnostic code-based comorbidity measurement tool that can predict in-hospital mortality. While it has been validated in the large in-patient population, the accuracy of the mortality prediction has not been assessed in HT.MethodsThis study assessed the in-hospital mortality risk prediction by ECI as well as demographic variables in HT patients in the National Inpatient Sample (NIS) database. Demographic information was included in the multivariable ECI with demographics (ECID) model to assess in-hospital mortality. Moreover, ECI and age were used to develop a single index adjusted ECI (aECI) for mortality prediction.ResultsAge best predicts (c-statistic = 0.673, 95% CI = 0.638-0.709) in-hospital mortality, followed by ECI (c-statistic = 0.638, 95% CI = 0.598-0.678), race (c-statistic = 0.571, 95% CI = 0.533-0.609). Sex did not have predictive power (c-statistic = 0.501, 95% CI = 0.467-0.535) for in-hospital mortality. The predictive power of ECI was improved (c-statistic = 0.753, 95% CI = 0.720-0.785) in the ECID model. The single measure aECI had comparable discriminative power (c-statistic = 0.763, 95% CI = 0.731-0.794) to ECID in predicting in-hospital mortality.ConclusionThis study showed that ECI was an effective measure to predict post-HT in-hospital mortality. The improved measure aECI can be easily derived from ECI as a quick reference to assess post-HT in-hospital mortality in both the clinic and health administration.
PyTorch-FEA: Autograd-enabled Finite Element Analysis Methods with Applications for Biomechanical Analysis of Human Aorta
Authors: Liang Liang , Liu Minliang , Elefteriades John , Sun Wei 
Published: March 2023

AbstractMotivationFinite-element analysis (FEA) is widely used as a standard tool for stress and deformation analysis of solid structures, including human tissues and organs. For instance, FEA can be applied at a patient-specific level to assist in medical diagnosis and treatment planning, such as risk assessment of thoracic aortic aneurysm rupture/dissection. These FEA-based biomechanical assessments often involve both forward and inverse mechanics problems. Current commercial FEA software packages (e.g., Abaqus) and inverse methods exhibit performance issues in either accuracy or speed.MethodsIn this study, we propose and develop a new library of FEA code and methods, named PyTorch-FEA, by taking advantage of autograd, an automatic differentiation mechanism in PyTorch. We develop a class of PyTorch-FEA functionalities to solve forward and inverse problems with improved loss functions, and we demonstrate the capability of PyTorch-FEA in a series of applications related to human aorta biomechanics. In one of the inverse methods, we combine PyTorch-FEA with deep neural networks (DNNs) to further improve performance.ResultsWe applied PyTorch-FEA in four fundamental applications for biomechanical analysis of human aorta. In the forward analysis, PyTorch-FEA achieved a significant reduction in computational time without compromising accuracy compared with Abaqus, a commercial FEA package. Compared to other inverse methods, inverse analysis with PyTorch-FEA achieves better performance in either accuracy or speed, or both if combined with DNNs.
Scaling deep phylogenetic embedding to ultra-large reference trees: a tree-aware ensemble approach
Authors: Jiang Yueyu , McDonald Daniel , Knight Rob , Mirarab Siavash 
Published: March 2023

AbstractPhylogenetic placement of a query sequence on a backbone tree is increasingly used across biomedical sciences to identify the content of a sample from its DNA content. The accuracy of such analyses depends on the density of the backbone tree, making it crucial that placement methods scale to very large trees. Moreover, a new paradigm has been recently proposed to place sequences on the species tree using single-gene data. The goal is to better characterize the samples and to enable combined analyses of marker-gene (e.g., 16S rRNA gene amplicon) and genome-wide data. The recent method DEPP enables performing such analyses using metric learning. However, metric learning is hampered by a need to compute and save a quadratically growing matrix of pairwise distances during training. Thus, DEPP (or any distance-based method) does not scale to more than roughly ten thousand species, a problem that we faced when trying to use our recently released Greengenes2 (GG2) reference tree containing 331,270 species. Scalability problems can be addressed in phylogenetics using divide- and-conquer. However, applying divide- and-conquer to data-hungry machine learning methods needs nuance. This paper explores divide- and-conquer for training ensembles of DEPP models, culminating in a method called C-DEPP that uses carefully crafted techniques to enable quasi-linear scaling while maintaining accuracy. C-DEPP enables placing twenty million 16S fragments on the GG2 reference tree in 41 hours of computation.
Hyaluronic acid turnover controls the severity of Cerebral Cavernous Malformations in bioengineered human micro-vessels
Authors: Yordanov Teodor E. , Martinez Marco A. E. , Esposito Tyron , Tefft Juliann B. , Labzin Larisa I. , Stehbens Samantha J. , Rowan Alan E. , Hogan Benjamin M. , Chen Christopher S. , Lauko Jan , Lagendijk Anne K. 
Published: March 2023

ABSTRACTCerebral Cavernous Malformations (CCMs) are vascular lesions that predominantly form in blood vessels of the central nervous system (CNS) upon loss of the CCM multimeric protein complex. The endothelial cells (ECs) within CCM lesions are characterised by overactive MEKK3 kinase and KLF2/4 transcription factor signalling, leading to pathological changes such as increased EC spreading and reduced junctional integrity. Concomitant to aberrant EC signalling, non-autonomous signals from the extracellular matrix (ECM) have also been implicated in CCM lesion growth and these factors might explain why CCM lesions mainly develop in the CNS. Here, we adapted a three dimensional (3D) microfluidic system to examine CCM1 deficient human micro-vessels in distinctive ECMs. We validate that EC pathological hallmarks are maintained in this 3D model. We further show that key genes responsible for homeostasis of Hyaluronic Acid (HA), a major ECM component of the CNS, are dysregulated in CCM. Supplementing the ECM in our model with forms of HA that are predicted to be reduced, inhibits CCM cellular phenotypes, independent of KLF2/4. This study thereby provides a proof-of-principle that ECM embedded 3D microfluidic models are ideally suited to identify how changes in ECM structure and signalling impact vascular malformations.
Novel role of bone morphogenetic protein 9 (BMP9) in innate host responses to HCMV infection
Authors: Stempel Markus , Drakesmith Hal , Brinkmann Melanie M. 
Published: March 2023

AbstractHerpesviruses modulate immune control to secure lifelong infection. The diverse mechanisms Human Cytomegalovirus (HCMV) employs in this regard can reveal unanticipated aspects of cellular signalling involved in antiviral immunity. Here, we describe a novel relationship between the transforming growth factor-beta (TGF-β) family cytokine bone morphogenetic protein 9 (BMP9) and HCMV infection. We identified cross-talk between BMP9-induced and type I interferon (IFN) receptor-mediated signaling, showing that BMP9 boosts the transcriptional response to and antiviral activity of IFNβ, and thereby enhances restriction of HCMV replication. We also show that BMP9 is secreted by human fibroblasts upon HCMV infection. However, HCMV infection impairs BMP9-induced enhancement of the response to IFNβ, indicating that this innate immune signalling role of BMP9 is actively targeted by HCMV. Indeed, we show that transmembrane proteins US18 and US20, which downregulate type I BMP receptors, are necessary and sufficient to cause inhibition of BMP9-mediated boosting of the antiviral response to IFNβ, so that HCMV lacking US18 and US20 is more sensitive to IFNβ. These results demonstrate that HCMV has a mutually antagonistic relationship with BMP9 and substantially extend the growing body of evidence that BMP signalling is an underappreciated modulator of innate immunity to viral infection.
Generating an efficient arginase variant for medical and industrial uses: In Silico engineering
Authors: Al-Madhagi Haitham Ahmed , Alsugoor Mahdi H. 
Published: March 2023

AbstractHuman arginase is a multifaceted enzyme that can be utilised for various medical and industrial applications, including as a replacement therapy for enzyme-deficient patients and for the industrial production of ornithine. However, no report has explored the in-silico engineering of this novel enzyme. The crystal structure of human arginase 1 was downloaded from the protein databank, and its quality was checked prior to further analysis. CUPSAT and DeepDDG webservers were then employed to nominate the most stable variants, which were prepared by the UCSF Chimera v1.16 modelling system and refined by the GalaxyRefine tool. Docking (i.e., to reference substrate and inhibitor), stability confirmation and dynamics simulations were conducted for all proposed variants, compared to the wild-type version of the enzyme. G119L was the best mutant in all the mentioned aspects, which was afterwards cloned in silico as a final step for the experimental testing thereof. Accordingly, G119L is found to be a valuable arginase mutant that deserves experimental validation to be employed for medical and industrial purposes.
Lysyl oxidase regulates epithelial differentiation and barrier integrity in eosinophilic esophagitis
Authors: Sasaki Masaru , Hara Takeo , Wang Joshua X. , Zhou Yusen , Kennedy Kanak V. , Umeweni Nicole N. , Alston Maiya A. , Spergel Zachary C. , Nakagawa Ritsu , Mcmillan Emily A. , Whelan Kelly A. , Karakasheva Tatiana A. , Hamilton Kathryn E. , Ruffner Melanie A. , Muir Amanda B. 
Published: March 2023

AbstractBackground & AimsEpithelial disruption in eosinophilic esophagitis (EoE) encompasses both impaired differentiation and diminished barrier integrity. We have shown that lysyl oxidase (LOX), a collagen cross-linking enzyme, is upregulated in the esophageal epithelium in EoE. However, the functional roles of LOX in the esophageal epithelium remains unknown.MethodsWe investigated roles for LOX in the human esophageal epithelium using 3-dimensional organoid and air-liquid interface cultures stimulated with interleukin (IL)-13 to recapitulate the EoE inflammatory milieu, followed by single-cell RNA sequencing, quantitative reverse transcription-polymerase chain reaction, western blot, histology, and functional analyses of barrier integrity.ResultsSingle-cell RNA sequencing analysis on patient-derived organoids revealed that LOX was induced by IL-13 in differentiated cells. LOX-overexpressing organoids demonstrated suppressed basal and upregulated differentiation markers. Additionally, LOX overexpression enhanced junctional protein genes and transepithelial electrical resistance. LOX overexpression restored the impaired differentiation and barrier function, including in the setting of IL-13 stimulation. Transcriptome analyses on LOX-overexpressing organoids identified enriched bone morphogenetic protein (BMP) signaling pathway compared to wild type organoids. Particularly, LOX overexpression increased BMP2 and decreased BMP antagonist follistatin. Finally, we found that BMP2 treatment restored the balance of basal and differentiated cells.ConclusionsOur data support a model whereby LOX exhibits non-canonical roles as a signaling molecule important for epithelial homeostasis in the setting of inflammation via activation of BMP pathway in esophagus. The LOX/BMP axis may be integral in esophageal epithelial differentiation and a promising target for future therapies.