bluuebunny/crossref_metadata_2025_split huggingface dataset:
Risk factors among Black and White COVID-19 patients from a Louisiana Hospital System, March, 2020 – August, 2021
Authors: Yu Qingzhao , Cao Wentao , Hamer Diana , Urbanek Norman , Straif-Bourgeois Susanne , Cormier Stephania , Ferguson Tekeda , Richmond-Bryant Jennifer 
Published: July 2022

AbstractObjectivesTo investigate relationships between race and COVID-19 hospitalizations, intensive care unit (ICU) admissions, and mortality over time and which characteristics, may mediate COVID-19 associations.MethodsWe analyzed hospital admissions, ICU admissions, and mortality among positive COVID-19 cases within the ten-hospital Franciscan Ministries of Our Lady Health System around the Mississippi River Industrial Corridor in Louisiana over four waves of the pandemic from March 1, 2020 – August 31, 2021. Associations between race and each outcome were tested, and multiple mediation analysis was performed to test if other demographic, socioeconomic, or air pollution variables mediate the race-outcome relationships.ResultsRace was associated with each outcome over the study duration and during most waves. Early in the pandemic, hospitalization, ICU admission, and mortality rates were greater among Black patients, but as the pandemic progressed these rates became greater in White patients. However, Black patients were still disproportionately represented in these measures. Age was a significant mediator for all outcomes across waves, while comorbidity and emissions of naphthalene and chloroprene acted as mediators for the full study period.ConclusionsThe role of race evolved throughout the pandemic in Louisiana, but Black patients bore a disproportionate impact. Naphthalene and chloroprene air pollution partially explained the long-term associations. Our findings imply that air pollution might contribute to the increased COVID-19 hospitalizations and mortality among Black residents in Louisiana but likely do not explain most of the effect of race.What is already known on this topicEarly in the pandemic, there was evidence of disparities in COVID-19 cases, hospitalizations, intensive care unit (ICU) admissions, and mortality due to race. Studies were emerging to indicate that strength of these relationships was waning over time.What this study addsThis study tests relationships between race and hospitalizations, ICU admissions, and mortality and finds that Black patients continue to be disproportionately represented, although that inequity diminished over time. This study, the first to use multiple mediation analysis to study COVID-19 associations, suggests that the relationship between race and health outcome can be explained by mediators including age and, to a lesser extent, comorbidity and air pollution.How this study might affect research, practice or policyThis study supports the need for healthcare resources to be available to Louisiana’s communities of color, for policy to support increased access to health care in the Industrial Corridor region, and for policy to support the reduction of air pollution emissions to disproportionately impacting the health of the Industrial Corridor’s communities of color.
Immune Heterogeneity and Epistasis Explain Punctuated Evolution of SARS-CoV-2
Authors: Nielsen Bjarke Frost , Li Yimei , Sneppen Kim , Simonsen Lone , Viboud Cécile , Levin Simon A. , Grenfell Bryan T. 
Published: July 2022

AbstractIdentifying drivers of viral diversity is key to understanding the evolutionary as well as epidemiological dynamics of the COVID-19 pandemic. Using rich viral genomic data sets, we show that periods of steadily rising diversity have been punctuated by sudden, enormous increases followed by similarly abrupt collapses of diversity. We introduce a mechanistic model of saltational evolution with epistasis and demonstrate that these features parsimoniously account for the observed temporal dynamics of inter-genomic diversity. Our results provide support for recent proposals that saltational evolution may be a signature feature of SARS-CoV-2, allowing the pathogen to more readily evolve highly transmissible variants. These findings lend theoretical support to a heightened awareness of biological contexts where increased diversification may occur. They also underline the power of pathogen genomics and other surveillance streams in clarifying the phylodynamics of emerging and endemic infections. In public health terms, our results further underline the importance of equitable distribution of up-to-date vaccines.
Border-associated macrophages transventricularly infiltrate the early embryonic cerebral wall to differentiate into microglia
Authors: Hattori Yuki , Kato Daisuke , Murayama Futoshi , Koike Sota , Naito Yu , Kawaguchi Ayano , Wake Hiroaki , Miyata Takaki 
Published: July 2022

SummaryThe relationships between microglia and macrophages, especially their lineage segregation outside the yolk sac, have been recently explored, providing a model in which a conversion from macrophages seeds microglia during brain development. However, spatiotemporal evidence to support such microglial seeding and to explain how it occurs has not been obtained. By cell tracking via slice culture, intravital imaging, and Flash tag-mediated labeling, we found that a group of intraventricular macrophages belonging to border-associated macrophages (BAMs), which were abundantly observed along the inner surface of the mouse cerebral wall at embryonic day 12, frequently entered the brain wall. Immunohistochemistry of the tracked cells showed that postinfiltrative BAMs acquired microglial properties while losing a macrophage phenotype. We also found that the intraventricular BAMs were supplied transepithelially from the roof plate. Thus, this study demonstrates that the “roof plate→ventricle→cerebral wall” route is an essential path for microglial colonization into the embryonic mouse brain.
Multiscale functional connectivity patterns of the aging brain learned from rsfMRI data of 4,259 individuals of the multi-cohort iSTAGING study
Authors: Zhou Zhen , Srinivasan Dhivya , Li Hongming , Abdulkadir Ahmed , Nasrallah Ilya , Wen Junhao , Doshi Jimit , Erus Guray , Mamourian Elizabeth , Bryan Nick R. , Wolk David A. , Beason-Held Lori , Resnick Susan M. , Satterthwaite Theodore D. , Davatzikos Christos , Shou Haochang , Fan Yong ,   the ISTAGING Consortium
Published: July 2022

AbstractTo learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4259 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and harmonization in the tangent space worked better than in the original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.
Amyloid beta peptides (Aβ) from Alzheimer’s disease neuronal secretome induce endothelial activation in a human cerebral microvessel model
Authors: Shin Yu Jung , Evitts Kira M. , Jin Solhee , Howard Caitlin , Sharp-Milgrom Margaret , Young Jessica E. , Zheng Ying 
Published: July 2022

AbstractIn Alzheimer’s disease (AD), secretion and deposition of amyloid beta peptides (Aβ) have been associated with blood-brain barrier dysfunction. However, the role of Aβ in endothelial cell (EC) dysfunction remains elusive. Here we investigated AD mediated EC activation by studying the effect of Aβ secreted from human induced pluripotent stem cell-derived cortical neurons (hiPSC- CN) harboring a familial AD mutation (Swe+/+) on human brain microvascular endothelial cells (HBMECs) in 2D and 3D perfusable microvessels. We demonstrated that increased Aβ levels in Swe+/+ conditioned media (CM) led to stress fiber formation and upregulation of genes associated with endothelial inflammation and immune-adhesion. Perfusion of Aβ-rich Swe+/+ CM induced acute formation of von Willebrand factor (VWF) fibers in the vessel lumen, which was attenuated by reducing Aβ levels in CM. Our findings suggest that Aβ can trigger rapid inflammatory and thrombogenic responses within cerebral microvessels, which may exacerbate AD pathology.
Proposal of a simple open-source quantitative tribometric assay and its implementation for the assessment of the effects of redox-related alterations on the lubrication capacity of a commercial water-based lubricant gel
Authors: Homolak Jan 
Published: July 2022

AbstractBackgroundUnderstanding molecular and biochemical mechanisms affecting biotribological properties of tissues, biological fluids, and drugs may accelerate the invention of novel drug formulations and targets and facilitate the discovery of etiopathogenetic mechanisms. Unfortunately, biotribometric equipment is expensive and unaffordable. The first aim was to assess whether PASTA, an open-source platform based on a hacked kitchen scale, can be adapted for the analysis of biotribometric properties. The second aim was to demonstrate the use of PASTA by studying the effects of oxidation on the lubricating properties of the commercial water-based lubricant.MethodsThe PASTA platform was adapted using a custom-made tribometric adapter attached to the bottom of the hacked kitchen scale connected to a computer via the NodeMCU ESP-32S. A commercial water-based lubricant was oxidized with 2,2’-azobis(2-amidinopropane) dihydrochloride and/or protected against oxidation with glutathione. The samples were analyzed using a simple tribometric assay with the PASTA platform and the ORP-146S redox microsensor.ResultsA mastPASTA custom-made adapter enables the use of PASTA for reliable quantitative tribometric studies. Oxidation of the commercial water-based lubricant does not reduce its lubrication capacity, however, the addition of the antioxidant glutathione protects against the loss of its lubrication capacity upon exposure to air by a redox-unrelated mechanism.ConclusionPASTA can easily be adapted for studying tribological properties. The effects of the addition of antioxidants to water-based vaginal lubricants should be explored as a possible way to enhance durability and efficacy and increase their health benefits.
Growing DAGs: Optimization Functions for Pathway Reconstruction Algorithms
Authors: Köse Tunç Başar , Li Jiarong , Ritz Anna 
Published: July 2022

AbstractA major challenge in molecular systems biology is to understand how proteins work to transmit external signals to changes in gene expression. Computationally reconstructing these signaling pathways from protein interaction networks can help understand what is missing from existing pathway databases. We formulate a new pathway reconstruction problem, one that iteratively grows directed acyclic graphs (DAGs) from a set of starting proteins in a protein interaction network. We present an algorithm that provably returns the optimal DAGs for two different cost functions and evaluate the pathway reconstructions when applied to six diverse signaling pathways from the NetPath database. The optimal DAGs outperform an existingk-shortest paths method for pathway reconstruction and the new reconstructions are enriched for different biological processes. Growing DAGs is a promising step towards reconstructing pathways that provably optimize a specific cost function.
Integrative analysis of a large real-world cohort of small cell lung cancer identifies distinct genetic subtypes and insights into histological transformation
Authors: Sivakumar Smruthy , Moore Jay A. , Montesion Meagan , Sharaf Radwa , Lin Douglas I. , Fleishmann Zoe , Ebot Ericka M. , Newberg Justin , Mills Jennifer M. , Hegde Priti S. , Frampton Garrett M. , Sage Julien , Lovly Christine M. 
Published: July 2022

ABSTRACTSmall cell lung cancer (SCLC) is a recalcitrant neuroendocrine carcinoma with dismal survival outcomes. A major barrier in the field has been the relative paucity of human tumors studied. Here we provide an integrated analysis of 3,600 “real-world” SCLC cases. This large cohort allowed us to identify new recurrent alterations and new genetic subtypes, including STK11-mutant tumors (1.7%) and TP53/RB1 wild-type tumors (5.5%), of which 12.7% were human papillomavirus-positive. In our cohort, gene amplifications on 4q12 are associated with increased overall survival while CCNE1 amplification is associated with decreased overall survival. We also identify more frequent alterations in the PTEN pathway in brain metastases. Finally, profiling cases of SCLC containing oncogenic drivers typically associated with NSCLC demonstrates that SCLC transformation may occur across multiple distinct molecular cohorts of NSCLC. These novel and unsuspected genetic features of SCLC may help personalize treatment approaches for this fatal form of cancer.STATEMENT OF SIGNIFICANCEMinimal changes in therapy and survival outcomes have occurred in SCLC for the past four decades. The identification of new genetic subtypes, novel recurrent mutations, and an improved understanding of the mechanisms of transformation to SCLC from NSCLC may guide the development of personalized therapies for subsets of patients with SCLC.
Velocity of Conduction Between Columns and Layers in Barrel Cortex Reported by Parvalbumin Interneurons
Authors: Scheuer Kate S. , Judge John , Zhao Xinyu , Jackson Meyer B. 
Published: July 2022

AbstractInhibitory interneurons that express parvalbumin (PV) play critical roles throughout the brain. Their rapid-spiking characteristics enable them to control the dynamics of neural circuits across a range of time scales, but the timing of their activation by different cortical pathways remains unclear. Here, we use a genetically encoded hybrid voltage sensor, hVOS, to image PV interneuron voltage changes with sub-millisecond precision in primary somatosensory barrel cortex (BC) of adult male and female mice. Electrical stimulation evoked depolarizing responses with a latency that increased with distance from the stimulating electrode, allowing us to determine conduction velocity. By focusing on conduction between cortical layers or between barrel columns we were able to measure interlaminar or intralaminar conduction velocity, respectively. Velocities ranged from 74 to 473 μm/msec depending on trajectory, and we found that interlaminar conduction velocity was about 71% faster than intralaminar conduction velocity. This suggests that computations within columns can be processed more rapidly than between columns. BC circuitry integrates thalamic and intracortical input for a variety of functions including texture discrimination and sensory tuning. The difference in timing between intra- and interlaminar activation of PV interneurons could impact these functions. This study illustrates how hVOS imaging of PV interneuron electrical activity can reveal differences in the dynamics of signaling within different elements of cortical circuitry, and this approach offers a unique opportunity to investigate conduction in populations of axons based on their targeting specificity.
MEPSi: A tool for simulating tomograms of membrane-embedded proteins
Authors: de Francisco Borja Rodríguez , Bezault Armel , Xu Xiao-Ping , Hanein Dorit , Volkmann Niels 
Published: July 2022

ABSTRACTThe throughput and fidelity of cryogenic cellular electron tomography (cryo-ET) is constantly increasing through advances in cryogenic electron microscope hardware, direct electron detection devices, and powerful image processing algorithms. However, the need for careful optimization of sample preparations and for access to expensive, high-end equipment, make cryo-ET a costly and time-consuming technique. Generally, only after the last step of the cryo-ET workflow, when reconstructed tomograms are available, it becomes clear whether the chosen imaging parameters were suitable for a specific type of sample in order to answer a specific biological question. Tools for a-priory assessment of the feasibility of samples to answer biological questions and how to optimize imaging parameters to do so would be a major advantage. Here we describe MEPSi (Membrane Embedded Protein Simulator), a simulation tool aimed at rapid and convenient evaluation and optimization of cryo-ET data acquisition parameters for studies of transmembrane proteins in their native environment. We demonstrate the utility of MEPSi by showing how to detangle the influence of different data collection parameters and different orientations in respect to tilt axis and electron beam for two examples: (1) simulated plasma membranes with embedded single-pass transmembrane αIIbβ3 integrin receptors and (2) simulated virus membranes with embedded SARS-CoV-2 spike proteins.HIGHLIGHTSTool to simulate tomograms of membrane-embedded proteinsDetangles influence of data acquisition parameters from sample quality issuesRapid evaluation and optimization of cryo-ET data acquisition parametersProof-of-concept provided with integrins and SARS-CoV-2 spike simulationsGRAPHICAL ABSTRACT
GCDPipe: risk gene, cell type, and drug ranking for complex traits
Authors: Pinakhina Daria , Loboda Alexander , Sergushichev Alexey , Artomov Mykyta 
Published: July 2022

AbstractWe introduce a user-friendly machine learning tool for risk gene, cell type, and drug ranking for complex traits - GCDPipe. It uses gene-level GWAS-derived data and publicly available expression data to train a model for prediction of disease risk genes and relevant cell types. Gene-ranking information is then coupled with known drug targets data to prioritize drugs based on their estimated functional effects associated with identified risk genes. The pipeline was tested in two case studies: inflammatory bowel disease (IBD) and schizophrenia, then it was applied to Alzheimer’s disease to investigate potential options for drug repurposing. The results show that GCDPipe is an effective tool to unify genetic risk factors with cellular context and known drug targets.
Towards 3D-bioprinting of osseous tissue of pre-defined shape using single-matrix cell-bioink constructs
Authors: Gu Yawei , Pigeot Sebastian , Ahrens Lucas , Tribukait-Riemenschneider Fabian , Sarem Melika , Wolf Francine , Barbero Andrea , Martin Ivan , Shastri V. Prasad 
Published: July 2022

AbstractEngineering living bone tissue of defined shape on-demand has remained a challenge. 3D-bioprinting (3DBP), a biofabrication process capable of yielding cell constructs of defined shape, when combined with developmental engineering can provide a possible path forward. Through the development of a bioink possessing appropriate rheological properties to carry a high cell load and concurrently yield physically stable structures, printing of stable, cell-laden, single-matrix constructs of anatomical shapes was realized without the need for fugitive or support phases. Using this bioink system, constructs of hypertrophic cartilage of predesigned geometry were engineered in vitro by printing human MSCs at a high density to drive spontaneous condensation and implanted in nude mice to evoke endochondral ossification. The implanted constructs retained their prescribed shape over a 12-week period and underwent remodeling to yield ossicles of the designed shape with neovascularization. Micro-CT, histological and immunohistochemistry assessments confirmed bone tissue characteristics and the presence of human cells. These results demonstrate the potential of 3DBP to fabricate complex bone tissue for clinical application.
MISPEL: A supervised deep learning harmonization method for multi-scanner neuroimaging data
Authors: Torbati Mahbaneh Eshaghzadeh , Minhas Davneet S. , Laymon Charles M. , Maillard Pauline , Wilson James D. , Chen Chang-Le , Crainiceanu Ciprian M. , DeCarli Charles S. , Hwang Seong Jae , Tudorascu Dana L. 
Published: July 2022

AbstractLarge-scale data obtained from aggregation of already collected multi-site neuroimaging datasets has brought benefits such as higher statistical power, reliability, and robustness to the studies. Despite these promises from growth in sample size, substantial technical variability stemming from differences in scanner specifications exists in the aggregated data and could inadvertently bias any downstream analyses on it. Such a challenge calls for data normalization and/or harmonization frameworks, in addition to comprehensive criteria to estimate the scanner-related variability and evaluate the harmonization frameworks. In this study, we propose MISPEL (Multi-scanner Image harmonization via Structure Preserving Embedding Learning), a supervised multi-scanner harmonization method that is naturally extendable to more than two scanners. We also designed a set of criteria to investigate the scanner-related technical variability and evaluate the harmonization techniques. As an essential requirement of our criteria, we introduced a multi-scanner matched dataset of 3T T1 images across four scanners, which, to the best of our knowledge is one of the few datasets of this kind. We also investigated our evaluations using two popular segmentation frameworks: FSL and segmentation in statistical parametric mapping (SPM). Lastly, we compared MISPEL to popular methods of normalization and harmonization, namely White Stripe, RAVEL, and CALAMITI. MISPEL outperformed these methods and is promising for many other neuroimaging modalities.
A developmental atlas of the mouse brain by single-cell mass cytometry
Authors: Van Deusen Amy L. , Goggin Sarah M. , Williams Corey M. , Keeler Austin B. , Fread Kristen I. , Cheng Irene , Deppmann Christopher D. , Zunder Eli R. 
Published: July 2022

AbstractDevelopment of the mammalian brain requires precisely controlled differentiation of neurons, glia, and nonneural cells. To investigate protein-level changes in these diverse cell types and their progenitors, we performed single-cell mass cytometry on whole brain (E11.5/E12.5) and microdissected telencephalon, diencephalon, mesencephalon, and rhombencephalon (E13.5–P4) collected at daily timepoints from C57/BL6 mice. Measuring 24,290,787 cells from 112 sample replicates with a 40-antibody panel, we quantified 85 molecularly distinct cell populations across embryonic and postnatal development, including microglia putatively phagocytosing neurites, neural cells, and myelin. Differentiation trajectory analysis also identified two separate pathways for producing oligodendrocyte precursor cells. Comparison with previous studies revealed considerable discrepancies between protein and mRNA abundances in the developing brain, demonstrating the value of protein-level measurements for identifying functional cell states. Overall, our findings demonstrate the utility of mass cytometry as a high-throughput, scalable platform for single-cell profiling of brain tissue.