bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Impact of Mask-Wearing in Mitigating the Spread of COVID-19 During the Early Phases of the Pandemic
Authors: Aravindakshan Ashwin , Boehnke Jörn , Gholami Ehsan , Nayak Ashutosh 
Published: September 2020

AbstractMasks have been widely recommended as a precaution against COVID-19 transmission. Several studies have shown the efficacy of masks at reducing droplet dispersion in lab settings. However, during the early phases of the pandemic, the usage of masks varied widely across countries. Using individual response data from the Imperial College London — YouGov personal measures survey, this study investigates the effect of mask use within a country on the spread of COVID-19. The survey shows that mask-wearing exhibits substantial variations across countries and over time during the pandemic’s early phase. We use a reduced form econometric model to relate population-wide variation in mask-wearing to the growth rate of confirmed COVID-19 cases. The results indicate that mask-wearing plays an important role in mitigating the spread of COVID-19. Widespread mask-wearing within a country associates with an expected 7% (95% CI: 3.94% — 9.99%) decline in the growth rate of daily active cases of COVID-19 in the country. This daily decline equates to an expected 88.5% drop in daily active cases over a 30-day period when compared to zero percent mask-wearing, all else held equal. The decline in daily growth rate due to the combined effect of mask-wearing, reduced outdoor mobility, and non-pharmaceutical interventions averages 28.1% (95% CI: 24.2%-32%).
Machine Learning for Integrating Social Determinants in Cardiovascular Disease Prediction Models: A Systematic Review
Authors: Zhao Yuan , Wood Erica P. , Mirin Nicholas , Vedanthan Rajesh , Cook Stephanie H. , Chunara Rumi 
Published: September 2020

SummaryBackgroundCardiovascular disease (CVD) is the number one cause of death worldwide, and CVD burden is increasing in low-resource settings and for lower socioeconomic groups worldwide. Machine learning (ML) algorithms are rapidly being developed and incorporated into clinical practice for CVD prediction and treatment decisions. Significant opportunities for reducing death and disability from cardiovascular disease worldwide lie with addressing the social determinants of cardiovascular outcomes. We sought to review how social determinants of health (SDoH) and variables along their causal pathway are being included in ML algorithms in order to develop best practices for development of future machine learning algorithms that include social determinants.MethodsWe conducted a systematic review using five databases (PubMed, Embase, Web of Science, IEEE Xplore and ACM Digital Library). We identified English language articles published from inception to April 10, 2020, which reported on the use of machine learning for cardiovascular disease prediction, that incorporated SDoH and related variables. We included studies that used data from any source or study type. Studies were excluded if they did not include the use of any machine learning algorithm, were developed for non-humans, the outcomes were bio-markers, mediators, surgery or medication of CVD, rehabilitation or mental health outcomes after CVD or cost-effective analysis of CVD, the manuscript was non-English, or was a review or meta-analysis. We also excluded articles presented at conferences as abstracts and the full texts were not obtainable. The study was registered with PROSPERO (CRD42020175466).FindingsOf 2870 articles identified, 96 were eligible for inclusion. Most studies that compared ML and regression showed increased performance of ML, and most studies that compared performance with or without SDoH/related variables showed increased performance with them. The most frequently included SDoH variables were race/ethnicity, income, education and marital status. Studies were largely from North America, Europe and China, limiting the diversity of included populations and variance in social determinants.InterpretationFindings show that machine learning models, as well as SDoH and related variables, improve CVD prediction model performance. The limited variety of sources and data in studies emphasize that there is opportunity to include more SDoH variables, especially environmental ones, that are known CVD risk factors in machine learning CVD prediction models. Given their flexibility, ML may provide opportunity to incorporate and model the complex nature of social determinants. Such data should be recorded in electronic databases to enable their use.FundingWe acknowledge funding from Blue Cross Blue Shield of Louisiana. The funder had no role in the decision to publish.
Ventilation and the SARS-CoV-2 Coronavirus
Authors: Birnir Björn 
Published: September 2020

AbstractIn a previous paper [10] a model of the distribution of respiratory droplets and aerosols by Lagrangian turbulent air-flow was developed. It is used to show how the SARS-CoV-2 Coronavirus can be spread by the breathing of single infected person. The model shows that the concentration of viruses in the cloud, exhaled by one person, can increase to infectious levels within a certain amount of time, in a confined space where the air re-circulates. In [10] the model was used to analyze the air-flow and SARS-CoV-2 Coronavirus build-up in a restaurant in Guangzhou, China [19, 18]. In this paper, we add the analysis of two more cases, an outbreak among lay-Buddhists, on a bus [26], traveling to a ceremony in Zhejiang province, China, and an outbreak in a Call Center in Seoul, Korea [20]. The analysis and comparison of these three cases, leads to the conclusion that the SARS-CoV-2 Coronavirus attacks in two steps: The first step is a linear spread between individuals with a couple of days delay. The second step is an exponential spread effected by the air-conditioning system affecting a much larger number of people. Thus in the second step, the ventilation becomes the super-spreader.
Clinical effectiveness of drugs in hospitalized patients with COVID-19: a systematic review and meta-analysis
Authors: Zuñiga R.A. Abeldaño , Coca S.M. , Abeldaño G.F. , Villoria R.A.M. González 
Published: September 2020

SummaryObjectiveThe aim was to assess the clinical effectiveness of drugs used in hospitalized patients with COVID-19 infection.MethodWe conducted a systematic review of randomized clinical trials assessing treatment with remdesivir, chloroquine, hydroxychloroquine, lopinavir, ritonavir, dexamethasone, and convalescent plasma, for hospitalized patients with a diagnosis of SARS-CoV-2 infection. The outcomes were mortality, clinical improvement, duration of ventilation, duration of oxygen support, duration of hospitalization), virological clearance, and severe adverse events.ResultsA total of 48 studies were retrieved from the databases. Ten articles were finally included in the data extraction and qualitative synthesis of results. The meta-analysis suggests a benefit of dexamethasone versus standard care in the reduction of risk of mortality at day 28; and the clinical improvement at days 14 and 28 in patients treated with remdesivir.ConclusionsDexamethasone would have a better result in hospitalized patients, especially in low-resources settings.Significance of resultsThe analysis of the main treatments proposed for hospitalized patients is of vital importance to reduce mortality in low-income countries; since the COVID-19 pandemic had an economic impact worldwide with the loss of jobs and economic decline in countries with scarce resources.
Adapting Lot Quality Assurance Sampling to accommodate imperfect tests: application to COVID-19 serosurveillance in Haiti
Authors: Fulcher Isabel R. , Clisbee Mary , Lambert Wesler , Leandre Fernet Renand , Hedt-Gauthier Bethany 
Published: September 2020

AbstractBackgroundLot Quality Assurance Sampling (LQAS), a tool used for monitoring health indicators in low resource settings resulting in “high” or “low” classifications, assumes that determination of the trait of interest is perfect. This is often not true for diagnostic tests, with imperfect sensitivity and specificity. Here, we develop Lot Quality Assurance Sampling for Imperfect Tests (LQAS-IMP) to address this issue and apply it to a COVID-19 serosurveillance study in Haiti.DevelopmentAs part of the standard LQAS procedure, the user specifies allowable classification errors for the system, which is defined by a sample size and decision rule. We show that when an imperfect diagnostic test is used, the classification errors are larger than specified. We derive a modified procedure, LQAS-IMP, that accounts for the sensitivity and specificity of a diagnostic test to yield correct classification errors.ApplicationAt Zanmi Lasante health facilities in Haiti, the goal was to assess the prior circulation of COVID-19 among healthcare workers (HCWs) using a limited number of antibody tests. As the COVID-19 antibody tests were known to have imperfect diagnostic accuracy, we used the LQAS-IMP procedure to define valid systems for sampling at eleven hospitals in Haiti.ConclusionsThe LQAS-IMP procedure accounts for imperfect sensitivity and specificity in system design; if the accuracy of a test is known, the use of LQAS-IMP extends LQAS to applications for indicators that are based on laboratory tests, such as COVID-19 antibodies.Key MessagesIt is imperative to account for the sensitivity and specificity of the diagnostic test in serosurveillance studies.For prevalence estimation and inference, adjustments for imperfect testing properties are available to researchers. However, no adjustments currently exist for the design of Lot Quality Assurance Sampling systems, resulting in invalid classification systems when used with an imperfect diagnostic test.When sensitivity and specificity of a diagnostic test is known, Lot Quality Assurance Sampling for Imperfect Tests should be used to determine the sample size and decision rule for a system as it will result in valid classification errors.
Deciphering the evolution of the transcriptional and regulatory landscape in human placenta
Authors: Sun Ming-an , Wolf Gernot , Wang Yejun , Ralls Sherry , Senft Anna D. , Jin Jinpu , Dunn-Fletcher Caitlin E. , Muglia Louis J. , Macfarlan Todd S. 
Published: September 2020

AbstractIn mammals, the placenta mediates maternal-fetal nutrient and waste exchange and provides immunomodulatory actions that facilitate maternal-fetal tolerance. The placenta is highly diversified among mammalian species, yet the molecular mechanisms that distinguish the placenta of human from other mammals are not fully understood. Using an interspecies transcriptomic comparison of human, macaque, and mouse term placentae, we identified hundreds of genes with lineage-specific expression – including dozens that are placentally-enriched and potentially related to pregnancy. We further annotated the enhancers for different human tissues using epigenomic data and demonstrate that the placenta and chorion are unique in that their enhancers display the least conservation. We identified numerous lineage-specific human placental enhancers, and found they are highly overlapped with specific families of endogenous retroviruses (ERVs), including MER21A, MER4A/B and MER39B that were previously linked to immune response and placental function. Among these ERV families, we further demonstrate that MER41 insertions create dozens of lineage-specific Serum Response Factor (SRF) binding loci in human, including one adjacent toFBN2, a placenta-specific gene with increased expression in humans that produces the peptide hormone placensin to stimulate glucose secretion and trophoblast invasion. Our results demonstrate the prevalence of lineage-specific human placental enhancers which are frequently associated with ERV insertions and likely facilitated the lineage-specific evolution of the mammalian placenta.
Integration of Biodynamic Imaging and RNA-seq classifies chemotherapy response in canine diffuse large B-cell lymphoma
Authors: Fine Jonathan , Utturkar Sagar , Dhawan Deepika , Miguel Phillip San , Chopra Gaurav , Turek John , Nolte David , Childress Michael O. , Lanman Nadia A. 
Published: September 2020

AbstractDiffuse large B-cell lymphoma (DLBCL) is a common, aggressive cancer of notorious genotypic and phenotypic heterogeneity. A major challenge is predicting response to drug treatment, which has typically been done using genomic tools alone with little success. A novel method that incorporates phenotypic profiling for predicting the effectiveness of therapy for individual patients is desperately needed. BioDynamic Imaging (BDI) is a technique for measuring time-dependent fluctuations in back-scattered light through living tumor tissues to identify critical changes in intracellular dynamics that are associated with phenotypic response to drugs. In this study, BDI and RNA sequencing (RNA-seq) data were collected on tumor samples from dogs with naturally occurring DLBCL, an animal model of increasingly recognized relevance to the human disease. BDI and RNA-seq data were combined to identify correlations between gene co-expression modules and linear combinations of biomarkers to provide biological mechanistic interpretations of BDI biomarkers. Using regularized multivariate logistic regression, we combined RNA-seq and BDI data to develop a novel model to accurately classify the clinical response of canine DLBCL to combination chemotherapy (i.e. CHOP). Our model incorporates data on the expression of 4 genes and 3 BDI-derived phenotypic biomarkers, capturing changes in transcription, microtubule related processes, and apoptosis. This pilot study suggests that the combination of multi-scale transcriptomic and phenotypic data can identify patients that respond to a given treatmenta prioriin a disease that has been difficult to treat. Our work provides an important framework for future development of strategies and treatments in precision cancer medicine.
Free ammonia pretreatment for anaerobic sludge digestion reduces the spread of antibiotic resistance
Authors: Zhang Zehao , Liu Huan , Wang Qilin 
Published: September 2020

AbstractSludge from the wastewater treatment plants (WWTPs) has been recognized as a reservoir of antibiotic resistance genes (ARGs). Free ammonia (FA, i.e. NH3-N) pretreatment has been demonstrated to be able to enhance anaerobic digestion, which is a widely used method for sludge treatment. However, the effect of combined FA pretreatment and anaerobic digestion on the fate of ARGs is still unknown. This study demonstrated for the first time that combined FA pretreatment (420 mg NH3-N/L for 24 h) and anaerobic digestion could reduce the abundances of the tested ARGs by 0.06 log10gene copies/g TS (total solids) compared with the anaerobic digestion alone. Specifically, the experimental results showed that combined FA pretreatment and anaerobic digestion reduced the abundances ofaac(6’)-Ib-cr, blaTEM, sul2, tetA,tetBandtetXby 0.07, 0.37, 0.09, 0.32, 0.24 and 0.59 log10gene copies/g TS compared with anaerobic digestion alone. In contrast, combined FA pretreatment and anaerobic digestion slightly increased the abundance oftetGby 0.05 log10gene copies/g TS compared with anaerobic digestion alone. In addition, FA pretreatment did not significantly affect the abundance ofsul1 and tetMduring anaerobic digestion. This study revealed that FA pretreatment for anaerobic digestion could potentially reduce the spread of antibiotic resistance from the sludge to soil (while agriculture reuse is used as the sludge disposal method), thereby protecting the environment and human health.
CUB and Sushi Multiple Domains 1 (CSMD1) opposes the complement cascade in neural tissues
Authors: Baum Matthew L. , Wilton Daniel K. , Muthukumar Allie , Fox Rachel G. , Carey Alanna , Crotty William , Scott-Hewitt Nicole , Bien Elizabeth , Sabatini David A. , Lanser Toby , Frouin Arnaud , Gergits Frederick , Håvik Bjarte , Gialeli Chrysostomi , Nacu Eugene , Blom Anna M. , Eggan Kevin , Johnson Matthew B. , McCarroll Steven A. , Stevens Beth 
Published: September 2020

AbstractSchizophrenia risk is associated with increased gene copy number and brain expression ofcomplement component 4(C4). Because the complement system facilitates synaptic pruning, theC4association has renewed interest in a hypothesis that excessive pruning contributes to schizophrenia pathogenesis. However, little is known about complement regulation in neural tissues or whether such regulation could be relevant to psychiatric illness. Intriguingly, common variation withinCSMD1, which encodes a putative complement inhibitor, has consistently associated with schizophrenia at genome-wide significance. We found that Csmd1 is predominantly expressed in the brain by neurons, and is enriched at synapses; that human stem cell-derived neurons lacking CSMD1 are more vulnerable to complement deposition; and that mice lacking Csmd1 have increased brain complement activity, fewer synapses, aberrant complement-dependent development of a neural circuit, and synaptic elements that are preferentially engulfed by cultured microglia. These data suggest that CSMD1 opposes the complement cascade in neural tissues.Graphic Abstract.Our findings support a model in which CSMD1 opposes actions of the complement cascade in neural tissues (top left). We investigated two models in which Csmd1 was genetically ablated: human cortical neurons derived from embryonic stem cells, and a back-crossed C57bl6-Tac mouse line (top right). Csmd1 is normally expressed by neurons and present at synapses where it can protect them from complement (bottom left); in the absence of Csmd1 (bottom right), we find more deposition of complement (on cultured human cortical neurons and in the mouse visual system), reduced numbers of synapses (in the mouse visual system), and synaptic fractions that are more readily engulfed by microglia (ex vivo). Created withBioRender.com.
An autism-associated calcium channel variant causes defects in neuronal polarity and axon termination in the ALM neuron ofC. elegans
Authors: Buddell Tyler , Quinn Christopher C. 
Published: September 2020

AbstractVariants of theCACNA1C voltage-gated calcium channelgene have been associated with autism and other neurodevelopmental disorders including bipolar disorder, schizophrenia, and ADHD. The Timothy syndrome mutation is a rarede novogain-of-function variant inCACNA1Cthat causes autism with high penetrance, providing a powerful avenue into investigating the role ofCACNA1Cvariants in neurodevelopmental disorders. In our previous work, we demonstrated that anegl-19(gof)mutation, that is equivalent to the Timothy syndrome mutation in the human homologCACNA1C,can disrupt termination of the PLM axon inC. elegans. Here, we find that theegl-19(gof)mutation disrupts the polarity of process outgrowth in the ALM neuron ofC. elegans. We also find that theegl-19(gof)mutation can disrupt termination of the ALM axon. These results suggest that the Timothy syndrome mutation can disrupt multiple steps of axon development. Further work exploring the molecular mechanisms that underlie these perturbations in neuronal polarity and axon termination will give us better understanding to how variants inCACNA1Ccontribute to the axonal defects that underlie autism.
Telomere-to-telomere assembly of the genome of an individualOikopleura dioicafrom Okinawa using Nanopore-based sequencing
Authors: Bliznina Aleksandra , Masunaga Aki , Mansfield Michael J. , Tan Yongkai , Liu Andrew W. , West Charlotte , Rustagi Tanmay , Chien Hsiao-Chiao , Kumar Saurabh , Pichon Julien , Plessy Charles , Luscombe Nicholas M. 
Published: September 2020

AbstractBackgroundThe larvaceanOikopleura dioicais an abundant tunicate plankton with the smallest (65-70 Mbp) non-parasitic, non-extremophile animal genome identified to date. Currently, there are two genomes available for the Bergen (OdB3) and Osaka (OSKA2016)O. dioicalaboratory strains. Both assemblies have full genome coverage and high sequence accuracy. However, a chromosome-scale assembly has not yet been achieved.ResultsHere, we present a chromosome-scale genome assembly (OKI2018_I69) of the OkinawanO. dioicaproduced using long-read Nanopore and short-read Illumina sequencing data from a single male, combined with Hi-C chromosomal conformation capture data for scaffolding. The OKI2018_I69 assembly has a total length of 64.3 Mbp distributed among 19 scaffolds. 99% of the assembly is in five megabase-scale scaffolds. We found telomeres on both ends of the two largest scaffolds, which represent assemblies of two fully contiguous autosomal chromosomes. Each of the other three large scaffolds have telomeres at one end only and we propose that they correspond to sex chromosomes split into a pseudo-autosomal region and X-specific or Y-specific regions. Indeed, these five scaffolds mostly correspond to equivalent linkage groups of OdB3, suggesting overall agreement in chromosomal organization between the two populations. At a more detailed level, the OKI2018_I69 assembly possesses similar genomic features in gene content and repetitive elements reported for OdB3. The Hi-C map suggests few reciprocal interactions between chromosome arms. At the sequence level, multiple genomic features such as GC content and repetitive elements are distributed differently along the short and long arms of the same chromosome.ConclusionsWe show that a hybrid approach of integrating multiple sequencing technologies with chromosome conformation information results in an accuratede novochromosome-scale assembly ofO. dioica’s highly polymorphic genome. This assembly will be a useful resource for genome-wide comparative studies betweenO. dioicaand other species, as well as studies of chromosomal evolution in this lineage.
Distinct mechanisms for TMPRSS2 expression explain organ-specific inhibition of SARS-CoV-2 infection by enzalutamide
Authors: Li Fei , Han Ming , Dai Pengfei , Xu Wei , He Juan , Tao Xiaoting , Wu Yang , Tong Xinyuan , Xia Xinyi , Guo Wangxin , Zhou Yunjiao , Li Yunguang , Zhu Yiqin , Zhang Xiaoyu , Liu Zhuang , Aji Rebiguli , Cai Xia , Li Yutang , Qu Di , Chen Yu , Jiang Shibo , Wang Qiao , Ji Hongbin , Xie Youhua , Sun Yihua , Lu Lu , Gao Dong 
Published: September 2020

AbstractThe coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly become a global public health threat due to the lack of effective drugs or vaccines against SARS-CoV-2. The efficacy of several repurposed drugs has been evaluated in clinical trials. Among these drugs, a relatively new antiandrogen agent, enzalutamide, was proposed because it reduces the expression of transmembrane serine protease 2 (TMPRSS2), a key component mediating SARS-CoV-2-driven entry into host cells, in prostate cancer cells. However, definitive evidence for the therapeutic efficacy of enzalutamide in COVID-19 is lacking. Here, we evaluated the antiviral efficacy of enzalutamide in prostate cancer cells, lung cancer cells, human lung organoids and SARS-CoV-2-infected Ad-ACE2-transduced Tmprss2 knockout (Tmprss2-KO) and wild-type (WT) mice. TMPRSS2 knockout significantly inhibited SARS-CoV-2 infectionin vivo. Enzalutamide effectively inhibited SARS-CoV-2 infection in human prostate cancer cells (LNCaP) but not in human lung cancer cells or patient-derived lung organoids. Although Tmprss2 knockout effectively blocked SARS-CoV-2 infection in ACE2-transduced mice, enzalutamide showed no antiviral activity due to the AR independence of TMPRSS2 expression in mouse and human lung epithelial cells. Moreover, we observed distinct AR binding patterns between prostate cells and lung cells and a lack of direct binding of AR to TMPRSS2 in human lung cells. Thus, our findings do not support the postulated protective role of enzalutamide in treating COVID-19.