bluuebunny/crossref_metadata_2025_split huggingface dataset:
Beyond the new normal: assessing the feasibility of vaccine-based elimination of SARS-CoV-2
Authors: Stoddard Madison , Sarkar Sharanya , Nolan Ryan P. , White Douglas E. , White Laura F. , Hochberg Natasha S. , Chakravarty Arijit 
Published: January 2021

AbstractAs the COVID-19 pandemic drags into its second year, there is hope on the horizon, in the form of SARS-CoV-2 vaccines which promise disease elimination and a return to pre-pandemic normalcy. In this study we critically examine the basis for that hope, using an epidemiological modeling framework to establish the link between vaccine characteristics and effectiveness in bringing an end to this unprecedented public health crisis. Our findings suggest that vaccines that do not prevent infection will allow extensive endemic SARS-CoV-2 spread upon a return to pre-pandemic social and economic conditions. Vaccines that only reduce symptomatic COVID-19 or mortality will fail to mitigate serious COVID-19 mortality risks, particularly in the over-65 population, likely resulting in hundreds of thousands of US deaths on a yearly basis. Our modeling points to the possibility of complete SARS-CoV-2 elimination with high population-level compliance and a vaccine that is highly effective at reducing SARS-CoV-2 infection. Notably, vaccine-mediated reduction of transmission is critical for elimination, and in order for partially-effective vaccines to play a positive role in SARS-CoV-2 elimination, other stackable (complementary) interventions must be deployed simultaneously.
Multinational Prevalence of Neurological Phenotypes in Patients Hospitalized with COVID-19
Authors: Le Trang T. , Gutiérrez-Sacristán Alba , Son Jiyeon , Hong Chuan , South Andrew M. , Beaulieu-Jones Brett K. , Loh Ne Hooi Will , Luo Yuan , Morris Michele , Ngiam Kee Yuan , Patel Lav P. , Samayamuthu Malarkodi J. , Schriver Emily , Tan Amelia LM , Moore Jason , Cai Tianxi , Omenn Gilbert S. , Avillach Paul , Kohane Isaac S. ,   4CE Consortium, Visweswaran Shyam , Mowery Danielle L. , Xia Zongqi 
Published: January 2021

AbstractOBJECTIVENeurological complications can worsen outcomes in COVID-19. We defined the prevalence of a wide range of neurological conditions among patients hospitalized with COVID-19 in geographically diverse multinational populations.METHODSUsing electronic health record (EHR) data from 348 participating hospitals across 6 countries and 3 continents between January and September 2020, we performed a cross-sectional study of hospitalized adult and pediatric patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test, both with and without severe COVID-19. We assessed the frequency of each disease category and 3-character International Classification of Disease (ICD) code of neurological diseases by countries, sites, time before and after admission for COVID-19, and COVID-19 severity.RESULTSAmong the 35,177 hospitalized patients with SARS-CoV-2 infection, there was increased prevalence of disorders of consciousness (5.8%, 95% confidence interval [CI]: 3.7%-7.8%,pFDR<.001) and unspecified disorders of the brain (8.1%, 95%CI: 5.7%-10.5%,pFDR<.001), compared to pre-admission prevalence. During hospitalization, patients who experienced severe COVID-19 status had 22% (95%CI: 19%-25%) increase in the relative risk (RR) of disorders of consciousness, 24% (95%CI: 13%-35%) increase in other cerebrovascular diseases, 34% (95%CI: 20%-50%) increase in nontraumatic intracranial hemorrhage, 37% (95%CI: 17%-60%) increase in encephalitis and/or myelitis, and 72% (95%CI: 67%-77%) increase in myopathy compared to those who never experienced severe disease.INTERPRETATIONUsing an international network and common EHR data elements, we highlight an increase in the prevalence of central and peripheral neurological phenotypes in patients hospitalized with SARS-CoV-2 infection, particularly among those with severe disease.
Unusual trend of respiratory syncytial virus burden of disease observed in primary care diagnosis of children under 5 years old in Catalonia during the COVID-19 pandemic
Authors: Coma Ermengol , Vila Jorgina , Méndez-Boo Leonardo , Antón Andrés , Mora Núria , Fina Francesc , Fàbregas Mireia , Medina Manuel 
Published: January 2021

AbstractWe observed an unusual pattern of respiratory syncytial virus (RSV) in children under 5 years in Catalonia (Spain). We observed a nearly absence during winter months and a subsequent surge late spring. Primary care electronic health records combined with hospital RSV laboratory confirmations could be a useful surveillance system to monitorize trends of respiratory pathogens.
Structuring clinical text with AI: old vs. new natural language processing techniques evaluated on eight common cardiovascular diseases
Authors: Zhan Xianghao , Humbert-Droz Marie , Mukherjee Pritam , Gevaert Olivier 
Published: January 2021

AbstractMining the structured data in electronic health records(EHRs) enables many clinical applications while the information in free-text clinical notes often remains untapped. Free-text notes are unstructured data harder to use in machine learning while structured diagnostic codes can be missing or even erroneous. To improve the quality of diagnostic codes, this work extracts structured diagnostic codes from the unstructured notes concerning cardiovascular diseases. Five old and new word embeddings were used to vectorize over 5 million progress notes from Stanford EHR and logistic regression was used to predict eight ICD-10 codes of common cardiovascular diseases. The models were interpreted by the important words in predictions and analyses of false positive cases. Trained on Stanford notes, the model transferability was tested in the prediction of corresponding ICD-9 codes of the MIMIC-III discharge summaries. The word embeddings and logistic regression showed good performance in the diagnostic code extraction with TF-IDF as the best word embedding model showing AU-ROC ranging from 0.9499 to 0.9915 and AUPRC ranging from 0.2956 to 0.8072. The models also showed transferability when tested on MIMIC-III data set with AUROC ranging from 0.7952 to 0.9790 and AUPRC ranging from 0.2353 to 0.8084. Model interpretability was showed by the important words with clinical meanings matching each disease. This study shows the feasibility to accurately extract structured diagnostic codes, impute missing codes and correct erroneous codes from free-text clinical notes with interpretable models for clinicians, which helps improve the data quality of diagnostic codes for information retrieval and downstream machine-learning applications.
A prediction model for COVID-19 prevalence based on demographic and healthcare parameters in Iran
Authors: Safavi Parimah Emadi , Rahimian Karim , Doustmohammadi Alireza , dastjerdei Mahla Safari , Rasouli Ahmadreza , Zahiri Javad 
Published: January 2021

AbstractCoronavirus Disease 2019 (COVID-19) pandemic has become the greatest threat to global health in only a matter of months. Iran struggling with COVID-19 coincidence with Nowruz vacations has led to horrendous consequences for both people and the public health workforce. Modeling approaches have been proved to be highly advantageous in taking appropriate actions in the early stages of the pandemic. To this date, no study has been conducted to model the disease to investigate the disease, especially after travel restrictions in Iran. In this study, we exploited the opportunities that Artificial neural networks offer to investigate contributing factors of early-stage coronavirus spread via generating a model to predict daily confirmed cases in Iran. We collected publicly available data of confirmed cases in 24 provinces from April 4, 2020, to May 2, 2020, with a list of explanatory factors. The factors were checked separately for any linear associations and to train and validate a multilayer perceptron network. The accuracy of the models was evaluated, the R2 scores were 0.842 for population distribution, 0.822 for health index, and 0.864 for the population in the provinces. Our results suggest the significant impact of the mentioned factors on disease spread in the time of travel restrictions when the vacation ended. Accordingly, this information can be implicated in assessing the risk of epidemics and future policy makings in this area.
The therapeutic effect of intra-articular facet joint injection with normal saline as a comparator for chronic low back pain: a systematic review and meta-analysis
Authors: Nopsopon Tanawin , Pongpirul Krit , Rittiphairoj Thanitsara , Lertparinyaphorn Irin , Suputtitada Areerat 
Published: January 2021

ABSTRACTBackgroundIntra-articular facet joint injection (FJI) has been increasingly used as a treatment for chronic low back pain (LBP). Choice of the substance has been based on clinical experience with unclear evidence on marginal effectiveness of active substance over normal saline as a placebo control. This systematic review investigates the comparative effectiveness between normal saline and active substances on patient-reported outcomes (PROs).MethodsSystematic search was conducted in five databases: PubMed, Embase, Scopus, Web of Science, and CENTRAL for randomized controlled trials and observational studies of evaluating the PROs of FJI comparing active injected substances with normal saline as placebo in chronic LBP patients in the English language without publication date restriction. Quality assessment was performed using ROB2 and ROBINS-I. The meta-analysis was done using a random-effects model. Mean difference with 95% CIs of efficacy outcomes including pain, numbness, disability, quality of life were measured.ResultsOf 2,467 potential studies, three were included in the systematic review and meta-analysis (247 patients). Compared to other active substances, normal saline provided similar therapeutic effects on pain outcome within one hour (MD 2.43, 95% CI –11.61 to 16.50), at 1–1.5 months follow up (MD –0.63, 95% CI –7.97 to 6.72), and at 3 to 6 months (MD 1.90, 95% CI –16.03 to 19.83) as well as the quality of life at one and six months follow-up.ConclusionsThe short-term and long-term clinical improvements of intra-articular FJI using normal saline are comparable to the other active substances in LBP patients.PROSPEROregistration number CRD42020216426
More than 50 Long-term effects of COVID-19: a systematic review and meta-analysis
Authors: Lopez-Leon Sandra , Wegman-Ostrosky Talia , Perelman Carol , Sepulveda Rosalinda , Rebolledo Paulina A , Cuapio Angelica , Villapol Sonia 
Published: January 2021

ABSTRACTCOVID-19, caused by SARS-CoV-2, can involve sequelae and other medical complications that last weeks to months after initial recovery, which has come to be called Long-COVID or COVID long-haulers. This systematic review and meta-analysis aims to identify studies assessing long-term effects of COVID-19 and estimates the prevalence of each symptom, sign, or laboratory parameter of patients at a post-COVID-19 stage. LitCOVID (PubMed and Medline) and Embase were searched by two independent researchers. All articles with original data for detecting long-term COVID-19 published before 1st of January 2021 and with a minimum of 100 patients were included. For effects reported in two or more studies, meta-analyses using a random-effects model were performed using the MetaXL software to estimate the pooled prevalence with 95% CI. Heterogeneity was assessed using I2 statistics. This systematic review followed Preferred Reporting Items for Systematic Reviewers and Meta-analysis (PRISMA) guidelines, although the study protocol was not registered. A total of 18,251 publications were identified, of which 15 met the inclusion criteria. The prevalence of 55 long-term effects was estimated, 21 meta-analyses were performed, and 47,910 patients were included. The follow-up time ranged from 14 to 110 days post-viral infection. The age of the study participants ranged between 17 and 87 years. It was estimated that 80% (95% CI 65-92) of the patients that were infected with SARS-CoV-2 developed one or more long-term symptoms. The five most common symptoms were fatigue (58%), headache (44%), attention disorder (27%), hair loss (25%), and dyspnea (24%). All meta-analyses showed medium (n=2) to high heterogeneity (n=13). In order to have a better understanding, future studies need to stratify by sex, age, previous comorbidities, severity of COVID-19 (ranging from asymptomatic to severe), and duration of each symptom. From the clinical perspective, multi-disciplinary teams are crucial to developing preventive measures, rehabilitation techniques, and clinical management strategies with whole-patient perspectives designed to address long COVID-19 care.
Evaluation of COVID-19 vaccination strategies with a delayed second dose
Authors: Moghadas Seyed M. , Vilches Thomas N. , Zhang Kevin , Nourbakhsh Shokoofeh , Sah Pratha , Fitzpatrick Meagan C. , Galvani Alison P. 
Published: January 2021

AbstractTwo of the COVID-19 vaccines currently approved in the United States require two doses, administered three to four weeks apart. Constraints in vaccine supply and distribution capacity, together with a deadly wave of COVID-19 from November 2020 to January 2021 and the emergence of highly contagious SARS-CoV-2 variants, sparked a policy debate on whether to vaccinate more individuals with the first dose of available vaccines and delay the second dose, or to continue with the recommended two-dose series as tested in clinical trials. We developed an agent-based model of COVID-19 transmission to compare the impact of these two vaccination strategies, while varying the temporal waning of vaccine efficacy following the first dose and the level of pre-existing immunity in the population. Our results show that for Moderna vaccines, a delay of at least 9 weeks could maximize vaccination program effectiveness and avert at least an additional 17.3 (95% CrI: 7.8 − 29.7) infections, 0.71 (95% CrI: 0.52 - 0.97) hospitalizations, and 0.34 (95% CrI: 0.25 - 0.44) deaths per 10,000 population compared to the recommended 4-week interval between the two doses. Pfizer-BioNTech vaccines also averted an additional 0.61 (95% CrI: 0.37 - 0.89) hospitalizations and 0.31 (95% CrI: 0.23 - 0.45) deaths per 10,000 population in a 9-week delayed second dose strategy compared to the 3-week recommended schedule between doses. However, there was no clear advantage of delaying the second dose with Pfizer-BioNTech vaccines in reducing infections, unless the efficacy of the first dose did not wane over time. Our findings underscore the importance of quantifying the characteristics and durability of vaccine-induced protection after the first dose in order to determine the optimal time interval between the two doses.
COVID-19 mortality: positive correlation with cloudiness but no correlation with sunlight and latitude in Europe
Authors: Omer S. , Iftime A. , Burcea V. 
Published: January 2021

AbstractWe systematically investigated an ongoing debate about the possible correlation between SARS-CoV-2 (COVID-19) epidemiological outcomes and solar exposure in European countries, in the period of March – December 2020. For each country, we correlated its mortality data with: i) objective sky cloudiness (as cloud fraction) derived from satellite weather data, ii) solar insolation (watt/square metre at ground level), iii) latitude. We found a positive correlation between the monthly mortality rate and the overall cloudiness in that month (Pearson’s r(35)=.68, P<.001; averaged linear model fitting the data, adjusted R2 =0.45). In an additional month-by-month analysis, 17% to 59% of the variance in COVID-19 mortality/million appears to be predicted by the cloudiness fraction of the sky, except in the last two months of 2020. We did not find any statistical significant correlation with insolation, nor with latitude of the countries, when the “latitude of a country” was precisely defined as the average landmass location (country centroid). The unexpected correlation found between cloudiness and mortality could perhaps be explained by the following: 1) heavy cloudiness is linked with colder outdoor surfaces, which might aid virus survival; 2) reduced evaporation rate; 3) moderate pollution may be linked to both cloudiness and mortality; and 4) large-scale behavioural changes due to cloudiness (which perhaps drives people to spend more time indoors and thus facilitates indoor contamination).
VeloViz: RNA-velocity informed embeddings for visualizing cellular trajectories
Authors: Atta Lyla , Sahoo Arpan , Fan Jean 
Published: January 2021

0AbstractSingle cell transcriptomic technologies enable genome-wide gene expression measurements in individual cells but can only provide a static snapshot of cell states. RNA velocity analysis can infer cell state changes from single cell transcriptomics data. To interpret these cell state changes as part of underlying cellular trajectories, current approaches rely on visualization with principal components, t-distributed stochastic neighbor embedding, and other 2D embeddings derived from the observed single cell transcriptional states. However, these 2D embeddings can yield different representations of the underlying cellular trajectories, hindering the interpretation of cell state changes. We developed VeloViz to create RNA-velocity-informed 2D and 3D embeddings from single cell transcriptomics data. Using both real and simulated data, we demonstrate that VeloViz embeddings are able to consistently capture underlying cellular trajectories across diverse trajectory topologies, even when intermediate cell states may be missing. By taking into consideration the predicted future transcriptional states from RNA velocity analysis, VeloViz can help visualize a more reliable representation of underlying cellular trajectories. VeloViz is available as an R package on GitHub (https://github.com/JEFworks-Lab/veloviz) with additional tutorials athttps://JEF.works/veloviz/.
Comparative metabolomics of fruits and leaves in a hyperdiverse lineage suggests fruits are a key incubator of phytochemical diversification
Authors: Schneider Gerald F. , Salazar Diego , Hildreth Sherry B. , Helm Richard F. , Whitehead Susan R. 
Published: January 2021

AbstractInteractions between plants and leaf herbivores have long been implicated as the major driver of plant secondary metabolite diversity. However, other plant-animal interactions, such as those between fruits and frugivores, may also be involved in phytochemical diversification. Using 12 species ofPiper, we conducted untargeted metabolomics and molecular networking with extracts of fruits and leaves. We evaluated organ-specific secondary metabolite composition and compared multiple dimensions of phytochemical diversity across organs, including richness, structural complexity, and variability across samples at multiple scales within and across species. Plant organ identity significantly influenced secondary metabolite composition, both independent of and in interaction with species identity. Leaves and fruit shared a majority of compounds, but fruits contained more unique compounds and had higher total estimated chemical richness. While organ-level chemical richness and structural complexity varied substantially across species, fruit diversity exceeded leaf diversity in more species than the reverse. Furthermore, the variance in chemical composition across samples was higher for fruits than leaves. By documenting a broad pattern of high phytochemical diversity in fruits relative to leaves, this study lays groundwork for incorporating fruit into a comprehensive and integrative understanding of the ecological and evolutionary factors shaping secondary metabolite composition at the whole-plant level.
Altered cleavage of Caspase-1 in hepatocytes limits control of malaria in the liver
Authors: Marques-da-Silva Camila , Poudel Barun , Baptista Rodrigo P. , Peissig Kristen , Hancox Lisa S. , Shiau Justine C. , Pewe Lecia L. , Shears Melanie J. , Kanneganti Thirumala-Devi , Sinnis Photini , Kyle Dennis E. , Gurung Prajwal , Harty John T. , Kurup Samarchith P. 
Published: January 2021

AbstractMalaria, caused byPlasmodiumparasites, is a devastating disease that kills over half a million people each year1.Plasmodiumsporozoites inoculated by mosquitoes into mammalian hosts undergo a clinically silent phase of obligatory development and replication in hepatocytes before initiating the life-threatening blood-stage of malaria2. Thus, understanding the immune responses elicited byPlasmodiuminfection in the liver is key to controlling clinical malaria and transmission3,4. Here, we show thatPlasmodiumDNA can be detected by AIM2 (absent in melanoma 2) sensors in the infected hepatocytes, resulting in Caspase-1 activation and pyroptotic cell-death. However, Caspase-1 was observed to undergo only partial cleavage in hepatocytes, limiting pyroptosis, and the maturation of pro-inflammatory cytokines classically associated with Caspase-1 activation. We discovered that the extent of Caspase-1 cleavage in cells is determined by the expression of ASC (apoptosis-associated speck-like protein containing a CARD). ASC expression is inherently low in hepatocytes, and transgenically enhancing it in the hepatocytes induced complete processing of Caspase-1, efficient secretion of pro-inflammatory cytokines, enhanced pyroptotic cell-death, and markedly improved control of malaria infection in the liver. In addition to describing a novel pathway of natural immunity to malaria, our findings uncover a key aspect of liver biology that may have been exploited during evolution by successful hepatotropic pathogens.
Lesion Shedding Model: unraveling site-specific contributions to ctDNA
Authors: Rhrissorrakrai Kahn , Utro Filippo , Levovitz Chaya , Parida Laxmi 
Published: January 2021

AbstractSampling circulating tumor DNA (ctDNA) using liquid biopsies offers clinically important benefits for monitoring cancer progression. A single ctDNA sample represents a mixture of shed tumor DNA from all known and unknown lesions within a patient. Although shedding levels have been suggested to hold the key to identifying targetable lesions and uncovering treatment resistance mechanisms, the amount of DNA shed by any one specific lesion is still not well characterized. We designed the Lesion Shedding Model (LSM) to order lesions from the strongest to the poorest shedding for a given patient. By characterizing the lesion-specific ctDNA shedding levels, we can better understand the mechanisms of shedding and more accurately interpret ctDNA assays to improve their clinical impact. We verified the accuracy of the LSM under controlled conditions using a simulation approach as well as testing the model on three cancer patients. The LSM obtained an accurate partial order of the lesions according to their assigned shedding levels in simulations and its accuracy in identifying the top shedding lesion was not impacted by number of lesions. Applying LSM to three cancer patients, we found that indeed there were lesions that consistently shed more than others into the patients’ blood. In two of the patients, the top shedding lesion was one of the only clinically progressing lesions at the time of biopsy suggesting a connection between high ctDNA shedding and clinical progression. The LSM provides a much needed framework with which to understand ctDNA shedding and interpret ctDNA assays.AvailabilityBinary is available athttps://github.com/ComputationalGenomics/LSM