bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis and Prediction of COVID-19 Patients’ False Negative Results for SARS-CoV-2 Detection with Pharyngeal Swab Specimen: A Retrospective Study
Authors: Xu Hui , Yan Li , Qiu Chun (Martin) , Jiao Bo , Chen Yanyan , Tan Xi , Chen Zhuo , Ai Ling , Xiao Yaru , Luo Ailin , Li Shusheng 
Published: March 2020

ABSTRACTBackgroundFalse negative results of SARS-CoV-2 nucleic acid detection pose threats to COVID-19 patients and medical workers alike.ObjectiveTo develop multivariate models to determine clinical characteristics that contribute to false negative results of SARS-CoV-2 nucleic acid detection, and use them to predict false negative results as well as time windows for testing positive.DesignRetrospective Cohort Study (Ethics number of Tongji Hospital: No. IRBID: TJ-20200320)SettingA database of outpatients in Tongji Hospital (University Hospital) from 15 January 2020 to 19 February 2020.Patients1,324 outpatients with COVID-19MeasurementsClinical information on CT imaging reports, blood routine tests, and clinic symptoms were collected. A multivariate logistic regression was used to explain and predict false negative testing results of SARS-CoV-2 detection. A multivariate accelerated failure model was used to analyze and predict delayed time windows for testing positive.ResultsOf the 1,324 outpatients who diagnosed of COVID-19, 633 patients tested positive in their first SARS-CoV-2 nucleic acid test (47.8%), with a mean age of 51 years (SD=14.9); the rest, which had a mean age of 47 years (SD=15.4), tested negative in the first test. “Ground glass opacity” in a CT imaging report was associated with a lower chance of false negatives (aOR, 0.56), and reduced the length of time window for testing positive by 26%. “Consolidation” was associated with a higher chance of false negatives (aOR, 1.57), and extended the length of time window for testing positive by 44%. In blood routine tests, basophils (aOR, 1.28) and eosinophils (aOR, 1.29) were associated with a higher chance of false negatives, and were found to extend the time window for testing positive by 23% and 41%, respectively. Age and gender also affected the significantly.LimitationData were generated in a large single-center study.ConclusionTesting outcome and positive window of SARS-CoV-2 detection for COVID-19 patients were associated with CT imaging results, blood routine tests, and clinical symptoms. Taking into account relevant information in CT imaging reports, blood routine tests, and clinical symptoms helped reduce a false negative testing outcome. The predictive AFT model, what we believe to be one of the first statistical models for predicting time window of SARS-CoV-2 detection, could help clinicians improve the accuracy and efficiency of the diagnosis, and hence, optimizes the timing of nucleic acid detection and alleviates the shortage of nucleic acid detection kits around the world.Primary Funding SourceNone.
Risks of use and non-use of antibiotics in primary care. Qualitative study of prescribers’ views
Authors: Boiko Olga , Burgess Caroline , Fox Robin , Ashworth Mark , Gulliford Martin C 
Published: March 2020

ABSTRACTPurposeThe emergence of antimicrobial resistance has led to increasing efforts to reduce unnecessary use of antibiotics in primary care, but potential hazards from bacterial infection continue to cause concern. This study investigated how primary care prescribers evaluate the risks of reduced antibiotic prescribing.MethodsQualitative study using semi-structured interviews conducted with primary care prescribers from 10 general practices in an urban area and a shire town in England. A thematic analysis was conducted.ResultsThirty participants were recruited, including 23 general practitioners, 5 nurses and 2 pharmacists. Three main themes were identified: risk assessment; balancing treatment risks; and negotiating decisions and risks. Respondents indicated that their decisions were grounded in clinical risk assessment, but this was informed by different approaches to antibiotic use, with most leaning towards reduced prescribing. Prescribers’ perceptions of risk included the consequences of both inappropriate prescribing and inappropriate withholding of antibiotics. Sepsis was viewed as the most concerning potential outcome of non-prescribing, leading to possible patient harm and potential litigation. Risks of antibiotic prescribing included antibiotic resistant and C. difficile infections, as well as side effects, such as rashes, that might lead to possible mislabelling as antibiotic allergy. Prescribers elicited patient preferences for use or avoidance of antibiotics to inform management strategies, which included educational advice, advice on self-management including warning signs, use of delayed prescriptions, and safety netting.ConclusionsAttitudes towards antibiotic prescribing are evolving, with reduced antibiotic prescribing now being approached more systematically. The safety trade-offs associated with either use or non-use of antibiotics present difficulties especially when prescribing decisions are inconsistent with patients’ expectations.Strengths and limitations of this studyThe study provides an investigation of primary care prescribers’ perceptions, emphasising safety perspectives in the context of antimicrobial stewardship.The main themes identified may inform the basis for future improvement and antimicrobial stewardship programs.The study is based on interviews with prescribers and may be susceptible to the limitations associated with qualitative interview studiesThe diverse sample of participants provide a good spread of opinions that are of a high validity and rigorously analysed.The study may lack generalisability beyond high-income countries.
Trend Analysis and Forecasting of COVID-19 outbreak in India
Authors: Gupta Rajan , Pal Saibal K 
Published: March 2020

AbstractCOVID-19 is spreading really fast around the world. The current study describes the situation of the outbreak of this disease in India and predicts the number of cases expected to rise in India. The study also discusses the regional analysis of Indian states and presents the preparedness level of India in combating this outbreak. The study uses exploratory data analysis to report the current situation and uses time-series forecasting methods to predict the future trends. The data has been considered from the repository of John Hopkins University and covers up the time period from 30th January 2020 when the first case occurred in India till the end of 24th March 2020 when the Prime Minister of India declared a complete lockdown in the country for 21 days starting 25th March 2020. The major findings show that number of infected cases in India is rising quickly with the average infected cases per day rising from 10 to 73 from the first case to the 300th case. The current mortality rate for India stands around 1.9. Kerala and Maharashtra are the top two infected states in India with more than 100 infected cases reported in each state, respectively. A total of 25 states have reported at least one infected case, however only 8 of them have reported deaths due to COVID-19. The ARIMA model prediction shows that the infected cases in India may reach up to 700 thousands in next 30 days in worst case scenario while most optimistic scenario may restrict the numbers up to 1000-1200. Also, the average forecast by ARIMA model in next 30 days is around 7000 patients from the current numbers of 536. Based on the forecasting model by Holt’s linear trends, an expected 3 million people may get infected if control measures are not taken in the near future. This study will be useful for the key stakeholders like Government Officials and Medical Practitioners in assessing the trends for India and preparing a combat plan with stringent measures. Also, this study will be helpful for data scientists, statisticians, mathematicians and analytics professionals in predicting outbreak numbers with better accuracy.
Extreme Climate Related Disasters: two-time points evaluation of the impact in children and youth mental health
Authors: de Sousa Magalhães Sabrina , Malloy-Diniz Leandro Fernandes , Valadão Rosa Daniela , Alvim-Soares Antônio , de Miranda Débora Marques , Aurélio Romano-Silva Marco 
Published: March 2020

AbstractWorldwide, floods and drought are the most frequent extreme climate-related disasters with a potential that might affect children and adolescent mental health. This study aimed to describe mental health impact on youth exposed to flood or drought (time 1), to provide a prospective follow up of symptoms (time 2), about 15 months apart, and to compare the child response with control groups based on ranges of socioeconomic status. Posttraumatic stress symptoms (PTSS) and general behavior problems were evaluated. Sociodemographic data from 275 children and adolescents (6 to 18 years old) were described and analyzed and they were gathered in four groups Control group with higher socioeconomic status, Control group with lower socioeconomic status, Flood group, and Drought group. At time point 1, children from all groups did not substantially differ in general behavioral problems, but PTSS scores significantly differentiate the groups. At time point 2, the Flood group presented a pattern of recovery about PTSS. For the Drought group, a persistence and stability pattern of PTSS was verified. Post-disaster longitudinal studies are essential to elucidate how psychological distress progress over time and to understand the relationship between mental health and exposure to trauma.
Persistent SARS-CoV-2 presence is companied with defects in adaptive immune system in non-severe COVID-19 patients
Authors: Liu Bing , Han Junyan , Cheng Xiaohuan , Yu Long , Zhang Li , Wang Wei , Ni Lan , Wei Chaojie , Huang Yafei , Cheng Zhenshun 
Published: March 2020

AbstractBackgroundCOVID-19 has been widely spreading. We aim to examine adaptive immune cells in non-severe patients with persistent SARS-CoV-2 shedding.Methods37 non-severe patients with persistent SARS-CoV-2 presence transferred to Zhongnan hospital of Wuhan University were retrospectively recruited to PP (persistently positive) group, which was further allocated to PPP group (n=19) and PPN group (n=18), according to their testing results after 7 days (N=negative). Epidemiological, demographic, clinical and laboratory data were collected and analyzed. Data from age- and sex-matched non-severe patients at disease onset (PA [positive on admission] patients, n=37), and lymphocyte subpopulation measurements from matched 54 healthy subjects were extracted for comparison.ResultsCompared with PA patients, PP patients had much improved laboratory findings, including WBCs, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, albumin, AST, CRP, SAA, and IL-6. The absolute numbers of CD3+ T cells, CD4+ T cells, and NK cells were significantly higher in PP group than that in PA group, and were comparable to that in healthy controls. PPP subgroup had markedly reduced B cells and T cells compared to PPN group and healthy subjects. Finally, paired results of these lymphocyte subpopulations from 10 PPN patients demonstrated that the number of T cells and B cells significantly increased when the SARS-CoV-2 tests turned negative.ConclusionPersistent SARS-CoV-2 presence in non-severe COVID-19 patients is associated with reduced numbers of adaptive immune cells. Monitoring lymphocyte subpopulations could be clinically meaningful in identifying fully recovered COVID-19 patients.SummaryDefects in adaptive immune system, including reduced T cells and B cells, were frequently observed in non-severe COVID-19 patients with persistent SARS-CoV-2 shedding. Assessment of immune system could be clinically relevant for discharge management.
Generating high quality assemblies for genomic analysis of transposable elements
Authors: Wierzbicki Filip , Schwarz Florian , Cannalonga Odontsetseg , Kofler Robert 
Published: March 2020

AbstractThe advent of long-read sequencing holds great promise for research on transposable elements (TEs). Long reads may finally allow us to obtain reliable assemblies of repetitive regions, and thus shed light on many open questions in TE biology, such as the evolution of piRNA clusters, i.e., the master loci controlling TE activity. Currently, many different assembly strategies exist and it is not clear how to obtain the most suitable assemblies for TE research. In fact, it is not even clear how to best identify suitable assemblies as classic quality metrics such as BUSCO and NG50 are ignorant of TEs. To address these problems, we introduce four novel quality metrics that assess i) how well piRNA clusters are assembled (CUSCO) and ii) to which extent an assembly captures the TE landscape of an organism (TE abundance, SNPs and internal deletions). Using these novel metrics, we evaluate the effect of assemblers, polishing, read length, coverage, residual polymorphisms, and finally, identify suitable assembly strategies. Using an optimized approach, we provide high-quality assemblies for the twoDrosophila melanogasterstrains Canton-S and Pi2. Around 80% of the piRNA clusters were contiguously assembled in these two strains. Such high-quality assemblies will provide novel insights into the biology of TEs. It is, for example, an open question of whether piRNA clusters contain abundant presence/absence polymorphism of TE insertions, as expected when piRNA clusters are responsible for stopping TE invasions. A comparison of the sequences of our assembled piRNA clusters reveals that such polymorphisms are indeed abundantly found in clusters.
Multimodal brain imaging study of 36,678 participants reveals adverse effects of moderate drinking
Authors: Daviet Remi , Aydogan Gökhan , Jagannathan Kanchana , Spilka Nathaniel , Koellinger Philipp D. , Kranzler Henry R. , Nave Gideon , Wetherill Reagan R. 
Published: March 2020

ABSTRACTHeavy alcohol consumption can have significant deleterious neural consequences, including brain atrophy, neuronal loss, poorer white matter fiber integrity, and cognitive decline. However, the effects of light-to-moderate alcohol consumption on brain structure remain unclear. Here, we examine the associations between alcohol intake and brain structure using multimodal imaging data from 36,678 generally healthy middle-aged and older adults from the UK Biobank, controlling for numerous potential confounds. We find negative associations between alcohol intake and global gray matter volume (GMV) and white matter volume (WMV), which become stronger as intake increases. An examination of the associations between alcohol intake and 139 regional GMV imaging-derived phenotypes (IDPs) and 375 WM microstructure IDPs yielded 304 (59.1%) significant findings, including 125 GMV IDPs that are spread across the brain and 179 WM microstructure IDPs across multiple tract regions. In general, findings comport with the existing literature. However, a daily alcohol intake of as little as one to two units – 250 to 500 ml of a 4% beer or 76 to 146 ml of a 13% wine – is already associated with GMV deficits and altered WMV microstructure, placing moderate drinkers at risk.One Sentence SummaryModerate alcohol intake, consuming one or more daily alcohol units, has adverse effects on brain health.
Failed detection of the full-length genome of SARS-CoV-2 by ultra-deep sequencing from the recovered and discharged patients retested viral PCR positive
Authors: Hu Fengyu , Chen Fengjuan , Wang Yaping , Xu Teng , Tang Xiaoping , Li Feng 
Published: March 2020

AbstractOver 10 percent of recovered and discharged patients retested positive for SARS-CoV-2, raising a public health concern whether they could be potential origins of infection. In this study, we found that detectable viral genome in discharged patients might only mean the presence of viral fragments, and could hardly form an infection origin for its extremely low concentration.
Evaluating the effectiveness of social distancing interventions against COVID-19
Authors: Matrajt Laura , Leung Tiffany 
Published: March 2020

AbstractSARS-CoV-2 has infected over 140,000 people as of March 14, 2020. We use a mathematical model to investigate the effectiveness of social distancing interventions lasting six weeks in a middle-sized city in the US. We explore four social distancing strategies by reducing the contacts of adults over 60 years old, adults over 60 years old and children, all adults (25, 75 or 95% compliance), and everyone in the population. Our results suggest that social distancing interventions can avert cases by 20% and hospitalizations and deaths by 90% even with modest compliance within adults as long as the intervention is kept in place, but the epidemic is set to rebound once the intervention is lifted. Our models suggest that social distancing interventions will buy crucial time but need to occur in conjunction with testing and contact tracing of all suspected cases to mitigate transmission of SARS-CoV-2.
Research on the Influence of Effective Distance Between Cities on the Cross-regional Transmission of COVID-19
Authors: Lin Shanlang , Qiao Yanning , Huang Junpei , Yan Na 
Published: March 2020

AbstractThe COVID-19 epidemic in China has been effectively controlled. It is of great significance to study the law of cross-regional spread of the epidemic, for the prevention and control of the COVID-19 in the future in China and other countries or regions. In this study, the cross-regional connection intensity between cities was characterized based on the probability and the effective distance of the shortest path tree, and the empirical analysis was carried out based on the high-frequency data such as the cases of COVID 19 outbreaks. It is concluded that the higher the intensity of inter-city connection, the larger scale the cross-regional spread of the epidemic.
When will the Covid-19 epidemic fade out?
Authors: Renna Ilaria 
Published: March 2020

AbstractA discrete-time deterministic epidemic model is proposed with the aim of reproducing the behaviour observed in the incidence of real infectious diseases. For this purpose, we analyse a SIRS model under the framework of a small world network formulation. Using this model, we make predictions about the peak of the Covid-19 epidemic in Italy. A Gaussian fit is also performed, to make a similar prediction.
Basic prediction methodology for covid-19: estimation and sensitivity considerations
Authors: Britton Tom 
Published: March 2020

SummaryThe purpose of the present paper is to present simple estimation and prediction methods for basic quantities in an emerging epidemic like the ongoing covid-10 pandemic. The simple methods have the advantage that relations between basic quantities become more transparent, thus shedding light to which quantities have biggest impact on predictions, with the additional conclusion that uncertainties in these quantities carry over to high uncertainty also in predictions.A simple non-parametric prediction method for future cumulative case fatalities, as well as future cumulative incidence of infections (assuming a given infection fatality risk f), is presented. The method uses cumulative reported case fatalities up to present time as input data. It is also described how the introduction of preventive measures of a given magnitude ρ will affect the two incidence predictions, using basic theory of epidemic models. This methodology is then reversed, thus enabling estimation of the preventive magnitude ρ, and of the resulting effective reproduction number RE. However, the effects of preventive measures only start affecting case fatalities some 3-4 weeks later, so estimates are only available after this time has elapsed. The methodology is applicable in the early stage of an outbreak, before, say, 10% of the community have been infected.Beside giving simple estimation and prediction tools for an ongoing epidemic, another important conclusion lies in the observation that the two quantities f (infection fatality risk) and ρ (the magnitude of preventive measures) have very big impact on predictions. Further, both of these quantities currently have very high uncertainty: current estimates of f lie in the range 0.2% up to 2% ([9], [7]), and the overall effect of several combined preventive measures is clearly very uncertain.The two main findings from the paper are hence that, a) any prediction containing f, and/or some preventive measures, contain a large amount of uncertainty (which is usually not acknowledged well enough), and b) obtaining more accurate estimates of in particular f, should be highly prioritized. Seroprevalence testing of random samples in a community where the epidemic has ended are urgently needed.
Short-term forecasts and long-term mitigation evaluations for the COVID-19 epidemic in Hubei Province, China
Authors: Yang Qihui , Yi Chunlin , Vajdi Aram , Cohnstaedt Lee W. , Wu Hongyu , Guo Xiaolong , Scoglio Caterina M. 
Published: March 2020

AbstractAs an emerging infectious disease, the 2019 coronavirus disease (COVID-19) has developed into a global pandemic. During the initial spreading of the virus in China, we demonstrated the ensemble Kalman filter performed well as a short-term predictor of the daily cases reported in Wuhan City. Second, we used an individual-level network-based model to reconstruct the epidemic dynamics in Hubei Province and examine the effectiveness of non-pharmaceutical interventions on the epidemic spreading with various scenarios. Our simulation results show that without continued control measures, the epidemic in Hubei Province could have become persistent. Only by continuing to decrease the infection rate through 1) protective measures and 2) social distancing can the actual epidemic trajectory that happened in Hubei Province be reconstructed in simulation. Finally, we simulate the COVID-19 transmission with non-Markovian processes and show how these models produce different epidemic trajectories, compared to those obtained with Markov processes. Since recent studies show that COVID-19 epidemiological parameters do not follow exponential distributions leading to Markov processes, future works need to focus on non-Markovian models to better capture the COVID-19 spreading trajectories. In addition, shortening the infectious period via early case identification and isolation can slow the epidemic spreading significantly.
Site-specific N-glycosylation Characterization of Recombinant SARS-CoV-2 Spike Proteins
Authors: Zhang Yong , Zhao Wanjun , Mao Yonghong , Chen Yaohui , Wang Shisheng , Zhong Yi , Su Tao , Gong Meng , Du Dan , Lu Xiaofeng , Cheng Jingqiu , Yang Hao 
Published: March 2020

SummaryThe glycoprotein spike (S) on the surface of SARS-CoV-2 is a determinant for viral invasion and host immune response. Herein, we characterized the site-specific N-glycosylation of S protein at the level of intact glycopeptides. All 22 potential N-glycosites were identified in the S-protein protomer and were found to be preserved among the 753 SARS-CoV-2 genome sequences. The glycosites exhibited glycoform heterogeneity as expected for a human cell-expressed protein subunits. We identified masses that correspond to 157 N-glycans, primarily of the complex type. In contrast, the insect cell-expressed S protein contained 38 N-glycans, primarily of the high-mannose type. Our results revealed that the glycan types were highly determined by the differential processing of N-glycans among human and insect cells. This N-glycosylation landscape and the differential N-glycan patterns among distinct host cells are expected to shed light on the infection mechanism and present a positive view for the development of vaccines and targeted drugs.