bluuebunny/crossref_metadata_2025_split huggingface dataset:
BCG VACCINES MAY NOT REDUCE COVID-19 MORTALITY RATES
Authors: Singh Samer 
Published: April 2020

ABSTRACTThe reason for the observed country-wise variability in incidence and severity of the COVID-19 outcome remains unknown. Few recent studies have suggested a positive protective correlation of the BCG vaccination policy of the countries with the observed COVID-19 severity. The current study was undertaken to reassess the existing data as of 4th April 2020. The incidence rates (cases per million population), Case Fatality Rates (CFR) and inherently more robust Infection Fatality Rates (IFR) were calculated across countries accounting for about 99% COVID-19 deaths. The initial scrutiny suggested a weaker association with BCG vaccination policy or BCG coverage, so positivity to the Tuberculin Sensitivity Test (TST)/ Interferon Gamma Release Assay (IGRA) as a measure of the potential protective effect of the resident populations’ exposure to Mycobacterium spp. whether from BCG vaccination or as a result of exposure to environmental mycobacteria was analyzed. The incidence rates (the number of cases per million population) decreased with an increase in % LTBI (TST/IGRA positivity) for the analyzed countries with R2 =0.6343, suggesting an exponentially negative covariation. However, the covariation of CFR estimates that ranged from 0.29% to 12.25 % (average 5.39%) among countries, was tenuous. Interim estimates of IFR (i-IFR), a more dependable measure for such studies, for the best and worst-case scenarios, i.e., i-IFR-l and i-IFR-h, predict on an average 20.57% to 30.15 % COVID-19 fatality rates globally, but individual country estimates display huge variation. Among countries accounting for 92.14% deaths (11 countries; top 20% countries included in current study) the estimate for lowest IFRs (i-IFR-l=4.16 (China) & i-IFR-h=4.61 (China)) and highest IFRs (i-IFR-l=96.39% (UK); & i-IFR-h=96.54% (UK)) displayed huge difference (average for the group: CFR=6.8±3.6%; i-IFR-l=34.97±30.55%; & i-IFR-h=44.20±29.08%). Currently, the worst affected countries Italy (CFR=12.25%; i-IFR-l=42.63%; i-IFR-h=48.69%) and Spain (CFR=9.39%; i- IFR-l=26.85%; i-IFR-h=36.60%) would seemingly cope with COVID-19 better than UK, Netherlands and USA while the countries Germany (CFR=1.40%; i-IFR-l=4.93%; i-IFR-h=17.49%) and Switzerland (CFR=3.01%; i-IFR-l=10.87%; i-IFR-h=16.23%) along with China could fare the best. The rest of the 80% countries (accounting for 6.74% deaths), seemed to have reduced mortality (CFR=2.45±2.01; i-IFR-l= 30.62±28.24%; i-IFR-h=40.99±30.47%) with associated high % LTBI (17.28±8.87) than top 20% countries. The inherent issues in the data set (e.g., heterogeneity, non- random sampling, different criteria of sampling and reporting, access to health care, genetic composition, underlying co-morbidities, etc) need to be taken into account for making informed decisions.
Serum Mycoplasma pneumoniae IgG in COVID-19: A Protective Factor
Authors: Mi Bobin , Chen Lang , Panayi Adriana C. , Xiong Yuan , Liu Guohui 
Published: April 2020

AbstractBackgroundA correlation between prior exposure to Mycoplasma pneumoniae (IgG positive) and better clinical response to COVID-19 was elusive.MethodsA retrospective review of all COVID-19 infected patients treated at Wuhan Union Hospital from Feb 1 to Mar 20 was carried out. Continuous variables were described as mean, median, and interquartile range (IQR), while categorical variables were compared by X2 test or Fisher’s exact test between COVID-19 infected patients with mycoplasma lgG (-) and mycoplasma lgG (+).ResultsStatistically significant differences were shown in terms of laboratory test results. COVID-19 infected patients with mycoplasma lgG positivity had a higher lymphocyte count and percentage (p=0.026, p=0.017), monocyte count and percentage (p=0.028, p=0.006) and eosinophil count and percentage (p=0.039, p=0.007), and a lower neutrophil count and percentage (p=0.044, p=0.006) than COVID-19 infected patients without mycoplasma lgG. Other routine blood tests, including coagulation tests, blood biochemistry and infection-related biomarkers did not significantly differ except for thrombin time (p=0.001) and lactate dehydrogenase (p=0.008). Furthermore, requirement and use of a nasal catheter or oxygen mask was significantly lower in COVID-19 infected patients with mycoplasma lgG positivity (p=0.029).ConclusionsOur findings indicate that mycoplasma IgG positivity is a potential protective factor for SARS-CoV-2 infection.
Immediate and Near Future Prediction of COVID-19 Patients in the U.S. Population Aged 65+ With the Prior Medical Conditions of Hypertension, Cardiovascular and Lung Diseases: Methods, Models and Acute Care Estimates
Authors: Srinivasa Rao Arni S.R. , Miller Douglas D. , Berman Adam E. , Hess David C. , Krantz Steven G. 
Published: April 2020

AbstractImportanceGiven the rapid rise of COVID-19 cases in the U.S. during March 2020 there has been a severe burden on the health care systems and care providers in the country. The impact of the virus so far was higher on the population aged 65+. Hospitalizations were higher among those with underlying medical conditions, namely, hypertension, cardiovascular and lung diseases. Hence, to have an idea of the number of new COVID-19 infections among these high-risk populations that could occur in the short-term could assist promptly to the country’s health care system for immediate health care planning. These estimates may aid us in better understanding the potential volumes of patients requiring inpatient care.ObjectiveTo provide immediate and short-term model-based predictions of COVID-19 patients in the U.S. population aged 65+ during April-June, 2020, those with the prior medical conditions of hypertension, cardiovascular and lung diseases.Design, Setting, and ParticipantsWe developed age-structured dynamic mathematical combined with wavelet analysis to understand the number of new cases that may emerge in the U.S. population aged 65+. We have estimated the number of people aged 65+ who might have three underlying conditions mentioned and a possible number of hospitalizations among them due to COVID-19 if they get infected. We have used publicly available data sources for developing our framework and estimates.ResultsWe estimate that there are 13 million individuals aged 65+ who have one or a combination of three major prior medical conditions in the U.S. who need to be protected against COVID-19 to reduce a large number of hospitalizations and associated deaths. Hospitalizations of patients both with and without ICU-admissions with more prevalent underlying conditions could range between 31,633 (20,310 non-ICU hospitalizations and 11,323 ICU-admissions) to 94,666 (60,779 non-ICU hospitalizations and 33,866 ICU-admissions) cases during the same period. Under a rapid spread of the virus environment, these hospitalizations could be beyond 430,000 within the above three-month period.Conclusions and RelevanceCOVID-19 continues to dramatically and adversely affect the lives of people aged 65+ in the U.S. During the next three months which could result in thousands of hospitalizations if precautions against the virus spread are not implemented and adhered to.
An SEIR Model for Assessment of Current COVID-19 Pandemic Situation in the UK
Authors: Sun Peiliang , Li Kang 
Published: April 2020

AbstractThe ongoing COVID-19 pandemic spread to the UK in early 2020 with the first few cases being identified in late January. A rapid increase in confirmed cases started in March, and the number of infected people is however unknown, largely due to the rather limited testing scale. A number of reports published so far reveal that the COVID-19 has long incubation period, high fatality ratio and non-specific symptoms, making this novel coronavirus far different from common seasonal influenza. In this note, we present a modified SEIR model which takes into account the time lag effect and probability distribution of model states. Based on the proposed model, it is estimated that the actual total number of infected people by 1 April in the UK might have already exceeded 610,000. Average fatality rates under different assumptions at the beginning of April 2020 are also estimated. Our model also reveals that the R0 value is between 7.5–9 which is much larger than most of the previously reported values. The proposed model has a potential to be used for assessing future epidemic situations under different intervention strategies.
Evaluation of lockdown impact on SARS-CoV-2 dynamics through viral genome quantification in Paris wastewaters
Authors: Wurtzer S , Marechal V , Mouchel JM , Maday Y , Teyssou R , Richard E , Almayrac JL , Moulin L 
Published: April 2020

SummarySARS-CoV-2 is the etiological agent of COVID-19. Most of SARS-CoV-2 carriers are assumed to exhibit no or mild non-specific symptoms. Thus, they may contribute to the rapid and mostly silent circulation of the virus among humans. Since SARS-CoV-2 can be detected in stool samples it has recently been proposed to monitor SARS-CoV-2 in wastewaters (WW) as a complementary tool to investigate virus circulation in human populations. In the present work we assumed that the quantification of SARS-CoV-2 genomes in wastewaters should correlate with the number of symptomatic or non-symptomatic carriers. To test this hypothesis, we performed a time-course quantitative analysis of SARS-CoV-2 by RT-qPCR in raw wastewater samples collected from several major wastewater treatment plant (WWTP) of the Parisian area. The study was conducted from 5 March to 23 April 2020, therefore including the lockdown period in France (since 17 March 2020). We confirmed that the increase of genome units in raw wastewaters accurately followed the increase of human COVID-19 cases observed at the regional level. Of note, the viral genomes could be detected before the beginning of the exponential growth of the epidemic. As importantly, a marked decrease in the quantities of genomes units was observed concomitantly with the reduction in the number of new COVID-19 cases which was an expected consequence of the lockdown. A s a conclusion, this work suggests that a quantitative monitoring of SARS-CoV-2 genomes in wastewaters should bring important and additional information for an improved survey of SARS-CoV-2 circulation at the local or regional scale.
Estimation of airborne viral emission: quanta emission rate of SARS-CoV-2 for infection risk assessment
Authors: Buonanno G. , Stabile L. , Morawska L. 
Published: April 2020

AbstractAirborne transmission is a pathway of contagion that is still not sufficiently investigated despite the evidence in the scientific literature of the role it can play in the context of an epidemic. While the medical research area dedicates efforts to find cures and remedies to counteract the effects of a virus, the engineering area is involved in providing risk assessments in indoor environments by simulating the airborne transmission of the virus during an epidemic. To this end, virus air emission data are needed. Unfortunately, this information is usually available only after the outbreak, based on specific reverse engineering cases. In this work, a novel approach to estimate the viral load emitted by a contagious subject on the basis of the viral load in the mouth, the type of respiratory activity (e.g. breathing, speaking), respiratory physiological parameters (e.g. inhalation rate), and activity level (e.g. resting, standing, light exercise) is proposed. The estimates of the proposed approach are in good agreement with values of viral loads of well-known diseases from the literature. The quanta emission rates of an asymptomatic SARS-CoV-2 infected subject, with a viral load in the mouth of 108 copies mL−1, were 10.5 quanta h−1 and 320 quanta h−1 for breathing and speaking respiratory activities, respectively, at rest. In the case of light activity, the values would increase to 33.9 quanta h−1 and 1.03×103 quanta h−1, respectively.The findings in terms of quanta emission rates were then adopted in infection risk models to demonstrate its application by evaluating the number of people infected by an asymptomatic SARS-CoV-2 subject in Italian indoor microenvironments before and after the introduction of virus containment measures. The results obtained from the simulations clearly highlight that a key role is played by proper ventilation in containment of the virus in indoor environments.
Pathologic Nodal Upstaging Predictive Models for CT-based Clinical Node Negative Non-Small Cell Lung Cancer
Authors: Chong Weelic , Hai Yang , Zhou Jian , Liu Lun-xu 
Published: April 2020

AbstractBackgroundAccurate clinical nodal staging of non-small cell lung cancer (NSCLC) is essential for surgical management. Some clinical node negative cases diagnosed preoperatively by CT were later staged as pathological N1 (pN1) or pN2. Our study aimed to evaluate factors related to pathological nodal upstaging and develop statistical models for predicting upstaging.MethodsWe retrospectively reviewed 1,735 patients with clinical node negative NSCLC from 2011 to 2016 in the West China Lung Cancer database. Demographic and clinical data were analyzed via univariate and multivariate approaches. Predictive models were developed on a training set and validated with independent datasets.Results171 (9.9%) clinical node negative patients have pathologic nodal upstaging to pN1. 191(11.0%) patients were upstaged to p(N1+N2). 91(5.2%) patients have pSN2 pathologic nodal upstaging. Preoperative factors were used to establish 3 statistical models for predicting pathological nodal upstaging. The area under the receiver operator characteristic (AUC) were 0.815, 0.768, and 0.726, for pN1, p(N1+N2) and pSN2 respectively.ConclusionOur models may help evaluate the possibility of nodal upstaging for clinical node negative NSCLC and enable surgeons to form appropriate plans preoperatively. External validation in a prospective multi-site study is needed before adoption into clinical practice.
Differential Effects of Pathological Beta Burst Dynamics Between Parkinson’s Disease Phenotypes Across Different Movements
Authors: Neuville Raumin S. , Petrucci Matthew N. , Wilkins Kevin B. , Anderson Ross. W. , Hoffman Shannon L. , Parker Jordan E. , Velisar Anca , Bronte-Stewart Helen M. 
Published: April 2020

AbstractBackgroundResting state beta band (13 – 30 Hz) oscillations represent pathological neural activity in Parkinson’s disease (PD). It is unknown how the peak frequency or dynamics of beta oscillations may change among fine, limb and axial movements and different disease phenotypes. This will be critical for the development of personalized closed loop deep brain stimulation (DBS) algorithms during different activity states.MethodsSubthalamic (STN) local field potentials (LFPs) were recorded from a sensing neurostimulator (Activa® PC+S, Medtronic PLC.,) in fourteen PD participants (six tremor-dominant, 8 akinetic-rigid) off medication/off STN DBS during thirty seconds of repetitive alternating finger tapping, wrist-flexion extension, stepping in place, and free walking. Beta power peaks and beta burst dynamics were identified by custom algorithms and were compared among movement tasks and between tremor-dominant and akinetic-rigid groups.ResultsBeta power peaks were evident during fine, limb, and axial movements in 98% of movement trials; the peak frequencies were similar during each type of movement. Burst power and duration were significantly larger in the high beta band, but not in the low beta band, in the akinetic-rigid group compared to the tremor-dominant group.ConclusionsThe conservation of beta peak frequency during different activity states supports the feasibility of patient-specific closed loop DBS algorithms driven by the dynamics of the same beta band during different activities. Akinetic-rigid participants had greater power and longer burst durations in the high beta band than tremor-dominant participants during movement, which may relate to the difference in underlying pathophysiology between phenotypes.
Association between gene expression profiling of skin lesion and autoantibody in patients with systemic sclerosis
Authors: Inamo Jun 
Published: April 2020

AbstractObjectiveThe aim of this study was to investigate relevance between type of autoantibody and gene expression profile in skin lesion of systemic sclerosis (SSc), and identify specifically dysregulated pathways.MethodsSixty-one patients with SSc from the Genetics versus Environment in Scleroderma Outcome Study cohort and thirty-six healthy controls (HC) are included. Differentially expressed genes (DEGs) were extracted and functional enrichment and pathways analysis were conducted.ResultsCompared with HC, lists consisting of 2, 71, 10, 144 and 78 DEGs were created for patients without specific autoantibody, anti-centromere (ACA), anti-U1 RNP (RNP), anti-RNA polymerase III (RNAP) and anti-topoisomerase I (ATA) antibody, respectively. While part of enriched pathways overlapped, distinct pathways were identified except those without specific autoantibody: keratinocyte differentiation in ACA, NF-kB signaling and cellular response to transforming growth factor beta stimulus in RNAP, interferon alpha/beta signaling of RNP and cellular response to stress in ATA.ConclusionPathogenic pathways were identified according to type of autoantibodies by leveraging gene expression data of patients and controls from multi-center cohort. The current study will promote to explore new therapeutic target for SSc.Key messageDistinct pathways are associated with type of autoantibody in skin lesion of systemic sclerosis.
Detection of neutralising antibodies to SARS coronavirus 2 to determine population exposure in Scottish blood donors between March and May 2020
Authors: Thompson CP. , Grayson N. , Paton R.S. , Bolton J. , Lourenco J. , Penman BS. , Lee L. , Odon V. , Mongkolsapaya J. , Chinnakannan S. , Dejnirattisai W. , Edmans M. , Fyfe A. , Imlach C. , Kooblall K. , Lim N. , Liu C. , Lopez-Camacho C. , McInally C. , McNaughton A.L. , Ramamurthy N. , Ratcliff J. , Supasa P. , Sampson O. , Wang B. , Mentzer A. , Turner M. , Semple M.G. , Baillie J.K. ,   ISARIC4C Investigators, Harvala H. , Screaton G.R. , Temperton N. , Klenerman P. , Jarvis L.M. , Gupta S. , Simmonds P. 
Published: April 2020

AbstractBackgroundThe progression and geographical distribution of SARS coronavirus 2 (SARS-CoV-2) infection in the UK and elsewhere is unknown because typically only symptomatic individuals are diagnosed. We performed a serological study of blood donors in Scotland between the 17th of March and the 18th of May to detect neutralising antibodies to SARS-CoV-2 as a marker of past infection and epidemic progression.AimTo determine if sera from blood bank donors can be used to track the emergence and progression of the SARS-CoV-2 epidemic.MethodsA pseudotyped SARS-CoV-2 virus microneutralisation assay was used to detect neutralising antibodies to SARS-CoV-2. The study group comprised samples from 3,500 blood donors collected in Scotland between the 17th of March and 19th of May, 2020. Controls were collected from 100 donors in Scotland during 2019.ResultsAll samples collected on the 17th March, 2020 (n=500) were negative in the pseudotyped SARS-CoV-2 virus microneutralisation assay. Neutralising antibodies were detected in 6/500 donors from the 23th-26th of March. The number of samples containing neutralising antibodies did not significantly rise after the 5th-6th April until the end of the study on the 18th of May. We find that infections are concentrated in certain postcodes indicating that outbreaks of infection are extremely localised. In contrast, other areas remain comparatively untouched by the epidemic.ConclusionThese data indicate that sero-surveys of blood banks can serve as a useful tool for tracking the emergence and progression of an epidemic like the current SARS-CoV-2 outbreak.
A vulnerability-based approach to human-mobility reduction for countering COVID-19 transmission in London while considering local air quality
Authors: Sasidharan Manu , Singh Ajit , Torbaghan Mehran Eskandari , Parlikad Ajith Kumar 
Published: April 2020

AbstractAn ecologic analysis was conducted to explore the correlation between air pollution, and COVID-19 cases and fatality rates in London. The analysis demonstrated a strong correlation (R2>0.7) between increment in air pollution and an increase in the risk of COVID-19 transmission within London boroughs. Particularly, strong correlations (R2>0.72) between the risk of COVID-19 fatality and NO2 and PM2.5 pollution concentrations were also found. Although this study assumed the same level of air pollution across a particular London borough, it demonstrates the possibility to employ air pollution as an indicator to rapidly identify the city’s vulnerable regions. Such an approach can inform the decisions to suspend or reduce the operation of different public transport modes within a city. The methodology and learnings from the study can thus aid in public transport’s response to COVID-19 outbreak by adopting different levels of human-mobility reduction strategies based on the vulnerability of a given region.One Sentence SummaryThis study introduces air pollution levels as an indicator for a region’s vulnerability to COVID-19 and suggests human-mobility reduction measures.
Time-variant strategies for optimizing the performance of non-pharmaceutical interventions (NPIs) in protecting lives and livelihoods during the COVID-19 pandemic
Authors: Aun Yap Wei , Raja Dhesi Baha 
Published: April 2020

AbstractA stochastic Individual Contact Model (ICM) using SIR compartments allowing for time-variant parameters was used to simulate 100 non-pharmaceutical intervention (NPI) strategies and exit trajectories for a hypothetical population, and to collect epidemiological and non-epidemiological outcomes to measure the performance of these strategies over the course of a period of intervention (up to six months) for a total duration of one-year to allow the full implications of the strategy and endgame to manifest. We find that variations in the time dimension and intensity of various strategies can have vastly different performance outcomes: (i) the timing of NPIs can ‘shrink the area under the curve’ (cumulative infections) not just ‘flatten the curve’; (ii) prolonged lockdowns have diminishing margins of returns; (iii) smooth, submaximal lockdowns perform better than pulsatile lockdowns; and (iv) the efficiency of various strategies incorporating both epidemiological and non-epidemiological outcomes vary substantially. Most sobering, none of the simulated strategies allow for an ‘acceptable’ path to exit within six months due to very large gaps in health system capacity.
A Predictive Model for the Evolution of COVID-19
Authors: Bhardwaj Rajneesh 
Published: April 2020

AbstractWe predict the evolution of the COVID-19 pandemic in several countries by using a regression-based predictive model. In particular, the growth rate of the infection has been fitted as an exponential decay, as compared to a linear decay, reported previously. The model has been validated with the data of China and South Korea, where the pandemic is nearing to its end. The data of Italy, Germany, Spain, and Sweden show that the peak of the infection has reached i.e. a time when the new infections will start to decrease as compared to the previous day. The model predicts the approximate number of total infections at the end of the outbreak. The model prediction of the USA, and Brazil show that the peak will reach in the next two-three weeks. The total number of infections in the USA is estimated to be around 4 million by the model. The reported data of India show the start of disease evolution, with a large initial scatter in the growth rate. The possible peak date and the total number of infections are predicted using the data available.