bluuebunny/crossref_metadata_2025_split huggingface dataset:
Contribution to COVID-19 spread modelling: A physical phenomenological dissipative formalism
Authors: Limam Oualid , Limam Mohamed 
Published: May 2020

AbstractIn this study, we propose an evolution law of COVID-19 transmission based first on a representation of population by a domain part of an infinite ordered lattice in which epidemic evolution is represented by a wave like free spread starting from a first case as an epicentre. Free energy of spread on a given day is defined equal to the natural logarithm of the number of infected cases. Dissipation of propagation is obtained using a postulated form of free energy built using thermodynamics of irreversible processes in analogy to isotherm wave propagation in solids and elastic damage behaviour of materials. The proposed expression of daily free energy rate leads to dissipation of propagation introducing a parameter quantifying measures taking by governments to restrict transmission. Entropy daily rate representing disorder produced in the initial system is also explicitly defined. In this context, a simple law of evolution of infected cases as function of time is given in an iterative form. The model predicts different effects on peak of infected cases Imax and epidemic period, including effects of population size N, effects of measures taking to restrict spread, effects of population density and effect of a parameter T similar to absolute temperature in thermodynamics. Different effects are presented first. The model is than applied to epidemic spread in Tunisia and compared with data registered since the report of the first confirmed case on Mar 2, 2020. It is shown that the low epidemic size in Tunisia is essentially due to a low population density and relatively strict restriction measures including lockdown and quarantine.
Forecasting COVID-19 new cases in Algeria using Autoregressive fractionally integrated moving average Models (ARFIMA)
Authors: Balah Belkacem , Djeddou Messaoud 
Published: May 2020

AbstractIn this research, an ARFIMA model is proposed to forecast new COVID-19 cases in Algeria two weeks ahead. In the present study, public health database from Algeria health ministry has been used to build an ARFIMA model and used to forecast COVID-19 new cases in Algeria until May 11, 2020.BackgroundThe aim of this study is first to find the best prediction method among the two techniques used and type of memory, either short or long, of the model constructed for the daily confirmed cases in Algeria, then make forecasts of the confirmed cases in the fifteen next days.MethodsThis study was conducted based on daily new cases of COVID-19 that were collected from the official website of Algerian Ministry of Health from March 1, 2020 to April 26, 2020. Auto Regressive Integrated Moving Average (ARFIMA) model was used to predict the trend of confirmed cases. The evaluation of the fractional differentiation parameter (d) is carried out using OxMetrics 6 software.ResultsThe ARFIMA model (0, 0.431779, 0) build for Algeria, has a long memory and an upward trend over the next fifteen days and which coincides with the holy month of Ramadhan.ConclusionsThe forecasted results obtained by the proposed ARFIMA model can be used as a decision support tool to manage medical efforts and facilities against the COVID-19 pandemic crisis.
Risk of secondary infection waves of COVID-19 in an insular region: the case of the Balearic Islands, Spain
Authors: Eguíluz Víctor M. , Fernández-Gracia Juan , Rodríguez Jorge P. , Pericás Juan M. , Melián Carlos 
Published: May 2020

AbstractThe Spanish government declared the lockdown on March 14th, 2020 to tackle the fast-spreading of COVID-19. As a consequence the Balearic Islands remained almost fully isolated due to the closing of airports and ports, These isolation measures and the home-based confinement have led to a low prevalence of COVID-19 in this region. We propose a compartmental model for the spread of COVID-19 including five compartments (Susceptible, Latent, Infected, Diseased, and Recovered), and the mobility between municipalities. The model parameters are calibrated with the temporal series of confirmed cases provided by the Spanish Ministry of Health. After calibration, the proposed model captures the trend of the official confirmed cases before and after the lockdown. We show that the estimated number of cases depends strongly on the initial dates of the local outbreak onset and the number of imported cases before the lockdown. Our estimations indicate that the population has not reached the level of herd immunization necessary to prevent future outbreaks. While the low prevalence, in comparison to mainland Spain, has prevented the saturation of the health system, this low prevalence translates into low immunization rates, therefore facilitating the propagation of new outbreaks that could lead to secondary waves of COVID-19 in the region. These findings warn about scenarios regarding after-lockdown-policies and the risk of second outbreaks, emphasize the need for widespread testing, and could potentially be extrapolated to other insular and continental regions.
Laboratory findings associated with severe illness and mortality among hospitalized individuals with coronavirus disease 2019 in Eastern Massachusetts
Authors: Castro Victor M. , McCoy Thomas H. , Perlis Roy H. 
Published: May 2020

AbstractImportanceThe coronavirus disease 2019 (COVID-19) pandemic has placed unprecedented stress on health systems across the world, and reliable estimates of risk for adverse hospital outcomes are needed.ObjectiveTo quantify admission laboratory and comorbidity features associated with critical illness and mortality risk across 6 Eastern Massachusetts hospitals.DesignRetrospective cohort study using hospital course, prior diagnoses, and laboratory values.SettingEmergency department and inpatient settings from 2 academic medical centers and 4 community hospitals.ParticipantsAll individuals with hospital admission and positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by PCR testing across these 6 hospitals through June 5, 2020.ExposureCoronavirus 2 (SARS-CoV-2).Main Outcome MeasuresSevere illness defined by ICU admission, mechanical ventilation, or death.ResultsAmong 2,511 hospitalized individuals who tested positive for SARS-CoV-2 (of whom 50.9% were male, 53.9% white, and 27.0% Hispanic, with mean age 62.6 years), 215 (8.6%) were admitted to the ICU, 164 (6.5%) required mechanical ventilation, and 292 (11.6%) died. L1-regression models developed in 3 of these hospitals yielded area under ROC curve (AUC) of 0.807 for severe illness and 0.847 for mortality in the 3 held-out hospitals. In total, 212/292 (78%) of the deaths occurred in the highest-risk mortality quintile.Conclusions and RelevanceIn this cohort, specific admission laboratory studies in concert with sociodemographic features and prior diagnosis facilitated risk stratification among individuals hospitalized for COVID-19.Funding1R56MH115187-01Trial RegistrationNoneKey PointsQuestionHow well can sociodemographic features, laboratory values, and comorbidities of individuals hospitalized with coronavirus disease 2019 (COVID-19) in Eastern Massachusetts through June 5, 2020 predict severe illness course?FindingsIn this cohort study of 2,511 hospitalized individuals positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by PCR who were admitted to one of six hospitals, 215 (8.6%) were admitted to the ICU, 164 (6.5%) required mechanical ventilation, and 292 (11.6%) died. In a risk prediction model, 212 (78%) deaths occurred in the top mortality-risk quintile.MeaningSimple prediction models may assist in risk stratification among hospitalized COVID-19 patients.
MantisCOVID: Rapid X-Ray Chest Radiograph and Mortality Rate Evaluation With Artificial Intelligence For COVID-19
Authors: Yaşar Yağmur , Karli Berat Tuna , Çöteli Cem , Çöteli Mert Burkay 
Published: May 2020

AbstractThe novel coronavirus pandemic has negative impacts over the health, economy and well-being of the global population. This negative effect is growing with the high spreading rate of the virus. The most critical step to prevent the spreading of the virus is pre-screening and early diagnosis of the individuals. This results in quaranteeing the patients not to effect the healthy population. COVID-19 is the name of the disease caused by the novel coronavirus. It has a high infection rate and it is urgent to diagnose many patients as we can to prevent the spread of the virus at the early stage. Rapid diagnostic tools development is urgent to save lives. MantisCOVID is a cloud-based pre-diagnosis tool to be accessed from the internet. This tool delivers a rapid screening test by analyzing the X-ray Chest Radiograph scans via Artificial Intelligence (AI) and it also evaluates the mortality rate of patients with the synthesis of the patient’s history with the machine learning methods. This study reveals the methods used over the platform and evaluation of the algorithms via open datasets.
Normalization of Cerebral Blood Flow, Neurochemicals, and White Matter Integrity After Kidney Transplantation
Authors: Lepping Rebecca J. , Montgomery Robert N. , Sharma Palash , Mahnken Jonathan D. , Vidoni Eric D. , Choi In-Young , Sarnak Mark J. , Brooks William M. , Burns Jeffrey M. , Gupta Aditi 
Published: May 2020

AbstractBackgroundChronic kidney disease (CKD) is associated with abnormalities in cerebral blood flow (CBF), cerebral neurochemical concentrations and white matter integrity, each of which are associated with adverse clinical consequences in the non-CKD population, and may explain the high prevalence of dementia and stroke in end stage kidney disease (ESKD). Since cognition improves after kidney transplantation (KT), we examined these brain abnormalities pre-to post-KT to identify potential reversibility in ESKD-associated brain abnormalities.MethodsWe measured the effects of KT on CBF assessed by arterial spin labeling, cerebral neurochemical concentrations (N-acetylaspartate, choline, glutamate and glutamine, myoinositol and total creatine) measured by magnetic resonance spectroscopic imaging, and white matter integrity measured by fractional anisotropy (FA) and mean diffusivity (MD) with diffusion tensor imaging. We used a linear mixed model analysis to compare longitudinal, repeated brain MRI measurements pre-KT, and 3 months and 12 months post-KT, and also compared findings with healthy controls.Results29 ESKD patients and 19 age-matched healthy controls participated in the study. 22 patients underwent post-KT MRI. CBF, which was higher pre-KT than in controls (p=0.003), decreased post-KT (p<0.0001) to values in controls. KT also normalized concentrations of osmotic neurochemicals choline (p<0.0001) and myo-inositol (p=0.0003) that were higher pre-KT compared to controls. Post-KT, FA increased (p=0.001) and MD decreased (p=0.0001).ConclusionsBrain abnormalities in CKD are reversible and normalize with KT. Further studies are needed to understand the mechanisms underlying these brain abnormalities and to explore interventions to mitigate them even in patients who cannot be transplanted.Significance statementKidney disease is accompanied by brain structural and physiological abnormalities and increased risk of dementia and stroke. Renal replacement therapy with dialysis does not normalize these brain abnormalities. We evaluated these brain abnormalities before and after kidney transplantation and demonstrated that unlike dialysis, kidney transplantation normalizes cerebral blood flow, neurochemical concentrations and white matter integrity. These changes persist beyond initial post-transplantation period and thus cannot be attributed to peri-procedural interventions like steroids. These results indicate reversibility of brain abnormalities in kidney disease. Further studies are needed to understand the mechanisms underlying these abnormalities and explore interventions for prevention and mitigation in patients who cannot be transplanted.
1-Methylnicotinamide is an immune regulatory metabolite in human ovarian cancer
Authors: Kilgour Marisa K. , MacPherson Sarah , Zacharias Lauren , Keyes Sarah , Pauly Brenna , Allard Bertrand , Smazynski Julian , Watson Peter H. , Stagg John , Nelson Brad H. , DeBerardinis Ralph J. , Hamilton Phineas T. , Lum Julian J. 
Published: May 2020

ABSTRACTImmune regulatory metabolites are key features of the tumor microenvironment (TME), yet with a few notable exceptions, their identities remain largely unknown. We uncovered the immune regulatory metabolic states and metabolomes of sorted tumor and stromal, CD4+, and CD8+ cells from the tumor and ascites of patients with high-grade serous ovarian cancer (HGSC) using high-dimensional flow cytometry and metabolomics supplemented with single cell RNA sequencing. Flow cytometry revealed that tumor cells show a consistently greater uptake of glucose than T cells, but similar mitochondrial activity. Cells within the ascites and tumor had pervasive metabolite differences, with a striking enrichment in 1-methylnicotinamide (MNA) in T cells infiltrating the tumor compared to ascites. Despite the elevated levels of MNA in T cells, the expression of nicotinamide N-methyltransferase, the gene encoding the enzyme that catalyses the transfer of a methyl group from S-adenosylmethionine to nicotinamide, was restricted to fibroblasts and tumor cells. Treatment of T cells with MNA resulted in an increase in T cell-mediated secretion of the tumor promoting cytokine tumor necrosis factor alpha. Thus, the TME-derived metabolite MNA contributes to an alternative and non-cell autonomous mechanism of immune modulation of T cells in HGSC. Collectively, uncovering the tumor-T cell metabolome may reveal metabolic vulnerabilities that can be exploited using T cell-based immunotherapies to treat human cancer.
CD8 co-receptor enhances T cell activation without any effect on initial attachment
Authors: Robert Philippe , Limozin Laurent , van der Merwe Anton , Bongrand Pierre 
Published: May 2020

SummaryThe scanning of surrounding tissues by T lymphocytes to detect cognate antigen is a highly demanding process that requires high rapidity, sensitivity and specificity. Co-receptors such as CD8 are known to increase detection performance, but the exact mechanism of this role remains incompletely understood. Here, we used interference reflection microscopy to image the initial spreading of 1G4 receptor transfected CD8+ and CD8− Jurkat cells dropped on surfaces exposing five cognate antigens of varying activating power, and we used a laminar flow chamber to measure the influence of CD8 on the kinetics of bond formation and rupture between cell-born T cell receptors (TCRs) and peptide-exposing major histocompatibility complex antigens (pMHCs) at the single molecule level. It is concluded that CD8 did not influence TCR-pMHC interaction during the first seconds following cell surface encounter, but it promoted the spreading responses during the first minutes, thus suggesting that CD8 was involved in early activation rather than binding. In addition, presented results were quantitatively compared with a recent report on the cell-free interaction between the same ligand-receptor couples : it is concluded that bond formation was strongly impaired by cell molecular environment, while bond rupture was comparable in both systems. Results from this and previous reports were used to propose a quantitative scheme of the strategy used by T lymphocytes to scan foreign surfaces. It is suggested that the understanding of the strategy used by cells to perform their basic functions may be a prerequisite to understand the function of molecular networks revealed by high throughput methods.
Optimizing experimental design for genome sequencing and assembly with Oxford Nanopore Technologies
Authors: Sutton John M. , Millwood Joshua D. , McCormack A. Case , Fierst Janna L. 
Published: May 2020

AbstractBackgroundHigh quality reference genome sequences are the core of modern genomics. Oxford Nanopore Technologies (ONT) produces inexpensive DNA sequences in excess of 100,000 nucleotides but high error rates make sequence assembly and analysis a non-trivial problem as genome size and complexity increases. To date there has been no comprehensive attempt to generate robust experimental design for ONT genome sequencing and assembly. In this study, we simulate ONT and Illumina DNA sequence reads for the model organismsEscherichia coli,Caenorhabditis elegans,Arabidopsis thaliana,andDrosophila melanogasterand assemble with Canu, Flye, and MaSuRCA software to quantify the influence of sequencing coverage and assembly approach. Heterozygosity in outbred eukaryotes is a common problem for genome assembly. We show broad applicability of our methods using real ONT data generated for four strains of the highly heterozygous nematodeCaenorhabditis remaneiandC. latens.ResultsONT libraries have a unique error structure and high sequence depth is necessary to assemble contiguous genome sequences.As sequence depth increases errors accumulate and assembly statistics plateau.High-quality assembled sequences require a combination of experimental techniques that increase sequence read length and computational protocols that reduce error through correction, read selection and ‘polishing’ with higher accuracy short sequence reads.Our robust experimental design results in highly contiguous and accurate genome assemblies for the four strains ofC. remaneiandC. latens.ConclusionsONT sequencing is inexpensive and accessible but the technology’s error structure requires robust experimental design. Our quantitative results will be helpful for a broad array of researchers seeking guidance forde novoassembly projects.
Small molecule enhancers of endosome-to-cytosol import augment anti-tumour immunity
Authors: Kozik Patrycja , Gros Marine , Itzhak Daniel N. , Joannas Leonel , Magalhaes Joao G. , Alloatti Andrés , Nery Elaine Del , Borner Georg H.H. , Amigorena Sebastian 
Published: May 2020

SummaryEfficient cross-presentation of antigens by dendritic cells (DCs) is critical for initiation of anti-tumour immune responses. Yet, several steps of antigen intracellular traffic during cross-presentation are incompletely understood: in particular, the molecular mechanisms and the relative importance of antigen import from endocytic compartments into the cytosol. Here, we asked whether antigen import into the cytosol is rate-limiting for cross-presentation and anti-tumour immunity. By screening 700 FDA-approved drugs, we identified 37 import enhancers. We focused on prazosin and tamoxifen, and generated proteomic organellar maps of drug-treated DCs, covering the subcellular localisations of over 2000 proteins. By combining organellar mapping, quantitative proteomics, microscopy, and bioinformatics, we conclude that import enhancers undergo lysosomal trapping leading to membrane permeation and antigen release into the cytosol. Enhancing antigen import facilitates cross-presentation of both soluble and cell-associated antigens. Systemic administration of prazosin also led to reduced growth of MC38 tumours and to a synergistic effect with checkpoint immunotherapy in a melanoma model. Thus, inefficient antigen import into the cytosol limits antigen cross-presentation, restraining the potency of anti-tumour immune responses and efficacy of checkpoint blockers.HighlightsQuinazolamine derivatives are potent enhancers of antigen import into the cytosolAntigen import enhancement occurs as a consequence of lysosomal trappingAntigen import enhancers facilitate cross-presentation and synergise with PD-L1Proteomic organellar maps of cDC1s as a tool to detect biological effects of drugs
Contact tracing strategies for COVID-19 containment with attenuated physical distancing
Authors: Bilinski Alyssa , Mostashari Farzad , Salomon Joshua A 
Published: May 2020

AbstractContact tracing has been recommended as a critical component of containment strategies for COVID-19. We used a simple epidemic model to evaluate how contact tracing might enable modification of current physical distancing restrictions. Testing and tracing coverage need to exceed 50% in order for contact tracing to reduce transmission by at least 10%. With 90% testing and tracing as well as high isolation and quarantine efficacy, contact tracing could reduce overall transmission by >45%, which would allow for partial loosening of physical distancing measures. Benefits of contact tracing could be enhanced by testing all contacts rather than only those with symptoms and by policies to support high adherence to voluntary isolation and quarantine.
Forecasting the spread of COVID-19 in Nigeria using Box-Jenkins Modeling Procedure
Authors: Ibrahim Rauf Rauf , Oladipo Hannah Oluwakemi 
Published: May 2020

AbstractObjectiveThis study is focused on the analysis of the spread of Covid-19 in Nigeria, applying statistical models and available data from the NCDC. We present an insight into the spread of Covid-19 in Nigeria in order to establish a suitable prediction model, which can be applied as a decision-supportive tool for assigning health interventions and mitigating the spread of the Covid-19 infection.MethodologyDaily spread data from February 27 to April 26, 2020, were collected to construct the autoregressive integrated moving average (ARIMA) model using the R software. Stability analysis and stationarity test, parameter test, and model diagnostic were also carried out. Finally, the fitting, selection and prediction accuracy of the ARIMA model was evaluated using the AICc model selection criteria.ResultsThe ARIMA (1,1,0) model was finally selected among ARIMA models based upon the parameter test and Box–Ljung test. A ten-day forecast was also made from the model, which shows a steep upward trend of the spread of the COVID-19 in Nigeria within the selected time frame.ConclusionFederal Government of Nigeria through the presidential task force can apply the forecasted trend of much more spread to make more informed decisions on the additional measures in place to curb the spread of the virus. Application of the model can also assist in studying the effectiveness of the lockdown on the on the spread of Covid-19 in Nigeria.
A Novel Intervention Recurrent autoencoder for real time forecasting and non-pharmaceutical intervention selection to curb the spread of Covid-19 in the world
Authors: Ge Qiyang , Hu Zixin , Li Shudi , Lin Wei , Jin Li , Xiong Momiao 
Published: May 2020

ABSTRACTAs the Covid-19 pandemic soars around the world, there is urgent need to forecast the number of cases worldwide at its peak, the length of the pandemic before receding and implement public health interventions to significantly stop the spread of Covid-19. Widely used statistical and computer methods for modeling and forecasting the trajectory of Covid-19 are epidemiological models. Although these epidemiological models are useful for estimating the dynamics of transmission od epidemics, their prediction accuracies are quite low. To overcome this limitation, we formulated the real-time forecasting and evaluating multiple public health intervention problem into forecasting treatment response problem and developed recurrent neural network (RNN) for modeling the transmission dynamics of the epidemics and Counterfactual-RNN (CRNN) for evaluating and exploring public health intervention strategies to slow down the spread of Covid-19 worldwide. We applied the developed methods to the real data collected from January 22, 2020 to May 8, 2020 for real-time forecasting the confirmed cases of Covid-19 across the world.