bluuebunny/crossref_metadata_2025_split huggingface dataset:
Climatic influences on the worldwide spread of SARS-CoV-2
Authors: Bariotakis Michail , Sourvinos George , Castanas Elias , Pirintsos Stergios A. 
Published: March 2020

AbstractThe rapid global spread of the novel, pathogenic, SARS-CoV-2 causing the severe acute respiratory disease COVID-19, becomes a major health problem worldwide and pose the need for international predictive programs. Given the lack of both specific drugs and an efficient preventive vaccine, the expectation that SARS-CoV-2’s transmission rate might decrease in temperate regions during summer, dominated the social scene. Here, we attempted a prediction of the worldwide spread of the infections based on climatic data, expressed by 19 bioclimatic variables. The calculated probability maps shown that potential areas of infection follow a shift from the Tropical to Temperate and Mediterranean Bioclimatic regions, and back to the Tropics again. Maps show an increased probability of infections in Europe, followed by an expansion covering areas of the Middle East and Northern Africa, as well as Eastern coastal areas of North America, South-Eastern coastal areas of Latin America and two areas of Southern Australia, and later return to areas of Southeastern Asia, in a manner similar to that of influenza strains (H3N2). Our approach may therefore be of value for the worldwide spread of SARS-CoV-2, suggesting an optimistic scenario of asynchronous seasonal global outbreaks, like other viral respiratory diseases. Consequently, we suggest the incorporation of a climatic impact in the design and implementation of public health policies. Maps of our model are available (constantly updated up to the saturation of the model) at: https://navaak.shinyapps.io/CVRisk/.
Bibliometric Analysis of Global Scientific Research on SARS-CoV-2 (COVID-19)
Authors: Nasab Fatemeh Rafiei , Rahim Fakher 
Published: March 2020

AbstractBackground and AimSince late 2019, an unknown-origin pneumonia outbreak detected in Wuhan city, Hubei Province, China. We aimed to build a model to qualitatively and quantitatively assess publications of research of COVID-19 from 2019 to 2020.Materials and MethodsData were obtained from the Web of Science (WOS), PubMed, and Scopus Core Collection on March 02, 2020, and updated on March 10. We conducted a qualitative and quantitative analysis of publication outputs, journals, authors, institutions, countries, cited references, keywords, and terms according to bibliometric methods using VOS viewer c software packages.ResultsInitially, we identified 227 papers, of which after an exclusion process, 92 studies were selected for statistical analyses. China accounted for the highest proportion of published research (44 papers, 40.48%), followed by the United States (21 papers, 19.32%), and Canada (7 papers, 6.44%). Adjusted by gross domestic product (GDP), ranked first, with 0.003 articles per billion GDP. In total, the top 10 journals published 47 articles, which accounted for 51.08% of all publications in this Feld. A total of 6 studies (05.52%) were supported by National Natural Science Foundation of China. Chinese Academy of Sciences ranked second 2, 2.76%).ConclusionBibliometric and visualized mapping may quantitatively monitor research performance in science and present predictions. The subject of this study was the fast growing publication on COVID-19. Most studies are published in journals with very high impact factors (IFs) and other journals are more interested in this type of research.HighlightsBibliometric description and mapping provided a birds-eye view of information on Covid-19 related researchReaders to comprehend the history of published Covid-19 articles in just a few minutes.We evaluated the research strength of countries and institutions,Scholars might refer to in order to find cooperative institutions.During our research using the selected database, we tried to guarantee comprehension and objectivity.
Modelling the epidemiological trend and behavior of COVID-19 in Italy
Authors: Rovetta Alessandro , Bhagavathula Akshaya Srikanth , Castaldo Lucia 
Published: March 2020

AbstractAs of May 14, 2020, Italy has been one of the red hotspots for the COVID-19 pandemic. With over 220,000 confirmed cases and almost 33,000 confirmed deaths reported from February, it is necessary to fully understand the spread of COVID-19 in this country. By S.E.I.R. simulation, we estimated the most representative basic reproduction number R0 for the three most affected regions from February 22 to March 14, 2020. In doing so, we have been able to evaluate the consistency of the first containment measures until the end of April, as well as identify possible SARS-CoV-2 local behavior mutations and specificities. Next to that, through new estimates of the infection mortality rate, we recalculated a more plausible number of real infected. Finally, given the absolutely anomalous trend of the Lombardy region, we looked for correlations between COVID-19 total cases and air pollutants such as PM 10 and PM 2.5.
Forecasting Ultra-early Intensive Care Strain from COVID-19 in England, v1.1.4
Authors: Deasy Jacob , Rocheteau Emma , Kohler Katharina , Stubbs Daniel J. , Barbiero Pietro , Liò Pietro , Ercole Ari 
Published: March 2020

AbstractThe COVID-19 pandemic has led to unprecedented strain on intensive care unit (ICU) admission in parts of the world. Strategies to create surge ICU capacity require complex local and national service reconfiguration and reduction or cancellation of elective activity. These measures have an inevitable lag-time before additional capacity comes on-line. An accurate short-range forecast would be helpful in guiding such difficult, costly, and ethically challenging decisions.At the time this work began, cases in England were starting to increase. If this represents a true spread in disease then ICU demand could increase rapidly. Here we present a short-range forecast based on published real-time COVID-19 case data from the seven National Health Service (NHS) commissioning regions in England (East of England, London, Midlands, North East and Yorkshire, North West, South East and South West). We use a Monte Carlo approach to model the likely impact of current diagnoses on regional ICU capacity over a 14-day horizon under the assumption that the increase in cases represents the start of an exponential growth in infections. Our model is designed to be parsimonious and based on available epidemiological data from the literature at the moment.On the basis of the modelling assumptions made, ICU occupancy is likely to increase dramatically in the days following the time of modelling. If the current exponential growth continues, case numbers will be comparable to current ICU bed numbers within weeks. Despite variable growth in absolute patients, all commissioning regions are forecast to be heavily burdened under the assumptions used.Whilst, like any forecast model, there remain uncertainties both in terms of model specification and robust epidemiological data in this early prospective phase, it would seem that surge capacity will be required in the very near future. Our findings should be interpreted with caution, but we hope that our model will help policy decision makers with their preparations. The uncertainties in the data highlight the urgent need for ongoing real-time surveillance to allow forecasts to be constantly updated using high quality local patient-facing data as it emerges.
Assessing the potential impact of COVID-19 in Brazil: Mobility, Morbidity and the burden on the Health Care System
Authors: Coelho Flávio C , Lana Raquel M , Cruz Oswaldo G , Villela Daniel , Bastos Leonardo S , Piontti Ana Pastore y , Davis Jessica T , Vespignani Alessandro , Codeço Claudia T , Gomes Marcelo F C 
Published: March 2020

AbstractBackgroundBrazil detected community transmission of COVID-19 on March 13, 2020. In this study we identify which areas in the country are most vulnerable for COVID-19, both in terms of the risk of arrival of COVID-19 cases and the risk of sustained transmission. The micro-regions with higher social vulnerability are also identified.MethodsProbabilistic models were used to calculate the probability of COVID-19 spread from São Paulo and Rio de Janeiro, according to previous data available on human mobility in Brazil. We also perform a multivariate cluster analysis of socio-economic indices to identify areas with similar social vulnerability.ResultsThe results consist of a series of maps of effective distance, outbreak probability, hospital capacity and social vulnerability. They show areas in the North and Northeast with high risk of COVID-19 outbreak that are also highly vulnerable.InterpretationThe maps produced are useful for authorities in their efforts to prioritize actions such as resource allocation to mitigate the effects of the pandemic and may help other countries to use a similar approach to predict the virus route in their countries as well.FundingNo funding
The local stability of a modified multi-strain SIR model for emerging viral strains
Authors: Fudolig Miguel , Howard Reka 
Published: March 2020

AbstractWe study a novel multi-strain SIR epidemic model with selective immunity by vaccination. A newer strain is made to emerge in the population when a preexisting strain has reached equilbrium. We assume that this newer strain does not exhibit cross-immunity with the original strain, hence those who are vaccinated and recovered from the original strain become susceptible to the newer strain. Recent events involving the COVID-19 virus demonstrates that it is possible for a viral strain to emerge from a population at a time when the influenza virus, a well-known virus with a vaccine readily available for some of its strains, is active in a population. We solved for four different equilibrium points and investigated the conditions for existence and local stability. The reproduction number was also determined for the epidemiological model and found to be consistent with the local stability condition for the disease-free equilibrium.
Basic and effective reproduction numbers of COVID-19 cases in South Korea excluding Sincheonji cases
Authors: Hwang Jeongeun , Park Hyunjung , Jung Jiwon , Kim Sung-Han , Kim Namkug 
Published: March 2020

AbstractIn February and March 2020, COVID-19 epidemic in South Korea met a large black swan effect by a Sincheonji cult mass infection in Daegu-Gyeongbuk area. The black swan made it difficult to evaluate that the current policies for infection prevention including social distancing, closing schools, hand washing, and wearing masks good enough or not. Therefore, in this study, we evaluated basic reproduction number (R0) and time-dependent reproduction number (Rt) of confirmed cases based on various kinds of populations, including total, Daegu-Geyoengbuk, except-Daegu-Gyeongbuk, Sincheonji, and except-Sincheonji. In total, it seems the infection is going to be under control, but this is never true because in the except-Sincheonji and except-Daegu-Geyongbuk cases, R0 is still above 1.0, and Rt is drifting around 1.0. This study could be used to determine government policies in the near future.
Investigating the potential effect of antihypertensive medication on psychiatric disorders: a mendelian randomisation study
Authors: Chauquet Solal , O’Donovan Michael , Walters James , Wray Naomi , Shah Sonia 
Published: March 2020

ABSTRACTBackgroundThere is growing evidence from observational studies that drugs used for the prevention and treatment of CVD may cause, exacerbate, or relieve neuropsychiatric symptoms.AimUse Mendelian randomisation (MR) analysis to investigate the potential effect of different antihypertensive drugs on schizophrenia, bipolar disorder and major depressive disorder.MethodsWe conduct two sample MR using expression quantitative trait loci (eQTLs) for antihypertensive drug target genes as genetic instruments, together with summary data from published genome-wide association studies, to investigate the causal effect of changes in drug target gene expression (as proxies of drug exposure) on psychiatric disorders.ResultsA 1 standard deviation lower expression of the ACE gene in blood was associated with 4.0 mmHg (95% CI = 2.7 – 5.3) lower systolic blood pressure, but increased risk of schizophrenia (OR (95% CI) = 1.75 (1.28 – 2.38)). A concordant direction of effect was observed with ACE expression in brain tissue.ConclusionsFindings suggest an adverse effect of lower ACE expression on schizophrenia risk. This warrants further investigation to determine if lowering ACE activity for treatment of hypertension using ACE inhibitors (particularly centrally-acting drugs) may worsen symptoms in patients with schizophrenia, and whether there is any association between ACE inhibitor use and risk of (mainly late-onset) schizophrenia.
maxnodf: an R package for fair and fast comparisons of nestedness between networks
Authors: Hoeppke Christoph , Simmons Benno I. 
Published: March 2020

AbstractNestedness is a widespread pattern in mutualistic networks that has high ecological and evolutionary importance due to its role in enhancing species persistence and community stability. Nestedness measures tend to be correlated with fundamental properties of networks, such as size and connectance, and so nestedness values must be normalised to enable fair comparisons between different ecological communities. Current approaches, such as using null-corrected nestedness values andz-scores, suffer from extensive statistical issues. Thus a new approach called NODFcwas recently proposed, where nestedness is expressed relative to network size, connectance and the maximum nestedness that could be achieved in a particular network. While this approach is demonstrably effective in overcoming the issues of collinearity with basic network properties, it is computationally intensive to calculate, and current approaches are too slow to be practical for many types of analysis, or for analysing large networks.We developed three highly-optimised algorithms, based on greedy, hillclimbing and simulated annealing approaches, for calculation of NODFc, spread along a speed-quality continuum. Users thus have the choice between a fast algorithm with a less accurate estimate, a slower algorithm with a more accurate estimate, and an intermediate option.We outline the package, and its implementation, as well as provide comparative performance benchmarking and two example analyses. We show thatmaxnodfenables speed increases of hundreds of times faster than existing approaches, with large networks seeing the biggest improvements. For example, for a large network with 3000 links, computation time was reduced from 50 minutes using the fastest existing algorithm to 11 seconds usingmaxnodf.maxnodfmakes correctly-normalised nestedness measures feasible for complex analyses of even large networks. Analyses that would previously take weeks to complete can now be finished in hours or even seconds. Given evidence that correctly normalising nestedness values can significantly change the conclusions of ecological studies, we believe this package will usher in necessary widespread use of appropriate comparative nestedness statistics.
Limits to flow detection in phase contrast MRI
Authors: Williamson Nathan H. , Komlosh Michal E. , Benjamini Dan , Basser Peter J. 
Published: March 2020

AbstractPulsed gradient spin echo (PGSE) complex signal behavior becomes dominated by attenuation rather than oscillation when displacements due to flow are similar or less than diffusive displacements. In this “slow-flow” regime, the optimal displacement encoding parameterqfor phase contrast velocimetry depends on the diffusive length scalerather than the velocity encoding parametervenc= π/(qΔ). The minimum detectable mean velocity using the difference between the phase at +qslowand −qslowis. These theories are then validated and applied to MRI by performing PGSE echo planar imaging experiments on water flowing through a column with a bulk region and a beadpack region at controlled flow rates. Velocities as slow as 6 µm/s are detected with velocimetry. Theories, MRI experimental protocols, and validation on a controlled phantom help to bridge the gap between porous media NMR and pre-clinical phase contrast and diffusion MRI.
OVERWHELMING GENETIC HETEROGENEITY AND EXHAUSTING MOLECULAR DIAGNOSTIC PROCESS IN CHRONIC AND PROGRESSIVE ATAXIAS: FACING IT UP WITH AN ALGORITHM, A GENE, A PANEL AT A TIME
Authors: Maturo J Perez , Zavala L , Vega P , González-Morón D , Medina N , Salinas V , Rosales J , Córdoba M , Arakaki T , Garretto N , Rodríguez-Quiroga S. , Kauffman MA 
Published: March 2020

ABSTRACTAtaxias is one of the most frequent complaints in Neurogenetics units worldwide. Currently, more than 50 subtypes of spinocerebellar ataxias and more than 60 recessive ataxias are recognized. We conducted an 11-year prospective, observational, analytical study in order to estimate the frequency of pediatric and adult genetic ataxias in Argentina, to describe the phenotypes of this cohort and evaluate the diagnostic yield of the algorithm used in our unit. We included 334 ataxic patients. Our diagnostic approach was successful in one third of the cohort. A final molecular diagnosis was reached in 113 subjects. This rate is significantly higher in the subgroup of patients with a positive family history, where the diagnostic yield increased to 55%. The most prevalent dominant and recessive ataxias in Argentina were SCA-2 (36% of dominant ataxias) and FA (62% of recessive ataxias), respectively. Next generation sequencing based assays were diagnostic in the 65% of the patients requiring these tests. These results provide relevant epidemiological information, bringing a comprehensive knowledge of the most prevalent subtypes of genetic ataxias and their phenotypes in our territory and laying the groundwork for rationally implementing genetic diagnostic programs for these disorders in our country.
Deep learning for predicting COVID-19 malignant progression
Authors: Fang Cong , Bai Song , Chen Qianlan , Zhou Yu , Xia Liming , Qin Lixin , Gong Shi , Xie Xudong , Zhou Chunhua , Tu Dandan , Zhang Changzheng , Liu Xiaowu , Chen Weiwei , Bai Xiang , Torr Philip H.S. 
Published: March 2020

AbstractAs COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosis and treatment, which has greatly challenged public medical systems. Treatment priority is often determined by the symptom severity based on first assessment. However, clinical observation suggests that some patients with mild symptoms may quickly deteriorate. Hence, it is crucial to identify patient early deterioration to optimize treatment strategy. To this end, we develop an early-warning system with deep learning techniques to predict COVID-19 malignant progression. Our method leverages clinical data and CT scans of outpatients and achieves an AUC of 0.920 in the single-center study and an average AUC of 0.874 in the multicenter study. Moreover, our model automatically identifies crucial indicators that contribute to the malignant progression, including Troponin, Brain natriuretic peptide, White cell count, Aspartate aminotransferase, Creatinine, and Hypersensitive C-reactive protein.
Early Outcomes of Preoperative 5-fraction Radiation Therapy for Soft Tissue Sarcoma Followed by Immediate Surgical Resection
Authors: Parsai Shireen , Lawrenz Joshua , Kilpatrick Scott , Rubin Brian , Hymes Cory , Gray Michele , Mesko Nathan , Shah Chirag , Nystrom Lukas , Scott Jacob G 
Published: March 2020

ABSTRACTPurpose/ObjectivesThere are limited data regarding the use of hypofractionated radiation therapy (RT) for soft tissue sarcoma. We report early oncologic outcomes and wound complications of patients undergoing preoperative hypofractionated (5 fraction) RT followed by immediate surgical resection.Materials/MethodsAn IRB-approved database of patients treated with preoperative RT for soft tissue sarcoma was queried. Patients treated with a hypofractionated dosing regimen followed by immediate (within 7 days) planned wide surgical resection were identified.ResultsBetween 2016 to 2019, sixteen patients met eligibility criteria. The median clinical follow-up was 10.7 months (range 1.7-33.2). The median patient age was 64 years old (range 33-88). Ten of the sarcomas were located in the lower extremity, 4 in the upper extremity, and two were located in the trunk. Five patients had metastatic disease at diagnosis. The majority of the patients received a total radiation dose of 30 Gy in 5 fractions (range 27.5-40 Gy) on consecutive days. All patients were planned with IMRT/VMAT. The median time to surgical resection following the completion of RT was 1 day (range 0-7 days). The median time from initial biopsy results to completion of primary oncologic therapy was 20 days (range 16-35). Ten patients achieved R0 resection, whereas the remaining 6 patients achieved R1 resection. Of the 13 patients assessed for local control, no patients developed local failure. Five patients developed wound healing complications (31%), of which only three patients (19%) required return to the operating room.ConclusionsTreatment of soft tissue sarcoma with preoperative hypofractionated RT followed by immediate resection resulted in a median of 20 days from biopsy results to completion of oncologic therapy. Early outcomes demonstrate favorable wound healing. Further prospective data with long-term follow-up is required to determine the oncologic outcomes and toxicity of hypofractionated preoperative RT.
Using a Simulation Centre to Evaluate the Effect of anArtificial Intelligence-Powered Clinical Decision Support System for Depression Treatment on the Physician-Patient Interaction
Authors: Benrimoh David , Tanguay-Sela Myriam , Perlman Kelly , Israel Sonia , Mehltretter Joseph , Armstrong Caitrin , Fratila Robert , Parikh Sagar V. , Karp Jordan F. , Heller Katherine , Vahia Ipsit V. , Blumberger Daniel M. , Karama Sherif , Vigod Simone , Myhr Gail , Martins Ruben , Rollins Colleen , Popescu Christina , Lundrigan Eryn , Snook Emily , Wakid Marina , Williams Jérôme , Soufi Ghassen , Perez Tamara , Tunteng Jingla-Fri , Rosenfeld Katherine , Miresco Marc , Turecki Gustavo , Cardona Liliana Gomez , Linnaranta Outi , Margolese Howard C. 
Published: March 2020

ABSTRACTObjectiveAifred is an artificial intelligence (AI)-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician-patient interaction.MethodsTwenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-hour study at a clinical interaction simulation centre with standardized patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-minute clinical scenarios with standardized patients portraying mild, moderate, and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews, and standardized patient feedback.ResultsAll twenty participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the AI’s predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool’s impact on the physician-patient interaction.ConclusionsThe simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician-patient interaction prior to clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.