bluuebunny/crossref_metadata_2025_split huggingface dataset:
Modelling the risk of SARS-CoV-2 infection through PPE doffing in a hospital environment
Authors: King Marco-Felipe , Wilson Amanda M , Weir Mark H. , López-García Martín , Proctor Jessica , Hiwar Waseem , Khan Amirul , Fletcher Louise A. , Sleigh P. Andrew , Clifton Ian , Dancer Stephanie J. , Wilcox Mark , Reynolds Kelly A. , Noakes Catherine J. 
Published: September 2020

AbstractSelf-contamination during doffing of personal protective equipment (PPE) is a concern for healthcare workers (HCW) following SARS-CoV-2 positive patient care. Staff may subconsciously become contaminated through improper glove removal, so quantifying this risk is critical for safe working procedures. HCW surface contact sequences on a respiratory ward were modelled using a discrete-time Markov chin for: IV-drip care, blood pressure monitoring and doctors’ rounds. Accretion of viral RNA on gloves during care was modelled using a stochastic recurrence relation. The HCW then doffed PPE and contaminated themselves in a fraction of cases based on increasing case load. The risk of infection from this exposure was quantified using a dose-response methodology. A parametric study was conducted to analyse the effect of: 1a) increasing patient numbers on the ward, 1b) the proportion of COVID-19 cases, 2) the length of a shift and 3) the probability of touching contaminated PPE. The driving factors for infection risk were surface contamination and number of surface contacts. HCWs on a 100% COVID-19 ward were less than 2-fold more at risk than on a 50% COVID ward (1.6% vs 1%), whilst on a 5% COVID-19 ward, the risk dropped to 0.1% per shift (sd=0.6%). IV-drip care resulted in higher risk than blood pressure monitoring (1.1% vs 1% p<0.0001), whilst doctors’ rounds produced a 0.6% risk (sd=0.8%). Recommendations include supervised PPE doffing procedures such as the “doffing buddy” scheme, maximising hand hygiene compliance post-doffing and targeted surface cleaning for surfaces away from the patient vicinity.ImportanceInfection risk from self-contamination during doffing PPE is an important concern in healthcare settings, especially on a COVID-19 ward. Fatigue during high workload shifts may result in increased frequency of mistakes and hence risk of exposure. Length of staff shift and number of COVID-19 patients on a ward correlate positively with the risk to staff through self-contamination after doffing. Cleaning of far-patient surfaces is equally important as cleaning traditional “high-touch surfaces”, given that there is an additional risk from bioaerosol deposition outside the patient zone(1).
At home and online during the early months of the COVID-19 pandemic and the relationship to alcohol consumption in a national sample of U.S. adults
Authors: Chartier Karen G. , Guidry Jeanine P.D. , Lee Catherine A. 
Published: September 2020

AbstractObjectiveThe current study seeks to understand the links between social media use and alcohol consumption during the early months of the COVID-19 pandemic.MethodData were from the national Understanding American Study, a probability-based Internet panel weighted to represent the U.S. population. Subjects (N=5874; 51% female) were adults, 18 years and older, who completed a March survey (wave 1) and a follow-up survey one month later (wave 3). Analyses assessed the relationship of social media use at wave 1 with wave 3 alcohol use, accounting for wave 1 alcohol use and the sociodemographic characteristics of the sample. We examined the effect of working or studying from home as a moderator.ResultsTwitter and Instagram users, but not Facebook users, drank more frequently at wave 3 than non-users. For Instagram users, more frequent alcohol use at wave 3 was at least partially attributed to the frequency of drinking at wave 1. The interaction between Twitter use and working or studying from home was statistically significant. The combination of being on Twitter and working or studying from home was associated with drinking more days a week.ConclusionsExposure to content about COVID-19 and increased alcohol consumption during the pandemic may contribute to more frequent alcohol use for some social media users, especially those sheltering at home. The study of public health messaging via social media to change alcohol use behaviors during traumatic events is warranted.
Establishment of National DRL for CT in Hybrid Imaging Studies (The Second Phase of the National NM CT (PET) Dose Audit for Kuwait Population -2019)
Authors: Masoomi Michael , Al-Shammeri Iman , Al-Kandari Latifah , Elrahman Hany , Al-Shammari Jehan 
Published: September 2020

ABSTRACTDRLs for CT part used in PET/CT examinations are limited. The aim was to execute the second phase of the national DRL for CT part of PET/CT imaging operating in KW, in support of optimisation and dose reduction as imaging technology is advancing. In this multicentre collaborative PET centers (No:8) audit, data collection was restricted to the adult oncology patients due to a limited number of the other studies and also due to the National MOH Ethical Committee recommendation. The CTDIvol, DLP and SL were recorded and the Median, Mean, SD, 75th, 25th percentiles as well as WB effective dose (ED) were calculated. Dose and scan length statistics for HB and WB scans (65% and 35% of total: 309) and the WB+HB presented together with the proposed NDRLs and the Achievable doses. Third quartile DLP (mGy x cm) and CTDIvol (mGy) values for the HB were (537, 5) which were higher than the UK NDRL (400, 4.3) but were lower than the Swiss NDRL (620, 6) and the France NDRL (762, 7.7). Comparatively, the Proposed NDRLs for (WB) were (684, 4.1) which were lower than Swiss National Data (720, 5.0). It is worth noted that, the Swiss had about 5000 (HB) & 706 (WB), the UK had 370 (HB) and France had 1000 (HB) entries. Calculated ED varied from 4.1 to 10.2 mSv, (mean values=6.9 mSv) for HB and from 2.6 to 7 mSv (mean value=4.6 mSv) for WB scans which were lower than the first phase (2018). Although, there was 9.1% improvement in NDRL, but the outcome suggested there is a continuous need for monitoring NDRL.
COVID-19 dynamics across the US: A deep learning study of human mobility and social behavior
Authors: Bhouri Mohamed Aziz , Costabal Francisco Sahli , Wang Hanwen , Linka Kevin , Peirlinck Mathias , Kuhl Ellen , Perdikaris Paris 
Published: September 2020

AbstractThis paper presents a deep learning framework for epidemiology system identification from noisy and sparse observations with quantified uncertainty. The proposed approach employs an ensemble of deep neural networks to infer the time-dependent reproduction number of an infectious disease by formulating a tensor-based multi-step loss function that allows us to efficiently calibrate the model on multiple observed trajectories. The method is applied to a mobility and social behavior-based SEIR model of COVID-19 spread. The model is trained on Google and Unacast mobility data spanning a period of 66 days, and is able to yield accurate future forecasts of COVID-19 spread in 203 US counties within a time-window of 15 days. Strikingly, a sensitivity analysis that assesses the importance of different mobility and social behavior parameters reveals that attendance of close places, including workplaces, residential, and retail and recreational locations, has the largest impact on the basic reproduction number. The model enables us to rapidly probe and quantify the effects of government interventions, such as lock-down and re-opening strategies. Taken together, the pro-posed framework provides a robust workflow for data-driven epidemiology model discovery under uncertainty and produces probabilistic forecasts for the evolution of a pandemic that can judiciously inform policy and decision making. All codes and data accompanying this manuscript are available at https://github.com/PredictiveIntelligenceLab/DeepCOVID19.
An expert judgment model to predict early stages of the COVID-19 outbreak in the United States
Authors: McAndrew Thomas , Reich Nicholas G. 
Published: September 2020

AbstractDuring early stages of the COVID-19 pandemic, forecasts provided actionable information about disease transmission to public health decision-makers. Between February and May 2020, experts in infectious disease modeling made weekly predictions about the impact of the pandemic in the U.S. We aggregated these predictions into consensus predictions. In March and April 2020, experts predicted that the number of COVID-19 related deaths in the U.S. by the end of 2020 would be in the range of 150,000 to 250,000, with scenarios of near 1m deaths considered plausible. The wide range of possible future outcomes underscored the uncertainty surrounding the outbreak’s trajectory. Experts’ predictions of measurable short-term outcomes had varying levels of accuracy over the surveys but showed appropriate levels of uncertainty when aggregated. An expert consensus model can provide important insight early on in an emerging global catastrophe.One Sentence SummaryExpert predictions provided valuable insight of future societal burden during the early stages of the COVID-19 pandemic in the US.
A simplified approach to monitoring the COVID-19 epidemiologic situation using waste water analysis and its application in Russia
Authors: Kuryntseva Polina Alexandrovna , Karamova Kamalya Oktay , Fomin Valentin Petrovich , Selivanovskaya Svetlana Yurevna , Galitskaya Polina Yurevna 
Published: September 2020

AbstractThe number of registered cases of COVID-19 is increasing in the world, and some countries are reporting a second wave of the pandemic. Accurate and real time information about epidemiological situation is therefore urgently needed for managing decisions in the countries, regions and municipalities which are affected. Massive testing of viral presence in people’s saliva, a smear from the nose, nasopharynx and / or oropharynx, bronchial lavage water obtained by fibrobronchoscopy (bronchoalveolar lavage), as well as from (endo) tracheal, nasopharyngeal aspirate, sputum, biopsy or autopsy material of the lungs, whole blood, serum or antibodies presence in blood cannot give relevant information about the COVID-19 infection rate in the community since simultaneous testing of the whole community is not technically possible, the information obtained in testing of specific groups is retarded and, in addition, such testing is expensive. The alternative to mass testing of the population is the testing of wastewater that could contain SARS-CoV-2 particles originating from excreta. Such testing has several limitations connected with the particularities of the testing procedure.In the present study, a modified approach for detection of COVID-19 infection rate using wastewater analysis has been developed. The approach includes i) the creation of a calibration curve on the basis of the serial dilution of excreta collected from people who are infected with COVID-19 and ii) the analysis of the wastewater samples and their serial dilutions, the approach excludes usage of concentrating techniques before wastewater sample analysis as well as usage of external control in RT-PCR reactions for calculation of numbers of viral particles. The minimum infection rate that can be detected using this approach is 10-2%. The approach developed was used to investigate wastewater from eleven sewage inspection chambers in the city of Kazan (Russia). It was demonstrated that the average infection rate of people using these sewers was over 0.4% in July 2020.
Early Humoral Response Correlates with Disease Severity and Outcomes in COVID-19 Patients
Authors: Hashem Anwar M , Algaissi Abdullah , Almahboub Sarah A , Alfaleh Mohamed A , Abujamel Turki S , Alamri Sawsan S , Alluhaybi Khalid A , Hobani Haya I , AlHarbi Rahaf H , Alsulaiman Reem M , ElAssouli M-Zaki , Hala Sharif , Alharbi Naif K , Alhabbab Rowa Y , AlSaieedi Ahdab A , Abdulaal Wesam H , Bukhari Abdullah , AL-Somali Afrah A , Alofi Fadwa S , Khogeer Asim A , Pain Arnab , Alkayyal Almohanad A , Almontashiri Naif AM , Mahmoud Ahmad Bakur , Li Xuguang 
Published: September 2020

AbstractThe Coronavirus Disease 2019 (COVID-19), caused by the novel SARS-CoV-2, continues to spread globally with significantly high morbidity and mortality rates. Immunological surrogate markers, in particular antigen-specific responses, are of unquestionable value for clinical management of patients with COVID-19. Here, we investigated the kinetics of IgM, IgG against the spike (S) and nucleoproteins (N) proteins and their neutralizing capabilities in hospitalized patients with RT-PCR confirmed COVID-19 infection. Our data show that SARS-CoV-2 specific IgG, IgM and neutralizing antibodies (nAbs) were readily detectable in almost all COVID-19 patients with various clinical presentations. Notably, anti-S and -N IgG, peaked 20-40 day after disease onset, and were still detectable for at least up to 70 days, with nAbs observed during the same time period. Moreover, nAbs titers were strongly correlated with IgG antibodies. Significantly higher levels of nAbs as well as anti-S1 and N IgG and IgM antibodies were found in patients with more severe clinical presentations, patients requiring admission to intensive care units (ICU) or those with fatal outcomes. Interestingly, lower levels of antibodies, particularly anti-N IgG and IgM in the first 15 days after symptoms onset, were found in survivors and those with mild clinical presentations. Collectively, these findings provide new insights into the characteristics and kinetics of antibody responses in COVID-19 patients with different disease severity.
Prevalences and healthcare expenditures related to 58 health conditions from 2012 to 2017 in France: diseases and healthcare expenditure mapping, a national population-based study
Authors: Rachas Antoine , Gastaldi-Ménager Christelle , Denis Pierre , Lesuffleur Thomas , Nicolas Muriel , Pestel Laurence , Mette Corinne , Drouin Jérôme , Rivière Sébastien , Tajahmady Ayden , Gissot Claude , Fagot-Campagna Anne 
Published: September 2020

AbstractBackgroundDescription of the prevalence of diseases and resources mobilized for the management of each disease is essential to identify public health priorities. We described the prevalences of 58 health conditions and all reimbursed healthcare expenditure by health condition in France between 2012 and 2017.Methods and FindingsAll national health insurance general scheme beneficiaries (87% of the French population) with at least one reimbursed healthcare expenditure were included from the French national health database. We identified health conditions (diseases, episodes of care, chronic treatments) by algorithms using ICD-10 codes for long-term diseases or hospitalisations, medications or medical procedures. We reported crude and age and sex-standardized annual prevalences between 2012 and 2017, and total and mean (per patient) reimbursed expenditure attributed to each condition without double counting, and according to the type of expenditure.In 2017, in a population of 57.6 million people (54% of women, median age: 40 years), the most prevalent diseases were diabetes (standardized prevalence: 5.8%), chronic respiratory diseases (5.5%) and chronic coronary heart disease (2.9%). Diseases concentrating the highest expenditures were active cancers (10% of total expenditure of €140.1 billion), mental illness (10%; neurotic and mood disorders: 4%; psychotic disorders: 3%), and chronic cardiovascular diseases (8%). Between 2012 and 2017, the most marked increase in total expenditure concerned liver and pancreatic diseases (+9.3%), related to the increased drug expenditure in 2014. Conversely, the increase in the number of patients (and the prevalence) explained the majority of the increase of total expenditures for cardiovascular disease, diabetes and mental illness.ConclusionsThese results showed a regular increase of the prevalence and expenditure of certain chronic diseases, probably related to ageing of the population, and increased expenditures related to major therapeutic innovations. The Diseases and Healthcare Expenditure Mapping therefore enlightens decision-makers in charge of public health and health accounts.
LOW PSYCHOLOGICAL WELL-BEING IN MEN WHO HAVE SEX WITH MEN (MSM) DURING THE SHELTER-IN-PLACE ORDERS TO PREVENT THE COVID-19 SPREAD: RESULTS FROM A NATIONWIDE STUDY
Authors: Silva Camargo Emerson Lucas , de Oliveira Bruna Isabela Adolpho , Siffoni Igor Fessina , de Sousa Anderson Reis , Teixeira Jules Ramon Brito , Amélia Costa Mendes Isabel , de Sousa Álvaro Francisco Lopes 
Published: September 2020

ABSTRACTIntroductionLittle is known about how sheltering in place to contain the spread of COVID-19 over extended periods affects individuals’ psychological well-being. This study’s objective was to analyze the factors associated with MSM’s low psychological well-being in the COVID-19 pandemic context.MethodThis cross-sectional study was conducted online in the entire Brazilian territory (26 states and federal district) in April and May 2020. The participants were recruited using an adapted version of Respondent-Driven Sampling (RDS). Data were collected using social media and MSM dating apps. We estimated the prevalence, crude prevalence ratio (PR), and respective confidence intervals (CI95%).ResultsOnly 44.4% of the sample presented high levels of well-being. Low psychological well-being appeared associated with the youngest age group (PR: 2.76; CI95%: 1.90-4.01), polyamorous relationships (PR: 2.78; CI95%: 1.51-5.11), not complying with social isolation measures (PR: 6.27; CI95%: 4.42-8.87), not using the social media to find partners (PR: 1.63; CI95%: 1.06-2.53), having multiple sexual partners (PR: 1.80; CI95%: 1.04-3.11), having reduced the number of partners (PR: 2.67; CI95%: 1.44-4.95), and group sex (PR: 1.82; CI95%: 1.23-2.69)ConclusionThe well-being of MSM living in Brazil was negatively affected during the social distancing measures intended to control the spread of COVID-19.Policy ImplicationsPlanning and implementing public policies and actions to promote psychological well-being are needed to improve MSM’s resilience through the adoption of safe strategies and behavior.
Treatment of Moderate to Severe Respiratory COVID-19—A Cost-Utility Analysis
Authors: Congly Stephen E. , Varughese Rhea A. , Brown Crystal E. , Clement Fiona M. , Saxinger Lynora 
Published: September 2020

AbstractBackgroundDue to COVID-19’s significant morbidity and mortality, identifying the most cost-effective pharmacologic treatment strategy is critical. As such, we determined the most cost-effective strategy for moderate to severe COVID-19 respiratory infections using the United States health care system as a representative model.MethodsA decision analytic model modelled a base case scenario of a 60-year-old patient admitted to hospital with COVID-19. Patients requiring oxygen were considered moderate severity, and patients with severe COVID-19 required intubation with intensive care. Strategies modelled included giving remdesivir to all patients, remdesivir in severe infections, remdesivir in moderate infections, dexamethasone to all patients, dexamethasone in severe infections, remdesivir in moderate/dexamethasone in severe infections, and best supportive care. Data for the model came from the published literature. The time horizon was 1 year; no discounting was performed due to the short duration. The perspective was of the payer in the United States health care system.ResultsSupportive care for moderate/severe COVID-19 cost $11,112.98/0.8256 QALY. Remdesivir in moderate/dexamethasone in severe infections was the most cost-effective with an incremental cost-effectiveness ratio of $19,764.56/QALY gained compared to supportive care. Probabilistic sensitivity analyses showed remdesivir for moderate/dexamethasone for severe COVID-19 infection was most cost-effective in 88.6% of scenarios and dexamethasone in moderate-severe infections in 11.4% of scenarios. With lower willingness to pay thresholds ($250-$37,500), dexamethasone for severe infections was favoured.ConclusionsRemdesivir for moderate/dexamethasone for severe COVID-19 infections was the0020most cost-effective strategy. Further data is required for remdesivir to better assess its cost effectiveness in treatment of COVID-19.
The N300: An Index For Predictive Coding Of Complex Visual Objects and Scenes
Authors: Kumar Manoj , Federmeier Kara D. , Beck Diane M. 
Published: September 2020

AbstractPredictive coding models can simulate known perceptual or neuronal phenomena, but there have been fewer attempts to identify a reliable neural signature of predictive coding for complex stimuli. In a pair of studies, we test whether the N300 component of the event-related potential, occurring 250-350 ms post-stimulus-onset, has the response properties expected for such a signature of perceptual hypothesis testing at the level of whole objects and scenes. We show that N300 amplitudes are smaller to representative (“good exemplars”) compared to less representative (“bad exemplars”) items from natural scene categories. Integrating these results with patterns observed for objects, we establish that, across a variety of visual stimuli, the N300 is responsive to statistical regularity, or the degree to which the input is “expected” (either explicitly or implicitly) based on prior knowledge, with statistically regular images evoking a reduced response. Moreover, we show that the measure exhibits context-dependency; that is, we find the N300 sensitivity to category representativeness when stimuli are congruent with, but not when they are incongruent with, a category pre-cue. Thus, we argue that the N300 is the best candidate to date for an index of perceptual hypotheses testing for complex visual objects and scenes.
EPISPOT: an epigenome-driven approach for detecting and interpreting hotspots in molecular QTL studies
Authors: Ruffieux Hélène , Fairfax Benjamin P. , Nassiri Isar , Vigorito Elena , Wallace Chris , Richardson Sylvia , Bottolo Leonardo 
Published: September 2020

AbstractWe present EPISPOT, a fully joint framework which exploits large panels of epigenetic annotations as variant-level information to enhance molecular quantitative trait locus (QTL) mapping. Thanks to a purpose-built Bayesian inferential algorithm, EPISPOT accommodates functional information for bothcisandtransactions, including QTLhotspoteffects. It effectively couples simultaneous QTL analysis of thousands of genetic variants and molecular traits, and hypothesis-free selection of biologically interpretable annotations which directly contribute to the QTL effects. This unified, epigenome-aided learning boosts statistical power and sheds light on the regulatory basis of the uncovered hits; EPISPOT therefore marks an essential step towards improving the challenging detection and functional interpretation oftrans-acting genetic variants and hotspots. We illustrate the advantages of EPISPOT in simulations emulating real-data conditions and in a monocyte expression QTL study, which confirms known hotspots and finds other signals, as well as plausible mechanisms of action. In particular, by highlighting the role of monocyte DNase-I sensitivity sites from > 150 epigenetic annotations, we clarify the mediation effects and cell-type specificity of major hotspots close to the lysozyme gene. Our approach forgoes the daunting and underpowered task of one-annotation-at-a-time enrichment analyses for prioritisingcisandtransQTL hits and is tailored to any transcriptomic, proteomic or metabolomic QTL problem. By enabling principled epigenome-driven QTL mapping transcriptome-wide, EPISPOT helps progress towards a better functional understanding of genetic regulation.