bluuebunny/crossref_metadata_2025_split huggingface dataset:
Safe Blues: A Method for Estimation and Control in the Fight Against COVID-19
Authors: Dandekar Raj , Henderson Shane G. , Jansen Marijn , Moka Sarat , Nazarathy Yoni , Rackauckas Christopher , Taylor Peter G. , Vuorinen Aapeli 
Published: May 2020

AbstractHow do fine modifications to social distancing measures really affect COVID-19 spread? A major problem for health authorities is that we do not know.In an imaginary world, we might develop a harmless biological virus that spreads just like COVID-19, but is traceable via a cheap and reliable diagnosis. By introducing such an imaginary virus into the population and observing how it spreads, we would have a way of learning about COVID-19 because the benign virus would respond to population behaviour and social distancing measures in a similar manner. Such a benign biological virus does not exist. Instead, we propose a safe and privacy-preserving digital alternative.Our solution is to mimic the benign virus by passing virtual tokens between electronic devices when they move into close proximity. As Bluetooth transmission is the most likely method used for such inter-device communication, and as our suggested “virtual viruses” do not harm individuals’ software or intrude on privacy, we call these Safe Blues.In contrast to many app-based methods that inform individuals or governments about actual COVID-19 patients or hazards, Safe Blues does not provide information about individuals’ locations or contacts. Hence the privacy concerns associated with Safe Blues are much lower than other methods. However, from the point of view of data collection, Safe Blues has two major advantages:Data about the spread of Safe Blues is uploaded to a central server in real time, which can give authorities a more up-to-date picture in comparison to actual COVID-19 data, which is only available retrospectively.Sampling of Safe Blues data is not biased by being applied only to people who have shown symptoms or who have come into contact with known positive cases.These features mean that there would be real statistical value in introducing Safe Blues. In the medium term and end game of COVID-19, information from Safe Blues could aid health authorities to make informed decisions with respect to social distancing and other measures.In this paper we outline the general principles of Safe Blues and we illustrate how Safe Blues data together with neural networks may be used to infer characteristics of the progress of the COVID-19 pandemic in real time. Further information is on the Safe Blues website: https://safeblues.org/.
The genetic analysis of a founder Northern American population of European descent identifiesFANCIas a candidate familial ovarian cancer risk gene
Authors: Fierheller Caitlin T , Guitton-Sert Laure , Alenezi Wejdan M , Revil Timothée , Oros Kathleen K , Bedard Karine , Arcand Suzanna L , Serruya Corinne , Behl Supriya , Meunier Liliane , Fleury Hubert , Fewings Eleanor , Subramanian Deepak N , Nadaf Javad , Provencher Diane , Foulkes William D , El Haffaf Zaki , Mes-Masson Anne-Marie , Majewski Jacek , Tischkowitz Marc , James Paul A , Campbell Ian G , Greenwood Celia M T , Ragoussis Jiannis , Masson Jean-Yves , Tonin Patricia N 
Published: May 2020

AbstractSome familial ovarian cancer (OC) could be due to rare risk alleles in genes that each account for a relatively small proportion of cases not due toBRCA1andBRCA2, major risk genes in the homologous recombination (HR) DNA repair pathway. We report a new candidate OC risk allele,FANCIc.1813C>T in a Fanconi anemia (FA) gene that plays a role upstream of the HR DNA repair pathway. This variant was identified by whole exome sequencing of aBRCA1andBRCA2mutation-negative French Canadian (FC) OC family from a population exhibiting founder effects. In FCs, the c.1813C>T allele was detected in 7% (3/43) of familial and 1.6% (7/439) of sporadic OC cases; and in 3.7% (3/82) of familial breast cancer (BC) cases with a family history of OC and in 1.9% (3/158) of BC only families. This allele was significantly associated with FCBRCA1andBRCA2mutation-negative OC families (OR=5.6; 95%CI=1.6-19; p=0.006). AlthoughFANCIc.1813C>T was detected in 2.5% (74/2950) of cancer-free FC females, carriers had a personal history of known OC risk reducing factors, and female/male carriers were more likely to have reported a first-degree relative with OC (ρ=0.037; p=0.011). Eight rare potentially pathogenicFANCIvariants were identified in 3.3% (17/516) of Australian OC cases, including 10 carriers ofFANCIc.1813C>T. Potentially pathogenicFANCIvariants were significantly more common in AUS OC cases with a family history of OC than in isolated OC cases (p=0.027). The odds ratios (OR) were >3 for carriers of any of the seven rarestFANCIalleles, and 1.5 for c.1813C>T. Data from the OC Association Consortium revealed that the ORs for the c.1813C>T allele were highest for the most common OC subtypes. Localization of FANCD2, part of the FANCI-FANCD2 (ID2) binding complex in the FA pathway, to sites of induced DNA damage was severely impeded in cells expressing the p.L605F isoform. This isoform was expressed at a reduced level; unstable by formaldehyde or mitomycin C treatment; and exhibited sensitivity to cisplatin but not to olaparib (a poly [ADP-ribose] polymerase inhibitor). By tissue microarray analyses, FANCI protein was robustly expressed in fallopian tube epithelial cells but expressed at low-to-moderate levels in 88% (83/94) of high-grade serous carcinoma OC samples. This is the first study to describe potentially pathogenic variants in OC in a member of the ID2 complex of the FA DNA repair pathway. Our data suggest that potentially pathogenicFANCIvariants may modify OC risk in cancer families.
Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland
Authors: Lemaitre Joseph. C. , Perez-Saez Javier , Azman Andrew S. , Rinaldo Andrea , Fellay Jacques 
Published: May 2020

AbstractFollowing the rapid dissemination of COVID-19 cases in Switzerland, large-scale non-pharmaceutical interventions (NPIs) were implemented by the cantons and the federal government between February 28 and March 20. Estimates of the impact of these interventions on SARS-CoV-2 transmission are critical for decision making in this and future outbreaks. We here aim to assess the impact of these NPIs on disease transmission by estimating changes in the basic reproduction number (R0) at national and cantonal levels in relation to the timing of these NPIs. We estimate the time-varying R0 nationally and in twelve cantons by fitting a stochastic transmission model explicitly simulating within hospital dynamics. We use individual-level data of >1,000 hospitalized patients in Switzerland and public daily reports of hospitalizations and deaths. We estimate the national R0 was 3.15 (95% CI: 2.13-3.76) at the start of the epidemic. Starting from around March 6, we find a strong reduction in R0 with a 85% median decrease (95% quantile range, QR: 83%-90%) to a value of 0.44 (95% QR: 0.27-0.65) in the period of March 29-April 5. At the cantonal-level R0 decreased over the course of the epidemic between 71% and 94%. We found that reductions in R0 were synchronous with changes in mobility patterns as estimated through smartphone activity, which started before the official implementation of NPIs. We found that most of the reduction of transmission is due to behavioural changes as opposed to natural immunity, the latter accounting for only about 3% of the total reduction in effective transmission. As Switzerland considers relaxing some of the restrictions of social mixing, current estimates of R0 well below one are promising. However most of inferred transmission reduction was due to behaviour change (<3% due to natural immunity buildup), with an estimated 97% (95% QR: 96.6%-97.2%) of the Swiss population still susceptible to SARS-CoV-2 as of April 24. These results warrant a cautious relaxation of social distance practices and close monitoring of changes in both the basic and effective reproduction numbers.
Projection of COVID-19 Cases and Deaths in the US as Individual States Re-open May 4, 2020
Authors: Yamana Teresa , Pei Sen , Kandula Sasikiran , Shaman Jeffrey 
Published: May 2020

AbstractIn March and April 2020, control measures enforcing social distancing and restricting individual movement and contact were adopted across the United States in an effort to slow the spread and growth of COVID-19. However, a number of states have now begun to ease these restrictions. Here, we evaluate the effects of loosening stay-at-home orders on COVID-19 incidence and related outcomes. We use a metapopulation model applied at county resolution to simulate the spread and growth of COVID-19 incidence in the United States. We calibrate the model against county-level daily case and death data collected from February 21, 2020 to May 2, 2020, and project the outbreak in 3,142 US counties for 6 weeks into the future. Projections for daily reported cases, daily new infections (both reported and unreported), new and cumulative hospital bed demand, ICU bed and ventilator demand, as well as daily mortality, are generated. We observe a rebound in COVID-19 incidence and deaths beginning in late May, approximately 2 to 4 weeks after states begin to reopen. Importantly, the lag between infection acquisition and case confirmation, coupled with insufficient broader testing and contact tracing, will mask any rebound and exponential growth of the COVID-19 until it is well underway.
COVID-19 DYNAMICS: A HETEROGENEOUS MODEL
Authors: Gerasimov Andrey , Lebedev Georgy , Lebedev Mikhail , Semenycheva Irina 
Published: May 2020

AbstractThe ongoing Coronavirus disease 2019 (COVID-19) epidemic is different from the previous epidemic of severe acute respiratory syndrome (SARS), which demands a rigorous analysis for the selection of anti-epidemic measures and their lifting when the epidemic subsides. Here we estimate the basic reproductive number for COVID-19 and propose a dynamical model for the time course of infection number. With this model, we assessed the effects of different measures for infection risk control. The model is different from the previous ones as it models the population as heterogeneous, with subpopulations having different infection risks. Our analyses showed that after this heterogeneity is incorporated in the model, several characteristics of the epidemic are estimated more accurately: the total number of cases and peak number of cases are lower compared to the homogeneous case, the early-stage growth rate in the number of infection cases is little affected, and the decrease in the number of infections slows down during the epidemic late stage. The comparison of our model results with the available data for COVID-19 indicates that the anti-epidemic measures undertaken in China and the rest of the world managed to decrease the basic reproductive number but did not assure an accumulation of sufficient collective immunity. Thus, the epidemic has a high likelihood to restart, which necessitates a careful approach to lifting the quarantine measures.
Scaling COVID-19 against inequalities: should the policy response consistently match the mortality challenge?
Authors: McCartney Gerry , Leyland Alastair H. , Walsh David , Dundas Ruth 
Published: May 2020

AbstractBackgroundThe mortality impact of COVID-19 has thus far been described in terms of crude death counts. We aimed to calibrate the scale of the modelled mortality impact of COVID-19 using age-standardised mortality rates and life expectancy contribution against other, socially-determined, causes of death in order to inform governments and the public.MethodsWe compared mortality attributable to suicide, drug poisoning and socioeconomic inequality with estimates of mortality from an infectious disease model of COVID-19. We calculated age-standardised mortality rates and life expectancy contributions for the UK and its constituent nations.ResultsMortality from a fully unmitigated COVID-19 pandemic is estimated to be responsible for a negative life expectancy contribution of −5.96 years for the UK. This is reduced to −0.33 years in the fully mitigated scenario. The equivalent annual life expectancy contributions of suicide, drug poisoning and socioeconomic inequality-related deaths are −0.25, −0.20 and −3.51 years respectively. The negative impact of fully unmitigated COVID-19 on life expectancy is therefore equivalent to 24 years of suicide deaths, 30 years of drug poisoning deaths, and 1.7 years of inequality-related deaths for the UK.ConclusionFully mitigating COVID-19 is estimated to prevent a loss of 5.63 years of life expectancy for the UK. Over 10 years there is a greater negative life expectancy contribution from inequality than around six unmitigated COVID-19 pandemics. To achieve long-term population health improvements it is therefore important to take this opportunity to introduce post-pandemic economic policies to ‘build back better’.Summary boxWhat is already known on this subject?COVID-19 has been modelled to create a substantial excess mortality in the UK, depending on the degree to which this is mitigated by social distancing measures. Best estimates of 510,000 and 20,000 crude deaths are predicted in unmitigated and fully mitigated scenarios respectively.What does this study add?We scale the mortality impact of the modelled COVID-19 on age-standardised mortality and life expectancy against suicide, drug poisonings and inequalities. The impact of COVID-19 on life expectancy is substantial (−5.96 years) if unmitigated, but over a decade the life expectancy impact of inequalities is around six times greater than even an unmitigated pandemic.
Assessment of medication Dosage Adjustment in Hospitalized Patients with Chronic Kidney Disease
Authors: Hassan Zair , Ali Iftikhar , Ullah Arslan Rahat , Ahmed Raheel , Rehman Shakeel , Khan Azizullah 
Published: May 2020

ABSTRACTBackgroundInappropriate medication dosing can develop adverse drug reactions (ADRs) or ineffective therapy due to declined renal function in patients with renal insufficiency. This necessitates proper renal dose adjustment. Using a retrospective analysis of medical records, this study was proposed to evaluate medication dosage adjustment in hospitalized chronic kidney disease (CKD) patients.MethodsThis retrospective review of medical records was conducted at the Institute of Kidney Disease (IKD), Peshawar. It included all CKD patients hospitalized between May 01, 2019 and April 25, 2020. Glomerular filtration rate was calculated using Modification of Diet in Renal Disease (MDRD) formula and dose appropriateness was established by evaluating practice with relevant reference books.ResultsOf the total 1537 CKD patients, 231(15.03%) had evidence of dosing error, which were considered for final analysis. Overall, 1549 drugs were prescribed, 480(30.99%) drugs required dose adjustment of which 196(40.42%) were adjusted properly and the remaining 286(59.58%) were unadjusted. The most common unadjusted drugs were meropenem, cefepime, ciprofloxacin and rosuvastatin, whereas captopril, aspirin, bisoprolol, pregabalin and levofloxacin had the highest percentage of adjusted drugs. On multivariate logistic regression, the number of drugs requiring dosing adjustments and obstructive nephropathy were found to be statistically significant factors that increased the likelihood of the medication dosing errors: A unit increase in the number of drugs requiring dose adjustment increases 5.241 times the likelihood of dosing error. Similarly the presence of obstructive nephropathy (OR 0.383, 95% Cl [0.153-0.960] P= 0.041) was found to be significantly associated with dosing error after adjustment for potential confounding factors.ConclusionThe dosing of more than half of the prescribed drugs that required adjustment in CKD patients were not adjusted which showed that medication dosing errors were high. This highlights the importance of medication prescription according to guidelines in these patients to improve the outcomes of pharmacotherapy and patients’ quality of life.
Does Ventricular Substrate Play a Role in Incident Stroke? The Atherosclerosis Risk in Communities (ARIC) Study
Authors: Johnson John A. , Haq Kazi T. , Lutz Katherine J. , Peters Kyle K. , Paternostro Kevin A. , Craig Natalie E. , Stencel Nathan , Hawkinson Lila , Khayyat-Kholghi Maedeh , Tereshchenko Larisa G. 
Published: May 2020

AbstractBackground—The goal of the study was to determine an association of ventricular substrate with thrombotic, cardioembolic, and hemorrhagic stroke.Methods—Participants from the Atherosclerosis Risk in Communities study with analyzable ECGs and no history of stroke were included (n=14,479; age 54±6 y; 55% female; 24% black). Ventricular substrate was characterized by cardiac memory [spatial QRS-T angle (QRS-Ta), sum absolute QRST integral (SAIQRST), spatial ventricular gradient magnitude (SVGmag)], premature ventricular contractions (PVC) and tachycardia-dependent intermittent bundle branch block (TD-IBBB) on 12-lead ECG recorded at visits 1-5. Incident strokes were adjudicated by physician reviewers. Cox proportional hazard risk models were constructed.Results—Over a median 24.5 y follow-up, there were 899 thrombotic, 400 cardioembolic, and 187 hemorrhagic strokes. After adjustment for cardiovascular disease (CVD) and its risk factors, atrial fibrillation / atrial substrate, and ECG-left ventricular hypertrophy, PVC (HR 1.72; 95%CI 1.02-2.92), QRS-Ta (HR 1.15; 95%CI 1.03-1.28), SAIQRST (HR 1.20; 95%CI 1.07-1.34) and time-updated SVGmag (HR 1.19; 95%CI 1.08-1.32) associated with cardioembolic stroke. After adjustment for CVD and its risk factors, PVC (HR 1.53; 95%CI 1.03-2.26), QRS-Ta (HR 1.08; 95%CI 1.01-1.16), SAIQRST (HR 1.07; 95%CI 1.01-1.14), and time-updated SVGmag (HR 1.11; 95%CI 1.04-1.19) associated with thrombotic stroke. In fully adjusted time-updated Cox model QRS-Ta (HR 1.20; 95%CI 1.04-1.38), SAI QRST (HR 1.23; 95%CI 1.06-1.43), SVGmag (HR 1.23; 95%CI 1.06-1.43) associated with hemorrhagic stroke, whereas TD-IBBB trended (HR 1.84; 95%CI 0.25-13.33).Conclusions—PVC burden reflected by cardiac memory is associated with ischemic stroke. Transient cardiac memory (likely through TD-IBBB) precedes hemorrhagic stroke.
Quantitative proteomics of coeliac gut during 14-day gluten challenge: low-level baseline inflammation despite clinical and histological normality predicts subsequent response
Authors: Stamnaes Jorunn , Stray Daniel , Stensland Maria , Sarna Vikas K. , Nyman Tuula A. , Lundin Knut E. A. , Sollid Ludvig M. 
Published: May 2020

ABSTRACTObjectiveTo shed light on gut mucosa processes provoked by gluten exposure in coeliac disease, we performed a quantitative proteomic analysis of duodenal tissue from well-treated coeliac patients before and after gluten challenge.DesignWe extracted and digested proteins from formalin-fixed paraffin-embedded tissue of 19 coeliac disease patients who had been challenged orally with gluten for 14 days. Protein identification and quantification was done by label-free quantitative mass spectrometry-based proteomics from total tissue and from laser capture microdissected epithelial cell layer. Proteomics data were compared with clinical, serological and histological data.ResultsAt baseline, all patients were in clinical and mucosal remission (Marsh 0-1) except one (Marsh 3). After challenge, five patients reached Marsh 3 scores. Proteome analysis categorised these five and additionally two patients as responders. Already at baseline, responder patients differed from the remaining patients in their gut tissue protein composition with altered levels of inflammatory and enterocyte function proteins – the same proteins that changed upon gluten challenge. Patients classified as responders from the proteomic analysis also differed from the remaining patients at baseline, with mild crypt hyperplasia and a slight increase in blood inflammatory parameters and gluten specific CD4+ T-cell frequencies.ConclusionDespite clinical and histological remission, coeliac disease patients that develop a mucosal response after 14-day gluten challenge have already at baseline altered protein compositions of their gut tissue with signs of ongoing inflammation. Thus, apparently well-treated coeliac disease is frequently not fully quiescent with presence of low-grade anti-gluten immunity in gut mucosa.
A prospective cohort study of prodromal Alzheimer’s disease: Prospective Imaging Study of Ageing: Genes, Brain and Behaviour (PISA)
Authors: Lupton Michelle K , Robinson Gail A , Adam Robert J , Rose Stephen , Byrne Gerard J , Salvado Olivier , Pachana Nancy A , Almeida Osvaldo P , McAloney Kerrie , Gordon Scott D , Raniga Parnesh , Fazlollahi Amir , Xia Ying , Ceslis Amelia , Sonkusare Saurabh , Zhang Qing , Kholghi Mahnoosh , Karunanithi Mohan , Mosley Philip E , Lv Jinglei , Adsett Jessica , Garden Natalie , Fripp Jurgen , Martin Nicholas G , Guo Christine C , Breakspear Michael 
Published: May 2020

AbstractThis prospective cohort study, “Prospective Imaging Study of Ageing: Genes, Brain and Behaviour” (PISA) seeks to characterise the phenotype and natural history of healthy adult Australians at high future risk of Alzheimer’s disease (AD). In particular, we are recruiting mid-life Australians with high and low genetic risk of dementia to discover biological markers of early neuropathology, identify modifiable risk factors, and establish the very earliest phenotypic and neuronal signs of disease onset. PISA utilises genetic prediction to recruit and enrich a prospective cohort and follow them longitudinally. Online surveys and cognitive testing are used to characterise an Australia-wide sample currently totalling nearly 3,000 participants. Participants from a defined at-risk cohort and positive controls (clinical cohort of patients with mild cognitive impairment or early AD) are invited for onsite visits for lifestyle monitoring, detailed neurocognitive testing, blood sample donation, plus functional, structural and molecular neuroimaging. This paper describes recruitment of the PISA cohort, study methodology and baseline demographics.Author ApprovalAll authors have seen and approved this manuscript.
Initial Model for the Impact of Social Distancing on CoVID-19 Spread
Authors: Eng Genghmun 
Published: May 2020

AbstractThe initial stages of the CoVID-19 coronavirus pandemic all around the world exhibit a nearly exponential rise in the number of infections with time. Planners, governments, and agencies are scrambling to figure out “How much? How bad?” and how to effectively treat the potentially large numbers of simultaneously sick people. Modeling the CoVID-19 pandemic using an exponential rise implicitly assumes a nearly unlimited population of uninfected persons (“dilute pandemic”). Once a significant fraction of the population is infected (“saturated pandemic”), an exponential growth no longer applies. A new model is developed here, which modifies the standard exponential growth function to account for factors such as Social Distancing. A Social Mitigation Parameter [SMP] αS is introduced to account for these types of society-wide changes, which can modify the standard exponential growth function, as follows:
The doubling-time tdbl = (In 2)/Ko can also be used to substitute for Ko, giving a {tdbl, αS} parameter pair for comparing to actual CoVID-19 data. This model shows how the number of CoVID-19 infections can self-limit before reaching a saturated pandemic level. It also provides estimates for: (a) the timing of the pandemic peak, (b) the maximum number of new daily cases that would be expected, and (c) the expected total number of CoVID-19 cases. This model shows fairly good agreement with the presently available CoVID-19 pandemic data for several individual States, and the for the USA as a whole (6 Figures), as well as for various countries around the World (9 Figures). An augmented model with two Mitigation Parameters, αS and βS, is also developed, which can give better agreement with the daily new CoVID-19 data. Data-to-model comparisons also indicate that using αS by itself likely provides a worst-case estimate, while using both αS and βS likely provides a best-case estimate for the CoVID-19 spread.