bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predicting COVID-19 spread and public health needs to contain the pandemic in West-Africa
Authors: Taboe Beaugard Hémaho , Salako Kolawolé Valère , Ngonghala Calistus N. , Glèlè Kakaï Romain 
Published: May 2020

AbstractThe novel coronavirus (COVID-19) pandemic is causing devastating demographic, social, and economic damage globally. Understanding current patterns of the pandemic spread and forecasting its long-term trajectory is essential in guiding policies aimed at curtailing the pandemic. This is particularly important in regions with weak economies and fragile health care systems such as West-Africa. We formulate and use a deterministic compartmental model to (i) assess the current patterns of COVID-19 spread in West-Africa, (ii) evaluate the impact of currently implemented control measures, and (iii) predict the future course of the pandemic with and without currently implemented and additional control measures in West-Africa. An analytical expression for the threshold level of control measures (involving a reduction in the effective contact rate) required to eliminate the pandemic is computed. Considering currently applied health control measures, numerical simulations of the model using baseline parameter values estimated from West-African COVID-19 data project a 60% reduction in the daily number of cases when the epidemic attains its peak. More reduction in the number of cases will be achieved if additional public health control measures that result in a reduction in the effective contact rate are implemented. We found out that disease elimination is difficult when more asymptomatic individuals contribute in transmission or are not identified and isolated in a timely manner. However, maintaining a baseline level of asymptomatic isolation and a low transmission rate will lead to a significant reduction in the number of daily cases when the pandemic peaks. For example, at the baseline level of asymptomatic isolation, at least a 53% reduction in the transmission rate is required for disease elimination, while disease elimination is also possible if asymptomatic individuals are identified and isolated within 2 days (after the incubation period). Combining two or more measures is better for disease control, e.g., if asymptomatic humans are contact traced or identified and isolated in less than 3 days then only about a 31% reduction in the disease transmission rate is required for disease elimination. Furthermore, we showed that the currently implemented measures caused the time-dependent effective reproduction number to reduce by approximately 37% from February 28, to August 24, 2020. We conclude that COVID-19 elimination requires more control measures than what is currently being applied in West-Africa and that mass testing and contact tracing in order to identify and isolate asymptomatic individuals early is very important in curtailing the burden of the pandemic.
Refining clinical algorithms for a neonatal digital platform for low-income countries: a modified Delphi technique
Authors: Evans Mari , Corden Mark H. , Crehan Caroline , Fitzgerald Felicity , Heys Michelle 
Published: May 2020

ABSTRACTObjectivesTo determine whether a panel of neonatal experts could address evidence gaps in neonatal guidelines by reaching a consensus on four clinical decision algorithms for a neonatal digital platform (NeoTree).DesignTwo-round, modified Delphi technique.Setting and participantsParticipants were neonatal experts from high-income and low-income countries (LICs).MethodsThis was a consensus-generating study. In round one, experts rated items for four clinical algorithms (neonatal sepsis, hypoxic ischaemic encephalopathy, respiratory distress of the newborn, hypothermia) and justified their responses. Items meeting consensus (≥80% agreement) were included. Items not meeting consensus were either excluded, included following revisions or included if they contained core elements of evidence-based guidelines. In round two, experts rated items from round one that did not reach consensus.ResultsFourteen experts participated in round one, ten in round two. Nine were from high-income countries, five from LICs. Experts included physicians and nurse practitioners with an average neonatal experience of 20 years, 12 in LICs. After two rounds, a consensus was reached on 43 of 84 items (52%). Experts consistently stated that items must be in line with local and WHO guidelines (irrespective of the level of supporting evidence or expert opinion). As a result, the final algorithms included 53 items (62%).ConclusionFour algorithms in a neonatal digital platform were reviewed and refined by consensus expert opinion. Revisions to the NeoTree application were made in response to these findings and will be clinically validated in an imminent study.STRENGTHS AND LIMITATIONS OF THIS STUDY➢In this study, a large number of algorithm items were reviewed and evaluated, and half met consensus for inclusion in the management pathways.➢The review was conducted with experts from a broad range of countries and neonatal experience who simultaneously refined the algorithms and highlighted gaps in current evidence, emphasising the need for future research to support international neonatal guidelines.➢Our study method meant that experts were not able to meet in person, which might have promoted dialogue that would have allowed greater clarity in their collective opinion.➢The representation of neonatal experts from LICs was not as robust as from high-income countries, which may have led to an uneven evaluation of the algorithms.
Computer code comprehension shares neural resources with formal logical inference in the fronto-parietal network
Authors: Liu Y. , Kim J. , Wilson C. , Bedny M. 
Published: May 2020

AbstractDespite the importance of programming to modern society, the cognitive and neural bases of code comprehension are largely unknown. Programming languages might ‘recycle’ neurocognitive mechanisms originally used for natural languages. Alternatively, comprehension of code could depend on fronto-parietal networks shared with other culturally derived symbol systems, such as formal logic and math. Expert programmers (average 11 years of programming experience) performed code comprehension and memory control tasks while undergoing fMRI. The same participants also performed language, math, formal logic, and executive control localizer tasks. A left-lateralized fronto-parietal network was recruited for code comprehension. Patterns of activity within this network distinguish between “for” loops and “if” conditional code functions. Code comprehension overlapped extensively with neural basis of formal logic and to a lesser degree math. Overlap with simpler executive processes and language was low, but laterality of language and code covaried across individuals. Cultural symbol systems, including code, depend on a distinctive fronto-parietal cortical network.
Juvenile hormone affects the development and strength of circadian rhythms in young bumble bee (Bombus terrestris) workers
Authors: Pandey Atul , Motro Uzi , Bloch Guy 
Published: May 2020

AbstractThe circadian and endocrine systems influence many physiological processes in animals, but little is known on the ways they interact in insects. We tested the hypothesis that juvenile hormone (JH) influences circadian rhythms in the social bumble beeBombus terrestris. JH is the major gonadotropin in this species coordinating processes such as vitellogenesis, oogenesis, wax production, and behaviors associated with reproduction. It is unknown however, whether it also influences circadian processes. We topically treated newly-emerged bees with the allatoxin Precocene-I (P-I) to reduce circulating JH titers and applied the natural JH (JH-III) for replacement therapy. We repeated this experiment in three trials, each with bees from different source colonies. Measurements of ovarian activity confirmed that our JH manipulations were effective; bees treated with P-I had inactive ovaries, and this effect was fully reverted by subsequent JH treatment. We found that JH augments the strength of circadian rhythms and the pace of rhythm development in individually isolated newly emerged worker bees. JH manipulation did not affect the free-running circadian period, overall level of locomotor activity, or the amount of sleep. Given that acute manipulation at an early age produced relatively long-lasting effects, we propose that JH effect on circadian rhythms is mostly organizational, accelerating the development or integration of the circadian system.
Highly multiplexed molecular and cellular mapping of breast cancer tissue in three dimensions using mass tomography
Authors: Catena Raúl , Özcan Alaz , Kütt Laura , Plüss Alex ,   IMAXT Consortium, Schraml Peter , Moch Holger , Bodenmiller Bernd 
Published: May 2020

ABSTRACTA holistic understanding of tissue and organ structures and their functions requires the detection of molecular constituents in their original three-dimensional (3D) context. Imaging mass cytometry (IMC) makes possible the detection of up to 40 antigens and specific nucleic acids simultaneously using metal-tagged antibodies or nucleic acid probes, respectively, but has so far been restricted to two-dimensional imaging. To enable use of IMC for 3D tissue analyses, we developed mass tomography, which combines quasi deformation-free serial sectioning with novel computational methods. We utilized mass tomography to analyze a breast cancer sample. The resulting 3D representation reveals spatial and cellular heterogeneity, preferential cell-to-cell interactions, detailed tissue-architecture motifs, and the unique microenvironment of a micro-invasion, where micro-metastases clonality is examined, showing that cells arising from the same invasive area, displaying very distinct phenotypes, are all able to produce initial invasive lesions. Mass tomography will provide invaluable insights into the tissue microenvironment, cellular neighborhoods, and tissue organization.
The cryoEM structure of the fibril-forming low-complexity domain of hnRNPA2 reveals distinct differences from pathogenic amyloid and shows how mutation converts it to the pathogenic form
Authors: Lu Jiahui , Cao Qin , Hughes Michael P. , Sawaya Michael R. , Boyer David R. , Cascio Duilio , Eisenberg David S. 
Published: May 2020

AbstracthnRNPA2 is one of a group of human ribonucleoproteins (RNPs) involved in RNA metabolism which form fibrils both under cellular stress and in mutated form in neurodegenerative conditions. Previous work established that the C-terminal low-complexity domain (LCD) of hnRNPA2 fibrillizes under stress, and that missense mutations in this domain are found in the disease multisystem proteinopathy (MSP) with symptoms indistinguishable from ALS and FTD. However, little is known at the atomic level about the hnRNPA2 LCD structure that is involved in those processes and how disease mutations cause structural change. Here we present the cryo-electron microscopy (cryoEM) structure of hnRNPA2 LCD fibril core and demonstrate its capability to form a reversible hydrogelin vitrocontaining amyloid-like fibrils. Whereas these fibrils, like pathogenic amyloid, are formed from protein chains stacked into β-sheets by backbone hydrogen bonds, they display distinct structural differences: the chains are kinked, enabling non-covalent cross-linking of fibrils and disfavoring formation of pathogenic steric zippers. Both their reversibility and energetic calculations suggest these fibrils are less stable than pathogenic amyloid. Moreover, the crystal structure of the disease-mutation-containing segment of hnRNPA2 suggests that the replacement fundamentally alters the fibril structure to a more stable energetic state. These findings illuminate how molecular interactions promote protein fibril networks and how mutation can transform fibril structure from functional to pathogenic form.
UHRF1 upregulation mediates exosome release and tumor progression in osteosarcoma
Authors: Wu Stephanie C. , Kim Ahhyun , Zocchi Loredana , Chen Claire , Lopez Jocelyne , Salcido Kelsey , Wu Jie , Benavente Claudia A. 
Published: May 2020

ABSTRACTLoss of function mutations at the retinoblastoma (RB1) gene are associated with increased mortality, metastasis and poor therapeutic outcome in several cancers, including osteosarcoma (OS). However, the mechanism(s) through whichRB1loss worsens clinical outcome remain to be elucidated. Ubiquitin-like with PHD and Ring Finger domains 1 (UHRF1) has been identified as a critical downstream effector of the RB/E2F signaling pathway that is overexpressed in various cancers. Here, we show that UHRF1 upregulation is critical in rendering OS cells more aggressive. Using novel OS genetically engineered mouse models, we determined that knocking-outUhrf1considerably reverses the poorer survival associated withRb1loss. We also found that highUHRF1expression correlates with increased clinical presentation of metastatic disease. Based on gain- and loss-of-function assays, we found that UHRF1 promotes cell proliferation, migration, invasion, and metastasis. UHRF1-mediated cell mobility results as a consequence of altered extracellular vesicles and their cargo, including urokinase-type plasminogen activator (uPA). Our work presents a new mechanistic insight intoRB1loss-associated poor prognosis and a novel oncogenic role of UHRF1 through regulation of exosome secretion that is critical for OS metastasis. This study provides substantial support for targeting UHRF1 or its downstream effectors as novel therapeutic options to improve current treatment for OS.
Estimating effective reproduction number using generation time versus serial interval, with application to COVID-19 in the Greater Toronto Area, Canada
Authors: Knight Jesse , Mishra Sharmistha 
Published: May 2020

AbstractBackgroundThe effective reproductive number Re(t) is a critical measure of epidemic potential. Re(t) can be calculated in near real time using an incidence time series and the generation time distribution—the time between infection events in an infector-infectee pair. In calculating Re(t), the generation time distribution is often approximated by the serial interval distribution—the time between symptom onset in an infector-infectee pair. However, while generation time must be positive by definition, serial interval can be negative if transmission can occur before symptoms, such as in covid-19, rendering such an approximation improper in some contexts.MethodsWe developed a method to infer the generation time distribution from parametric definitions of the serial interval and incubation period distributions. We then compared estimates of Re(t) for covid-19 in the Greater Toronto Area of Canada using: negative-permitting versus non-negative serial interval distributions, versus the inferred generation time distribution.ResultsWe estimated the generation time of covid-19 to be Gamma-distributed with mean 3.99 and standard deviation 2.96 days. Relative to the generation time distribution, non-negative serial interval distribution caused overestimation of Re(t) due to larger mean, while negative-permitting serial interval distribution caused underestimation of Re(t) due to larger variance.ImplicationsApproximation of the generation time distribution of covid-19 with non-negative or negative-permitting serial interval distributions when calculating Re(t) may result in over or underestimation of transmission potential, respectively.
Noninvasive respiratory support in acute hypoxemic respiratory failure associated with COVID-19 and other viral infections
Authors: Crimi Claudia , Noto Alberto , Cortegiani Andrea , Impellizzeri Pietro , Elliott Mark , Ambrosino Nicolino , Gregoretti Cesare 
Published: May 2020

ABSTRACTIntroductionNoninvasive respiratory support (NRS) such as noninvasive ventilation (NIV) and high flow nasal therapy (HFNT) have been used in the treatment of acute hypoxemic respiratory failure (AHRF) related to the coronavirus disease (COVID-19) and other viral infections. However, there is a lack of consensus in favor of or against NRS use due to the risks of worsening hypoxemia, intubation delay, and aerosols environmental contamination associated with the use of these tools. We aimed to summarize the evidence on the use of NRS in adult patients with COVID-19 and other viral pneumonia (i.e. H1N1, SARS, MERS) and AHRF. We also searched for studies evaluating the risk of aerosolization/contamination with these tools.Evidence AcquisitionWe searched MEDLINE, PubMed EMBASE and two major preprint servers (biorXiv and medRxiv) from inception to April 14, 2020, for studies on the use of respiratory support in AHRF and viral pneumonia.Evidence SynthesisThe search identified 4086 records and we found only one randomized controlled trial out of 58 studies included, with great variabilities in support utilization and failure rates. Fifteen studies explored the issue of aerosolization/contamination showing a high risk of airborne transmission via droplets generation during the use of these modalitiesConclusionsUse of NRS and treatment failure in the context of COVID-19 and viral infection associated-AHRF, varied widely. Dispersion of exhaled air is different depending on the type of respiratory therapies and interfaces. Data from randomized controlled trials are lacking.
Comparison of epidemic control strategies using agent-based simulations
Authors: del Árbol Pablo Martínez Ruiz , Iglesias Lara Lloret 
Published: May 2020

AbstractA simulation of the dynamics of a small population is used to assess the impact of different confinement and testing strategies in the control of an epidemic. The simulation considers individuals as agents moving randomly across the habitat according to predefined urban patterns. Agents carry a simple tracing device that identifies signals emitted by other agents, recording the position and time of the encounter. The information of every device is propagated daily to an epidemic observatory based on an online graph database. Infections are simulated as stochastic processes depending on the proximity among individuals. Different epidemic control strategies are tested with and without the information of the tracing device under several scenarios. We observe that the success of the strategies strongly depends on the duration of the period of infectiousness before the presence of symptoms and the fraction of asymptomatic agents. If these values are high, strategies based on the presence of symptoms or on testing campaigns can hardly contain the epidemic. Strategies using massive confinement of the agents are able to control the epidemic at the cost of sending a large fraction of the population into quarantine. In cases with moderate and low values for these parameters, the tracing devices can provide a slightly better performance but only if a large fraction of the agents carry the device. Otherwise, the impact of these devices is found to be negligible in comparison with other strategies not using them. Finally, we provide a methodology allowing to use the information of the graph database to estimate basic parameters of the disease such as the infection probability.
COVID-19: Impact of Obesity and Diabetes in Disease Severity
Authors: Al-Sabah Salman , Al-Haddad Mohannad , Al-Youha Sarah , Jamal Mohammad , Almazeedi Sulaiman 
Published: May 2020

AbstractBackgroundThe Coronavirus disease 2019 (COVID-19) pandemic is straining the healthcare system, particularly for patients with severe outcomes who require admittance to the intensive care unit (ICU). This study aimed to investigate the potential associations of obesity and diabetes with COVID-19 severe outcomes, assessed as ICU admittance.SubjectsDemographic and patient characteristics from a retrospective cohort of 1158 patients hospitalized with COVID-19 in a single center in Kuwait, along with their medical history, were analyzed. Univariate and multivariate analyses were performed to explore the associations between different variables and ICU admittance.ResultsFrom the 1158 hospitalized patients, 271 (23.4%) had diabetes, 236 (20.4%) had hypertension and 104 (9%) required admittance into the ICU. From patients with available measurements, 157 (21.6%) had body mass index (BMI)≥25 kg/m2. Univariate analysis showed that overweight (BMI=25.0–29.9 kg/m2), obesity class I (BMI=30–34.9 kg/m2) and morbid obesity (BMI≥40 kg/m2) associated with ICU admittance (odds ratio (OR) [95% confidence intervals (CI)]: 2.45 [1.26–4.74]p-value=0.008; OR [95% CI]: 3.51 [1.60–7.69]p-value=0.002; and OR [95% CI]: 5.18 [1.50–17.85]p-value=0.009], respectively). Patients with diabetes were more likely to be admitted to ICU (OR [95% CI]: 9.38 [5.49–16.02]). Two models for multivariate regression analysis were used, assessing either BMI or diabetes on ICU outcomes. In the BMI model, class I obesity and morbid obesity were associated with ICU admittance (adjusted OR (AOR) [95% CI]: 2.7 [1.17–6.20]p-value=0.019 and AOR [95% CI]: 3.95 [1.00–15.20]p-value=0.046, respectively). In the diabetes model, diabetes was associated with higher ICU admittance (AOR [95% CI]: 5.49 [3.13–9.65]p-value<0.001) whereas hypertension had a protective effect on ICU admittance (AOR [95% CI]: 0.51 (0.28–0.91).ConclusionsIn our cohort, overweight, obesity and diabetes in patients with COVID-19 were associated with ICU admittance, putting these patients at higher risk of poor outcomes.
Novel mammogram-based measures improve breast cancer risk prediction beyond an established measure of mammographic density
Authors: Nguyen Tuong L. , Schmidt Daniel F. , Makalic Enes , Maskarinec Gertraud , Li Shuai , Dite Gillian , Aung Ye K. , Evans Christopher F. , Trinh Ho N. , Baglietto Laura , Stone Jennifer , Song Yun-Mi , Sung Joohon , MacInnis Robert J. , Dugué Pierre-Antoine , Dowty James G. , Jenkins Mark A. , Milne Roger L. , Southey Melissa C. , Giles Graham G. , Hopper John L. 
Published: May 2020

ABSTRACTBackgroundMammograms contain information that predicts breast cancer risk. We recently discovered two novel mammogram-based breast cancer risk measures based on image brightness (Cirrocumulus) and texture (Cirrus). It is not known whether these measures improve risk prediction when fitted together, and with an established measure of mammographic density (Cumulus).MethodsWe used three studies consisting of: 168 interval cases and 498 matched controls; 422 screen-detected cases and 1,197 matched controls; and 354 younger-diagnosis cases and 944 frequency-matched controls. We conducted conditional and unconditional logistic regression analyses of individually-and frequency-matched studies, respectively. We reported risk gradients as change in odds ratio per standard deviation of controls after adjusting for age and body mass index (OPERA). For models involving multiple measures, we calculated the OPERA equivalent to the area under the receiver operating characteristic curve.ResultsFor interval, screen-detected and younger-diagnosis cancer, the best fitting models (OPERAs [95% confidence intervals]) were: Cumulus (1.81 [1.41 to 2.31]) and Cirrus (1.7 [1.38 to 2.14]); Cirrus (1.49 [1.32 to 1.67]) and Cirrocumulus (1.16 [1.03 to 1.31]); and Cirrus (1.70 [1.48 to 1.94]) and Cirrocumulus (1.46 [1.27 to 1.68]), respectively. Their OPERA equivalents were: 2.35, 1.58, and 2.28, respectively.ConclusionsOur mammogram-based measures improved risk prediction beyond and, except for interval cancers, negated the influence of conventional mammographic density. Combined, these new mammogram-based risk measures are at least as accurate as the current polygenetic risk scores (OPERA ~ 1.6) in predicting, on a population basis, women who will be diagnosed with breast cancer.