bluuebunny/crossref_metadata_2025_split huggingface dataset:
Complex Autoantibody Responses Occur Following Moderate to Severe Traumatic Brain Injury
Authors: Needham Edward J , Stoevesandt Oda , Thelin Eric P , Zetterberg Henrik , Zanier Elisa R , Nimer Faiez Al , Ashton Nicholas J , Outtrim Joanne G , Newcombe Virginia FJ , Mousa Hani S , Simren Joel , Blennow Kaj , Yang Z Zhihui , Hutchinson Peter J , Piehl Fredrik , Helmy Adel E , Taussig Mike J , Wang Kevin KW , Jones Joanne L , Menon David K , Coles Alasdair J 
Published: July 2020

AbstractThe majority of variation in outcome following severe traumatic brain injury (TBI) remains unexplained by currently recognised prognostic factors, suggesting a contribution from unaccounted variables. One key candidate variable is neuroinflammation, including the generation of autoantibodies against brain specific antigens which have been described in some individuals following TBI. Here we hypothesised that autoantibody responses following TBI would differ between individuals, and would explain a proportion of outcome variance.We developed a custom protein microarray to characterise the generation of autoantibodies to both central nervous system and systemic antigens in the acute-phase of TBI (within ten days of injury), and to determine their late (6-12 months) and long-term (6-13 years) persistence.We identified two distinct patterns of response. The first was a broad response to the majority of antigens tested, predominantly IgM-mediated in the acute-phase, then IgG-dominant at late and long-term time-points. The second was of dominant responses to certain antigens, most frequently myelin-associated glycopeptide (MAG), which persisted for several months post-TBI but then subsequently resolved.Exploratory analyses suggested that patients with a greater acute IgM response experienced worse outcomes than predicted. Furthermore, late persistence of anti-MAG IgM autoantibodies correlated with serum neurofilament light concentrations, suggesting an association with ongoing neurodegeneration over the first year post-injury.Our results show that autoantibody production occurs in some individuals following TBI, can persist for many years, and may affect patient outcome. The complexity of responses mean that conventional approaches based on measuring responses to single antigenic targets may be misleading.
Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine-Learning
Authors: Delafiori Jeany , Navarro Luiz Claudio , Siciliano Rinaldo Focaccia , de Melo Gisely Cardoso , Busanello Estela Natacha Brandt , Nicolau José Carlos , Sales Geovana Manzan , de Oliveira Arthur Noin , Almeida Val Fernando Fonseca , de Oliveira Diogo Noin , Eguti Adriana , dos Santos Luiz Augusto , Falcão Dalçóquio Talia , Bertolin Adriadne Justi , Cardoso Alonso João Carlos , Abreu-Netto Rebeca Linhares , Salsoso Rocio , Baía-da-Silva Djane , Sampaio Vanderson Souza , Judice Carla Cristina , Maranhão Costa Fabio Trindade , Durán Nelson , Perroud Mauricio Wesley , Sabino Ester Cerdeira , Guimarães Lacerda Marcus Vinicius , Reis Leonardo Oliveira , José Fávaro Wagner , Monteiro Wuelton Marcelo , Rocha Anderson Rezende , Catharino Rodrigo Ramos 
Published: July 2020

ABSTRACTCOVID-19 is still placing a heavy health and financial burden worldwide. Impairments in patient screening and risk management play a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile.We combined a machine learning-based algorithm with instrumental analysis using mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study with 728 patients (369 confirmed COVID-19 and 359 controls) was enrolled from three Brazilian epicentres (São Paulo capital, São Paulo countryside and Manaus) in the months of April, May, June and July 2020.We were able to elect and identify 21 molecules that are related to the disease’s pathophysiology and 26 features to patient’s health-related outcomes. With specificity >97% and sensitivity >83% from blinded data, this screening approach is understood as a tool with great potential for real-world application.
Validating layer-specific VASO across species
Authors: Huber Laurentius Renzo , Poser Benedikt A , Kaas Amanda L , Fear Elizabeth J , Desbach Sebastian , Berwick Jason , Goebel Rainer , Turner Robert , Kennerley Aneurin J 
Published: July 2020

AbstractCerebral blood volume (CBV) has been shown to be a robust and important physiological parameter for quantitative interpretation of functional (f)MRI, capable of delivering highly localized mapping of neural activity. Indeed, with recent advances in ultra-high-field (>=7T) MRI hardware and associated sequence libraries, it has become possible to capture non-invasive CBV weighted fMRI signals across cortical layers. One of the most widely used approaches to achieve this (in humans) is through vascular-space-occupancy (VASO) fMRI. Unfortunately, the exact contrast mechanisms of layer-dependent VASO fMRI have not been validated and thus interpretation of such data is confounded. Here we cross-validate layer-dependent VASO fMRI contrast in a preclinical rat model using well established (but invasive) imaging methods in response to neuronal activation (somatosensory cortex) and respiratory challenge (hypercapnia). In particular VASO derived CBV measures are directly compared to concurrent measures of total haemoglobin changes from high resolution intrinsic optical imaging spectroscopy (OIS). Through direct comparison of response magnitude, across time, negligible changes in hematocrit ratio during activation (neuronal or vascular) are inferred. Quantified cortical layer profiling is demonstrated and in agreement between both VASO and contrast enhanced fMRI (using monocrystalline iron oxide nanoparticles, MION). Responses show high spatial localisation to layers of cortical excitatory and inhibitory processing independent of confounding large draining veins which hamper BOLD fMRI studies. While we find increased VASO based CBV reactivity (3.1 ± 1.2 fold increase) in humans compared to rats it is demonstrated that this reflects differences in stimulus design rather than confounds of the VASO signal source. Together, our findings confirm that the VASO contrast is indeed a reliable estimate of layer-specific CBV changes. This validation study increases the neuronal interpretability of human layer-dependent fMRI results and should supersede BOLD fMRI as the method of choice in neuroscience application studies.HighlightsOur goal is to validate layer-specific VASO fMRI with gold standard methodsLayer-specific VASO sequences are implemented for 7T imaging in humans and ratsComparisons of VASO, optical imaging, and MION confirm the expected contrast originSomatosensory stimulation in humans and rats reveal the same layer-fMRI signaturesWe confirm that VASO is a valid measure to estimate layer-specific neural activityGraphical abstract
Opportunities and challenges in using remote sensing for identifying grassland restoration sites and invasive tree species management in a global biodiversity hotspot
Authors: Arasumani M. , Bunyan Milind , Robin V. V. 
Published: July 2020

ABSTRACTTropical montane grasslands (TMG) support biodiverse and endemic taxa and provide vital ecosystems services to downstream communities. Yet invasive alien tree species across the world have threatened tropical grasslands and grassland endemic species. In India, TMG in the Shola Sky Islands of the Western Ghats have been reduced due to exotic tree invasions (acacias, pines, and eucalyptus species). The loss of grassland habitat has, in turn, reduced the range sizes of species endemic to grasslands (plants, birds, amphibians, and mammals), driving some populations to local extinction. Grassland conversion to exotic trees has also reduced the annual runoff in the Western Ghats. Conserving existing grassland and restoring invaded habitat is critical to reverse these losses. This research focused on identifying grassland restoration sites using satellite images with a high spatial resolution (RapidEye). We used an object-oriented Random Forest classification to map the area for grassland restoration. We have identified an area of 254 sq. km. as suitable for grassland restoration and an area of 362 sq. km. for grassland conservation and prevention from invasion by exotic tree species. For restoration, we recommend a cautious removal of young and isolated exotic trees at the invasion front and restoring grasslands, instead of removing dense stands of mature exotic trees. We find that areas with low fire frequency areas tend to be invaded, but areas invaded by exotic trees tend to burn hotter which may be harmful to grassland species and ecosystems. We assume that removing exotic tree species in the identified restoration sites and restoring the grassland will be helpful in recovering lost habitat and ensuring the viability of populations of indigenous and endemic species and increasing streamflow.
Combined Vapor Exposure to THC and Alcohol in Pregnant Rats: Maternal Outcomes and Pharmacokinetic Effects
Authors: Breit Kristen R. , Rodriguez Cristina , Lei Annie , Thomas Jennifer D. 
Published: July 2020

ABSTRACTCannabis is the most frequently used illicit drug among pregnant women, yet the potential consequences of prenatal cannabis exposure on development are not well understood. Electronic cigarettes have become an increasingly popular route of administration among pregnant women, in part to user’s perception that e-cigarettes are a safer route for consuming cannabis products. Importantly, half of pregnant women who consume cannabis also report consuming alcohol, but research investigating co-consumption of these drugs is limited, particularly with current routes of administration. The purpose of this study was to establish a co-exposure vapor inhalation model of alcohol and THC in pregnant rats, to ultimately determine the effects on fetal development. Pregnant Sprague-Dawley rats were exposed to moderate doses of THC via e-cigarettes, alcohol, the combination, or vehicle daily from gestational days 5-20. Importantly, pharmacokinetic interactions of alcohol and THC were observed during pregnancy. Combined exposure consistently increased blood alcohol concentrations, indicating that THC alters alcohol metabolism. In addition, THC levels also increased over the course of pregnancy and THC metabolism was altered by alcohol. Alcohol, but not THC, exposure during pregnancy reduced maternal weight gain, despite no group differences in food intake. Neither prenatal alcohol nor THC exposure altered gestational length, litter size, sex ratio or birth weight. However, prenatal alcohol exposure delayed eye opening, and prenatal THC exposure decreased body weights during adolescence among offspring. These individual and synergistic effects suggest that this novel co-exposure vapor inhalation paradigm can effectively be used to expose pregnant dams, exerting some effects on fetal development, while avoiding nutritional confounds, birth complications, or changes in litter size. With this model, we have demonstrated that combining THC and alcohol alters drug metabolism, which could have important consequences on prenatal development.
Degenerate circuits use distinct mechanisms to respond similarly to the same perturbation
Authors: Powell D.J. , Marder E. , Nusbaum M.P. 
Published: July 2020

AbstractThere is considerable flexibility embedded within neural circuits. For example, separate modulatory inputs can differently configure the same underlying circuit but these different configurations generate comparable, or degenerate, activity patterns. However, little is known about whether these mechanistically different circuits in turn exhibit degenerate responses to the same inputs. We examined this issue using the crab (Cancer borealis) stomatogastric nervous system, in which stimulating the modulatory projection neuron MCN1 and bath applying the neuropeptide CabPK II elicit similar gastric mill (chewing) rhythms in the stomatogastric ganglion, despite differentially configuring the same neural circuit. We showed previously that bath applying the peptide hormone CCAP or stimulating the muscle stretch-sensitive sensory neuron GPR during the MCN1-elicited gastric mill rhythm selectively prolongs the protraction or retraction phase, respectively. Here, we found that these two influences on the CabPK-rhythm elicited some unique and unexpected consequences compared to their actions on the MCN1-rhythm. For example, in contrast to its effect on the MCN1-rhythm, CCAP selectively decreased the CabPK-rhythm retraction phase and thus increased the rhythm speed, whereas the CabPK-rhythm response to stimulating GPR during the retraction phase was similar its effect on the MCN1-rhythm (i.e. prolonging retraction). Interestingly, despite the comparable GPR actions on these degenerate rhythms, the underlying synaptic mechanism was distinct. Thus, degenerate circuits do not necessarily exhibit degenerate responses to the same influence, but when they do, it can occur via different underlying mechanisms.Significance StatementCircuits generating seemingly identical behaviors are often thought to arise from identical circuit states, as that is the most parsimonious explanation. Here we take advantage of an alternate scenario wherein a well-defined circuit with known connectivity generates similar activity patterns using distinct circuit states, via known mechanisms. The same peptide hormone modulation of these distinct circuit states produced divergent activity patterns, whereas the same sensory feedback altered these circuit outputs similarly but via different synaptic pathways. The latter observation limits the insights available from comparable studies in systems lacking detailed access to the underlying circuit.
Impact of COronaVirus Disease-2019 (COVID-19) pandemic on Haemodialysis care delivery pattern in Karnataka, India - a cross-sectional, questionnaire based survey
Authors: Anupama Y J , Conjeevaram Arvind , Ravindra Prabhu A , Doshetty Manjunath , Srinivasa Sanjay , Moger Venkatesh 
Published: July 2020

AbstractThe COVID-19 pandemic has disrupted health care delivery globally. Patients on in-centre haemodialysis(HD) are particularly affected due to their multiple hospital visits and the need for uninterrupted care for their well-being and survival. We studied the impact of the pandemic and the national policy for pandemic control on the HD care delivery in Karnataka state in India in April 2020, when the first and second national lockdown were in place. An online, questionnaire based survey of dialysis facilities was conducted and the responses analysed. The questions were pertaining to the key areas such as changes in number of dialysis treatments, frequency, duration, expenses, transportation to and from dialysis units, impact on availability of consumables, effect on dialysis personnel and on machine maintenance. 62 centres participated. Median of dialysis treatments for the months of March and April 2020 were 695.5 and 650 respectively. Reduction in dialysis treatments was noted in 29(46.8%) facilities, decreased frequency reported by 60 centres. In at least 35(56.5%) centres, dialysis patients had to bear increased expenses. Cost and availability of dialysis consumables were affected in 40(64.5%) and 55(88.7%) centres respectively. Problems with transportation and movement restriction were the two key factors affecting both patients and dialysis facilities.This survey documents the collateral impact of COVID-19 on the vulnerable group of patients on HD, even when not affected by COVID. It identifies the key areas of challenges faced by the patients and the facilities and implores the care-providers for finding newer avenues for mitigation of the problems.
Adapting for the COVID-19 pandemic in Ecuador, a characterization of hospital strategies and patients
Authors: Garzon-Chavez Daniel , Romero-Alvarez Daniel , Bonifaz Marco , Gaviria Juan , Mero Daniel , Gunsha Narcisa , Perez Asiris , Garcia María , Espejo Hugo , Espinosa Franklin , Ligña Edison , Espinel Mauricio , Quentin Emmanuelle , Teran Enrique , Mora Francisco , Reyes Jorge. 
Published: July 2020

AbstractThe World Health Organization (WHO) declared coronavirus disease-2019 (COVID-19) a global pandemic on March 11th. In Ecuador, the first case of COVID-19 was recorded on February 29th. Despite efforts to control its spread, SARS-CoV-2 overrun the Ecuadorian public health system which became one of the most affected in Latin America with 22,719 cases reported up to April, 24th. Public health control measures, including social distancing and lockdowns, were implemented at different times in the biggest cities of the country: Guayaquil and Quito. The Hospital General del Sur de Quito (HGSQ) had to transition from a general to a specific COVID-19 health center in a short period of time to fulfill the health demand from patients with respiratory afflictions. Here, we summarized the implementations applied in the HGSQ to become a COVID-19 exclusive hospital, including the rearrangement of hospital rooms and a triage strategy based on a severity score calculated through an artificial intelligence (AI)-assisted chest computed tomography (CT). Moreover, we present clinical, epidemiological, and laboratory data from 75 laboratory tested COVID-19 patients, which represent the first outbreak of Quito city.
Mathematical modelling based study and prediction of COVID-19 epidemic dissemination under the impact of lockdown in India
Authors: Tiwari Vipin , Deyal Namrata , Bisht Nandan S. 
Published: July 2020

ABSTRACTCOVID-19 (SARS-CoV-2) is rapidly spreading in South Asian countries especially in India. India is the fourth most COVID-19 affected country at present (as on July 10, 2020). With limited medical facilities and high transmission rate, study of COVID-19 progression and its subsequence trajectory need to be analyzed in India. Epidemiologic mathematical models have the potential to predict the epidemic peak of COVID-19 under different scenario. Lockdown is one of the most effective mitigation policy adapted worldwide to control the transmission rate of COVID-19 cases. In this study, we use an improvised five compartment mathematical model i.e. Susceptible (S) - exposed (E)- infected (I)- recovered (R)- death (D) (SEIRD) to investigate the progression of COVID-19 and predict the epidemic peak under the impact of lockdown in India. The aim of this study to provide the most accurate prediction of epidemic peak and to evaluate the impact of lockdown on epidemic peak shift in India. For this purpose, we examine most recent data (up to July 10, 2020) to enhance the accuracy of outcomes from the proposed model. The obtained results indicate that COVID-19 epidemic peak would appear on around mid-August 2020 in India and corresponding estimated cases would be 2.5×106under current scenario. In addition, our study indicates that existence of under-reported cases (∼105) during post-lockdown period in India. It is expected that nationwide lockdown would lead to epidemic peak suppression in India. The obtained results would be beneficial for determining further COVID-19 mitigation policies not only in India but globally.
Power Law in COVID-19 Cases in China
Authors: Ahundjanov Behzod B. , Akhundjanov Sherzod B. , Okhunjanov Botir B. 
Published: July 2020

ABSTRACTThe novel coronavirus (COVID-19) was first identified in China in December 2019. Within a short period of time, the infectious disease has spread far and wide. This study focuses on the distribution of COVID-19 confirmed cases in China—the original epicenter of the outbreak. We show that the upper tail of COVID-19 cases in Chinese cities is well described by a power law distribution, with exponent around one in the early phases of the outbreak (when the number of cases was growing rapidly) and less than one thereafter. This finding is significant because it implies that (i) COVID-19 cases in China is heavy-tailed and disperse; (ii) a few cities account for a disproportionate share of COVID-19 cases; and (iii) the distribution generally has no finite mean or variance. We find that a proportionate random growth model predicated by Gibrat’s law offers a plausible explanation for the emergence of a power law in the distribution of COVID-19 cases in Chinese cities in the early phases of the outbreak.
Global dynamics of microbial communities emerge from local interaction rules
Authors: van Vliet Simon , Hauert Christoph , Ackermann Martin , Dal Co Alma 
Published: July 2020

AbstractInteractions between cells drive biological processes across all of life, from microbes in the environment to cells in multicellular organisms. Interactions often arise in spatially structured settings, where cells mostly interact with their neighbors. A central question is how local interactions shape the properties of biological systems. This question is very relevant in the context of microbial communities, such as biofilms, where cells live close by in space and are connected via a dense network of biochemical interactions. To understand and control the functioning of these communities, it is essential to uncover how community-level properties, such as the community composition, spatial arrangement, and growth rate, arise from these interactions. Here, we develop a mathematical framework that can predict community-level properties from the molecular mechanisms underlying the cell-cell interactions for systems consisting of two cell types. Our predictions can qualitatively reproduce measurements from an experimental cross-feeding community. For these cross-feeding communities, the community growth rate is reduced when cells interact only with few neighbors; as a result, some communities can co-exist in a well-mixed system, where cells can interact with all other cells, but not in systems where cells can interact only with close by neighbors. In general, our framework shows that key molecular parameters underlying the cell-cell interactions (e.g. the uptake and leakage rates of molecules) determine community-level properties. Our framework can be extended to a variety of systems of two interacting cell types, within and beyond the microbial world, and contributes to our understanding of how community-level dynamics and biological functions emerge from microscopic interactions between single cells.