bluuebunny/crossref_metadata_2025_split huggingface dataset:
VAERS data reveals no increased risk of neuroautoimmune adverse events from COVID-19 vaccines
Authors: Csefalvay Chris von 
Published: June 2021

AbstractNeuroautoimmune disorders, such as multiple sclerosis and Guillain-Barre syndrome, have been documented in relation to various vaccines in the past. This paper uses passive reporting information from the CDC/FDA’s VAERS system to analyse whether neuroautoimmune presentations are reported at a relatively higher or lower rate, vis-a-vis other adverse effects, for COVID-19 vaccines than for other vaccines. Through computing the reporting odds ratios for a range of symptoms and comparator vaccines, a clear indication in favour of the safety of COVID-19 vaccines emerges, with reports of neuroautoimmune adverse events in relation to other adverse events being over 70% less likely for COVID-19 than for comparator vaccines (ROR : 0.292, p < 0.0001). In comparison with other vaccines given as part of routine care in adulthood, COVID-19 vaccines have the lowest reporting odds ratio of neuroautoimmune adverse effects (median ROR: 0.246).
Multimodal deep learning enhances diagnostic precision in left ventricular hypertrophy
Authors: Soto Jessica Torres , Hughes J. Weston , Sanchez Pablo Amador , Perez Marco , Ouyang David , Ashley Euan 
Published: June 2021

AbstractDetermining the etiology of left ventricular hypertrophy (LVH) can be challenging due to the similarity in clinical presentation and cardiac morphological features of diverse causes of disease. In particular, distinguishing individuals with hypertrophic cardiomyopathy (HCM) from the much larger set of individuals with manifest or occult hypertension (HTN) is of major importance for family screening and the prevention of sudden death. We hypothesized that deep learning based joint interpretation of 12 lead electrocardiograms and echocardiogram videos could augment physician interpretation. We chose not to train on proximate data labels such as physician over-reads of ECGs or echocardiograms but instead took advantage of electronic health record derived clinical blood pressure measurements and diagnostic consensus (often including molecular testing) among physicians in an HCM center of excellence. Using over 18,000 combined instances of electrocardiograms and echocardiograms from 2,728 patients, we developed LVH-Fusion. On held-out test data, LVH-Fusion achieved an F1-score of 0.71 in predicting HCM, and 0.96 in predicting HTN. In head-to-head comparison with human readers LVH-Fusion had higher sensitivity and specificity rates than its human counterparts. Finally, we use explainability techniques to investigate local and global features that positively and negatively impact LVH-Fusion prediction estimates providing confirmation from unsupervised analysis the diagnostic power of lateral T wave inversion on the ECG and proximal septal hypertrophy on the echocardiogram for HCM. In conclusion, these results show that deep learning can provide effective physician augmentation in the face of a common diagnostic dilemma with far reaching implications for the prevention of sudden cardiac death.
Single-Cell Classification Using Graph Convolutional Networks
Authors: Wang Tianyu , Bai Jun , Nabavi Sheida 
Published: June 2021

AbstractBackgroundAnalyzing single-cell RNA sequencing (scRNAseq) data plays an important role in understanding the intrinsic and extrinsic cellular processes in biological and biomedical research. One significant effort in this area is the identification of cell types. With the availability of a huge amount of single cell sequencing data and discovering more and more cell types, classifying cells into known cell types has become a priority nowadays. Several methods have been introduced to classify cells utilizing gene expression data. However, incorporating biological gene interaction networks has been proved valuable in cell classification procedures.ResultsIn this study, we propose a multimodal end-to-end deep learning model, named sigGCN, for cell classification that combines a graph convolutional network (GCN) and a neural network to exploit gene interaction networks. We used standard classification metrics to evaluate the performance of the proposed method on the within-dataset classification and the cross-dataset classification. We compared the performance of the proposed method with those of the existing cell classification tools and traditional machine learning classification methods.ConclusionsResults indicate that the proposed method outperforms other commonly used methods in terms of classification accuracy and F1 scores. This study shows that the integration of prior knowledge about gene interactions with gene expressions using GCN methodologies can extract effective features improving the performance of cell classification.
Changes to household income in a Kenyan informal settlement during COVID-19
Authors: Tompsett Anna , Baum Aaron , Bukachi Vera , Kipkemboi Pascal , K’oyoo Allan Ouko , Varela Ana Varela , Mulligan Joseph 
Published: June 2021

AbstractMore than a billion people live in densely-populated informal settlements worldwide. Crowded living conditions and limited resources may render these populations vulnerable to the health and economic effects of the COVID-19 pandemic. Representative and longitudinal survey data are needed to accurately measure impacts in these populations, but such data are scarce. Using satellite data and spatial sampling to ensure representativeness, we use longitudinal survey data on 1,033 households comprising 3,681 individuals collected pre- and post-pandemic to measure the economic effects of the COVID-19 pandemic households in six areas of the informal settlement of Kibera, Nairobi. The economic impacts are sizable and long-lasting. Household incomes declined at the start of the pandemic by 59% (95% CI: 50% to 69%) and remained 21% (95% CI: 13% to 28%) below the baseline level after six months. Respondents primarily attributed these declines in income to fewer labor market opportunities or lower demand for services, rather than the direct health impacts of the pandemic. The findings raise serious concerns about the welfare consequences of the COVID-19 pandemic for residents of informal settlements.Summary boxResidents of informal settlements may be highly vulnerable to the economic impacts of the COVID-19 pandemic, but the longitudinal and representative survey data needed to measure impacts in these populations are scarce.We use an innovative spatial sampling strategy to collect representative survey data from residents in six areas of the informal settlement of Kibera, Nairobi.Relative to a pre-pandemic baseline, household incomes declined by 59% on average during the first three months of the pandemic. Six months later, incomes were still 21% below baseline.Households attributed declines in income primarily to fewer labour market opportunities or lower demand for services (43% of respondents) rather than the direct health impacts of the pandemic (1%).The declines in income we document raise concerns about the welfare of residents of informal settlements during the COVID-19 pandemic.
Study on the mucosal and serological immune response to the Novel Coronavirus (SARS-CoV-2) vaccines
Authors: Chan Renee WY , Liu Shaojun , Cheung Jonathan Y , Tsun Joseph GS , Chan Kate C , Chan Kathy YY , Fung Genevieve PG , Li Albert M , Lam Hugh Simon 
Published: June 2021

AbstractVaccines that elicit mucosal immune responses against SARS-CoV-2 could potentially be of exceptional importance in providing first line defense at the site of viral entry. The serological antibody response induced by SARS-CoV-2 vaccines have already been well characterized. In order to understand the mucosal immune response profiles of SARS-CoV-2 vaccines, we examined both the mucosal and systemic responses of subjects vaccinated by two different vaccination platforms: mRNA (Comirnaty) and inactivated virus (CoronaVac). Serial nasal epithelial lining fluid (NELF) and peripheral blood samples were collected in ten subjects who had received CoronaVac and thirty-two subjects who had received Comirnaty. We quantified IgA and IgG specific to SARS-CoV-2 S1 protein by ELISA in NELF and plasma samples. The neutralization effect of these two sample types were evaluated by surrogate ACE-SARS-CoV-2 Spike protein ELISA. Only Comirnaty induced nasal SARS-CoV-2 S1 protein-specific (S1-specific) IgA and IgG responses, which were evident as early as on 14±2 days after the first dose. The NELF samples of 72% of subjects became IgA+IgG+, while in 62.5% of subjects the samples were neutralizing by 7±2 days after the second dose. In 45% of the subjects their NELF remained neutralizing 50 days after the booster of Comirnaty. In plasma, 91% and 100% Comirnaty subjects possessed S1-specific IgA+IgG+ on 14±2 days after the first dose and 7±2 days after booster, respectively. The plasma collected on 7±2 days after booster was 100% neutralizing. The induction of S1-specific antibody by CoronaVac was IgG dominant, and 70% of the subjects possessed S1-specific IgG by 7±2 days after booster and were all neutralizing. This study reveals that Comirnaty is able to induce S1-specific IgA and IgG response with neutralizing activity in the nasal mucosa in addition to a consistent systemic response. The clinical implications and the biological mechanism of an additional nasal immune response induced by vaccines such as Comirnaty warrant further investigation.One Sentence SummarymRNA vaccine (CoronaVac) elicits mucosal IgA and IgG in the nasal epithelial lining fluid together with ELISA-detected anti-wild-type spike neutralizing antibodies as early as day 14 post vaccination.
Grey and white matter volume changes after preterm birth: A meta-analytic approach
Authors: Schmitz-Koep Benita , Haller Bernhard , Coupé Pierrick , Menegaux Aurore , Gaser Christian , Zimmer Claus , Wolke Dieter , Bartmann Peter , Sorg Christian , Hedderich Dennis M. 
Published: June 2021

ABSTRACTObjectiveLower brain grey matter volumes (GMV) and white matter volumes (WMV) have been reported at single time points in preterm-born individuals. While large MRI studies in the normative population have led to a better understanding of brain growth trajectories across the lifespan, such results remain elusive for preterm-born individuals since large, aggregated datasets of preterm-born individuals do not exist. To close this gap, we investigated GMV and WMV in preterm-born individuals as reported in the literature and contrasted it against individual volumetric data and trajectories from the general population.Study designSystematic database search of PubMed and Web of Science in March 2021 and extraction of outcome measures by two independent reviewers. Individual data on full-term controls was extracted from freely available databases. Mean GMV, WMV, total intracranial volume (TIV), and mean age at scan were the main outcome measures.ResultsOf 532 identified records, nine studies were included with 538 preterm-born subjects between 1.1 and 28.5 years of age. Reference data was generated from 880 full-term controls between 1 and 30 years of age. GMV was consistently lower in preterm-born individuals from infancy to early adulthood with no evidence for catch-up growth. While GMV changes followed a similar trajectory as full-term controls, WMV was particularly low in adolescence after preterm birth.ConclusionsResults demonstrate altered brain volumes after premature birth across the first half of lifespan with particularly low white matter volumes in adolescence. Future studies should address this issue in large aggregated datasets of preterm-born individuals.
Stopping vs Resting state during motor imagery paradigm
Authors: Orset Bastien , Lee Kyuhwa , Chavarriaga Ricardo , Millán José del. R 
Published: June 2021

AbstractCurrent non-invasive Brain Machine interfaces commonly rely on the decoding of sustained motor imagery activity (MI). This approach enables a user to control brain-actuated devices by triggering predetermined motor actions. One major drawback of such strategy is that users are not trained to stop their actions. Indeed, the termination process involved in BMI is poorly understood with most of the studies assuming that the end of an MI action is similar to the resting state. Here we hypothesize that the process of stopping MI (MI termination) and resting state are two different processes that should be decoded independently due to the exhibition of different neural pattens. We compared the detection of both states transitions of an imagined movement, i.e. rest-to-movement (onset) and movement-to-rest (offset). Our results shows that both decoders show significant differences in term of performances and latency (N=17 Subjects) with the offset decoder able to detect faster and better MI termination. While studying this difference, we found that the offset decoder is primarily based on the use of features in Beta band which appears earlier. Based on this finding, we also proposed a Random Forrest based decoder which enable to distinguish three classes (MI, MI termination and REST).
Synthetic Natural Product Inspired Peptides
Authors: Hostetler Matthew A. , Smith Chloe , Nelson Samantha , Budimir Zachary , Modi Ramya , Woolsey Ian , Frerk Autumn , Baker Braden , Gantt Jessica , Parkinson Elizabeth I. 
Published: June 2021

ABSTRACTNatural products (NPs) are a bountiful source of bioactive molecules. Unfortunately, discovery of novel bioactive NPs is challenging due to cryptic biosynthetic gene clusters (BGCs), low titers, and arduous purifications. Herein, we describe SNaPP (Synthetic Natural Product Inspired Peptides), a method for identifying NP-inspired bioactive molecules. SNaPP expedites bioactive molecule discovery by combining bioinformatics predictions of non-ribosomal peptide synthetases (NRPS) with chemical synthesis of the predicted NPs (pNPs). SNaPP utilizes a recently discovered cyclase, the penicillin binding protein (PBP)-like cyclase, as the lynchpin for the development of a library of cyclic peptide pNPs. Analysis of 500 BGCs allowed for identification of 131 novel pNPs. 51 diverse pNPs were synthesized using solid phase peptide synthesis and in-solution cyclization. Antibacterial testing revealed 14 pNPs with antibiotic activity, including activity against multidrug-resistant Gram-negative bacteria. Overall, SNaPP demonstrates the power of combining bioinformatics predictions with chemical synthesis to accelerate the discovery of bioactive molecules.
DOPAL initiates αSynuclein-mediated impaired proteostasis in neuronal projections leading to enhanced vulnerability in Parkinson’s disease
Authors: Masato A. , Plotegher N. , Thor A. , Adams S. , Sandre M. , Cogo S. , De Lazzari F. , Fontana C. M. , Martinez P. A. , Strong R. , Bellucci A. , Bisaglia M. , Greggio E. , Valle L. Dalla , Boassa D. , Bubacco L. 
Published: June 2021

AbstractDopamine dyshomeostasis has been acknowledged to be among the determinants of nigrostriatal neuron degeneration in Parkinson’s disease (PD). Several studies in experimental models and postmortem PD patients underlined increasing levels of the aldehydic dopamine metabolite 3,4-dihydroxyphenylacetaldehyde (DOPAL), which is highly reactive towards proteins. DOPAL has been shown to covalently modify the presynaptic protein αSynuclein (αSyn), whose misfolding and aggregation represent a major trait of PD pathology, triggering αSyn oligomerization in dopaminergic neurons. Here, we demonstrated that DOPAL elicits αSyn neuronal accumulation and hampers αSyn clearance at synapses and the soma. By combining cellular and in vivo models, we provided evidence that DOPAL-induced αSyn buildup lessens neuronal resilience, compromises synaptic integrity, and overwhelms protein quality control pathways, specifically at neuronal projections. The resulting progressive decline of neuronal homeostasis leads to dopaminergic neuron loss and motor impairment, corroborating the αSyn-DOPAL interplay as an early event in PD neurodegeneration.
Structure of mycobacterial cytochrome bcc in complex with Q203 and TB47, two anti-TB drug candidates
Authors: Zhou Shan , Wang Weiwei , Zhou Xiaoting , Zhang Yuying , Lai Yuezheng , Tang Yanting , Xu Jinxu , Yang Xiaolin , Guddat Luke W. , Wang Quan , Gao Yan , Rao Zihe , Gong Hongri 
Published: June 2021

AbstractPathogenic mycobacteria pose a sustained threat to global human health. Recently, cytochrome bcc complexes have gained interest as targets for antibiotic drug development. However, there is currently no structural information for the cytochrome bcc complex from these pathogenic mycobacteria. Here, we report the structures of M. tuberculosis cytochrome bcc alone (2.68 Å resolution) and in complex with clinical drug candidates Q203 (2.67 Å resolution) and TB47 (2.93 Å resolution) determined by single-particle cryo-electron microscopy. M. tuberculosis cytochrome bcc forms a dimeric assembly with endogenous menaquinone/menaquinol bound at the quinone/quinol binding pockets. Q203 and TB47 are bound to the quinol-binding site. Hydrogen bonds are formed between the inhibitor and the side chains of QcrBThr313 and QcrBGlu314, residues that are conserved across pathogenic mycobacteria. These high-resolution structures provide a basis for the design of new mycobacterial cytochrome bcc inhibitors that could be developed into broad spectrum drugs to treat mycobacterial infections.
Nitrates increase abscisic acid levels to regulate haustoria formation in the parasitic plant Phtheirospermum japonicum
Authors: Kokla Anna , Leso Martina , Zhang Xiang , Simura Jan , Cui Songkui , Ljung Karin , Yoshida Satoko , Melnyk Charles W. 
Published: June 2021

AbstractParasitic plants are globally prevalent pathogens that withdraw nutrients from their host plants using an organ known as the haustorium. Some, the obligate parasites are entirely dependent on their hosts for survival, whereas others, the facultative parasites, are independent of their hosts and infect depending on environmental conditions and the presence of the host. How parasitic plants regulate their haustoria in response to their environment is largely unknown. Using the facultative root parasite Phtheirospermum japonicum, we found that external nutrient levels modified haustorial numbers. This effect was independent of phosphate and potassium but nitrates were sufficient and necessary to block haustoria formation. Elevated nitrate levels prevented the activation of hundreds of genes associated with haustoria formation, downregulated genes associated with xylem development and increased levels of abscisic acid (ABA). Enhancing ABA levels independently of nitrates blocked haustoria formation whereas reducing ABA biosynthesis allowed haustoria to form in the presence of nitrates suggesting that nitrates mediated haustorial regulation in part via ABA production. Nitrates also inhibited haustoria formation and reduced infectivity of the obligate root parasite Striga hermonthica, suggesting a more widely conserved mechanism by which parasitic plants adapt their extent of parasitism according to nitrogen availability in the external environment.
Repression of Hypoxia-Inducible Factor-1 Contributes to Increased Mitochondrial Reactive Oxygen Species Production in Diabetes
Authors: Zheng Xiaowei , Narayanan Sampath , Xu Cheng , Angelstig Sofie Eliasson , Grünler Jacob , Zhao Allan , Toro Alessandro Di , Bernardi Luciano , Mazzone Massimiliano , Carmeliet Peter , Sole Marianna Del , Solaini Giancarlo , Forsberg Elisabete A , Zhang Ao , Brismar Kerstin , Schiffer Tomas A. , Ekberg Neda Rajamand , Botusan Ileana Ruxandra , Palm Fredrik , Catrina Sergiu-Bogdan 
Published: June 2021

AbstractBackgroundExcessive production of mitochondrial reactive oxygen species (ROS) is a central mechanism for the development of diabetes complications. Recently, hypoxia has been identified to play an additional pathogenic role in diabetes. In this study, we hypothesized that ROS overproduction was secondary to the impaired responses to hypoxia due to the inhibition of hypoxia-inducible factor-1 (HIF-1) by hyperglycemia.MethodsThe dynamic of ROS levels was analysed in the blood of healthy subjects and individuals with type 1 diabetes after exposure to hypoxia (ClinicalTrials.gov registration no. NCT02629406). The relation between HIF-1, glucose levels, ROS production and its functional consequences were analyzed in renal mIMCD-3 cells and in kidneys of mouse models of diabetes.ResultsExposure to hypoxia increased circulating ROS in subjects with diabetes, but not in subjects without diabetes. High glucose concentrations repressed HIF-1 both in hypoxic cells and in kidneys of animals with diabetes, through a HIF prolyl-hydroxylase (PHD) - dependent mechanism. The impaired HIF-1 signaling contributed to excess production of mitochondrial ROS through increased mitochondrial respiration that was mediated by Pyruvate dehydrogenase kinase 1 (PDK1) and was followed by functional consequences. The restoration of HIF-1 function attenuated ROS overproduction despite persistent hyperglycemia, and conferred protection against apoptosis and renal injury in diabetes.ConclusionsWe conclude that the repression of HIF-1 plays a central role in mitochondrial ROS overproduction in diabetes and is a potential therapeutic target for diabetic complications. These findings are highly significant and timely since the first PHD inhibitor that can activate HIF-1 has been newly approved for clinical use.
Conservation of endangered galaxiid fishes in the Falkland Islands requires urgent action on invasive brown trout
Authors: Minett J. F. , Fowler D.M. , Jones J.A.H. , Brickle P. , Crossin G.T. , Consuegra S. , de Leaniz C. Garcia 
Published: June 2021

AbstractNon-native salmonids are protected in the Southern hemisphere where they sustain aquaculture and valuable sport fisheries, but also impact on native galaxiid fishes, which poses a conservation conundrum. Legal protection and human-assisted secondary releases may have helped salmonids to spread, but this has seldom been tested. We reconstructed the introduction of brown trout (Salmo trutta) to the Falkland Islands using historical records and modelled its dispersal. Our results indicate that establishment success was ∼88%, and that dispersal was facilitated by proximity to introduction sites and density of stream-road crossings, suggesting it was human assisted. Brown trout has already invaded 54% of Falkland rivers, which are 2.9-4.5 times less likely to contain native galaxiids than uninvaded streams. Without strong containment we predict brown trout will invade nearly all suitable freshwater habitats in the Falklands within the next ∼70 years, which might put native freshwater fishes at a high risk of extinction.