bluuebunny/crossref_metadata_2025_split huggingface dataset:
Komodo-dragon cathelicidin-inspired peptides are antibacterial against carbapenem-resistant Klebsiella pneumoniae
Authors: Hitt Samantha J. , Bishop Barney M. , van Hoek Monique L. 
Published: October 2020

Introduction.The rise of carbapenem-resistant enterobacteriaceae (CRE) is a growing crisis that requires development of novel therapeutics.Hypothesis.To this end, cationic antimicrobial peptides (CAMPs) represent a possible source of new potential therapeutics to treat difficult pathogens such as carbapenem-resistantKlebsiella pneumoniae(CRKP), which has gained resistance to many if not all currently approved antibiotics, making treatment difficult.Aim.To examine the anti-CRKP antimicrobial activity of the predicted cathelicidins derived fromVaranus komodoensis(Komodo dragon) as well as synthetic antimicrobial peptides that we created.Methodology.We determined the minimum inhibitory concentrations of the peptides against CRKP. We also characterized the abilities of these peptides to disrupt the hyperpolarization of the bacterial membrane as well as their ability to form pores in the membrane.Results.We did not observe significant anti-CRKP activity for the predicted native Komodo cathelicidin peptides. We found that the novel peptides DRGN-6,-7 and -8 displayed significant antimicrobial activity against CRKP with MICs of 4–8 µg ml−1. DRGN-6 peptide was the most effective peptide against CRKP. Unfortunately, these peptides showed higher than desired levels of hemolysis, althoughin vivotesting in the waxwormGalleria mellonellashowed no mortality associated with treatment by the peptide; however, CRKP-infected waxworms treated with peptide did not show an improvement in survival.Conclusion.Given the challenges of treating CRKP, identification of peptides with activity against it represents a promising avenue for further research. Given DRGN-6′s similar level of activity to colistin, DRGN-6 is a promising template for the development of novel antimicrobial peptide-based therapeutics.
Prevalence of antibodies against sars-cov-2 in professionals of a public health laboratory at são paulo, sp, brazil
Authors: Silva Valéria Oliveira , de Oliveira Elaine Lopes , Castejon Marcia Jorge , Yamashiro Rosemeire , Ahagon Cintia Mayumi , López-Lopes Giselle Ibette , da Silveira Edilene Peres Real , Hong Marisa Ailin , Carmo Timenetsky Maria do , Freitas Carmem Aparecida , Brígido Luís Fernando de Macedo 
Published: October 2020

AbstractBackgroundCovid-19 Serology may document exposure and perhaps protection to the virus and serological test may help understand epidemic dynamics. We tested health workers form a public laboratory to evaluate previous exposure to the virus and estimate the prevalence of antibodies against-SARS-CoV-2 in Adolfo Lutz Institute, State of São Paulo, Brazil.MethodsThis study was an open, prospective evaluation among professionals of Adolfo Lutz Institute some administrative personnel from the Secretary of Health that shares common areas with the institute. We used a lateral flow immunoassay (rapid test) to detect IgG and IgM for SARS-CoV-2; positive samples were further evaluated using Roche Electrochemiluminescence assay. SARS-CoV-2 RNA by real time reverse transcriptase polymerase chain reaction (RT-PCR) was also offered to participants.ResultsA total of 406 HPs participated. Thirty five (8.6%) tested positive on rapid test and 32 these rapid test seropositive cases were confirmed by ECLIA. 43 HPs had SARS-CoV-2 RNA detected at a median of 33 days, and the three cases not reactive at Roche ECLIA had a previous positive RNA. Outsourced professionals (34% seropositive), males (15%) workers referring COVID-19 patients at home (22%) and those living farther form the institute tended to have higher prevalence of seropositivity, but in multivariable logistic analysis only outsourced workers and those with COVID patients at home remained independently associated to seropositivity. We observed no relation of seropositivity to COVID samples handling. Presence of at least one symptom was common but some clinical manifestations as anosmia/dysgeusia. Fatigue, cough and fever were associated to seropositivity.ConclusionsWe documented a relatively high (8.6%) of anti-SARS-CoV-2 serological reactivity in this population, higher among outsourced workers and those residing with COVID-19 patients. COVID related work did not increased seropositivity. Some symptoms show strong association to COVID-19 serology and may be used in scoring tools for screening or diagnosis in resort limited settings.
SI epidemic model applied to COVID-19 data in mainland China
Authors: Demongeot J. , Griette Q. , Magal P. 
Published: October 2020

AbstractThe article is devoted to the parameters identification in the SI model. We consider several methods, starting with an exponential fit of the early cumulative data of Sars-CoV2 in mainland China. The present methodology provides a way to compute the parameters at the early stage of the epidemic. Next, we establish an identifiability result. Then we use the Bernoulli-Verhulst model as a phenomenological model to fit the data and derive some results on the parameters identification. The last part of the paper is devoted to some numerical algorithms to fit a daily piecewise constant rate of transmission.
Modelling the dispersion of SARS-CoV-2 on a dynamic network graph
Authors: Bryant Patrick , Elofsson Arne 
Published: October 2020

AbstractBackgroundWhen modelling the dispersion of an epidemic using R0, one only considers the average number of individuals each infected individual will infect. However, we know from extensive studies of social networks that there is significant variation in the number of connections and thus social contacts each individual has. Individuals with more social contacts are more likely to attract and spread infection. These individuals are likely the drivers of the epidemic, so-called superspreaders. When many superspreaders are immune, it becomes more difficult for the disease to spread, as the connectedness of the social network dramatically decreases. If one assumes all individuals being equally connected and thus as likely to spread disease as in a SIR model, this is not true.MethodsTo account for the impact of social network structure on epidemic development, we model the dispersion of SARS-CoV-2 on a dynamic preferential attachment graph which changes appearance proportional to observed mobility changes. We sample a serial interval distribution that determines the probability of dispersion for all infected nodes each day. We model the dispersion in different age groups using age-specific infection fatality rates. We vary the infection probabilities in different age groups and analyse the outcome.ResultsThe impact of movement on network dynamics plays a crucial role in the spread of infections. We find that higher movement results in higher spread due to an increased probability of new connections being made within a social network. We show that saturation in the dispersion can be reached much earlier on a preferential attachment graph compared to spread on a random graph, which is more similar to estimations using R0.ConclusionsWe provide a novel method for modelling epidemics by using a dynamic network structure related to observed mobility changes. The social network structure plays a crucial role in epidemic development, something that is often overlooked.
Epidemic Curve of Contamination in a Hospital That Served as Sentinel of the Spread of the SARS-Cov-2 Epidemic in the City of Rio de Janeiro
Authors: Santos Marisa , Felippe Guimarães Tereza Cristina , Cramer Helena , Mucillo Fabiana , Silva Bezerra Izabella Pereira da , Quintanilha Barbosa Raiana Andrade , Kasai Brunswick Tais Hanae , Carvalho Adriana Bastos , Dias Glauber Monteiro , Issa Aurora , Carvalho Adriana Bastos , Carvalho Antonio Carlos Campos de 
Published: October 2020

AbstractThe COVID-19 pandemic had a profound impact on the operation of Brazilian hospital units, even those dedicated to non-infectious diseases. This study aims to describe the Covid-19 epidemic curve from a cardiovascular specialized nosocomial unit. All symptomatic employees were submitted to RT-qPCR. A total of 613 tests were performed on 548 employees between March 23, 2020, and June 4, 2020; with 45.7% positivity from the samples, representing 11.9% of the total employees. The epidemic curve showed a profound drop after first week of May. The data showed a high contamination rate despite widespread availability of personal protective equipment and employees’ training.
An NIH Investment in Health Equity - The Economic Impact of the Flint Center for Health Equity Solutions
Authors: Meghea Cristian I. , Montgomery Barrett Wallace , Ellington Roni , Wang Ling , Barajas Clara , Lewis E. Yvonne , Yeary Sheridan T. , Van Egeren Laurie A. , Furr-Holden Debra 
Published: October 2020

ABSTRACTBackgroundHealth disparities are pervasive and are linked to economic losses in the United States of up to $135 billion per year. The Flint Center for Health Equity Solutions (FCHES) is a Transdisciplinary Collaborative Center for health disparities research funded by the National Institute of Minority Health and Health Disparities (NIMHD). The purpose of this study was to estimate the economic impact of the 5-year NIH investment in FCHES in Genesee County, Michigan.MethodsThe estimated impacts of FCHES were calculated using a U.S.-specific input/output (I/O) model, IMPLAN, from IMPLAN Group, LLC., which provides a software system to access geographic specific data regarding economic sector interactions from a variety of sources. This allowed us to model the cross-sector economic activity that occurred throughout Genesee County, Michigan, as a result of the FCHES investment. The overall economic impacts were estimated as the sum of three impact types: 1. Direct (the specific expenditures impact of FCHES and the Scientific Research and Development Services sector); 2. Indirect (the impact on suppliers to FCHES and the Scientific Research and Development Services sector); and 3. Induced (the additional economic impact of the spending of these suppliers and employees in the county economy).ResultsThe total FCHES investment amounted to approximately $11 million between 2016-2020. Overall, combined direct, indirect, and induced impacts of the total FCHES federal investment in Genesee County included over 161 job-years, over $7.6 million in personal income, and more than $19.2 million in economic output. In addition, this combined economic activity generated close to $2.3 million in state/local and federal tax revenue. The impact multipliers show the ripple effect of the FCHES investment. For example, the overall output of over $19.2 million led to an impact multiplier of 1.75 – every $1 of federal FCHES investment led to an additional $.75 of economic output in Genesee County.ConclusionsThe FCHES research funding yields significant U.S. direct economic impacts above and beyond the direct NIH investment of $11 million. The economic impact estimation method may be relevant and generalizable to other TCCs or other large research centers such as FCHES.
GLIOBLASTOMA MULTIFORME: A META-ANALYSIS OF DRIVER GENES, CURRENT DIAGNOSIS, AND TUMOR HETEROGENEITY
Authors: Herrera-Oropeza Gabriel Emilio , Angulo-Rojo Carla , Gástelum-López Santos Alberto , Varela-Echavarría Alfredo , Hernández-Rosales Maribel , Aviña-Padilla Katia 
Published: October 2020

AbstractGlioblastoma (GBM) is the most aggressive and common brain cancer in adults with the lowest life expectancy. The current neuro-oncology practice has incorporated genes involved in key molecular events that drive GBM tumorigenesis as biomarkers to guide diagnosis and design treatment. This study summarizes findings describing the significant heterogeneity of GBM at the transcriptional and genomic levels, emphasizing eighteen driver genes with clinical relevance. A pattern was identified fitting the stem cell model for GBM ontogenesis, with an up-regulation profile forMGMTand down-regulation forATRX, H3F3A, TP53, andEGFRin the mesenchymal subtype. We also detected overexpression ofEGFR, NES, VIM, andTP53in the classical subtype and ofMKi67andOLIG2genes in the proneural subtype. In keeping with this, we found a panel of nine biomarkers with a strong potential to determine the GBM molecular subtype. A unique distribution of somatic mutations was found for the young and adult population, particularly for genes related to DNA repair and chromatin remodeling, highlightingATRX, MGMT, andIDH1. Our results also revealed that highly lesioned genes undergo differential regulation with particular biological pathways for young patients. This meta-analysis will help delineate future strategies related to the use of these molecular markers for clinical decision-making in the medical routine.
Deep learning segmentation model for automated detection of the opacity regions in the chest X-rays of the Covid-19 positive patients and the application for disease severity
Authors: Tang Haiming , Sun Nanfei , Li Yi , Xia Haoran 
Published: October 2020

AbstractPurposeThe pandemic of Covid-19 has caused tremendous losses to lives and economy in the entire world. The machine learning models have been applied to the radiological images of the Covid-19 positive patients for disease prediction and severity assessment. However, a segmentation model for detecting the opacity regions like haziness, ground-glass opacity and lung consolidation from the Covid-19 positive chest X-rays is still lacking.MethodsThe recently published collection of the radiological images for a rural population in United States had made the development of such a model a possibility, for the high quality images and consistent clinical measurements. We manually annotated 221 chest X-ray images with the lung fields and the opacity regions and trained a segmentation model for the opacity region using the Unet framework and the Resnet18 backbone. In addition, we applied the percentage of the opacity region over the area of the total lung fields for predicting the severity of patients.ResultsThe model has a good performance regarding the overlap between the predicted and the manually labelled opacity regions. The performance is comparable for both the testing data set and the validation data set which comes from very diverse sources. However, careful manual examinations by experienced radiologists show mistakes in the predictions, which could be caused by the anatomical complexities. Nevertheless, the percentage of the opacity region can predict the severity of the patients well in regards to the ICU admissions and mortality.ConclusionIn view of the above, our model is a successful first try in the development of a segmentation model for the opacity regions for the Covid-19 positive chest X-rays. However, additional work is needed before a robust model can be developed for the ultimate goal of the implementations in the clinical setting.Model and supporting materials can be found in https://github.com/haimingt/opacity_segmentation_covid_chest_X_ray.
Prevalence of SARS-CoV-2 IgG antibodies in a population from Veracruz (Southeastern Mexico)
Authors: Remes-Troche Jose María , Ramos-De-la-Medina Antonio , Manríquez-Reyes Marisol , Martínez-Pérez Maldonado Laura , Solis-Gonzalez María Antonieta , Hernández Flores Karina Guadalupe , Cid Héctor Vivanco 
Published: October 2020

AbstractIntroduction/AimRecent studies have shown that seroprevalence is quite variable depending on the country, the population and the time of the pandemic in which the serological tests are performed. Here, we investigated the prevalence of IgG antibodies against SARS-CoV-2 in a population living in Veracruz City, México.MethodsFrom of June 1 to July 31, 2020, the consecutive adult patients (age ≥18 years) that attended 2 ambulatory diagnostic private practice centers for testing were included. Samples were run on the Abbott Architect instrument using the commercial Abbott SARS-CoV-2 IgG assay. The main outcome was seroprevalence. Demographics, previous infection to SARS-CoV-2 (according to a previous positive polymerase-chain reaction nasopharyngeal swab), self-suspicious of virus of infection (according to have in the previous 4 weeks either fever, headache, respiratory symptoms but not a confirmatory PCR) or no having symptoms were also evaluated.ResultsA total of 2174 subjects were tested, included 53.6% women (mean age 41.8±15.17 years, range 18-98 years). One thousand and forty-one (52.5%) subjects were asymptomatic, 722 (33.2%) had suspicious of infection and 311 (14.3%) had previous infection. Overall, 642 of 2174 (29.5% [95% CI 27.59%-31.47%]) of our population were seropositive. Seropositivity among groups was 21.3% in asymptomatic, 23.4% in self-suspicious patients and 73.9% in previous infection patients.ConclusionsWe found one of the highest seroprevalences reported for SARS-CoV-2 worldwide in asymptomatic subjects (21.3%) as well in subjects with self-suspicious of COVID-19 (23.4%). The number of infected subjects in our population is not encouraging and it should be interpreted with caution.
On nonlinear incidence rate of Covid-19
Authors: Paul Swarna Kamal , Jana Saikat , Bhaumik Parama 
Published: October 2020

AbstractClassical Susceptible-Infected-Removed model with constant transmission rate and removal rate may not capture real world dynamics of epidemic due to complex influence of multiple external factors on the spread. On top of that transmission rate may vary widely in a large region due to non-stationarity of spatial features which poses difficulty in creating a global model. We modified discrete global Susceptible-Infected-Removed model by using time varying transmission rate, recovery rate and multiple spatially local models. No specific functional form of transmission rate has been assumed. We have derived the criteria for disease-free equilibrium within a specific time period. A single Convolutional LSTM model is created and trained to map multiple spatiotemporal features to transmission rate. The model achieved 8.39% mean absolute percent error in terms of cumulative infection cases in each locality in a 10-day prediction period. Local interpretations of the model using perturbation method reveals local influence of different features on transmission rate which in turn is used to generate a set of generalized global interpretations. A what-if scenario with modified recovery rate illustrates rapid dampening of the spread when forecasted with the trained model. A comparative study with current normal scenario reveals key necessary steps to reach baseline.
White matter brain aging In Relationship to Schizophrenia and Its Cognitive Deficit
Authors: Wang Jingtao , Kochunov Peter , Sampath Hemalatha , Hatch Kathryn S. , Ryan Meghann C. , Xue Fuzhong , Neda Jahanshad , Paul Thompson , Hahn Britta , Gold James , Waltz James , Hong L. Elliot , Chen Shuo 
Published: October 2020

AbstractWe hypothesized that cerebral white matter deficits in schizophrenia (SZ) are driven in part by accelerated white matter aging and are associated with cognitive deficits. We used machine learning model to predict individual age from diffusion tensor imaging features and calculated the delta age (Δage) as the difference between predicted and chronological age. Through this approach, we translated multivariate white matter imaging features into an age-scaled metric and used it to test the temporal trends of accelerated aging-related white matter deficit in SZ and its association with the cognition. Followed feature selection, a machine learning model was trained with fractional anisotropy values in 34 of 43 tracts on a training set consisted of 107 healthy controls (HC). The brain age of 166 SZs and 107 HCs in the testing set were calculated using this model. Then, we examined the SZ-HC group effect on Δage and whether this effect was moderated by chronological age using the regression spline model. The results showed that Δage was significantly elevated in the age >30 group in patients(p< 0.001) but not in age ⩽ 30 group(p= 0.364). Δage in patients was significantly and negatively associated with both working memory(β= −0.176,p= 0.007) and processing speed(β= −0.519,p= 0.035) while adjusting sex and chronological age. Overall, these findings indicate that the Δage is elevated in SZs and become significantly from middle life stage; the increase of Δage in SZs is associated with the decline neurocognitive performance.
Unearthing the Boreal Soil Resistome Associated with Permafrost Thaw
Authors: Haan Tracie J. , Drown Devin M. 
Published: October 2020

ABSTRACTUnderstanding the distribution and mobility of antibiotic resistance genes (ARGs) in soil bacteria from diverse ecological niches is critical in assessing their impacts on the global spread of antibiotic resistance. In permafrost associated soils, climate and human driven forces augment near-surface thaw altering the overlying active layer. Physiochemical changes shift bacterial community composition and metabolic functioning, however, it is unknown if permafrost thaw will affect ARGs comprising the boreal soil resistome. To assess how thaw shifts the resistome, we performed susceptibility testing and whole genome sequencing on soil isolates from a disturbance-induced thaw gradient in Interior Alaska. We found resistance was widespread in the Alaskan isolates, with 87% of the 90 isolates resistant to at least one of the five antibiotics. We also observed positive trends in both the proportion of resistant isolates and the abundance of ARGs with permafrost thaw. However, the number of ARGs per genome and types of genes present were shown to cluster more strongly by bacterial taxa rather than thaw emphasizing the evolutionary origins of resistance and the role vertical gene transfer has in shaping the predominantly chromosomally encoded ARGs. The observed higher proportion of plasmid-borne and distinct ARGs in our isolates compared to RefSoil+ suggests local conditions affect the composition of the resistome along with selection for ARG mobility. Overall taxonomy and geography shape the resistome, suggesting that as microbial communities shift in response to permafrost thaw so will the ARGs in the boreal active layer.IMPORTANCEAs antibiotic resistance continues to emerge and rapidly spread in clinical settings, it is imperative to generate studies that build insight into the ecology of environmental resistance genes that pose a threat to human health. This study provides insight into the occurrence of diverse ARGs found in Alaskan soil bacteria which is suggestive of the potential to compromise health. The observed differences in ARG abundance with increasing permafrost thaw suggest the role of soil disturbance in driving the distribution of resistant determinants and the predominant taxa that shape the resistome. Moreover, the high-quality whole genome assemblies generated in this study are an extensive resource for microbial researchers interested in permafrost thaw and will provide a steppingstone for future research into ARG mobility and transmission risks.