bluuebunny/crossref_metadata_2025_split huggingface dataset:
The female protective effect against autism spectrum disorder
Authors: Wigdor Emilie M. , Weiner Daniel J. , Grove Jakob , Fu Jack M. , Thompson Wesley K. , Carey Caitlin E. , Baya Nikolas , van der Merwe Celia , Walters Raymond K. , Satterstrom F. Kyle , Palmer Duncan S. , Rosengren Anders , Bybjerg-Grauholm Jonas ,   iPSYCH Consortium, Hougaard David M. , Mortensen Preben Bo , Daly Mark J. , Talkowski Michael E. , Sanders Stephan J. , Bishop Somer L. , Børglum Anders D. , Robinson Elise B. 
Published: April 2021

AbstractAutism spectrum disorder (ASD) is diagnosed 3-4 times more frequently in males than in females. Genetic studies of rare variants support a female protective effect (FPE) against ASD. However, sex differences in common, inherited genetic risk for ASD are less studied. Leveraging the nationally representative Danish iPSYCH resource, we found siblings of female ASD cases had higher rates of ASD than siblings of male ASD cases (P < 0.01). In the Simons Simplex and SPARK collections, mothers of ASD cases carried more polygenic risk for ASD than fathers of ASD cases (P = 7.0 × 10−7). Male unaffected siblings under-inherited polygenic risk (P = 0.03); female unaffected siblings did not. Further, female ASD cases without a high-impact de novo variant over-inherited nearly three-fold the polygenic risk of male cases with a high-impact de novo (P = 0.02). Our findings support a FPE against ASD that includes common, inherited genetic variation.
Predictive modelling of COVID-19 New Confirmed Cases in Algeria using Artificial Neural Network
Authors: Djeddou Messaoud , Hameed Ibrahim A. , Hellal Aouatef , Nejatian Abolfazel 
Published: April 2021

AbstractThis study investigates the potential of a simple artificial neural network for the prediction of COVID-19 New Confirmed Cases in Algeria (CNCC).Four different ANN models were built (GRNN, RBFNN, ELM, and MLP). The performance of the predictive models is evaluated based on four numerical parameters, namely root mean squared error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and Pearson correlation coefficient (R). Taylor diagram was also used to examine the similarities and differences between the observed and predicted values obtained from the proposed models.The results showed the potential of the multi-layer perceptron neural network (MLPNN) which exhibited a high level of accuracy in comparison to the other models.
RELATIONSHIP OF LIVER ENZYME LEVELS WITH THE CLINICAL COURSE OF COVID-19
Authors: İnce Ulku , Duran Harun Tolga 
Published: April 2021

ABSTRACTIntroductionCovid 19 infection, which can affect many systems in the human body, can cause organ dysfunction. High liver serum enzymes can be found in covid-19 patients, and many factors cause this stop. Patients with high levels of liver enzymes that require invasive mechanical ventilation during their follow-up were examined, and it was aimed to determine whether it was among the predictive indicators of mortality.MATERIAL AND METHODSPatients infected with covid 19 who were hospitalized in the intensive care unit between March 30 and December 1, 2020 according to the criteria of hospitalization in the intensive care unit, clinical trials such as age, gender, length of stay, additional diseases, liver enzyme levels and whether invasive mechanical ventilation is required their characteristics were recorded and analyzed retrospectively and compared.RESULTSData were collected from 111 patients whose liver enzyme levels were measured from 131 patients included in the study. It was found that aspartate transaminase, alanine aminotransferase, and gamma-glutamyl transferase levels were statistically higher in the invasive mechanical ventilation group compared to the patients who did not undergo invasive mechanical ventilation.CONCLUSIONAlanine aminotransferase, aspartate aminotransferase, and gamma-glutamyl transferase levels were statistically higher in COVID19-infected patients who were treated in intensive care and undergoing invasive mechanical ventilation. These enzymes are easily accessible and are shown among predictive values in mortality.
Smoking modulates different secretory subpopulations expressing SARS-CoV-2 entry genes in the nasal and bronchial airways
Authors: Xu Ke , Shi Xingyi , Husted Chris , Hong Rui , Wang Yichen , Ning Boting , Sullivan Travis , Rieger-Christ Kimberly M , Duan Fenghai , Marques Helga , Gower Adam C. , Xiao Xiaohui , Liu Hanqiao , Liu Gang , Duclos Grant , Spira Avrum , Mazzilli Sarah A. , Billatos Ehab , Lenburg Marc E. , Campbell Joshua D. , Beane Jennifer 
Published: April 2021

AbstractCoronavirus Disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 SARS-CoV-2), which infects host cells with help from the Viral Entry (VE) proteins ACE2, TMPRSS2, and CTSL1–4. Proposed risk factors for viral infection, as well as the rate of disease progression, include age5,6, sex7, chronic obstructive pulmonary disease7,8, cancer9, and cigarette smoking10–13. To investigate whether the proposed risk factors increase viral infection by modulation of the VE genes, we examined gene expression profiles of 796 nasal and 1,673 bronchial samples across four lung cancer screening cohorts containing individuals without COVID-19. Smoking was the only clinical factor reproducibly associated with the expression of any VE gene across cohorts. ACE2 expression was significantly up-regulated with smoking in the bronchus but significantly down-regulated with smoking in the nose. Furthermore, expression of individual VE genes were not correlated between paired nasal and bronchial samples from the same patients. Single-cell RNA-seq of nasal brushings revealed that an ACE2 gene module was detected in a variety of nasal secretory cells with the highest expression in the C15orf48+ secretory cells, while a TMPRSS2 gene module was most highly expressed in nasal keratinizing epithelial cells. In contrast, single-cell RNA-seq of bronchial brushings revealed that ACE2 andTMPRSS2 gene modules were most enriched in MUC5AC+ bronchial goblet cells. The CTSL gene module was highly expressed in immune populations of both nasal and bronchial brushings. Deconvolution of bulk RNA-seq showed that the proportion of MUC5AC+ goblet cells was increased in current smokers in both the nose and bronchus but proportions of nasal keratinizing epithelial cells, C15orf48+ secretory cells, and immune cells were not associated with smoking status. The complex association between VE gene expression and smoking in the nasal and bronchial epithelium revealed by our results may partially explain conflicting reports on the association between smoking and SARS-CoV-2 infection.
Differences in risk for SARS-CoV-2 infection among healthcare workers
Authors: Elfström K. Miriam , Blomqvist Jonas , Nilsson Peter , Hober Sophia , Pin Elisa , Månberg Anna , Pimenoff Ville N. , Arroyo Mühr Laila Sara , Lundgren Kalle Conneryd , Dillner Joakim 
Published: April 2021

AbstractHealthcare workers (HCWs) are a risk group for SARS-CoV-2 infection, but which healthcare work that conveys risk and to what extent such risk can be prevented is not clear. Starting on April 24th, 2020, all employees at work (n=15,300) at the Karolinska University Hospital, Stockholm, Sweden were invited and 92% consented to participate in a SARS-CoV-2 cohort study. Complete SARS-CoV-2 serology was available for n=12,928 employees and seroprevalences were analyzed by age, sex, profession, patient contact, and hospital department. Relative risks were estimated to examine the association between type of hospital department as a proxy for different working environment exposure and risk for seropositivity, adjusting for age, sex, sampling week, and profession. Wards that were primarily responsible for COVID-19 patients were at increased risk (adjusted OR 1.95 (95% CI 1.65-2.32) with the notable exception of the infectious diseases and intensive care units (adjusted OR 0.86 (95% CI 0.66-1.13)), that were not at increased risk despite being highly exposed. Several units with similar types of work varied greatly in seroprevalences. Among the professions examined, nurse assistants had the highest risk (adjusted OR 1.62 (95% CI 1.38-1.90)). Although healthcare workers, in particular nurse assistants, who attend to COVID-19 patients are a risk group for SARS-CoV-2 infection, several units caring for COVID-19 patients had no excess risk. Large variations in seroprevalences among similar units suggest that healthcare work-related risk of SARS-CoV-2 infection may be preventable.
Impact of COVID-19 in Individuals with Autism Spectrum Disorders: Analysis of a National Private Claims Insurance Database
Authors: Karpur Arun , Vasudevan Vijay , Shih Andy , Frazier Thomas 
Published: April 2021

AbstractThe COVID-19 pandemic continues to have a detrimental impact on individuals with disabilities. Data from FAIR Health’s FH® NPIC (National Private Insurance Claims) database,1 one of the nation’s largest databases of private insurance claim records, were analyzed to understand the experiences of individuals with ASD in COVID-19 pandemic. Multivariate logistic regression models revealed that individuals with ASD + ID were nine times more likely to be hospitalized (OR = 9.3; 95% CI: 6.9 – 12.5) and were nearly six times more likely have an elevated length of hospital stay(OR = 5.9; 95% CI: 3.5 – 10.1) compared those without ASD + ID. These findings point to their need for prioritization in access to vaccines for preventing COVID-19 infection and morbidities.
Commercialized kit to assess T-cell responses against SARS-CoV-2 S peptides. A pilot study in Health Care Workers
Authors: Martínez-Gallo Mónica , Esperalba-Esquerra Juliana , Pujol-Borrell Ricardo , Sandá Víctor , Arrese-Muñoz Iria , Fernández-Naval Candela , Antón-Pagarolas Andrés , Cardona Victoria , Labrador-Horrillo Moisés , Pumarola-Suñé Tomás , Hernandéz-González Manuel 
Published: April 2021

AbstractBackgroundIt is crucial to assess the levels of protection generated by natural infection or SARS-CoV-2 vaccines, mainly in individuals professionally exposed and in vulnerable groups. Measuring T-cell responses may complement antibody tests currently in use as correlates of protection. Our aim was to assess the feasibility of a validated assay of T-cell responses.MethodsTwenty health-care-workers (HCW) were included. Antibody test to SARS-CoV-2 N and S-proteins in parallel with a commercially available whole-blood-interferon-gamma-release-assay (IGRA) to S-peptides and two detection methods, CLIA and ELISA were determined.ResultsIGRA test detected T-cell responses in naturally exposed and vaccinated HCW already after first vaccination dose. The correlation by the two detection methods was very high (R>0.8) and sensitivity and specificity ranged between 100 and 86% and 100-73% respectively. Even though there was a very high concordance between specific antibody levels and the IGRA assay in the ability to detect immune response to SARS-CoV-2, there was a relatively low quantitative correlation. In the small group primed by natural infection, one vaccine dose was sufficient to reach immune response plateau. IGRA was positive in one, with Ig(S) antibody negative vaccinated immunosuppressed HCW illustrating another advantage of the IGRA-test.ConclusionWhole-blood-IGRA-tests amenable to automation and constitutes a promising additional tool for measuring the state of the immune response to SARS-CoV-2; they are applicable to large number of samples and may become a valuable correlate of protection to COVID-19, particularly for vulnerable groups at risk of being re-exposed to infection, as are health-care-workers.
Autoantibodies stabilize neutrophil extracellular traps in COVID-19
Authors: Zuo Yu , Yalavarthi Srilakshmi , Navaz Sherwin , Hoy Claire , Harbaugh Alyssa , Gockman Kelsey , Zuo Melanie , Madison Jacqueline A. , Shi Hui , Kanthi Yogendra , Knight Jason S. 
Published: April 2021

ABSTRACTThe release of neutrophil extracellular traps (NETs) by hyperactive neutrophils is recognized to play an important role in the thromboinflammatory milieu inherent to severe presentations of COVID-19. At the same time, a variety of functional autoantibodies have been observed in individuals with severe COVID-19 where they likely contribute to immunopathology. Here, we aimed to determine the extent to which autoantibodies might target NETs in COVID-19 and, if detected, to elucidate their potential functions and clinical associations. We measured anti-NET antibodies in 328 individuals hospitalized with COVID-19 alongside 48 healthy controls. We found high anti-NET activity in the IgG and IgM fractions of 27% and 60% of patients, respectively. There was a strong correlation between anti-NET IgG and anti-NET IgM (r=0.4, p<0.0001). Both anti-NET IgG and IgM tracked with high levels of circulating NETs, impaired oxygenation efficiency, and high circulating D-dimer. Furthermore, patients who required mechanical ventilation had a greater burden of anti-NET antibodies than did those not requiring oxygen supplementation. Levels of anti-NET IgG (and to a lesser extent anti-NET IgM) demonstrated an inverse correlation with the efficiency of NET degradation by COVID sera. Furthermore, purified IgG from COVID sera with high levels of anti-NET antibodies impaired the ability of healthy control serum to degrade NETs. In summary, many individuals hospitalized with COVID-19 have anti-NET antibodies, which likely impair NET clearance and may potentiate SARS-CoV-2-mediated thromboinflammation.
Growth Functions as a tool to model SARS-CoV-2 pandemic trajectory and related-deaths worldwide
Authors: Fonseca Vagner , Mascarenhas Edson , Ramos Paulo , Coelho Leandro , Frias Diego 
Published: April 2021

AbstractThe international scientific community from different areas of knowledge has made efforts to provide information and methods that contribute to the adoption of the most appropriate measures to curb the spread of the COVID-19 disease. In particular, the data analysis community has been very active in publishing a large number of papers. A good part of them is related to the prediction of epidemic variables (number of cases and deaths) in different time horizons. To solve the problem of the prediction of COVID-19, an important place is occupied by the sigmoidal growth functions, as they have often been used successfully in previous epidemic outbreaks. The objective of this work was to investigate, on a statistical basis, the ability of classical growth functions to model the data from the COVID-19 pandemic. But for that, it was necessary to establish a clear classification of the 5 types of problems that can be faced with data analysis techniques in this specific context and to define a methodology based on quantitative metrics to measure the performance in solving these different types of problems. The basic concept used was that of an epidemic wave consisting of an initial-increasing and a final-decreasing phase. A classification of the COVID-19 waves in 4 types was done based on mining data from all available countries. Thus, it was possible to determine the resolvability of each type of problem depending on the stage of the epidemic wave. The biggest conclusion was the impossibility of solving the long-term forecasting problems (problem 5 – to estimate the total value of an epidemic wave) with data from the first phase only. Using this theoretical-methodological framework, we evaluated, using metrics specifically designed for these types of problems, the performance of 3 classic growth functions: Logistics, Gompertz and Richards (a generalization of the previous two) in 2 types of problems: (1) Description of the trajectory of the epidemic and (2) Prediction of the total numbers of cases and deaths. We used data from 10 countries, 7 of them with more than 100 daily deaths on the peak day. The results show a generalized underperformance of the logistic function in all aspects and place the Gompertz function as the best cost-benefit alternative, as it has performance comparable to the Richards function, but it has one less parameter to be adjusted, in the process of regression of the model to the observed data.
Is convalescent plasma futile in COVID-19? A Bayesian re-analysis of the RECOVERY randomised controlled trial
Authors: Hamilton FW , Lee TC , Arnold DT , Lilford R , Hemming K 
Published: April 2021

IntroductionRandomised trials are generally performed from a frequentist perspective reporting point estimates and 95% confidence intervals. This approach can confuse “evidence of no effect” with “no evidence of an effect” and does not allow for contextual knowledge. The RECOVERY trial evaluated convalescent plasma for patients hospitalised with COVID-19, the interaction test for the primary outcome was not statistically significant, and the trial concluded no evidence of an effect. From the clinical immunology perspective, there is strong justification to expect differential responses to convalescent plasma in patients who already have their own antibodies to SARS-CoV2 (seropositive) versus those who do not (seronegative).MethodsOutcome data was extracted from the RECOVERY trial both overall and for seronegative participants. A Bayesian re-analysis with a wide variety of priors (vague, optimistic, skeptical and pessimistic) was performed calculating the posterior probability for both any benefit or a modest benefit (number needed to treat of 100).ResultsAcross all patients, when analysed with a vague prior the likelihood of any benefit or a modest benefit was estimated to be 64% and 18% respectively. In contrast, in the seronegative subgroup, the likelihood of any benefit or a modest benefit was estimated to be 90% and 74%. Results were broadly consistent across all prior distributions.ConclusionPerforming clinical trials during a pandemic is challenging, and RECOVERY has provided high quality evidence for numerous therapies. However, the use of frequentist hypothesis testing in this trial has led to the trialists and governing bodies to conclude a strong evidence of no effect. Based on this trial, and other prior knowledge there remains a strong probability that convalescent plasma provides at least a modest benefit in seronegative patients.
Mechanisms and fluid dynamics of foraging in heterotrophic nanoflagellates
Authors: Suzuki Sei , Andersen Anders , Kiørboe Thomas 
Published: April 2021

ABSTRACTHeterotrophic nanoflagellates are the main consumers of bacteria and picophytoplankton in the ocean. In their micro-scale world, viscosity impedes predator-prey contact, and the mechanisms that allow flagellates to daily clear a volume of water for prey corresponding to 106times their own volume is unclear. It is also unclear what limits observed maximum ingestion rates of about 104bacterial prey per day. We used high-speed video-microscopy to describe feeding flows, flagellum kinematics, and prey searching, capture, and handling in four species with different foraging strategies. In three species, prey-handling times limit ingestion rates and account well for their reported maximum values. Similarly, observed feeding flows match reported clearance rates. Simple point-force models allowed us to estimate the forces required to generate the feeding flows, between 4-13 pN, and consistent with the force produced by the hairy (hispid) flagellum, as estimated using resistive force theory. Hispid flagella can produce a force that is much higher than the force produced by a naked flagellum with similar kinematics, and the hairy flagellum is therefore key to foraging in most nanoflagellates. Our findings provide a mechanistic underpinning of observed functional responses of prey ingestion rates in nanoflagellates.
Equity Impacts of Dollar Store Vaccine Distribution
Authors: Chevalier Judith A. , Schwartz Jason L. , Su Yihua , Williams Kevin R. 
Published: April 2021

AbstractWe use geospatial data to examine the unprecedented national program currently underway in the United States to distribute and administer vaccines against COVID-19. We quantify the impact of the proposed federal partnership with the company Dollar General to serve as vaccination sites and compare vaccine access with Dollar General to the current Federal Retail Pharmacy Partnership Program. Although dollar stores have been viewed with skepticism and controversy in the policy sector, we show that, relative to the locations of the current federal program, Dollar General stores are disproportionately likely to be located in Census tracts with high social vulnerability; using these stores as vaccination sites would greatly decrease the distance to vaccines for both low-income and minority households. We consider a hypothetical alternative partnership with Dollar Tree and show that adding these stores to the vaccination program would be similarly valuable, but impact different geographic areas than the Dollar General partnership. Adding Dollar General to the current pharmacy partners greatly surpasses the goal set by the Biden administration of having 90% of the population within 5 miles of a vaccine site. We discuss the potential benefits of leveraging these partnerships for other vaccinations, including against influenza.
Asymmetric learning facilitates human inference of transitive relations
Authors: Ciranka Simon , Linde-Domingo Juan , Padezhki Ivan , Wicharz Clara , Wu Charley M. , Spitzer Bernhard 
Published: April 2021

AbstractHumans and other animals are capable of inferring never-experienced relations (e.g., A>C) from other relational observations (e.g., A>B and B>C). The processes behind such transitive inference are subject to intense research. Here, we demonstrate a new aspect of relational learning, building on previous evidence that transitive inference can be accomplished through simple reinforcement learning mechanisms. We show in simulations that inference of novel relations benefits from an asymmetric learning policy, where observers update only their belief about the winner (or loser) in a pair. Across 4 experiments (n=145), we find substantial empirical support for such asymmetries in inferential learning. The learning policy favoured by our simulations and experiments gives rise to a compression of values which is routinely observed in psychophysics and behavioural economics. In other words, a seemingly biased learning strategy that yields well-known cognitive distortions can be beneficial for transitive inferential judgments.
Bioinformatics analysis of SARS-CoV-2 RBD mutant variants and insights into antibody and ACE2 receptor binding
Authors: Ranjan Prashant , Neha  , Devi Chandra , Das Parimal 
Published: April 2021

AbstractPrevailing COVID-19 vaccines are based on the spike protein of earlier SARS-CoV-2 strain that emerged in Wuhan, China. The continuously evolving nature of SARS-CoV-2 resulting emergence of new variants raises the risk of immune absconds. During the last few months, several RBD (receptor-binding domain) variants have been reported to affect the vaccine efficacy considerably. Soon after reporting of a new double mutant variant (L452R & E484Q) in India, the country facing a deadlier second wave of infections which prompts researchers to suspects this variant to be accountable. To address the relevant concerns about this new variant affecting vaccine efficacy, we performed molecular simulation dynamics based structural analysis of spike protein of double mutant (L452R & E484Q) along with K417G variants and earlier reported RBD variants and found structural changes in RBD region after comparing with the wild type. Comparison of the binding affinity of the double mutant and earlier reported RBD variant for ACE2 (angiotensin 2 altered enzymes) receptor and CR3022 antibody with the wild-type strain revealed the lowest binding affinity of the double mutant for CR3022 among all other variants. These findings suggest that the newly emerged double mutant could significantly reduce the impact of the current vaccine which threatens the protective efficacy of current vaccine therapy.