bluuebunny/crossref_metadata_2025_split huggingface dataset:
“The drug development pipeline for glioblastoma - a cross sectional assessment of the FDA Orphan Drug Product designation database”
Authors: Johann Pascal , Lenz Dominic , Ries Markus 
Published: January 2021

AbstractBackgroundGlioblastoma multiforme (GBM) is the most common malignant brain tumor among adult patients and represents an almost universally fatal disease. Novel therapies for GBM are being developed under the orphan drug legislation and the knowledge on the molecular makeup of this disease has been increasing rapidly. However, the clinical outcomes in GBM patients with currently available therapies are still dismal. An insight into the current drug development pipeline for GBM is therefore of particular interest.ObjectivesTo provide a quantitative clinical-regulatory insight into the status of FDA orphan drug designations for compounds intended to treat GBM.MethodsQuantitative cross-sectional analysis of the U.S. Food and Drug Administration Orphan Drug Product database between 1983 and 2020. STROBE criteria were respected.ResultsFour orphan drugs out of 161 (2,4%) orphan drug designations were approved for the treatment for GBM by the FDA between 1983 and 2020. Fourteen orphan drug designations were subsequently withdrawn for unknown reasons. The number of orphan drug designations per year shows a growing trend. In the last decade, the therapeutic mechanism of action of designated compounds intended to treat glioblastoma shifted from cytotoxic drugs (median year of designation 2008) to immunotherapeutic approaches and small molecules (median year of designation 2014 and 2015 respectively) suggesting an increased focus on precision in the therapeutic mechanism of action for compounds the development pipeline.ConclusionDespite the fact that current pharmacological treatment options in GBM are sparse, the drug development pipeline is steadily growing. In particular, the surge of designated immunotherapies detected in the last years raises the hope that elaborate combination possibilities between classical therapeutic backbones (radiotherapy) and novel, currently experimental therapeutics may help to provide better therapies for this deadly disease in the future.Article summaryStrengths and limitationsThis study provides a quantitative overview on the drug development pipeline for pediatric and adult oncology in general and specifically for the indication glioblastomaAnalyzing the therapeutic mechanisms of designated compounds in glioblastoma reveals an increased focus on personalized and targeted therapiesThe precise reasons for failure of approved drugs and for withdrawal of approved drugs in glioblastoms are unknownFor the analysis, only the databases “FDALabel” (https://nctr-crs.fda.gov/fdalabel/ui/search) and the FDA Orphan drug product designation database (https://www.accessdata.fda.gov/scripts/opdlisting/oopd/) were considered
Mining underutilized whole-genome sequencing projects to improve 16S rRNA databases
Authors: Nolan Ben , Abram Florence , Brennan Fiona , Holmes Ashleigh , O’Flaherty Vincent , Pritchard Leighton , Waters Nicholas R. 
Published: January 2021

AbstractCurrent approaches to interpreting 16S rDNA amplicon data are hampered by several factors. Among these are database inaccuracy or incompleteness, sequencing error, and biased DNA/RNA extraction. Existing 16S rRNA databases source the majority of sequences from deposited amplicon sequences, draft genomes, and complete genomes. Most of the draft genomes available are assembled from short reads. However, repeated ribosomal regions are notoriously difficult to assemble well from short reads, and as a consequence the short-read-assembled 16S rDNA region may be an amalgamation of different loci within the genome. This complicates high-resolution community analysis, as a draft genome’s 16S rDNA sequence may be a chimera of multiple loci; in such cases, the draft-derived sequences in a database may not represent a 16S rRNA sequence as it occurs in biology. We present Focus16, a pipeline for improving 16S rRNA databases by mining NCBI’s Sequence Read Archive for whole-genome sequencing runs that could be reassembled to yield additional 16S rRNA sequences. Using riboSeed (a genome assembly tool for correcting rDNA misassembly), Focus16 provides a way to augment 16S rRNA databases with high-quality re-assembled sequences. In this study, we augmented the widely-used SILVA 16S rRNA database with the novel sequences disclosed by Focus16 and re-processed amplicon sequences from several benchmarking datasets with DADA2. Using this augmented SILVA database increased the number of amplicon sequence variants that could be assigned taxonomic annotations. Further, fine-scale classification was improved by revealing ambiguities. We observed, for example, that amplicon sequence variants (ASVs) may be assigned to a specific genus where Focus16-correction would indicate that the ASV is represented in two or more genera. Thus, we demonstrate that improvements can be made to taxonomic classification by incorporating these carefully re-assembled 16S rRNA sequences, and we invite the community to expand our work to augment existing 16S rRNA reference databases such as SILVA, GreenGenes, and RDP.
Spatial-temporal relationship between population mobility and COVID-19 outbreaks in South Carolina: A time series forecasting analysis
Authors: Zeng Chengbo , Zhang Jiajia , Li Zhenlong , Sun Xiaowen , Olatosi Bankole , Weissman Sharon , Li Xiaoming 
Published: January 2021

AbstractBackgroundPopulation mobility is closely associated with coronavirus 2019 (COVID-19) transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive non-pharmaceutical interventions for disease control. South Carolina (SC) is one of the states which reopened early and then suffered from a sharp increase of COVID-19.ObjectiveTo examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility to predict daily new cases at both state- and county- levels in SC.MethodsThis longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020 in SC and its top five counties with the largest number of cumulative confirmed cases. Daily new case was calculated by subtracting the cumulative confirmed cases of previous day from the total cases. Population mobility was assessed using the number of users with travel distance larger than 0.5 mile which was calculated based on their geotagged twitters. Poisson count time series model was employed to carry out the research goals.ResultsPopulation mobility was positively associated with state-level daily COVID-19 incidence and those of the top five counties (i.e., Charleston, Greenville, Horry, Spartanburg, Richland). At the state-level, final model with time window within the last 7-day had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3-, 7-, 14- days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9-, 14-, 28-, 20-, and 9- days, respectively. The 14-day prediction accuracy ranged from 60.3% to 74.5%.ConclusionsPopulation mobility was positively associated with COVID-19 incidences at both state- and county- levels in SC. Using Twitter-based mobility data could provide acceptable prediction for COVID-19 daily new cases. Population mobility measured via social media platform could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.
Burden of predominant psychological reactions among the healthcare workers and general during COVID-19 pandemic phase: a systematic review and meta-analysis
Authors: Thakur Bhaskar , Pathak Mona 
Published: January 2021

ABSTRACTAimPresent systematic review and meta-analysis examined the burden of psychological reactions predominantly anxiety, depression, stress and insomnia during novel COVID-19 pandemic phase among the frontline healthcare, non-frontline healthcare and general.MethodologyPubMed, EMBASE and SCOPUS were searched for studies between Jan 1, 2020 to May 25, 2020. Brief protocol of the systematic review was registered with the PROSPERO database, (CRD42020186229).Any study that reported the burden of at least one of psychological reactions including anxiety or depression or stress or insomnia was eligible. Heterogeneity was assessed using I2statistic and results were synthesized using random effect meta-analysis.ResultsOut of 52eligible studies, 43 reported anxiety, 43 reported depression, 20 reported stress and 11 reported insomnia. Overall prevalence for anxiety, depression, stress and insomnia were 26.6%, 26.2%,26.2% and 34.4% respectively. Anxiety and depression were found highest among the COVID-19 patients (43.3% and 51.75 respectively). Apart from COVID-19 patients, prevalence of anxiety, depression, stress and insomnia were found highest among the frontline healthcare (27.2%, 32.1%,55.6% and 34.4% respectively) as compared to general healthcare workers (26.9%, 15.7%, 7.0% and 34.0% respectively) and general population (25.9%, 25.9%,25.4% and 29.4% respectively).ConclusionAnxiety and depression were found highest among the COVID-19 patients. Apart from COVID-19 patients, the anxiety, depression, stress and insomnia were more prevalent among frontline healthcare workers compared to general. Such increased prevalence is prompting towards the global mental health emergency. Therefore a call of urgent attention and pan-region effective mental-health intervention are required to mitigate these psychological reactions.
Race/ethnicity differences in vitamin D levels and impact on cardiovascular disease, bone health, and oral health
Authors: Thamattoor Ajoy 
Published: January 2021

ABSTRACTVitamin D and its biomarker 25(OH)D are known to vary by race/ethnicity with African Americans (AAs) having significantly lower levels than non-Hispanic whites (white Americans). However, AAs have better bone mineral density (BMD) and less arterial calcification, one marker of cardiovascular risk, than white Americans, with some studies showing higher vit. D levels harmful to AAs. This study analyzes NHANES data from 2011 to 2014, NHANES being a biennially published national survey of nearly 10,000 people, with interview, examination, and lab data components. The analyses, using count regression and linear regression models to avoid thresholding of variables, find that abdominal aortic calcification scores rise with 24(OH)D in white Americans, with no statistically significant effect in AAs; femoral BMD falls with 25(OH)D in both groups; osteoporotic fracture risks fall with 25(OH)D in white Americans; and periodontal attachment loss falls with rising 25(OH)D in both groups. Overall, higher 25(OH)D seems protective for oral and skeletal health in white Americans, protective for periodontal health in AAs, negative for their skeletal health, and negative for arterial calcification in white Americans, after controlling for the demographic factors of age and sex, the physiological elements of blood pressure and BMI, the biochemical variables of LDL and cholesterol levels, the socioeconomic indicators of income-to-poverty-level ratio and education levels, and the environmental influence of the season. As periodontitis is low on the disease hazard scale compared to arterial calcification and skeletal health, the results point to a lack of significant protection with rising OH(D) levels in AAs, even after their low base levels, and some harmful impact from those higher levels. That combination should trigger a closer look at the single population-wide vitamin D threshold of 30 to 50 ng/mL currently recommended in the US.
Priming mycobacterial ESX-secreted protein B to form a channel-like structure
Authors: Gijsbers Abril , Vinciauskaite Vanesa , Siroy Axel , Gao Ye , Tria Giancarlo , Mathew Anjusha , Sánchez-Puig Nuria , López-Iglesias Carmen , Peters Peter J. , Ravelli Raimond B. G. 
Published: January 2021

AbstractESX-1 is a major virulence factor ofMycobacterium tuberculosis, a secretion machinery directly involved in the survival of the microorganism from the immune system defence. It disrupts the phagosome membrane of the host cell through a contact-dependent mechanism. Recently, the structure of the inner-membrane core complex of the homologous ESX-3 and ESX-5 was resolved; however, the elements involved in the secretion through the outer membrane or those acting on the host cell membrane are unknown. Protein substrates might form this missing element. Here, we describe the oligomerisation process of the ESX-1 substrate EspB, which occurs upon cleavage of its C-terminal region and is favoured by an acidic environment. Cryo-electron microscopy data are presented which show that EspB from different mycobacterial species have a conserved quaternary structure, except for the non-pathogenic speciesM. smegmatis. EspB assembles into a channel with dimensions and characteristics suitable for the transit of ESX-1 substrates, as shown by the presence of another EspB trapped within. Our results provide insight into the structure and assembly of EspB, and suggests a possible function as a structural element of ESX-1.
Humoral and cell-mediated response in colostrum after exposure to severe acute respiratory syndrome coronavirus 2
Authors: Narayanaswamy Vignesh , Pentecost Brian , Alfandari Dominique , Chin Emily , Minor Kathleen , Kastrinakis Alyssa , Lieberman Tanya , Arcaro Kathleen F. , Leftwich Heidi 
Published: January 2021

AbstractBackgroundColostrum provides an immune sharing between a mother and her infant. The transfer in colostrum of antibodies against SARS-CoV-2 and the elicited cytokines may provide crucial protection to the infant. There is limited literature on the immune response to SARS-CoV-2 present in colostrum.ObjectiveTo evaluate the presence of antibodies specific to SARS-CoV-2 and the associated cytokines in colostrum from women who tested positive for the virus.Study DesignBetween March and September 2020 we obtained bilateral colostrum samples collected on spot cards within 48 hours of delivery from 15 new mothers who had previously tested positive for SARS-CoV-2. Five of these 15 COVID-19 positive women also provided bilateral liquid colostrum within 1-2 days of providing the spot card samples. Archived bilateral colostrum samples collected from 8 women during 2011-2013 were used as pre-COVID-19 controls. All samples were tested for reactivity to the Receptor Binding Domain (RBD) of the SARS-CoV-2 spike protein using an ELISA that measures SARS-CoV-2 RBD-specific IgA, IgG, and IgM, and for concentrations of 10 inflammatory cytokines (IFNγ, TNFα, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10, IL-12, IL-13) using a multiplex electrochemiluminescent sandwich assay.ResultsBilateral colostrum samples from 73%, 73% and 33% of the 15 COVID-19 mothers exhibited IgA, IgG, and IgM reactivity to RBD respectively. Colostrum samples from two of the 8 pre-pandemic controls showed IgA and IgG reactivity to RBD. Additionally, COVID-19 mothers had significantly higher levels of 9 of the 10 inflammatory markers (all except IFNγ) as compared to the pre-COVID-19 controls. Comparable results were obtained with both the spot card-eluates and liquid samples.ConclusionsA strong humoral immune response is present in the colostrum of women who were infected with SARS-CoV-2 before delivering. High levels of 9 inflammatory markers were also present in the colostrum. The evolution and duration of the antibody response, as well as dynamics of the cytokine response, remain to be determined. Our results also indicate that future large-scale studies can be conducted with milk easily collected on paper spot cards.
How vaccination and contact isolation might interact to suppress transmission of Covid-19: a DCM study
Authors: Friston Karl J. , Costello Anthony , Flandin Guillaume , Razi Adeel 
Published: January 2021

AbstractThis report describes a dynamic causal model that could be used to address questions about the rollout and efficacy of vaccines in the United Kingdom. For example, is suppression of community transmission a realistic aspiration? And, if not, what kind of endemic equilibrium might be achieved? What percentage of the population needs to be vaccinated? And over what timescale? It focuses on the synergies among (i) vaccination, (ii) the supported isolation of contacts of confirmed cases and (iii) restrictions on contact rates (i.e., lockdown and social distancing). To model these mitigations, we used a dynamic causal model that embeds an epidemiological model into agent-based behavioural model. The model structure and parameters were optimised to best explain responses—to the first and subsequent waves—enabling predictions over the forthcoming year under counterfactual scenarios. Illustrative analyses suggest that the full potential of vaccination is realised by increasing the efficacy of contact tracing: for example, under idealised (best case) assumptions—of an effective vaccine and efficient isolation of infected pre-symptomatic cases— suppression of community transmission would require 50% herd immunity by vaccinating 22% by the end of 2021; i.e., 15 million people or about 50,000 per day. With no change in the isolation of contacts, 36% would require vaccination, i.e., 25 million people. These figures should not be read as estimates of the actual number of people requiring vaccination; however, they illustrate the potential of this kind of model to quantify interactions among public health interventions. We anticipate using this model in a few months—to estimate the average effectiveness of vaccines when more data become available.
Age-seroprevalence curves for the multi-strain structure of influenza A virus
Authors: Vinh Dao Nguyen , Duy Nhat Nguyen Thi , de Bruin Erwin , Thao Vy Nguyen Ha , Nhu Thao Tran Thi , Phuong Huynh Thi , Anh Pham Hong , Todd Stacy , Quan Tran Minh , Le Thanh Nguyen Thi , Nam Lien Nguyen Thi , Hong Ha Nguyen Thi , Kim Hong Tran Thi , Thai Pham Quang , Choisy Marc , Nguyen Tran Dang , Simmons Cameron P , Thwaites Guy E , Clapham Hannah E , Van Vinh Chau Nguyen , Koopmans Marion , Boni Maciej F 
Published: January 2021

AbstractThe relationship between age and seroprevalence provides the simplest and least expensive approach to computing the annual attack rate of an infectious disease. However, many pathogens circulate as multiple serologically distinct strains, with no single assay able to determine seropositivity or seronegativity to an entire clade or family of co-circulating pathogens. An approach is needed to describe population exposure to an antigenically variable group of pathogens without focusing on any particular strain or serotype in the group. In this study, we focus on the two-subtype multi-strain taxonomy of human influenza A virus. We describe a data set of 24,402 general-population serum samples collected in central and southern Vietnam between 2009 to 2015, and assayed for influenza HA1 antibodies to eleven different strains of human influenza A (both H3 and H1 subtypes). We find that a principal components decomposition of the data results in the first principal component PC1 being an appropriate surrogate for seroprevalence (or composite antibody titer) which can be further decomposed for H1 and H3 contribution to the serological profile. Using this approach, we are able to provide the first ELISA-based standardized measurements of serology to reconstruct population exposure history, which correlates well with known influenza epidemiology. Annual attack rates in Vietnam are estimated at 25.6% (95% CI: 24.1% – 27.1%) for H3 and 16.0% (95% CI: 14.7% – 17.3%) for H1, with some variation in location-specific attack rates. The remaining principal components act as descriptors of influenza history and sort the population by birth year. The novel contributions of this analysis are (1) the introduction of dimensionality reduction on human antibody profiles to construct an age-seroprevalence relationship for an antigenically variable pathogen, (2) an analysis of >24,000 individuals, using >260,000 serological data points in total, allowing us to construct age-seroprevalence relationships with the precision of modern big data studies, and (3) a description of long-term attack rates in a non-vaccinated setting showing the natural history of influenza A virus in a densely-populated tropical country.
Saving Lives with Pre-Financed Rules-Based Disaster Aid: Evidence from Mexico
Authors: Valle Alejandro del 
Published: January 2021

AbstractDeveloping economies are not disproportionately exposed to natural disasters, but they experience significantly more deaths. Exploiting a discontinuity in the eligibility rules to Mexico’s pre-financed indexed disaster fund (Fonden), I show that accelerated reconstruction of public infrastructure can fully reduce postdisaster excess mortality in the short-run and up to 75 percent two years after. I also find that Fonden’s impact is concentrated in areas with medical infrastructure and among conditions responsive to basic medical interventions. These findings suggest that Fonden operates by restoring access to health services after a disaster. Annual benefits from Fonden amount to 197 thousand life-years saved. (JEL: G22, H12, H84, Q54, I15, O18.)
Mental health of health care workers during the COVID-19 pandemic and evidence-based frameworks for mitigation: A rapid review
Authors: Major Ajay , Hlubocky Fay J. 
Published: January 2021

AbstractBackgroundThe ongoing COVID-19 pandemic has profoundly affected the mental health of health care workers (HCWs), and optimal strategies to provide psychological support for HCWs are not currently established.AimsTo rapidly review recently-published literature on the mental health of HCWs during the COVID-19 pandemic.MethodsQuery of all quantitative research through the PubMed database on the mental health of HCWs during the COVID-19 pandemic which utilized validated mental health instruments. 723 articles were screened and 87 articles were included.ResultsNearly all included studies were cross-sectional, survey-based assessments of the prevalence of and risk factors for mental illness. Only one interventional study was identified. Prevalence of mental health outcomes varied widely: 7.0-97.3% anxiety, 10.6-62.1% depression, 2.2-93.8% stress, 3.8-56.6% post traumatic stress, 8.3-88.4% insomnia, and 21.8-46.3% burnout. Risk and protective factors were identified in personal and professional domains, including degree of COVID-19 exposure, adequacy of protective equipment, and perception of organizational support.ConclusionsThe myriad risk factors for poor mental health among HCWs suggests that a comprehensive psychosocial support model with individual- and organization-level interventions is necessary. Further longitudinal research on specific evidence-based interventions to mitigate adverse mental health outcomes among HCWs is urgently needed as the pandemic continues.