bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association of hypertension, diabetes, stroke, cancer, kidney disease, and high-cholesterol with COVID-19 disease severity and fatality: a systematic review
Authors: Zaki Nazar , Alashwal Hany , Ibrahim Sahar 
Published: June 2020

AbstractObjectiveTo undertake a review and critical appraisal of published/preprint reports that offer methods of determining the effects of hypertension, diabetes, stroke, cancer, kidney issues, and high-cholesterol on COVID-19 disease severity.Data sourcesGoogle Scholar, PubMed, COVID-19 Open Research Dataset: a resource of over 128,000 scholarly articles, including over 59,000 articles with full text related to COVID-19, SARS-CoV-2, and coronaviruses.MethodsA search was conducted by two authors independently on the freely available COVID-19 Open Research Dataset (CORD-19). We developed an automated search engine to screen a total of 59,000 articles in a few seconds. The search engine was built using a retrieval function that ranks a set of documents based on the query terms appearing in each document regardless of their proximity within the document. Filtering of the articles was then undertaken using keywords and questions, e.g. “Effects of diabetes on COVID/normal coronavirus/SARS-CoV-2/nCoV/COVID-19 disease severity, mortality?”. The search terms were repeated for all the comorbidities considered in this paper. Additional articles were retrieved by searching via Google Scholar and PubMed.FindingsA total of 54 articles were considered for a full review. It was observed that diabetes, hypertension, and cholesterol levels possess an apparent relation to COVID-19 severity. Other comorbidities, such as cancer, kidney disease, and stroke, must be further evaluated to determine a strong relationship to the virus. Reports associating cancer, kidney disease, and stroke with COVID-19 should be carefully interpreted, not only because of the size of the samples, but also because patients could be old, have a history of smoking, or have any other clinical condition suggesting that these factors might be associated with the poor COVID-19 outcomes rather than the comorbidity itself. Such reports could lead many oncologists and physicians to change their treatment strategies without solid evidence and recommendations. Further research regarding this relationship and its clinical management is warranted. Additionally, treatment options must be examined further to provide optimal treatment and ensure better outcomes for patients suffering from these comorbidities. It should be noted that, whether definitive measurements exist or not, the care of patients as well as the research involved should be largely prioritized to tackle this deadly pandemic.
Machine learning for identifying relevant publications in updates of systematic reviews of diagnostic test studies
Authors: Lange Toni , Schwarzer Guido , Datzmann Thomas , Binder Harald 
Published: June 2020

AbstractBackgroundUpdating systematic reviews is often a time-consuming process involving a lot of human effort and is therefore not carried out as often as it should be. Our aim was therefore to explore the potential of machine learning methods to reduce the human workload, and to particularly also gauge the performance of deep learning methods as compared to more established machine learning methods.MethodsWe used three available reviews of diagnostic test studies as data basis. In order to identify relevant publications we used typical text pre-processing methods. The reference standard for the evaluation was the human-consensus based binary classification (inclusion, exclusion). For the evaluation of models various scenarios were generated using a grid of combinations of data preprocessing steps. Furthermore, we evaluated each machine learning approach with an approach-specific predefined grid of tuning parameters using the Brier score metric.ResultsThe best performance was obtained with an ensemble method for two of the reviews, and by a deep learning approach for the other review. Yet, the final performance of approaches is seen to strongly depend on data preparation. Overall, machine learning methods provided reasonable classification.ConclusionIt seems possible to reduce the human workload in updating systematic reviews by using machine learning methods. Yet, as the influence of data preprocessing on the final performance seems to be at least as important as choosing the specific machine learning approach, users should not blindly expect good performance just by using approaches from a popular class, such as deep learning.
Assessing the impact of coordinated COVID-19 exit strategies across Europe
Authors: Ruktanonchai N.W. , Floyd J.R. , Lai S. , Ruktanonchai C.W. , Sadilek A. , Rente-Lourenco P. , Ben X. , Carioli A. , Gwinn J. , Steele J.E. , Prosper O. , Schneider A. , Oplinger A. , Eastham P. , Tatem A.J. 
Published: June 2020

AbstractAs rates of new COVID-19 cases decline across Europe due to non-pharmaceutical interventions such as social distancing policies and lockdown measures, countries require guidance on how to ease restrictions while minimizing the risk of resurgent outbreaks. Here, we use mobility and case data to quantify how coordinated exit strategies could delay continental resurgence and limit community transmission of COVID-19. We find that a resurgent continental epidemic could occur as many as 5 weeks earlier when well-connected countries with stringent existing interventions end their interventions prematurely. Further, we found that appropriate coordination can greatly improve the likelihood of eliminating community transmission throughout Europe. In particular, synchronizing intermittent lockdowns across Europe meant half as many lockdown periods were required to end community transmission continent-wide.One Sentence SummaryEU coordination in easing restrictions is key to preventing resurgent COVID-19 outbreaks and stopping community transmission.
Improved Global Response Outcome After Intradetrusor Injection of Adult Muscle-Derived Cells for the Treatment of Underactive Bladder
Authors: Gilleran Jason , Diokno Ananias C. , Ward Elijah , Sirls Larry , Hasenau Deborah , Giordano Jennifer , Shea Evelyn , Bartolone Sarah N. , Lamb Laura E. , Chancellor Michael B. 
Published: June 2020

AbstractWe report on the first regulatory approved clinical trial of a prospective open-label physician-initiated study assessing the safety and efficacy of intradetrusor injected Autologous Muscle Derived Cells (AMDC) treatment for underactive bladder (UAB).20 non-neurogenic UAB patients were treated. Approximately 50-250 mg of quadriceps femoris muscle was collected using a spirotome 8-gauge needle. The muscles biopsy samples were sent to Cook MyoSite (Pittsburgh, PA) for processing, isolation, and propagation of cells. Research patients received approximately 30 intradetrusor injections of 0.5 mL delivered to the bladder, for a total of 15 mL and 125 million AMDC, performed utilizing a flexible cystoscope under direct vision using topical local anesthesia. Follow-up assessments included adverse events and efficacy via voiding diary and urodynamic testing at 1, 3, 6 & 12-month post-injection. An optional second injection was offered at the end of the 6 months visit.20 patients received the first injection and all 20 patients requested and received a second injection. Median patient age was 65 years old (range 41-82 years). There were 16 male (80%) and 4 female (20%) patients. Etiology included 7 men (35%) with persistent urinary retention after transurethral resection of the prostate for benign prostatic hyperplasia and 13 patients (65%) with idiopathic chronic urinary retention. At the primary outcome time point of 12 months, 11/19 patients (58%) reported a global response assessment (GRA) ≥ 5, showing slight to marked improvement in their UAB symptoms, compared to 6/20 (30%) patients at 3-months post-injection. No serious procedure or treatment-related adverse events occurred. Noted improvements included: decreased post void residual urine volume, increased voiding efficiency, and decreased catheter use.Intradetrusor injected AMDC as a treatment for UAB was successfully completed in a 20-patient trial without serious adverse event and with signal of efficacy. Cellular therapy may be a promising novel treatment for catheter dependent chronic urinary retention. A multicenter controlled trial is needed to further assess the promise of regenerative medicine in the treatment of lower urinary tract dysfunction.
Genetic spectrum and distinct evolution patterns of SARS-CoV-2
Authors: Liu Sheng , Shen Jikui , Fang Shuyi , Li Kailing , Liu Juli , Yang Lei , Hu Chang-Deng , Wan Jun 
Published: June 2020

AbstractFour signature groups of frequently occurred single-nucleotide variants (SNVs) were identified in over twenty-eight thousand high-quality and high-coverage SARS-CoV-2 complete genome sequences, representing different viral strains. Some SNVs predominated but were mutually exclusively presented in patients from different countries and areas. These major SNV signatures exhibited distinguishable evolution patterns over time. A few hundred patients were detected with multiple viral strain-representing mutations simultaneously, which may stand for possible co-infection or potential homogenous recombination of SARS-CoV-2 in environment or within the viral host. Interestingly nucleotide substitutions among SARS-CoV-2 genomes tended to switch between bat RaTG13 coronavirus sequence and Wuhan-Hu-1 genome, indicating the higher genetic instability or tolerance of mutations on those sites or suggesting that major viral strains might exist between Wuhan-Hu-1 and RaTG13 coronavirus.
Estimation of the incubation period of COVID-19 using viral load data
Authors: Ejima Keisuke , Kim Kwang Su , Ludema Christina , Bento Ana I. , Iwanami Shoya , Fujita Yasuhisa , Ohashi Hirofumi , Koizumi Yoshiki , Watashi Koichi , Aihara Kazuyuki , Nishiura Hiroshi , Iwami Shingo 
Published: June 2020

AbstractThe incubation period, or the time from infection to symptom onset of COVID-19 has been usually estimated using data collected through interviews with cases and their contacts. However, this estimation is influenced by uncertainty in recalling effort of exposure time. We propose a novel method that uses viral load data collected over time since hospitalization, hindcasting the timing of infection with a mathematical model for viral dynamics. As an example, we used the reported viral load data from multiple countries (Singapore, China, Germany, France, and Korea) and estimated the incubation period. The median, 2.5, and 97.5 percentiles of the incubation period were 5.23 days (95% CI: 5.17, 5.25), 3.29 days (3.25, 3.37), and 8.22 days (8.02, 8.46), respectively, which are comparable to the values estimated in previous studies. Using viral load to estimate the incubation period might be a useful approach especially when impractical to directly observe the infection event.
Downsides of face masks and possible mitigation strategies: a systematic review and meta-analysis
Authors: Bakhit Mina , Krzyzaniak Natalia , Scott Anna Mae , Clark Justin , Glasziou Paul , Del Mar Chris 
Published: June 2020

AbstractObjectiveTo identify, appraise, and synthesise studies evaluating the downsides of wearing facemasks in any setting. We also discuss potential strategies to mitigate these downsides.MethodsPubMed, Embase, CENTRAL, EuropePMC were searched (inception-18/5/2020), and clinical registries were searched via CENTRAL. We also did forward-backward citation search of the included studies. We included randomised controlled trials and observational studies comparing facemask use to any active intervention or to control. Two author pairs independently screened articles for inclusion, extracted data and assessed the quality of included studies. The primary outcomes were compliance, discomforts, harms, and adverse events of wearing facemasks.FindingsWe screened 5471 articles, including 37 (40 references); 11 were meta-analysed. For mask wear adherence, 47% more people wore facemasks in the facemask group compared to control; adherence was significantly higher (26%) in the surgical/medical mask group than in N95/P2 group. The largest number of studies reported on the discomfort and irritation outcome (20-studies); fewest reported on the misuse of masks, and none reported on mask contamination or risk compensation behaviour. Risk of bias was generally high for blinding of participants and personnel and low for attrition and reporting biases.ConclusionThere are insufficient data to quantify all of the adverse effects that might reduce the acceptability, adherence, and effectiveness of face masks. New research on facemasks should assess and report the harms and downsides. Urgent research is also needed on methods and designs to mitigate the downsides of facemask wearing, particularly the assessment of alternatives such as face shields.
Laser-activated drug implant for controlled release to the posterior segment of the eye
Authors: He Xingyu , Yuan Zheng , Gaeke Samantha , Kao Winston W.-Y. , Li S. Kevin , Miller Daniel , Williams Basil , Park Yoonjee C. 
Published: June 2020

AbstractThe current standard of care for posterior segment eye diseases, such as age-related macular degeneration and diabetic macular edema, is frequent intravitreal injections or sustained-release drug implants. Drug implants have side effects due to the burst release of the drugs, and their release cannot be easily controlled after implantation. Present study attempts to develop a dosage-controllable drug delivery implant which consists of a nanoporous biodegradable PLGA capsule and light-activated liposomes. Controllable drug release from the implant was achieved by using pulsed near-infrared (NIR) laser both in vitro and in vivo. The in vitro drug release kinetics from two different initial dose implants, 1000 μg and 500 μg, was analyzed by fitting zero order and first order kinetics, as well as the Korsmeyer-Peppas and Higuchi models. The 1000 μg and 500 μg implants fit the first-order and zero-order kinetics model, respectively, the best. The multiple drug releases in the vitreous was determined by in vivo fluorimeter, which was consistent with the in vitro data. The dose released was also clinically relevant. Histology and optical and ultrasound imaging data showed no abnormality in the eyes received implant treatment suggesting that the drug delivery system was safe to the retina. This on-demand dose-controllable drug delivery system could be potentially used for long-term posterior eye disease treatment.
Neuronal Variability Reflects Probabilistic Inference Tuned to Natural Image Statistics
Authors: Festa Dylan , Aschner Amir , Davila Aida , Kohn Adam , Coen-Cagli Ruben 
Published: June 2020

AbstractNeuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences. In primary visual cortex (V1), modulation of variability by sensory and non-sensory factors supports this view. However, it is unknown whether V1 variability reflects the statistical structure of visual inputs, as would be required for inferences correctly tuned to the statistics of the natural environment. Here we combine analysis of image statistics and recordings in macaque V1 to show that probabilistic inference tuned to natural image statistics explains the widely observed dependence between spike-count variance and mean, and the modulation of V1 activity and variability by spatial context in images. Our results show that the properties of a basic aspect of cortical responses — their variability — can be explained by a probabilistic representation tuned to naturalistic inputs.
A program to automate the discovery of drugs for West Nile and Dengue virus – programmatic screening of over a billion compounds on PubChem, generation of drug leads and automatedIn Silicomodelling
Authors: Ben Geoffrey A S , Sanker Akhil , Madaj Rafal , Valdés Tresanco Mario Sergio , Upadhyay Manish , Gracia Judith 
Published: June 2020

AbstractOur work is composed of a python program for programmatic data mining of PubChem to collect data to implement a machine learning based AutoQSAR algorithm to generate drug leads for the flaviviruses – Dengue and West Nile. The drug leads generated by the program are feed as programmatic inputs to AutoDock Vina package for automatedIn Silicomodelling of interaction between the compounds generated as drug leads by the program and the chosen Dengue and West Nile drug target methyltransferase, whose inhibition leads to the control of viral replication. The machine learning based AutoQSAR algorithm involves feature selection, QSAR modelling, validation and prediction. The drug leads generated each time the program is run is reflective of the constantly growing PubChem database is an important dynamic feature of the program which facilitates fast and dynamic drug lead generation against the West Nile and Dengue virus in way which is reflective of the constantly growing PubChem database. The program prints out the top drug leads after screening PubChem library which is over a billion compounds. The leads generated by the program are fed as programmatic inputs to anIn Silicomodelling package. The interaction of top drug lead compounds generated by the program and drug targets of West Nile and Dengue virus, was modelled in an automated way through programmatic commands. Thus our program ushers in a new age of automatic ease in the virtual drug screening and drug identification through programmatic data mining of chemical data libraries and drug lead generation through machine learning based AutoQSAR algorithm and an automatedIn Silicomodelling run through the program to study the interaction between the drug lead compounds and the drug target protein of West Nile and Dengue virus
Endogenization from diverse viral ancestors is common and widespread in parasitoid wasps
Authors: Burke Gaelen R. , Hines Heather M. , Sharanowski Barbara J. 
Published: June 2020

AbstractThe Ichneumonoidea (Ichneumonidae and Braconidae) is an incredibly diverse superfamily of parasitoid wasps that includes species that produce virus-like entities in their reproductive tracts to promote successful parasitism of host insects. Research on these entities has traditionally focused upon two viral generaBracovirus(in Braconidae) andIchnovirus(in Ichneumonidae). These viruses are produced using genes known collectively as endogenous viral elements (EVEs) that represent historical, now heritable viral integration events in wasp genomes. Here, new genome sequence assemblies for eleven species and six publicly available genomes from the Ichneumonoidea were screened with the goal of identifying novel EVEs and characterizing the breadth of species in lineages with known EVEs. Exhaustive similarity searches combined with the identification of ancient core genes revealed sequences from both known and novel EVEs. Two species harbored novel, independently derived EVEs related to a divergent large double-stranded DNA (dsDNA) virus that manipulates behavior in other hymenopteran species. While bracovirus or ichnovirus EVEs were identified as expected in three species, the absence of ichnoviruses in several species suggests that they are independently derived and present in two younger, less widespread lineages than previously thought. Overall, this study presents a novel bioinformatic approach for EVE discovery in genomes and shows that three divergent virus families (nudiviruses, the ancestors of ichnoviruses, and LbFV-like viruses) are recurrently acquired as EVEs in parasitoid wasps. Virus acquisition in the parasitoid wasps is a common process that has occurred in many more than two lineages from a diverse range of arthropod-infecting dsDNA viruses.SignificanceParasitoid wasps are an extremely diverse group of animals that are known to harbor Endogenous Virus Elements (EVEs) that produce virions or virus-like particles of key importance in wasps’ parasitism success. However, the prevalence and diversity of independently acquired EVEs in parasitoid wasp lineages has remained largely uncharacterized on a widespread scale. This study represents an important first step and hints at the massive, untapped diversity of EVEs in parasitoid wasps via the identification of several novel virus co-option events from diverse groups of double-stranded DNA virus pathogens.
Infinity Flow: High-throughput single-cell quantification of 100s of proteins using conventional flow cytometry and machine learning
Authors: Becht Etienne , Tolstrup Daniel , Dutertre Charles-Antoine , Ginhoux Florent , Newell Evan W. , Gottardo Raphael , Headley Mark B. 
Published: June 2020

AbstractModern immunologic research increasingly requires high-dimensional analyses in order to understand the complex milieu of cell-types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the co-expression patterns of 100s of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and to identify novel cellular heterogeneity in the lungs of melanoma metastasis bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost and accessible solution to single cell proteomics in complex tissues.
Norepinephrine, olfactory bulb and memory stability
Authors: Linster Christiane , Midroit Maellie , Forest Jeremy , Thenaisie Yohann , Cho Christina , Richard Marion , Didier Anne , Mandairon Nathalie 
Published: June 2020

AbstractMemory stability is essential for animal survival when environment and behavioral state change over short or long time spans. The stability of a memory can be expressed by its duration, its perseverance when conditions change as well as its specificity to the learned stimulus. Using optogenetic and pharmacological manipulations, we show that stability of an odor-reward association can be modulated by noradrenergic inputs to the first olfactory network, the olfactory bulb. We show that while manipulations of noradrenaline during an odor-reward acquisition have no acute effects, they impact learning flexibility as well as the duration and the specificity of the memory. We use a computational approach to propose a proof of concept model showing that a single, simple network effect of noradrenaline on olfactory bulb dynamics can underlie these seemingly different behavioral effects. Our results show how acute changes in network dynamics can have long term effects that extend beyond the network that was manipulated.