bluuebunny/crossref_metadata_2025_split huggingface dataset:
Original research: How accurate are digital symptom assessment apps for suggesting conditions and urgency advice?: a clinical vignettes comparison to GPs
Authors: Gilbert Stephen , Mehl Alicia , Baluch Adel , Cawley Caoimhe , Challiner Jean , Fraser Hamish , Millen Elizabeth , Multmeier Jan , Pick Fiona , Richter Claudia , Türk Ewelina , Upadhyay Shubhanan , Virani Vishaal , Vona Nicola , Wicks Paul , Novorol Claire 
Published: May 2020

AbstractObjectivesTo compare breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of 8 popular symptom assessment apps with each other and with 7 General Practitioners.DesignClinical vignettes study.Setting200 clinical vignettes representing real-world scenarios in primary care.Intervention/comparatorCondition coverage, suggested condition accuracy, and urgency advice performance was measured against the vignettes’ gold-standard diagnoses and triage level.Primary outcome measuresOutcomes included (i) proportion of conditions “covered” by an app, i.e. not excluded because the patient was too young/old, pregnant, or comorbid, (ii) proportion of vignettes in which the correct primary diagnosis was amongst the top 3 conditions suggested, and, (iii) proportion of “safe” urgency level advice (i.e. at gold standard level, more conservative, or no more than one level less conservative).ResultsCondition-suggestion coverage was highly variable, with some apps not offering a suggestion for many users: in alphabetical order, Ada: 99.0%; Babylon: 51.5%; Buoy: 88.5%; K Health: 74.5%; Mediktor: 80.5%; Symptomate: 61.5%; Your.MD: 64.5%. The top-3 suggestion accuracy (M3) of GPs was on average 82.i±5.2%. For the apps it was - Ada: 70.5%; Babylon: 32.0%; Buoy: 43.0%; K Health: 36.0%; Mediktor: 36.0%; Symptomate: 27.5%; WebMD: 35.5%; Your.MD: 23.5%. Some apps exclude certain user groups (e.g. younger users) or certain conditions - for these apps condition-suggestion performance is generally greater with exclusion of these vignettes. For safe urgency advice, tested GPs had an average of 97.0±2.5%. For the vignettes with advice provided, only three apps had safety performance within 1 S.D. of the GPs (mean) - Ada: 97.0%; Babylon: 95.i%; Symptomate: 97.8%. One app had a safety performance within 2 S.D.s of GPs - Your.MD: 92.6%. Three apps had a safety performance outside 2 S.D.s of GPs - Buoy: 80.0% (p<0.001); K Health: 81.3% (p <0.001); Mediktor: 87.3% (p =1.3×10-3).ConclusionsThe utility of digital symptom assessment apps relies upon coverage, accuracy, and safety. While no digital tool outperformed GPs, some came close, and the nature of iterative improvements to software offers scalable improvements to care.
Nonlinear Classification of EEG recordings from patients with Alzheimer’s Disease using Gaussian Process Latent Variable Model
Authors: Gunawardena S. Rajintha. A. S. , He Fei , Sarrigiannis Ptolemaios , Blackburn Daniel J. 
Published: May 2020

AbstractIn this work, nonlinear temporal features from multi-channel EEGs are used for the classification of Alzheimer’s disease patients from healthy individuals. This was achieved by temporal manifold learning using Gaussian Process Latent Variable Models (GPLVM) as a nonlinear dimensionality reduction technique. Classification of the extracted features was undertaken using a nonlinear Support Vector Machine. Comparisons were made against the linear counterpart, Principle Component Analysis while exploring the effect of the time window or EEG epoch length used. It was demonstrated that temporal manifold learning using GPLVM is better in extracting features that attain high separability and prediction accuracy. This work aims to set the significance of using GPLVM temporal manifold learning for EEG feature extraction in the classification of Alzheimer’s disease.
COVID-19 diagnosis prediction by symptoms of tested individuals: a machine learning approach
Authors: Zoabi Yazeed , Shomron Noam 
Published: May 2020

AbstractMotivationEffective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed in hopes of assisting medical staff worldwide in triaging patients when allocating limited healthcare resources.ResultsWe established a machine learning approach that trained on records from 51,831 tested individuals (of whom 4,769 were confirmed COVID-19 cases) while the test set contained data from the following week (47,401 tested individuals of whom 3,624 were confirmed COVID-19 cases). Our model predicts COVID-19 test results with high accuracy using only eight features: gender, whether age is above 60, known contact with an infected individual, and five initial clinical symptoms.SummaryOverall, based on the nationwide data publicly reported by the Israeli Ministry of Health, we developed a model that detects COVID-19 cases by simple features accessed by asking basic questions. Our framework can be used, among other considerations, to prioritize testing for COVID-19 when allocating limited testing resources.AvailabilityAll data used in this study was retrieved from the Israeli Ministry of Health website.Contactyazeed@tauex.tau.ac.il, nshomron@tauex.tau.ac.il
Utility and limitations of Google searches on sensory loss as markers for new COVID-19 cases
Authors: Asseo Kim , Fierro Fabrizio , Slavutsky Yuli , Frasnelli Johannes , Niv Masha Y 
Published: May 2020

AbstractEvidence of smell loss in COVID-19 is growing. Researchers and analysts have suggested to use Google searches on smell loss as indicators of COVID-19 cases. However, such searches may be due to interest elicited by media coverage of the COVID-19-related smell loss, rather than attempts to understand self-symptoms. We analyzed searches related to 4 senses: smell and taste (both recently shown to be impaired in some COVID-19 patients), vision and sight (senses not currently known to be impaired in COVID-19 patients), and an additional general control (“COVID-19 symptoms”). Focusing on two countries with a large number of cases, Italy and the United States, we have compared Google Trends results per region or state to the number of new cases prevalence in that region. The analysis was performed for each of the 8 weeks ranging from March 4th till April 28th.No correlation with vision loss or sight loss searches was identified, while taste and smell loss searches were correlated with new COVID-19 cases during a limited time window, that starts when the number of weekly new cases reached for the first time 21357 cases in Italy (11-17 March) and 47553 in the US (18-24 March).Media effect on the specific symptoms searches was also analyzed, establishing a different impact according to the country.Our results suggest that Google Trends for taste loss and smell loss searches captured a genuine connection between these symptoms and new COVID-19 cases prevalence in the population. However, due to variability in correlation from week to week, and overall decrease in correlation as taste and smell loss are becoming known COVID-19 symptoms, recognized now by CDC and World Health Organization, Google Trends is no longer a reliable marker for monitoring the disease spread. The “surprise rise” followed by decrease, probably attributable to knowledge saturation, should be kept in mind for future digital media analyses of potential new symptoms of COVID-19 or future pandemics.
Transmission in Latent Period Causes A Large Number of Infected People in the United States
Authors: Liu Qinghe , Zhu Junkai , Liu Zhicheng , Zhu Yuhao , Zhou Liuling , Gao Zefei , Li Deqiang , Tang Yuanbo , Zhang Xiang , Yang Junyan , Wang Qiao 
Published: May 2020

ABSTRACTThe cumulative number of confirmed cases in the United States exceeded one million on 29 April 2020, becoming the country of the most serious pandemic in the world. We proposed a model to analyze the real situation and follow-up trend of the epidemic in the US.The proposed model divides the epidemic period into two phases, and includes three different categories of transmitters: the latent population, the documented infectious population, and the undocumented infectious population. We use metapopulation network to simulate the spread of the COVID-19 in the US, and apply the Bayesian inference to estimate the key parameters of the model. We also perform component analysis and sensitivity analysis, researching the compositions of the people with COVID-19.The results show that the basic reproduction number in the early period of propagation is 4.06. As of April 13, 2020, only 45% (95% CI: 35% - 73%) of symptom onset cases in the United States were documented. The incubation period of COVID-19 is 10.69 days (95% CI: 10.02 – 11.74). If the current level of interventions is continued, the cumulative number of confirmed cases is expected to reach more than 1.7 million in July and continue to grow.
Epidemiology of CoVID-19 and predictors of recovery in the Republic of Korea
Authors: Das Ashis Kumar , Gopalan Saji Saraswathy 
Published: May 2020

AbstractBackgroundThe recent CoVID-19 pandemic has emerged as a threat to global health. Though current evidence on the epidemiology of the disease is emerging, very little is known about the predictors of recovery.ObjectivesTo describe the epidemiology of confirmed CoVID-19 patients in Republic of Korea and identify predictors of recovery.Materials and methodsUsing publicly available data for confirmed CoVID-19 cases from the Korea Centers for Disease Control and Prevention from January 20, 2020 to April 30, 2020, we undertook descriptive analyses of cases stratified by sex, age group, place of exposure, date of confirmation and province. Correlation was tested among all predictors (sex, age group, place of exposure and province) with the Pearson’s correlation coefficient. Associations between recovery from CoVID-19 and predictors were estimated using a multivariable logistic regression model.ResultsMajority of the confirmed cases were females (56%), from 20-29 age group (24.3%), and primarily from three provinces –– Gyeongsangbuk-do (36.9%), Gyeonggi-do (20.5%) and Seoul (17.1%). Case fatality ratio was 2.1% and 41.6% cases recovered. Older patients, patients from provinces such as Daegu, Gyeonggi-do, Gyeongsangbuk-do, Jeju-do, Jeollabuk-do and Jeollanam-do, and those contracting the disease from healthcare settings had lower recovery.ConclusionsOur study adds to the very limited evidence base on potential predictors of survival among confirmed CoVID-19 cases. We call additional research to explore the predictors of recovery and support development of policies to protect the vulnerable patient groups.
E-cigarette use frequency by smoking status among youth in the United States, 2014-2019
Authors: Tam Jamie 
Published: May 2020

AbstractAimsTo report annual 2014-2019 youth estimates of past 30-day e-cigarette use frequency by smoking status in the United States (US).DesignWeighted prevalence estimates of student’s e-cigarette use using the 2014-2019 National Youth Tobacco Surveys (NYTS). For each year, t-tests for significance were used to compare estimates with those from the preceding year; t-tests were not performed on data for 2019 due to the change in survey format from paper to electronic.SettingThe NYTS is an annual school-based cross-sectional survey of US middle school (MS) and high school (HS) students.Participants117,472 students.MeasurementsSelf-report of past 30 day e-cigarette use based on students’ smoking status. Smoking status is assessed by asking if students have ever tried smoking, “even one or two puffs”, with never smokers responding “no”. Former smokers respond “yes” but have not smoked at all in the past 30 days. Current smokers used cigarettes at least once in the past 30 days. Frequent e-cigarette use is defined as use on ≥20 days in the past month.FindingsPast 30-day and frequent e-cigarette use increased among never, former, and current smoker youth from 2014-2019. In 2019, a greater proportion of current smokers used e-cigarettes frequently (HS = 46.1%, 95% CI: 39.1, 53.2; MS = 27.4%, 95% CI: 21.1, 33.6) compared to former smokers (HS = 23.2%, 95% CI: 18.1, 28.2; MS = 10.9%, 95% CI: 6.1, 15.7) and never smokers (HS = 3.7%, 95% CI: 3.0, 4.3; MS = 0.7%, 95% CI: 0.4, 0.9). From 2018 to 2019, the total number of youth using e-cigarettes frequently who were never smokers (2018: 180,000; 2019: 490,000) or former smokers (2018: 260,000; 2019: 640,000) surpassed that of current smokers (2018: 420,000; 2019: 460,000).ConclusionsThe proportion and number of never smoker youth using e-cigarettes frequently increased greatly since 2014.
Relationship between odor intensity estimates and COVID-19 population prediction in a Swedish sample
Authors: Iravani Behzad , Arshamian Artin , Ravia Aharon , Mishor Eva , Snitz Kobi , Shushan Sagit , Roth Yehudah , Perl Ofer , Honigstein Danielle , Weissgross Reut , Karagach Shiri , Ernst Gernot , Okamoto Masako , Mainen Zachary , Monteleone Erminio , Dinnella Caterina , Spinelli Sara , Mariño-Sánchez Franklin , Ferdenzi Camille , Smeets Monique , Touhara Kazushige , Bensafi Moustafa , Hummel Thomas , Sobel Noam , Lundström Johan N. 
Published: May 2020

ABSTRACTIn response to the COVID-19 pandemic, countries have implemented various strategies to reduce and slow the spread of the disease in the general population. For countries that have implemented restrictions on its population in a step-wise manner, monitoring of COVID-19 prevalence is of importance to guide decision on when to impose new, or when to abolish old, restrictions. We are here determining whether measures of odor intensity in a large sample can serve as one such measure. Online measures of how intense common household odors are perceived and symptoms of COVID-19 were collected from 2440 Swedes. Average odor intensity ratings were then compared to predicted COVID-19 population prevalence over time in the Swedish population and were found to closely track each other (r=-0.83). Moreover, we found that there was a large difference in rated intensity between individuals with and without COVID-19 symptoms and number of symptoms was related to odor intensity ratings. Finally, we found that individuals progressing from reporting no symptoms to subsequently reporting COVID-19 symptoms demonstrated a large drop in olfactory performance. These data suggest that measures of odor intensity, if obtained in a large and representative sample, can be used as an indicator of COVID-19 disease in the general population. Importantly, this simple measure could easily be implemented in countries without wide-spread access to COVID-19 testing or implemented as a fast early response before widespread testing can be facilitated.
Early Mandated Social Distancing is a Strong Predictor of Reduction in Highest Number of New COVID-19 cases per Day within Various Geographic Regions
Authors: Qureshi Adnan I. , Suri M. Fareed K. , Chu Haitao , Suri Habibullah Khan , Suri Ayehsa Khan 
Published: May 2020

AbstractMandated social distancing has been globally applied to limit the spread of corona virus disease 2019 (COVID-19) from highly pathogenic severe acute respiratory syndrome (SARS)-associated coronavirus 2 (SARS-CoV-2). The benefit of this community-based intervention in limiting COVID-19 has not been proven nor quantified. We examined the effect of timing of mandated social distancing on the rate of COVID-19 in 119 geographic regions derived from 41 states within United States and 78 countries. We found that highest number of new COVID-19 cases per day per million persons was significantly associated with total number of COVID-19 cases per million persons on the day before mandated social distancing (β=0.66, p<0.0001). Our findings suggest that the initiation of mandated social distancing for each doubling in number of existing COVID-19 cases would result in eventual peak with 58% higher number of COVID-19 infections per day. Subgroup analysis on those regions where the highest number of new COVID-19 cases per day have peaked increased β to .85 (p<0.0001). We demonstrate that initiating mandated social distancing at a 10 times smaller number of COVID-19 cases will reduce the number of daily new COVID-19 cases at peak by 80% highlighting the importance of this community-based intervention.
Connectome and microcircuit models implicate atypical subcortico-cortical interactions in autism pathophysiology
Authors: Park Bo-yong , Hong Seok-Jun , Valk Sofie , Paquola Casey , Benkarim Oualid , Bethlehem Richard A. I. , Di Martino Adriana , Milham Michael P. , Gozzi Alessandro , Yeo B. T. Thomas , Smallwood Jonathan , Bernhardt Boris C. 
Published: May 2020

AbstractBoth macroscale connectome miswiring and microcircuit anomalies have been suggested to play a role in the pathophysiology of autism. However, an overarching framework that consolidates these macro and microscale perspectives of the condition is lacking. Here, we combined connectome-wide manifold learning and biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. Our analysis established that autism showed significant differences in structural connectome organization relative to neurotypical controls, with strong effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models revealed that the degree of macroscale anomalies was related to atypical increases of subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic decoding and developmental gene enrichment analyses provided biological context and pointed to genes expressed in cortical and thalamic areas during childhood and adolescence. Supervised machine learning showed the macroscale perturbations predicted socio-cognitive symptoms and repetitive behaviors. Our analyses provide convergent support that atypical subcortico-cortical interactions may contribute to both microcircuit and macroscale connectome anomalies in autism.
Differential neural circuitry behind autism subtypes with imbalanced social-communicative and restricted repetitive behavior symptoms
Authors: Bertelsen Natasha , Landi Isotta , Bethlehem Richard A. I. , Seidlitz Jakob , Busuoli Elena Maria , Mandelli Veronica , Satta Eleonora , Trakoshis Stavros , Auyeung Bonnie , Kundu Prantik , Loth Eva , Dumas Guillaume , Baumeister Sarah , Beckmann Christian F. , Bölte Sven , Bourgeron Thomas , Charman Tony , Durston Sarah , Ecker Christine , Holt Rosemary J. , Johnson Mark H. , Jones Emily J. H. , Mason Luke , Meyer-Lindenberg Andreas , Moessnang Carolin , Oldehinkel Marianne , Persico Antonio , Tillmann Julian , Williams Steven C. R. , Spooren Will , Murphy Declan G. M. , Buitelaar Jan K. ,   the EU-AIMS LEAP group, Baron-Cohen Simon , Lai Meng-Chuan , Lombardo Michael V. 
Published: May 2020

AbstractSocial-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here we developed a phenotypic stratification model that makes highly accurate (97-99%) out-of-sample SC=RRB, SC>RRB, and RRB>SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n=509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD). Specifically, the somatomotor network is hypoconnected with perisylvian circuitry in SC>RRB and visual association circuitry in SC=RRB. The SC=RRB subtype show hyperconnectivity between medial motor and anterior salience circuitry. Genes that are highly expressed within these networks show a differential enrichment pattern with known autism-associated genes, indicating that such circuits are affected by differing autism-associated genomic mechanisms. These results suggest that SC-RRB imbalance subtypes share many commonalities, but also express subtle differences in functional neural circuitry and the genomic underpinnings behind such circuitry.
Fast scanned widefield scheme provides tunable and uniform illumination for optimized SMLM on large fields of view
Authors: Mau Adrien , Friedl Karoline , Leterrier Christophe , Bourg Nicolas , Lévêque-Fort Sandrine 
Published: May 2020

AbstractQuantitative analyses in classical fluorescence microscopy and Single Molecule Localization Microscopy (SMLM) require uniform illumination over the field of view; ideally coupled with optical sectioning techniques such as Total Internal Reflection Fluorescence (TIRF) to remove out of focus background. In SMLM, high irradiances (several kW/cm²) are crucial to drive the densely labeled sample into the single molecule regime, and conventional gaussian-shaped lasers will typically restrain the usable field of view to around 40 µm x 40 µm. Here we present Adaptable Scanning for Tunable Excitation Regions (ASTER), a novel and versatile illumination technique that generates uniform illumination over adaptable fields of view and is compatible with illumination schemes from epifluorescence to speckle-free TIRF. For SMLM, ASTER delivers homogeneous blinking kinetics at reasonable laser power, providing constant precision and higher throughput over fields of view 25 times larger than typical. This allows improved clustering analysis and uniform size measurements on sub-100 nm objects, as we demonstrate by imaging nanorulers, microtubules and clathrin-coated pits in COS cells, as well as periodic β2-spectrin along the axons of neurons. ASTER’s sharp, quantitative TIRF and SMLM images up to 200 µm x 200 µm in size pave the way for high-throughput quantification of cellular structures and processes.
Stress granules display bistable dynamics modulated by Cdk
Authors: Yahya G , Pérez AP , Mendoza MB , Parisi E , Moreno DF , Gallego C , Aldea M 
Published: May 2020

AbstractStress granules are conserved biomolecular condensates that originate in response to many stress conditions. These membraneless organelles contain nontranslating mRNAs and a diverse subproteome, but our knowledge on their regulation and functional relevance is still incipient. Here we describe a mutual-inhibition interplay between stress granules and Cdc28, the budding yeast Cdk. Amongst Cdc28 interactors acting as negative modulators of Start we have identified Whi8, an RNA-binding protein that localizes to SGs and recruits the mRNA ofCLN3, the most upstream G1 cyclin, for efficient translation inhibition and Cdk inactivation under stress. However, Whi8 also contributes to recruiting Cdc28 to SGs, where it acts to promote their dissolution. As predicted by a mutual-inhibition framework, the SG constitutes a bistable system that is modulated by Cdk. Since mammalian cells display a homologous mechanism, we propose that the opposing functions of specific mRNA-binding proteins and Cdks subjugate SG dynamics to a conserved hysteretic switch.