bluuebunny/crossref_metadata_2025_split huggingface dataset:
Correlation Between Ophthalmologic and Neuroradiologic Findings in Type 1 Neurofibromatosis
Authors: Godinho Gonçalo , Esteves-Leandro João , Alves Gonçalo , Madeira Carolina , Faria Olinda , Brandão Elisete , Magalhães Augusto , Falcão-Reis Fernando , Penas Susana 
Published: March 2021

Background:
Neurofibromatosis Type 1 (NF-1) is a genetic disease affecting the eye, and ocular findings such as Lisch nodules (LN) or optic pathway gliomas (OPGs) are a part of its diagnostic criteria. Recent imaging technologies such as infrared (IR) imaging and optical coherence tomography (OCT) have highlighted the visualization of choroidal focal abnormalities in these patients, even in the absence of other ocular lesions. This study aimed to establish a morphological multimodal evaluation of choroidal findings in patients with NF-1, correlating them with central nervous system (CNS) findings.

Methods:
This retrospective study included 44 eyes from 22 patients with NF-1. Central 30° IR imaging was obtained, and the number and total area of detectable lesions were calculated. Both macular and optic disc scanning with OCT were performed, with and without the enhanced depth imaging technique, to assess the presence of choroidal focal hyperreflective lesions. Central macular thickness, ganglion cell layer, and outer nuclear layer thickness were assessed, as well as subfoveal choroidal thickness. The peripapillary retinal nerve fiber layer (RNFL) thickness was also assessed. Patients' magnetic resonance images (MRI) were reviewed and categorized by a neuroradiology specialist, determining the presence of OPGs and CNS hamartomas. Correlations between the ophthalmological and neuroradiological findings were established.

Results:
Patients' mean age was 16.4 ± 7.3 years and 59.1% were women. On the MRI, 86.4% of the patients had CNS hamartomas, and 34.1% of the eyes had OPGs. LN were described in 29.5% of the eyes, whereas a total of 63.4% of the eyes presented the characteristic hyperreflective lesions in IR imaging, all of them matching the underlying choroidal lesions. A mean of 2.9 ± 3.3 lesions per eye and a median total lesion area of 1.52 mm2 were found. The presence of OPGs was correlated with a greater number (P = 0.004) and a larger area (P = 0.006) of IR lesions. For a cut-off of 3.5 lesions per eye, the sensitivity and specificity for the presence of OPGs were 75% and 80%, respectively. For a total lesion area of 2.77 mm2, the sensitivity and specificity for the presence of OPGs were 69.2% and 93.1%, respectively. Eyes with OPGs presented a significant reduction in the temporal RNFL (P = 0.018) thickness, as well as a reduction in subfoveal choroid thickness (P = 0.04). No relations were found between CNS hamartomas and ophthalmological findings.

Conclusions:
This study suggests that focal choroidal abnormalities are correlated with the presence of CNS lesions as OPGs in patients with NF-1, and it might be a surrogate for the need for CNS imaging in these patients.
Supporting COVID-19 policy response with large-scale mobility-based modeling
Authors: Chang Serina , Wilson Mandy L. , Lewis Bryan , Mehrab Zakaria , Dudakiya Komal K. , Pierson Emma , Koh Pang Wei , Gerardin Jaline , Redbird Beth , Grusky David , Marathe Madhav , Leskovec Jure 
Published: March 2021

ABSTRACTSocial distancing measures, such as restricting occupancy at venues, have been a primary intervention for controlling the spread of COVID-19. However, these mobility restrictions place a significant economic burden on individuals and businesses. To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures.In this paper, we present our work motivated by our interactions with the Virginia Department of Health on a decision-support tool that utilizes large-scale data and epidemiological modeling to quantify the impact of changes in mobility on infection rates. Our model captures the spread of COVID-19 by using a fine-grained, dynamic mobility network that encodes the hourly movements of people from neighborhoods to individual places, with over 3 billion hourly edges. By perturbing the mobility network, we can simulate a wide variety of reopening plans and forecast their impact in terms of new infections and the loss in visits per sector. To deploy this model in practice, we built a robust computational infrastructure to support running millions of model realizations, and we worked with policymakers to develop an intuitive dashboard interface that communicates our model’s predictions for thousands of potential policies, tailored to their jurisdiction. The resulting decision-support environment provides policymakers with much-needed analytical machinery to assess the tradeoffs between future infections and mobility restrictions.
Three-dimensional cranio-facial landmark detection in CT slices from a publicly available database, using multi-phased regression networks on a personal computer
Authors: Nishimoto Soh , Saito Takuya , Ishise Hisako , Fujiwara Toshihiro , Kawai Kenichiro , Kakibuchi Maso 
Published: March 2021

AbstractAimGeometrical assessments to comprehend the shape of an object are done based on characteristic landmarks. Computer assisted tomography (CT) images, horizontal slices as two-dimensional pictures, can be digitally restructured into virtual three-dimensional objects. Automatic detection of the landmarks, if developed, will be a great help not only medically, but also for anthropologically. The aim of this study is to develop an automated system to predict three-dimensional coordinate values of cranio-facial landmarks in sequences of CT slices.MethodsCT images were obtained from a publicly available database. Digital reconstruction was done to obtain three dimensional models. Sixteen landmarks were plotted on the models and coordinate values of them were recorded. Multi-phased deep learning system was constructed. For the first phase, 512 x 512 pixels images were resized to 96 x 96 pixels. A regression deep learning network was trained with 90 training data. For the second phase, for each landmark, 100 x 100 pixels images were cropped from the original images. Sixteen models were trained. For the third phase, 50 x 50 pixels images were cropped, and models were trained.ResultsThree-dimensional error for the first phase, testing 30 data, was 11.60 pixels in average. (1 pixel = 500 / 512 mm) For the second phase, it was significantly improved to 4.66 pixels. For the third phase, it was significantly progressed to 2.89. This was comparable to the gaps between the landmarks, plotted by two experienced practitioners.DiscussionThe calculation volume required to process three-dimensional pile of images is tremendous. One solution may be to compress the images, but detailed information may be lost during the process. Our proposing method of multi-phased prediction, coarse detection first and narrowing down the detection area, may be a possible solution, within the physical limitation of memory and computation.
Reportable range of quantitative assays for SARS-CoV-2 antibodies determination. An overlooked issue?
Authors: Dittadi Ruggero , Bertoli Isabella , Carraro Paolo 
Published: March 2021

AbstractIn order to identify the quantization capability of two methods for SARS-CoV-2 IgG determination (Anti-SARS-CoV-2 QuantiVac ELISA IgG from Euroimmun and SARS-CoV-2 IgG anti-RBD from SNIBE), the linearity of the reportable range with respect to the calibration curve was evaluated.
COVID-19 reinfection: A Rapid Systematic Review of Case Reports and Case Series
Authors: Wang Jingzhou , Kaperak Christopher , Sato Toshiro , Sakuraba Atsushi 
Published: March 2021

AbstractThe COVID-19 pandemic has infected millions of people worldwide and many countries have been suffering from a large number of deaths. Acknowledging the ability of SARS-CoV-2 to mutate into distinct strains as an RNA virus and investigating its potential to cause reinfection is important for future health policy guidelines. It was thought that individuals who recovered from COVID-19 generate a robust immune response and develop protective immunity, however, since the first case of documented reinfection of COVID-19 in August 2020, there have been a number of cases with reinfection. Many cases are lacking genomic data of the two infections and it remains unclear whether they were caused by different strains. In the present study, we undertook a rapid systematic review to identify cases infected with different genetic strains of SARS-CoV-2 confirmed by polymerase-chain reaction and viral genome sequencing. A total of 17 cases of genetically confirmed COVID-19 reinfection were found. One immunocompromised patient had mild symptoms with the first infection, but developed severe symptoms resulting in death with the second infection. Overall, 68.8% (11/16) had similar severity, 18.8% (3/16) had worse symptoms, and 12.5% (2/16) had milder symptoms with the second episode. Our case series shows that reinfection with different strains is possible and some cases may experience more severe infections with the second episode. The findings also suggest that COVID-19 may continue to circulate even after achieving herd immunity through natural infection or vaccination suggesting the need for longer term transmission mitigation efforts.
Pregnancy and risk of COVID-19
Authors: Magnus Maria C. , Oakley Laura , K. Gjessing Håkon , Stephansson Olof , Engjom Hilde M. , Macsali Ferenc , Juliusson Petur B. , Andersen Anne-Marie Nybo , Håberg Siri E. 
Published: March 2021

AbstractBackgroundStudies report that pregnant women with coronavirus disease 2019 (COVID-19) are at increased risk of severe disease, intensive-care and death. Whether pregnant women in general are more susceptible of contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unclear.MethodsLinked registry-data on all women ages 15 to 45 living in Norway on March 1st, 2020 (N=1,033,699) were used in Cox regression models to estimate hazard ratios (HR) comparing pregnant to non-pregnant women, of having a positive test for SARS-CoV-2, a diagnosis of COVID-19 in specialist healthcare, or hospitalization with COVID-19, adjusting for age, marital status, education, income, country of birth and underlying medical conditions.ResultsCompared to non-pregnant women, pregnant women had a similar risk of a positive SARS-CoV-2 test (adjusted HR, 0.99; 95% confidence interval [CI], 0.92 to 1.07), a higher risk of a COVID-19 diagnosis in specialist care (HR, 3.46; 95% CI, 2.89 to 4.14), and to be hospitalized (HR, 4.70; 95% CI, 3.51 to 6.30). Pregnant women were in general not more likely to be tested for SARS-CoV-2. Pregnant women born outside Scandinavia were less likely to be tested, but at higher risk of a positive test (HR, 2.37; 95% CI, 2.51 to 8.87) and of hospitalization with COVID-19 (HR, 4.72; 95% CI, 2.51 to 8.87) than pregnant Scandinavian born women.ConclusionPregnant women were not more likely to be infected with SARS-CoV-2. However, pregnant women with COVID-19, especially those born outside of Scandinavia, were more likely to receive specialist care and to be hospitalized.
Direct Primary Care: Family Physician Perceptions of a Growing Model
Authors: Brekke Gayle , Saint Onge Jarron M. , Kimminau Kim S. , Ellis Shellie 
Published: March 2021

ABSTRACTPurposeDirect Primary Care (DPC) is a relatively new primary care practice model in which patients receive unlimited access to a defined set of primary care services in exchange for a monthly practice-specific membership fee. DPC is a bottom-up physician-driven approach that contrasts to typical top-down insurer-centric health care delivery reform efforts. The degree to which physicians are aware of this practice model and whether they believe it addresses two key challenges facing primary care, access and administrative burden, are unclear.MethodsAn online survey distributed in July 2017 gauged family physicians’ awareness of DPC and views about the model.ResultsMost respondents (85%) had heard of DPC and eight percent practiced in a DPC model at the time of the survey. In general, respondents reported that DPC can offer positive outcomes through lower administrative burden for physicians, improved doctor-patient relationships, and better access. Respondents also suggested DPC may result in improved patient health outcomes and lower overall healthcare spending. Respondents’ concerns included inappropriateness of the model for vulnerable populations and physician shortages. Survey responses differed depending on whether the respondent practiced in a DPC model; DPC physicians had a more favorable view of the model and were focused on benefits to patients rather than benefits to physicians.ConclusionsWhile some perceive challenges of DPC, others think that this model may benefit both patients and physicians.
Environment-wide association study (EWAS) on cardiometabolic traits: A systematic assessment of the association of lifestyle variables on a longitudinal setting
Authors: Poveda Alaitz , Chen Yan , Pomares-Millan Hugo , Kurbasic Azra , Patel Chirag J , Renström Frida , Hallmans Göran , Johansson Ingegerd , Franks Paul W. 
Published: March 2021

AbstractThe present study aims to assess the over-time association of ∼300 lifestyle exposures with nine cardiometabolic traits with the ultimate aim of identifying exposures/exposure groups that could inform lifestyle interventions aiming at controlling cardiometabolic diseases. The analyses were undertaken in a longitudinal sample comprising >31000 adults living in northern Sweden. Linear mixed models were used to assess the average associations of lifestyle exposures and linear regression models were used to test association with 10-year change of the cardiometabolic traits. ‘Physical activity’ and ‘General Health’ were the exposure categories containing the highest number of ‘tentative signals’ in analyses assessing the average association of lifestyle variables, while ‘Tobacco use’ was the top-category for the 10-year change association analyses. Thirteen modifiable variables showed a consistent average association among the majority of cardiometabolic traits. These variables belonged to four main groups: i) Smoking, ii) Diet (secoisolariciresinol intake and brewed coffee), iii) Leisure time physical activity and iv) a group of variables more specific to the Swedish lifestyle (snuff status, hunting/fishing during leisure time and boiled coffee). Interestingly, sweet drinks, fish intake and salt content, all lifestyle exposures frequently mentioned in public health recommendations were not broadly associated with the analysed cardiometabolic traits.
A global database of COVID-19 vaccinations
Authors: Mathieu Edouard , Ritchie Hannah , Ortiz-Ospina Esteban , Roser Max , Hasell Joe , Appel Cameron , Giattino Charlie 
Published: March 2021

AbstractAn effective rollout of vaccinations against COVID-19 offers the most promising prospect of bringing the pandemic to an end. We present the Our World in Data COVID-19 vaccination dataset, a global public dataset that tracks the scale and rate of the vaccine rollout across the world. This dataset is updated regularly, and includes data on the total number of vaccinations administered; first and second doses administered; daily vaccination rates; and population-adjusted coverage for all countries for which data is available (138 countries as of 17 March 2021). It will be maintained as the global vaccination campaign continues to progress. This resource aids policymakers and researchers in understanding the rate of current and potential vaccine rollout; the interactions with non-vaccination policy responses; the potential impact of vaccinations on pandemic outcomes such as transmission, morbidity, and mortality; and global inequalities in vaccine access.
Bridging the polar and hydrophobic metabolome in single-run untargeted liquid chromatography-mass spectrometry dried blood spot metabolomics for clinical purposes
Authors: Skogvold Hanne Bendiksen , Mørk Sandås Elise , Østeby Anja , Løkken Camilla , Rootwelt Helge , Rønning Per Ola , Wilson Steven Ray , Prestø Elgstøen Katja Benedikte 
Published: March 2021

AbstractDried blood spot (DBS) metabolite analysis is a central tool for the clinic, e.g. newborn screening. Instead of applying multiple analytical methods, a single liquid chromatography-mass spectrometry (LC-MS) method was developed for metabolites spanning from highly polar glucose to hydrophobic long-chain acylcarnitines. For liquid chromatography, a diphenyl column and a multi-linear solvent gradient operated at elevated flow rates allowed for an even-spread resolution of diverse metabolites. Injecting moderate volumes of DBS organic extracts directly, in contrast to evaporation and reconstitution, provided substantial increases in analyte recovery. Q Exactive MS settings were also tailored for sensitivity increases, and the method allowed for analyte retention time and peak area repeatabilities of 0.1-0.4 % and 2-10 %, respectively, for a wide polarity range of metabolites (logP −4.4 to 8.8). The method’s performance was suited for both untargeted analysis as well as targeted approaches, evaluated in clinically relevant experiments.Graphical Abstract
Predicting disability progression and cognitive worsening in multiple
sclerosis using grey matter network measures
Authors: Colato Elisa , Stutters Jonathan , Tur Carmen , Sridar Narayanan , Arnold Douglas , Kingshott Claudia Wheeler , Barkhof Frederik , Ciccarelli Olga , Chard Declan , Eshaghi Arman 
Published: March 2021

Abstract

Objective
In multiple sclerosis (MS), magnetic resonance imaging (MRI) measures at
the whole brain or regional level are only modestly associated with
disability, while network-based measures are emerging as promising
prognostic markers. We sought to demonstrate whether data-driven
network-based measures of regional grey matter (GM) volumes predict future
disability in secondary progressive MS (SPMS).

Methods

We used cross-sectional structural MRI, and baseline and longitudinal
data of Expanded Disability Status Scale [EDSS], 9-Hole Peg Test [9HPT], and
Symbol Digit Modalities Test [SDMT], from a clinical trial in 988 people
with progressive MS. We processed T1-weighted scans to obtain GM probability
maps and applied spatial independent component analysis (ICA) to identify
co-varying patterns of GM volume change. We used survival models to
determine whether baseline GM network measures predict cognitive and motor
worsening.

Results
We identified 15 networks of regionally co-varying GM features. Compared
with whole brain GM, deep GM, and lesion volumes, ICA-components correlated
more closely with clinical outcomes. A mainly basal ganglia component had
the highest correlations at baseline with the SDMT and was associated with
cognitive worsening (HR= 1.29, 95% CI [1.09-1.52], p< 0.005). Two
ICA-components were associated with 9HPT worsening (HR=1.30, 95% CI
[1.06:1.60], p<0.01; and HR= 1.21, 95%CI [1.01:1.45], p<0.05).
Post-hoc analyses revealed that for 9HPT and SDMT survival models including
network-based measures reported a higher discrimination power (respectively,
C-index= 0.69, se= 0.03; C-index= 0.71, se= 0.02) compared to models
including only whole and regional MRI measures (respectively, C-index= 0.65,
se= 0.03; C-index= 0.69, se= 0.02).

Conclusions
The disability progression was better predicted by networks of covarying
GM regions, rather than by single regional or whole-brain measures. Network
analysis can be applied in future clinical trials and may play a role in
stratifying participants who have the most potential to show a treatment
effect.
Development of a Codebook of Online Anti-Vaccination Rhetoric to Manage COVID-19 Vaccine Misinformation
Authors: Hughes B. , Miller-Idriss C. , Piltch-Loeb R. , White K. , Creizis M. , Cain Caleb , Savoia E. 
Published: March 2021

AbstractVaccine hesitancy (delay in obtaining a vaccine, despite availability) represents a significant hurdle to managing the COVID-19 pandemic. Vaccine hesitancy is in part related to the prevalence of anti-vaccine misinformation and disinformation, which are spread through social media and user-generated content platforms. This study uses qualitative coding methodology to identify salient narratives and rhetorical styles common to anti-vaccine and COVID-denialist media. It organizes these narratives and rhetorics according to theme, imagined antagonist, and frequency. Most frequent were narratives centered on “corrupt elites” and rhetorics appealing to the vulnerability of children. The identification of these narratives and rhetorics may assist in developing effective public health messaging campaigns, since narrative and emotion have demonstrated persuasive effectiveness in other public health communication settings.
Behavioral and Magnetoencephalographic Correlates of Fear Generalization Are Associated with Responses to Later Virtual Reality Exposure Therapy in Spider Phobia
Authors: Roesmann Kati , Leehr Elisabeth Johanna , Böhnlein Joscha , Steinberg Christian , Seeger Fabian , Schwarzmeier Hanna , Gathmann Bettina , Siminski Niklas , Herrmann Martin J. , Dannlowski Udo , Lueken Ulrike , Klucken Tim , Hilbert Kevin , Straube Thomas , Junghöfer Markus 
Published: March 2021

AbstractAs overgeneralization of fear is a pathogenic marker of anxiety disorders, we investigated whether pre-treatment levels of fear generalization in spider-phobic patients are associated with their response to exposure-based treatment, in order to identify pre-treatment correlates of treatment success. Ninety patients with spider phobia completed pre-treatment clinical and magnetoencephalography (MEG) assessments, one session of virtual reality exposure therapy, and a post-treatment clinical assessment. Based on the primary outcome (30% symptom reduction in self-reported symptoms from pre-to post-treatment) they were categorized as responders or non-responders. In a pre-treatment MEG fear generalization paradigm involving fear conditioning with two unconditioned stimuli (UCS), we obtained fear ratings, UCS-expectancy ratings, and event-related fields to conditioned stimuli (CS+, CS-) and 7 different generalization stimuli (GS) on a perceptual continuum from CS+ to CS-. Prior to treatment, non-responders showed behavioral overgeneralization indicated by more linear generalization gradients in fear ratings. Analyses of MEG source estimations revealed that non-responders showed a decline of their (inhibitory) frontal activations to safety-signaling CS- and GS compared to CS+ over time, while responders maintained these activations at early (<300ms) and late processing stages. Results provide initial evidence that pre-treatment differences of behavioral and neural markers of fear generalization are associated with later responses to behavioral exposure. Findings demonstrate the relevance of inhibitory learning functions and their spatio-temporal neural reflections in this interplay. Findings stimulate research on mechanism-based augmentation strategies for behavioral therapies.