bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cov2clusters: genomic clustering of SARS-CoV-2 sequences
Authors: Sobkowiak Benjamin , Kamelian Kimia , Zlosnik James E. A. , Tyson John , da Silva Anders Gonçalves , Hoang Linda M. N. , Prystajecky Natalie , Colijn Caroline 
Published: March 2022

AbstractBackgroundThe COVID-19 pandemic remains a global public health concern. Advances in sequencing technologies has allowed for high numbers of SARS-CoV-2 whole genome sequence (WGS) data and rapid sharing of sequences through global repositories to enable almost real-time genomic analysis of the pathogen. WGS data has been used previously to group genetically similar viral pathogens to reveal evidence of transmission, including methods that identify distinct clusters on a phylogenetic tree. Identifying clusters of linked cases can aid in the regional surveillance and management of the disease. In this study, we present a novel method for producing stable genomic clusters of SARS-CoV-2 cases, cov2clusters, and compare the sensitivity and stability of our approach to previous methods used for phylogenetic clustering using real-world SARS-CoV-2 sequence data obtained from British Columbia, Canada,ResultsWe found that cov2clusters produced more stable clusters than previously used phylogenetic clustering methods when adding sequence data through time, mimicking an increase in sequence data through the pandemic. Our method also showed high sensitivity when compared to epidemiologically informed clusters.ConclusionsOur new approach allows for the identification of stable clusters of SARS-CoV-2 from WGS data. Producing high-resolution SARS-CoV-2 clusters from sequence data alone can a challenge and, where possible, both genomic and epidemiological data should be used in combination.
Semi-automatic segmentation of the fetal brain from Magnetic Resonance Imaging
Authors: Wang Jianan , Nichols Emily S. , Mueller Megan E. , de Vrijer Barbra , Eagleson Roy , McKenzie Charles A. , de Ribaupierre Sandrine , Duerden Emma G. 
Published: March 2022

AbstractTemplate-based segmentation techniques have been used for targeting deep brain structures in fetal MR images. In this study, two registration algorithms were compared to determine the optimal strategy of segmenting subcortical structures in T2-weighted images acquired during the third trimester of pregnancy. Adult women with singleton pregnancies (n=9) ranging from 35-39 weeks gestational age were recruited. Fetal MRI were performed on 1.5 T and 3 T scanners. Automatic fetal brain segmentation and volumetric reconstruction algorithms were performed on all subjects using the NiftyMIC software. An atlas of cortical and subcortical structures (36 weeks’ gestation) was registered into native space using ANTs (Automatic Normalization Tools) and FLIRT (FMRIB’s linear image registration tool). The cerebellum and thalamus were manually segmented. Dice coefficients were calculated to validate the reliability of automatic methods and to compare the performance between ANTs (nonlinear) and FLIRT (affine) registration algorithms compared to the gold-standard manual segmentations. The Dice-kappa values for the automated labels were compared using the Wilcoxon test. Comparing cerebellum and thalamus masks against the manually segmented masks, the median Dice-kappa coefficients for ANTs and FLIRT were 0.76 (interquartile range [IQR]= 0.56-0.83) and 0.65 (IQR=0.5-0.73), respectively. The Wilcoxon test (Z=4.9, P<0.01) indicated that the ANTs registration method performed better than FLIRT for the fetal cerebellum and thalamus. We found that a nonlinear registration method, provided improved results compared to an affine transformation. Nonlinear registration methods may be preferable for subcortical segmentations in MR images acquired in third-trimester fetuses.
Spatial patterns of excess mortality in the first year of the COVID-19 pandemic in Germany
Authors: Wieland Thomas 
Published: March 2022

AbstractIn order to quantify the impact of the SARS-CoV-2/COVID-19 pandemic, several studies have estimated excess mortality rather than infections or COVID-19-related deaths. The current study investigates excess mortality in Germany in 2020 at a small-scale spatial level (400 counties) and under consideration of demographic changes. Mortality is operationalized using standardized mortality ratios (SMRs), visualized on maps, and analyzed descriptively. Regional mortality and COVID-19-related morbidity are tested for spatial dependence by the Moran’s I index. It is, furthermore, tested whether all-cause mortality is associated with COVID-19-related morbidity by correlation coefficients. Excess mortality only occurrs in a minority of counties. There are large regional disparities of all-cause mortality and COVID-19-related morbidity. In older age groups, both indicators show spatial dependence. (Excess) mortality in older age groups is impacted by COVID-19, but this association is not found for young and middle age groups.
Stable Sparse Classifiers predict cognitive impairment from gait patterns
Authors: Aznielle-Rodríguez Tania , Ontivero-Ortega Marlis , Galán-García Lídice , Sahli Hichem , Valdés-Sosa Mitchell 
Published: March 2022

AbstractBackgroundAlthough gait patterns disturbances are known to be related to cognitive decline, there is no consensus on the possibility of predicting one from the other. It is necessary to find the optimal gait features, experimental protocols, and computational algorithms to achieve this purpose.PurposesTo assess the efficacy of the Stable Sparse Classifiers procedure (SSC) for discriminating young and older adults, as well as healthy and cognitively impaired elderly groups from their gait patterns. To identify the walking tasks or combinations of tasks and specific spatio-temporal gait features (STGF) that allow the best prediction with SSC.MethodsA sample of 125 participants (40 young- and 85 older-adults) was studied. They underwent assessment with five neuropsychological tests that explore different cognitive domains. A summarized cognitive index (MDCog), based on the Mahalanobis distance from normative data, was calculated. The sample was divided into three groups (young adults, healthy and cognitively impaired elderly adults) using k-means clustering of MDCog in addition to Age. The participants executed four walking tasks (normal, fast, easy- and hard-dual tasks) and their gait patterns, measured with a body-fixed Inertial Measurement Unit, were used to calculate 16 STGF and dual-task costs. SSC was then employed to predict which group the participants belonged to. The classification’s performance was assessed using the area under the receiver operating curves (AUC). The set of STGF features and tasks producing the most accurate classifications were identified.ResultsThe comparison between the three groups revealed significant differences for all STGF in all tasks, while the global AUC of the classification using SSC was 0.87. The classification between the groups of elderly people revealed that the combination of the easy dual-task and the fast walking task had the best prediction performance (AUC = 0.86). Gait variability in step and stride time and the RMS value of vertical acceleration were the features with the largest predictive power. SSC prediction accuracy was better than the accuracies obtained with linear discriminant analysis and support vector machine classifiers.ConclusionsThe study corroborated that the changes in gait patterns can be used to discriminate between young and older adults and more importantly between healthy and cognitively impaired adults. A subset of gait tasks and STGF optimal for achieving this goal with SSC were identified, with the latter method superior to other classification techniques.
Tau, β-amyloid, and glucose metabolism following service-related Traumatic Brain Injury in Vietnam war veterans: The AIBL-VETS study
Authors: Doré Vincent , Cummins Tia L. , Feizpour Azadeh , Krishnadas Natasha , Bourgeat Pierrick , Elias Alby , Lamb Fiona , Williams Robert , Hopwood Malcolm , Villemagne Victor L. , Weiner Michael , Rowe Christopher C. ,   Alzheimer’s Disease Neuroimaging Initiative,   AIBL Research Group
Published: March 2022

AbstractTraumatic Brain Injury (TBI) is common amongst military veterans and has been associated with an increased risk of dementia. It is unclear if this is due to increased risk for Alzheimer’s disease (AD) or other mechanisms. This case control study sought evidence for AD, as defined by the 2018 NIA-AA research framework1, by measuring tau, β-amyloid and glucose metabolism using positron emission tomography (PET) in veterans with service-related TBI.Seventy male Vietnam war veterans — 40 with TBI (aged 68.0±2.5 years) and 30 controls (aged 70.1±5.3 years) — with no prior diagnosis of dementia or mild cognitive impairment underwent β-amyloid (18F-Florbetaben), tau (18F-Flortaucipir) and18F-FDG PET. The TBI cohort included 15 participants with mild, 16 with moderate, and 9 with severe injury. β-amyloid level was calculated using the Centiloid (CL) method and tau was measured by Standardized Uptake Value Ratios (SUVR) using the cerebellar cortex as reference region. Analyses were adjusted for age and APOE-e4. The findings were validated in an independent cohort from the ADNI-DOD study.There were no significant nor trending differences in β-amyloid or tau levels or18F-FDG uptake between the TBI and control groups before and after controlling for covariates. The β-amyloid and tau findings were replicated in the ADNI-DOD validation cohort and persisted when the AIBL-VETS and ADNI-DOD cohorts were combined (114 TBI vs 87 controls in total). These findings suggest that TBI is not associated with the later life accumulation of the neuropathological markers of AD.
How often do cancer researchers make their data and code available and what factors are associated with sharing?
Authors: Hamilton Daniel G. , Page Matthew J. , Finch Sue , Everitt Sarah , Fidler Fiona 
Published: March 2022

BackgroundVarious stakeholders are calling for increased availability of data and code from cancer research. However, it is unclear how commonly these products are shared, and what factors are associated with sharing. Our objective was to evaluate how frequently oncology researchers make data and code available, and explore factors associated with sharing.MethodsA cross-sectional analysis of a random sample of 306 articles indexed in PubMed in 2019 presenting original cancer research was performed. Outcomes of interest included the prevalence of affirmative sharing declarations and the rate with which declarations connected to useable data. We also investigated associations between sharing rates and several journal characteristics (e.g., sharing policies, publication models), study characteristics (e.g., cancer rarity, study design), open science practices (e.g., pre-registration, pre-printing) and citation rates between 2020-2021.ResultsOne in five studies declared data were publicly available (95% CI: 15-24%). However, when actual data availability was investigated this percentage dropped to 16% (95% CI: 12-20%), and then to less than 1% (95% CI: 0-2%) when data were checked for compliance with key FAIR principles. While only 4% of articles that used inferential statistics reported code to be available (10/274, 95% CI: 2-6%), the odds of reporting code to be available were 5.6 times higher for researchers who shared data. Compliance with mandatory data and code sharing policies was observed in 48% and 0% of articles, respectively. However, 88% of articles included data availability statements when required. Policies that encouraged data sharing did not appear to be any more effective than not having a policy at all. The only factors associated with higher rates of data sharing were studying rare cancers and using publicly available data to complement original research.ConclusionsData and code sharing in oncology occurs infrequently, and at a lower frequency than would be expected due to non-compliance with journal policies. There is also a large gap between those declaring data to be available, and those archiving data in a way that facilitates its reuse. We encourage journals to actively check compliance with sharing policies, and researchers consult community accepted guidelines when archiving the products of their research.
‘The Association of Scoliosis Properties with Spinal Cord Tethering: A Statistical Model for Prognostication’
Authors: Yarandi Kourosh Karimi , Mohammadi Esmaeil , Amirjamshidi Abbas , Bidabadi Mohammad Shirani , Rashidi Ahmad Pour , Azadnajafabad Sina , Maroufi Seyed Farzad , Alimohammadi Maisam 
Published: March 2022

AbstractObjectiveTo evaluate the relationship between the structural measures of scoliosis and underlying spinal cord tethering (SCT) and proposing a statistical prognostication model.Study designCross-sectional.SettingAcademic healthcare centerMethods128 definite scoliosis cases that were candidates for corrective surgery were enrolled. Anterior-posterior whole column digital radiographs and whole-spine MRI (supine for all samples and adjuvant prone MRI for suspected cases with tight filum terminal) were performed. Univariate and multiple logistic regression were used for the analysis of association and interaction. Association of SCT with structural features of scoliosis –Cobb angle, convexity, and type (idiopathic and congenital)– age, and sex were assessed.ResultsNone of the study variables showed a statistical association with SCT in univariable and multiple logistic regressions. After inclusion of Cobb angle-convexity-type interaction, higher Cobb angle, idiopathic scoliosis, dextrosoliosis, and male gender had a significant effect. Stratification for convexity discovered a positive association of Cobb angle and SCT in idiopathic patients with dextroscoliosis (1.02 [1.01–1.03], 0.049). In contrast, in congenital cases, the rate of SCT decreased by higher left-sided Cobb angles but it was not statistically significant (0.94 [0.88–1.01], 0.104).ConclusionThe risk of spinal cord tethering was not zero in any of the subgroups and no SCT-free group could be detected. Conventional MRI should be preoperatively performed for every case of scoliosis and thoroughly examined for signs of tethering. Clear imaging of patients at higher risk of SCT should not be decisive and further workup should be utilized before proceeding with reconstructive surgery.
Inferring Insulin Secretion Rate From Sparse Patient Glucose and Insulin Measures
Authors: Abohtyra Rammah M. , Chan Christine L. , Albers David J. , Gluckman Bruce J. 
Published: March 2022

ABSTRACTThe insulin secretion rate (ISR) contains information that can provide a personal, quantitative understanding of endocrine function. The ISR, if it can be reliably inferred from sparse measurements, could be used both for understanding the source of a problem with glucose regulation system and for monitoring people with diabetes.ObjectiveThis study aims to develop a model-based method for inferring ISR and related physiological information among people with different glycemic conditions in a robust manner.MethodsAn algorithm for estimating and validating ISR for different compartmental models with unknown but estimable ISR function and absorption/decay rates describing the dynamics of insulin and C-peptide accumulation was developed. The algorithm was applied to data from 17 subjects with normal glucose regulatory systems and 9 subjects with cystic fibrosis related diabetes (CFRD) in which glucose, insulin and C-peptide were measured in course of oral glucose tolerance tests (OGTT).ResultsThe model-based algorithm was able to successfully estimate ISR for a diverse set of patients. For CFRD subjects, due to high maximum observed glucose values, we observed better estimation for the degradation rates than for healthy patients. Even though ISR and C-peptide secretion rate (CSR) have different physiological characteristics and nonlinear relations with plasma glucose concentration, our model-based algorithm reproduced the expected linear relationship between ISR and CSR. And the estimated ISR can differentiate normal and CFRD patients: the ISR for individuals with CFRD is substantially lower compared with the ISR for individuals’ normal glucose regulatory systems.SignificanceA new estimation method is available to infer the ISR profile, plasma insulin, and C-peptide absorption rates from sparse measurements of insulin, C-peptide, and plasma glucose concentrations.
EMS prehospital response to the COVID-19 pandemic in the US: A brief literature review
Authors: Ventura Christian Angelo I. , Denton Edward E. , David Jessica A. , Schoenfelder Brianna J. , Mela Lillian , Lumia Rebecca P. , Rudi Rachel B. , Haldar Barnita 
Published: March 2022

ABSTRACTThis study aimed to analyze prehospital Emergency Medical Services (EMS) response to the COVID-19 pandemic in the US through a brief systematic review of available literature in context with international prehospital counterparts. An exploration of the NCBI repository was performed using a search string of relevant keywords which returned n=5128 results; articles that met the inclusion criteria (n=77) were reviewed and analyzed in accordance with PRISMA and PROSPERO recommendations. Methodical quality was assessed using critical appraisal tools, and the Egger’s test was used for risk of bias reduction upon linear regression analysis of a funnel plot. Sources of heterogeneity as defined by P < 0.10 or I^2 > 50% were interrogated. Findings were considered within ten domains: structural/systemic; clinical outcomes; clinical assessment; treatment; special populations; dispatch/activation; education; mental health; perspectives/experiences; and transport. Findings suggest, EMS clinicians have likely made significant and unmeasured contributions to care during the pandemic via nontraditional roles, i.e., COVID-19 testing and vaccine deployment. EMS plays a critical role in counteracting the COVID-19 pandemic in addition to the worsening opioid epidemic, both of which disproportionately impact patients of color. As such, being uniquely influential on clinical outcomes, these providers may benefit from standardized education on care and access disparities such as racial identity. Access to distance learning continuing education opportunities may increase rates of provider recertification. Additionally, there is a high prevalence of vaccine hesitancy among surveyed nationally registered EMS providers. Continued rigorous investigation on the impact of COVID-19 on EMS systems and personnel is warranted to ensure informed preparation for future pandemic and infectious disease response.
Long-COVID in Children and Adolescents: A Systematic Review and Meta-analyses
Authors: Lopez-Leon Sandra , Wegman-Ostrosky Talia , Ayuzo del Valle Norma Cipatli , Perelman Carol , Sepulveda Rosalinda , Rebolledo Paulina A , Cuapio Angelica , Villapol Sonia 
Published: March 2022

ABSTRACTThe objective of this systematic review and meta-analyses is to estimate the prevalence of long-COVID in children and adolescents and to present the full spectrum of symptoms present after acute COVID-19. We have used PubMed and Embase to identify observational studies published before February 10th, 2022 that included a minimum of 30 patients with ages ranging from 0 to 18 years that met the National Institute for Healthcare Excellence (NICE) definition of long-COVID, which consists of both ongoing (4 to 12 weeks) and post-COVID-19 (≥12 weeks) symptoms. Random-effects meta-analyses were performed using the MetaXL software to estimate the pooled prevalence with a 95% confidence interval (CI). Heterogeneity was assessed using I2 statistics. The Preferred Reporting Items for Systematic Reviewers and Meta-analysis (PRISMA) reporting guideline was followed (registration PROSPERO CRD42021275408). The literature search yielded 8,373 publications, of which 21 studies met the inclusion criteria, and a total of 80,071 children and adolescents were included. The prevalence of long-COVID was 25.24%, and the most prevalent clinical manifestations were mood symptoms (16.50%), fatigue (9.66%), and sleep disorders (8.42%). Children infected by SARS-CoV-2 had a higher risk of persistent dyspnea, anosmia/ageusia, and/or fever compared to controls. Limitations of the studies analyzed include lack of standardized definitions, recall, selection, misclassification, nonresponse and/or loss of follow-up, and a high level of heterogeneity.
Predicting systemic and pulmonary tissue barrier concentration of orally inhaled drug products
Authors: Singh Narender , Kannan Ravi , Arey Ryan , Walenga Ross , Babiskin Andrew , Przekwas Andrzej 
Published: March 2022

AbstractThe complex physiology and anatomy of the lungs and the range of processes involved in pulmonary drug transport and disposition make it challenging to predict the fate of orally inhaled drugs. This study aimed to develop an integrated computational pharmacology approach to mechanistically describe the spatio-temporal dynamics of inhaled drugs in both systemic circulation and site-specific lung tissue. The model included all the physiologically relevant pulmonary processes, such as deposition, dissolution, transport across lung barriers, and mucociliary clearance, to predict the inhaled drug pharmacokinetics. For validation test cases, the model predicted the fate of orally inhaled budesonide (highly soluble, mildly lipophilic) and fluticasone propionate (practically insoluble, highly lipophilic) in healthy subjects for: i) systemic and site-specific lung retention profiles, ii) aerodynamic particle size-dependent deposition profiles, and iii) identified the most impactful drug-specific, formulation-specific, and system-specific property factors that impact the fate of both the pulmonary and systemic concentration of the drugs. In summary, the presented multiscale computational model can guide the design of orally inhaled drug products to target specific lung areas, identify the effects of product differences on lung and systemic pharmacokinetics, and be used to better understand bioequivalence of generic orally inhaled drug products.Author summaryDespite widespread use of available orally inhaled drug products (OIDPs), much is unknown regarding their optimal lung deposition, targeted delivery to specific lung regions, and the effects of various device, formulation, and physiological factors on deposition, absorption, transport, and clearance. In this study, we have presented a multiscale computational framework that integrates a full-scale 24 generation 3D lung model with distinct barrier regions spanning trachea, tracheobronchial, alveolar, and the terminal alveolar sacs with multiple other modules to track the OIDP levels (concentration) in both blood and pulmonary tissue regions. Along with validating the framework on two different inhaled drug types, we have also presented a sensitivity analysis to highlight the most impactful drug and formulation parameters, and therefore, potential optimization parameters to modulate lung selectivity and to better understand the pulmonary retention of drugs in distinct lung regions.