bluuebunny/crossref_metadata_2025_split huggingface dataset:
THE RELATIONSHIP OF THE FOOT AND ANKLE STRUCTURE WITH OVERUSE INJURIES IN LICENSED FOOTBALLERS, A PROSPECTIVE COHORT
Authors: Sahillioglu Ali , Cerrahoglu Lale 
Published: July 2020

ABSTRACTThe main purpose of this study was to compare the values determined in the clinical examination of the foot and ankle and the plantar pressure measurements of the foot in athletes who developed an overuse-type disability and in athletes who did not develop overuse-type disability, and was to find factors that might predispose to disability, during the one-year follow-up. 100 licensed football players were included in the study. Presence of joint hypermobility, foot posture assessment, ankle and first metatarsophalangeal (MTP) joint range of motion (ROM) measurements, pedobarographic plantar pressure assessment of foot was carried out. Then, the footballers were followed for 12 months for the development of new foot and ankle overuse injuries and the clinical and pedobarographic data of the footballers with at least one injury were compared with the group without injury. We found asymmetric pressure distribution between the preferred and non-preferred foot in the group who had an injury in the pedobarographic static foot plantar pressure measurements(p = .040). When we compared the ROM values of footballers who had an injury and footballers who did not have an injury, we found a significant limitation in the group that suffered an injury, in the ankle eversion, first MTP joint dorsiflexion and ankle plantarflexion degrees(p = .029, p = .023, p=.044, respectively). These findings suggest that impairments in foot plantar pressure distribution and limitations in ankle and foot joint ROM may be risk factors for the development of foot and ankle overuse injury.
Enhancing MR imaging driven Alzheimer’s disease classification performance using generative adversarial learning
Authors: Zhou Xiao , Qiu Shangran , Joshi Prajakta S. , Xue Chonghua , Killiany Ronald J. , Mian Asim , Chin Sang P. , Au Rhoda , Kolachalama Vijaya B. ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: July 2020

AbstractBackgroundGenerative adversarial networks (GAN) can generate images of improved quality but their ability to augment image-based classification tasks is not fully explored.PurposeWe evaluated if a modified GAN can learn from MRI scans of multiple magnetic field strength to enhance Alzheimer’s disease (AD) classification performance.Materials and methodsT1-weighted brain MRI scans from 151 participants of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), who underwent both 1.5 Tesla (1.5T) and 3 Tesla imaging at the same time were selected to construct a GAN model. This model was trained along with a three-dimensional fully convolutional network (FCN) using the generated images (1.5T*) as inputs to predict AD status. Quality of the generated images was evaluated using signal to noise ratio (SNR), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE). Data from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n=107), and the National Alzheimer’s Coordinating Center (NACC, n=565) was used for model validation.ResultsThe mean quality of the generated (1.5T*) images was consistently higher than the 1.5T images, as measured using SNR, BRISQUE and NIQE on the validation datasets. The 1.5T*-based FCN classifier performed better than the FCN model trained using the 1.5T scans.Specifically, the mean area under curve increased from 0.907 to 0.932, from 0.934 to 0.940 and from 0.870 to 0.907 on the ADNI test, AIBL and NACC datasets, respectively.ConclusionThis study demonstrates that GAN frameworks can be constructed to simultaneously improve image quality and augment classification performance.Key pointsOur proposed generative adversarial network used 1.5 and 3 Tesla MR scans of the brain to generate images of improved quality, as estimated using no-reference image quality algorithms.Classification models of Alzheimer’s disease risk developed using the generated images had higher classification performance than the models developed using the original 1.5 Tesla scans.
D614G Spike Mutation Increases SARS CoV-2 Susceptibility to Neutralization
Authors: Weissman Drew , Alameh Mohamad-Gabriel , de Silva Thushan , Collini Paul , Hornsby Hailey , Brown Rebecca , LaBranche Celia C. , Edwards Robert J , Sutherland Laura , Santra Sampa , Mansouri Katayoun , Gobeil Sophie , McDanal Charlene , Pardi Norbert , Hengartner Nick , Lin Paulo J.C. , Tam Ying , Shaw Pamela A. , Lewis Mark G. , Boesler Carsten , Şahin Uğur , Acharya Priyamvada , Haynes Barton F. , Korber Bette , Montefiori David C. 
Published: July 2020

SummaryThe SARS-CoV-2 Spike protein acquired a D614G mutation early in the COVID-19 pandemic that appears to confer on the virus greater infectivity and is now the globally dominant form of the virus. Certain of the current vaccines entering phase 3 trials are based on the original D614 form of Spike with the goal of eliciting protective neutralizing antibodies. To determine whether D614G mediates neutralization-escape that could compromise vaccine efficacy, sera from Spike-immunized mice, nonhuman primates and humans were evaluated for neutralization of pseudoviruses bearing either D614 or G614 Spike on their surface. In all cases, the G614 pseudovirus was moderately more susceptible to neutralization. The G614 pseudovirus also was more susceptible to neutralization by monoclonal antibodies against the receptor binding domain and by convalescent sera from people known to be infected with either the D614 or G614 form of the virus. These results indicate that a gain in infectivity provided by D614G came at the cost of making the virus more vulnerable to neutralizing antibodies, and that the mutation is not expected to be an obstacle for current vaccine development.
Homebound by COVID19: The Benefits and Consequences of Non-Pharmaceutical Intervention Strategies
Authors: Oruc Buse Eylul , Baxter Arden , Keskinocak Pinar , Asplund John , Serban Nicoleta 
Published: July 2020

ABSTRACTObjectivesTo evaluate the tradeoffs between potential benefits (e.g., reduction in infection spread and deaths) of non-pharmaceutical interventions for COVID19 and being homebound (i.e., refraining from community/workplace interactions).MethodsAn agent-based simulation model to project the disease spread and estimate the number of homebound people and person-days under multiple scenarios, including combinations of shelter-in- place, voluntary quarantine, and school closure in Georgia from March 1 to September 1, 2020.ResultsCompared to no intervention, under voluntary quarantine, voluntary quarantine with school closure, and shelter-in-place with school closure scenarios 3.43, 19.8, and 200+ homebound adult-days were required to prevent one infection, with the maximum number of adults homebound on a given day in the range of 121K-268K, 522K-567K, 5,377K-5,380K, respectively.ConclusionsVoluntary quarantine combined with school closure significantly reduced the number of infections and deaths with a considerably smaller number of homebound person-days compared to shelter-in-place.Three-question Summary BoxWhat is the current understanding of this subject?Recent research has been conducted by various countries and regions on the impact of non-pharmaceutical interventions (NPIs) on reducing the spread of COVID19.What does this report add to the literature?Our report assessed which intervention strategies provided the best results in terms of both reducing infection outcomes (cases, deaths, etc.) and minimizing their social and economic effects (e.g., number of people homebound, providing childcare, etc.).What are the implications for public health practice?Voluntary quarantine proved to be the most beneficial in terms of reducing infections and deaths compared to the number of people who were homebound.
Using mobile phone data for epidemiological simulations of lockdowns: government interventions, behavioral changes, and resulting changes of reinfections
Authors: Müller Sebastian A , Balmer Michael , Charlton Billy , Ewert Ricardo , Neumann Andreas , Rakow Christian , Schlenther Tilmann , Nagel Kai 
Published: July 2020

AbstractEpidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. Results show that in Berlin (Germany), behavioral changes of the population mostly happened before the government-initiated so-called contact ban came into effect. Also, the model is used to determine differentiated changes to the reinfection rate for different interventions such as reductions in activity participation, the wearing of masks, or contact tracing followed by quarantine-at-home. One important result is that successful contact tracing reduces the reinfection rate by about 30 to 40%, and that if contact tracing becomes overwhelmed then infection rates immediately jump up accordingly, making rather strong lockdown measures necessary to bring the reinfection rate back to below one.
COVID-19 in-hospital mortality and mode of death in a dynamic and non-restricted tertiary care model in Germany
Authors: Rieg Siegbert , Cube Maja von , Kalbhenn Johannes , Utzolino Stefan , Pernice Katharina , Bechet Lena , Baur Johanna , Lang Corinna N. , Wagner Dirk , Wolkewitz Martin , Kern Winfried V. , Biever Paul 
Published: July 2020

AbstractBackgroundReported mortality of hospitalised COVID-19 patients varies substantially, particularly in critically ill patients. So far COVID-19 in-hospital mortality and modes of death under optimised care conditions have not been systematically studied.MethodsThis retrospective observational monocenter cohort study was performed after implementation of a non-restricted, dynamic tertiary care model at the University Medical Center Freiburg, an experienced ARDS and ECMO referral center. All hospitalised patients with PCR-confirmed SARS-CoV-2 infection were included. The primary endpoint was in-hospital mortality, secondary endpoints included major complications and modes of death. A multistate analysis and a Cox regression analysis for competing risk models were performed. Modes of death were determined by two independent reviewers.ResultsBetween February 25, and May 8, 213 patients were included in the analysis. The median age was 65 years, 129 patients (61%) were male. 70 patients (33%) were admitted to the intensive care unit (ICU), of which 57 patients (81%) received mechanical ventilation and 23 patients (33%) extracorporeal membrane-oxygenation (ECMO) support. According to the multistate model the probability to die within 90 days after COVID-19 onset was 24% in the whole cohort. If the levels of care at time of study entry were accounted for, the probabilities to die were 16% if the patient was initially on a regular ward, 47% if in the ICU and 57% if mechanical ventilation was required at study entry. Age ≥65 years and male sex were predictors for in-hospital death. Predominant complications – as judged by two independent reviewers – determining modes of death were multi-organ failure, septic shock and thromboembolic and hemorrhagic complications.ConclusionIn a dynamic care model COVID-19-related in-hospital mortality remained substantial. In the absence of potent antiviral agents, strategies to alleviate or prevent the identified complications should be investigated. In this context, multistate analyses enable comparison of models-of-care and treatment strategies and allow estimation and allocation of health care resources.RegistrationGerman Clinical Trials Register (identifier DRKS00021775), retrospectively registered June 10, 2020.
The Optimal Allocation of Covid-19 Vaccines
Authors: Babus Ana , Das Sanmay , Lee SangMok 
Published: July 2020

AbstractCovid-19 vaccine prioritization is key if the initial supply of the vaccine is limited. A reasonable policy will surely prioritize populations facing high risk of severe illness in high-exposure occupations. The challenge is deciding between high-risk populations in low-exposure occupations and those that are young and healthy but work in high-exposure occupations. We estimate occupation-based infection risks and use age-based infection fatality rates in a model to assign priorities over populations with different occupations and ages. Among others, we find that 50 year-old food-processing workers and 60 year-old financial advisors are equally prioritized. Our model suggests a vaccine distribution that emphasizes age-based mortality risk more than occupation-based exposure risk. Designating some occupations as essential does not affect the optimal vaccine allocation, unless a stay-at-home order is also in effect. Even with vaccines allocated optimally, 1.37% of the employed workforce is still expected to be infected with the virus until the vaccine becomes widely available, provided the vaccine is 50% effective and assuming a supply of 60mil doses.
Decreased incidence, virus transmission capacity, and severity of COVID-19 at altitude on the American continent
Authors: Arias-Reyes Christian , Carvajal-Rodriguez Favio , Poma-Machicao Liliana , Aliaga-Raudan Fernanda , Marques Danuzia A. , DeUrioste Natalia Zubieta , Accinelli Roberto Alfonso , Schneider-Gasser Edith M. , Zubieta-Calleja Gustavo , Dutschmann Mathias , Soliz Jorge 
Published: July 2020

AbstractThe coronavirus disease 2019 (COVID-19) outbreak in North, Central, and South America has become the epicenter of the current pandemic. We have suggested previously that the infection rate of this virus might be lower in people living at high altitude (over 2,500 m) compared to that in the lowlands. Based on data from official sources, we performed a new epidemiological analysis of the development of the pandemic in 23 countries on the American continent as of May 23, 2020. Our results confirm our previous finding, further showing that the incidence of COVID-19 on the American continent decreases significantly starting at 1,000 m above sea level (masl). Moreover, epidemiological modeling indicates that the virus transmission rate capacity is lower in the highlands (>1,000 masl) than in the lowlands (<1,000 masl). Finally, evaluating the differences in the recovery percentage of patients, the death-to-case ratio, and the theoretical fraction of undiagnosed cases, we found that the severity of COVID-19 is also decreased above 1,000 m. We conclude that the impact of the COVID-19 decreases significantly with altitude.HighlightsThere is a negative correlation between altitude and COVID-19 incidence on the American Continent starting from 1,000 m above sea level.The transmission rate of SARS-CoV-2 is lower in the highlands than in the lowlands.The severity of COVID-19 decreases significantly with increased altitude.
Neuroanatomic markers of post-traumatic epilepsy based on magnetic resonance imaging and machine learning
Authors: Akrami Haleh , Leahy Richard M. , Irimia Andrei , Kim Paul E. , Heck Christianne N. , Joshi Anand A. 
Published: July 2020

AbstractAlthough post-traumatic epilepsy (PTE) is a common complication of traumatic brain injury (TBI), the relationship between these conditions is unclear, early PTE detection and prevention being major unmet clinical challenges. This study aims to identify imaging biomarkers that distinguish PTE and non-PTE subjects among TBI survivors based on a magnetic resonance imaging (MRI) dataset. We performed tensor-based morphometry to analyze brain shape changes associated with TBI and to derive imaging features for statistical group comparison. Additionally, machine learning was used to identify structural anomalies associated with brain lesions. Automatically generated brain lesion maps were used to identify brain regions where lesion load may indicate an increased incidence of PTE. Statistical analysis suggests that lesions in the temporal lobes, cerebellum, and the right occipital lobe are associated with an increased PTE incidence.
Pediatric intensive care unit admissions for COVID-19: insights using state-level data
Authors: Loomba Rohit S. , Villarreal Enrique G. , Farias Juan S. , Bronicki Ronald A. , Flores Saul 
Published: July 2020

AbstractIntroductionIntensive care has played a pivotal role during the COVID-19 pandemic as many patients developed severe pulmonary complications. The availability of information in pediatric intensive care (PICUs) remains limited. The purpose of this study is to characterize COVID-19 positive admissions (CPAs) in the United States and to determine factors that may impact those admissions.Materials and MethodsThis is a retrospective cohort study using data from the COVID-19 dashboard virtual pediatric system containing information regarding respiratory support and comorbidities for all CPAs between March and April 2020. The state level data contained 13 different factors from population density, comorbid conditions and social distancing score. The absolute CPAs count was converted to frequency using the state’s population. Univariate and multivariate regression analyses were performed to assess the association between CPAs frequency and endpoints.ResultsA total of 205 CPAs were reported by 167 PICUs across 48 states. The estimated CPAs frequency was 2.8 per million children. A total of 3,235 tests were conducted with 6.3% positive tests. Children above 11 years of age comprised 69.7% of the total cohort and 35.1% had moderated or severe comorbidities. The median duration of a CPA was 4.9 days [1.25-12.00 days]. Out of the 1,132 total CPA days, 592 [52.2%] were for mechanical ventilation. The inpatient mortalities were 3 [1.4%]. Multivariate analyses demonstrated an association between CPAs with greater population density [beta-coefficient 0.01, p<0.01] and increased percent of children receiving the influenza vaccination [beta-coefficient 0.17, p=0.01].ConclusionsInpatient mortality during PICU CPAs is relatively low at 1.4%. CPA frequency seems to be impacted by population density while characteristics of illness severity appear to be associated with ultraviolet index, temperature, and comorbidities such as Type 1 diabetes. These factors should be included in future studies using patient-level data.
Circulating cytokines and lymphocyte subsets in patients who have recovered from COVID-19
Authors: Hasichaolu  , Zhang Xinri , Li Xin , Li Xin , Li Dongyan 
Published: July 2020

AbstractTo investigate the immune status of people who previously had COVID-19 infections, we recruited patients 2 weeks post-recovery and analyzed circulating cytokines and lymphocyte subsets. We measured levels of total lymphocytes, CD4+ T cells, CD8+ T cells, CD19+ B cells, CD56+ NK cells, and the serum concentrations of interleukin (IL)-1, IL-4, IL-6, IL-8, IL-10, transforming growth factor beta (TGF-β), tumor necrosis factor alpha (TNF-α), and interferon gamma (IFN-γ) by flow cytometry. We found that in most post-recovery patients, levels of total lymphocytes (66.67%), CD3+ T cells (54.55%), CD4+ T cells (54.55%), CD8 + T cells (81.82%), CD19+ B cells (69.70%), and CD56+ NK cells(51.52%) remained lower than normal, whereas most patients showed normal levels of IL-2 (100%), IL-4 (80.88%), IL-6 (79.41%), IL-10 (98.53%), TNF-α (89.71%), IFN-γ (100%) and IL-17 (97.06%). Compared to healthy controls, 2-week post-recovery patients had significantly lower absolute numbers of total lymphocytes, CD3+ T cells, CD4+ T cells, CD8+ T cells, CD19+ B cells, and CD56+ NK cells, along with significantly higher levels of IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ and IL-17. Among post-recovery patients, T cells, particularly CD4+ T cells, were positively correlated with CD19+ B cell counts. Additionally, CD8+ T cells positively correlated with CD4+ T cells and IL-2 levels, and IL-6 positively correlated with TNF-α and IFN-γ. These correlations were not observed in healthy controls. By ROC curve analysis, post-recovery decreases in lymphocyte subsets and increases in cytokines were identified as independent predictors of rehabilitation efficacy. These findings indicate that the immune system has gradually recovered following COVID-19 infection; however, the sustained hyper-inflammatory response for more than 14 days suggests a need to continue medical observation following discharge from the hospital. Longitudinal studies of a larger cohort of recovered patients are needed to fully understand the consequences of the infection.
Heat-based Decontamination of N95 Masks Using a Commercial Laundry Dryer
Authors: Lensky Yuri D. , Mazenc Edward A. , Ranard Daniel , Vilim Matthew , Prakash Manu , Brooks Bill , Bradley Amanda , Engelschenschilt Alain , Plutz Jason , Zellmer Todd 
Published: July 2020

AbstractWe propose a dry heat method for decontaminating N95 masks of SARS-CoV-2, designed around placing them in resealable plastic bags, packed in large cardboard boxes installed at the rear end of commercial laundry dryers. Our protocol rests on data collected in collaboration with Alliance Laundry Systems (ALS) and the CDC/NIOSH laboratories, under the “NPPTL Respirator Assessments to Support the COVID-19 Response” initiative. We test the two most widely available ALS tumbler models, the UTF75N and UT075N, and show that if our procedure is carefully followed, the masks will be subject to suitably high and stable temperatures for decontamination; in particular the masks will be heated to at least 80 °C for at least 65 min. For the mask models 3M 1860, 3M 8511, and Halyard 62126, we establish that they pass quantitative fit tests and retain sufficient filtration performance after three cycles of our decontamination procedure. All masks used in this study were new and uncontaminated: the evidence for the levels of biological inactivation of SARS-CoV-2 is provided by [1]. While the protocol outlined here is currently specific to certain tested dryer models, this equipment is widely available, with machines estimated to be within 15 minutes of most US hospitals. Models from other manufacturers may also be appropriate for this decontamination method, though we stress the need for explicit testing on alternative models before use.
High-Plex and High-throughput Digital Spatial Profiling of non-small-cell lung cancer (NSCLC)
Authors: Monkman James , Taheri Touraj , Warkiani Majid Ebrahimi , O’leary Connor , Ladwa Rahul , Richard Derek , O’ Byrne Ken , Kulasinghe Arutha 
Published: July 2020

AbstractProfiling the tumour microenvironment(TME) has been informative in understanding the underlying tumour-immune interactions. Multiplex immunohistochemistry(mIHC) coupled with molecular barcoding technologies have revealed greater insights into the TME. In this study, we utilised the Nanostring GeoMX™ Digital Spatial Profiler (DSP) platform to profile a NSCLC tissue microarray for protein markers across immune cell profiling, immuno-oncology(IO) drug target, immune activation status, immune cell typing, and pan-tumour protein modules. Regions of interest(ROIs) were selected that described tumour, TME and normal adjacent tissue(NAT) compartments. Our data revealed that paired analysis (n=18) of patient matched compartments indicated that the TME was significantly enriched in CD27, CD3, CD4, CD44, CD45, CD45RO, CD68, CD163, and VISTA relative to tumour. Unmatched analysis indicated that the NAT(n=19) was significantly enriched in CD34, fibronectin, IDO1, LAG3, ARG1 and PTEN when compared to the TME(n=32). Univariate Cox proportional hazards indicated that the presence of cells expressing CD3(HR:0.5, p=0.018), CD34(HR:0.53, p=0.004) and ICOS (HR:0.6, p=0.047) in tumour compartments were significantly associated with improved overall survival(OS). We implemented both high-plex and high-throughput methodologies to the discovery of protein biomarkers and molecular phenotypes within biopsy samples and demonstrate the power of such tools for a new generation of pathology research.Conflict of interest statementThe authors have declared that no conflict of interest exists.