bluuebunny/crossref_metadata_2025_split huggingface dataset:
COVID-19 AND PATIENTS UNDERGOING PHARMACOLOGICAL TREATMENTS FOR IMMUNE-MEDIATED INFLAMMATORY DISEASES: PROTOCOL FOR A RAPID LIVING SYSTEMATIC REVIEW
Authors: da Rocha Aline Pereira , Atallah Alvaro Nagib , Pinto Ana Carolina Pereira Nunes , Rocha-Filho César Ramos , de Assis Reis Felipe Sebastião , Milby Keilla Martins , Civile Vinicius Tassoni , Carvas Junior Nelson , Santos Rodolfo Rodrigo Pereira , Ferla Laura Jantsch , Trevisani Giulia Fernandes Moça , Ramalho Gabriel Sodré , Puga Maria Eduarda Santos , Trevisani Virgínia Fernandes Moça 
Published: May 2020

ABSTRACTCONTEXT AND OBJECTIVEWe propose to systematically review the available evidence to evaluate if patients with immune mediated inflammatory diseases under pharmacological treatment with immunosuppressants, immunobiologics, Disease-Modifying Anti-Rheumatic Drugs (DMARD) or targeted synthetic DMARDs have better or worse outcomes when infected by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). This study is a protocol for our rapid living systematic review. METHODS: Protocol for a rapid living systematic review methodology following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidance. To conduct the rapid systematic review, we will employ abbreviated systematic review methods, including: not performing independent screens of abstracts and not searching grey literature. As this will be a living review, it will be continuously updated.
Personalized Early Stage Alzheimer’s Disease Detection: A Case Study of President Reagan’s Speeches
Authors: Wang Ning , Peddagangireddy Vishal , Luo Fan , Subbalakshmi K.P. , Chandramouli R. 
Published: May 2020

AbstractAlzheimer’s disease (AD)-related global healthcare cost is estimated to be $1 trillion by 2050. Currently, there is no cure for this disease; however, clinical studies show that early diagnosis and intervention helps to extend the quality of life and inform technologies for personalized mental healthcare. Clinical research indicates that the onset and progression of Alzheimer’s disease lead to dementia and other mental health issues. As a result, the language capabilities of patient start to decline.In this paper, we show that machine learning-based unsupervised clustering of and anomaly detection with linguistic biomarkers are promising approaches for intuitive visualization and personalized early stage detection of Alzheimer’s disease. We demonstrate this approach on 10 year’s (1980 to 1989) of President Ronald Reagan’s speech data set. Key linguistic biomarkers that indicate early-stage AD are identified. Experimental results show that Reagan had early onset of Alzheimer’s sometime between 1983 and 1987. This finding is corroborated by prior work that analyzed his interviews using a statistical technique. The proposed technique also identifies the exact speeches that reflect linguistic biomarkers for early stage AD.
Spatial Inequities in COVID-19 Testing, Positivity, Confirmed Cases and Mortality in 3 US Cities: an Ecological Study
Authors: Bilal Usama , Tabb Loni P. , Barber Sharrelle , Roux Ana V. Diez 
Published: May 2020

AbstractBackgroundPreliminary evidence has shown inequities in COVID-19 related cases and deaths in the US.ObjectiveWe explored the emergence of spatial inequities in COVID-19 testing, positivity, confirmed cases, and mortality in New York City, Philadelphia, and Chicago during the first six months of the pandemic.DesignEcological, observational study at the zip code tabulation area (ZCTA) level from March to September 2020.SettingChicago, New York City and Philadelphia.ParticipantsAll populated ZCTAs in the three cities.MeasuresOutcomes were ZCTA-level COVID-19 testing, positivity, confirmed cases, and mortality cumulatively through the end of September. Predictors were the CDC social vulnerability index and its four domains, obtained from the 2014-2018 American Community Survey. We examined the spatial autocorrelation of COVID-19 outcomes using global and local Moran’s I and estimated associations using spatial conditional autoregressive negative binomial models.ResultsWe found spatial clusters of high and low positivity, confirmed cases and mortality, co-located with clusters of low and high social vulnerability. We also found evidence for the existence of spatial inequities in testing, positivity, confirmed cases and mortality for the three cities. Specifically, neighborhoods with higher social vulnerability had lower testing rates, higher positivity ratios, confirmed case rates and mortality rates.LimitationsZCTAs are imperfect and heterogeneous geographical units of analysis. We rely on surveillance data, which may be incomplete.ConclusionWe found spatial inequities in COVID-19 testing, positivity, confirmed cases, and mortality in three large cities of the US.RegistrationN/AFunding sourceNIH (DP5OD26429) and RWJF (77644)
Safe clinical practice for patients hospitalised in mental health wards during a suicidal crisis: a qualitative study of patient experiences
Authors: Berg Siv Hilde , Rørtveit Kristine , Walby Fredrik A. , Aase Karina 
Published: May 2020

AbstractAimThe aim of this study was to explore suicidal patients’ experiences of safe clinical practice during hospitalisation in mental healthcare. The study was guided by the following research question: How do suicidal patients experience safe clinical practice during hospitalisation in mental health wards?Design, setting and participantsA qualitative design with semi-structured individual interviews was applied. Eighteen patients hospitalised with suicidal behaviour in specialised mental healthcare for adults at a Norwegian hospital participated in the study. Data were analysed thematically and inductively using qualitative content analysis.ResultsPatients in a suicidal crisis experienced safe clinical care in mental health wards characterised by the following three themes: (1) being detected by mindful healthcare professionals, (2) receiving tailor-made treatment and (3) being protected by adaptive practice.ConclusionThis study illuminates the experiences of safe clinical practice for patients in a suicidal crisis. The patient group was multifaceted with fluctuating suicidal behaviour, which highlights the importance of embracing personalised activities. Safe clinical practice needs to recognise rather than efface patients’ variability.Strengths and limitations of this study•This study used qualitative interviews to provide rich and variable in-depth data of inpatients with suicidal behaviour, which is an under-researched group.•Patient experience consultants were involved in the design of the study.•The study results are suitable for conceptual development of safe clinical practice for suicidal patients.•The patient sample provided rich variability regarding diagnoses, symptom/function level, sex, number of previous hospital admissions and compulsory/voluntary admissions.•The qualitative methodological approach is not suited for assessing the effects of interventions.
COVID-19 in Children with Brain-Based Developmental Disabilities: A Rapid Review
Authors: Dugas Michèle , Carnovale Valérie , Poirier Andrée-Anne , Mailhot Benoit , Skidmore Becky , Faust Lena , Costello Carrie , Thomson Donna , Majnemer Annette , Goldowitz Dan , Miller Steven P. , LeBlanc Annie 
Published: May 2020

AbstractBackgroundThe prevalence of symptomatic COVID-19 in children remains low to date. In just a few months, COVID-19 has affected millions of people worldwide, and as of the date of this publication, the pandemic continues. Based on the current available evidence, children do not appear to be at higher risk of contracting COVID-19 than adults. However, children with neurological and neuromuscular conditions are vulnerable to the respiratory complications of other viral infections.ObjectivesTo assess whether children with brain-based developmental disabilities were more likely to develop COVID-19 and have complications or poorer outcomes following infection.MethodsWe conducted a two-week rapid review on studies with primary data regarding children aged between zero and 18 years old with brain-based developmental disabilities, or who were at risk of developing such disabilities, with confirmed or suspected COVID-19. We performed our literature searches on April 18, 2020.ResultsOur search strategy identified 538 individual records, of which four were included in our review. Of the 50 COVID-19 pediatric patients reported in the included studies, a total of seven children were at risk of developing brain-based disabilities. Symptoms ranged in severity. However, generally, patients were discharged or saw improvements in their symptoms by the end of the study period. No deaths were reported.DiscussionOur study highlights a knowledge gap regarding the impact of COVID-19 in children with brain-based developmental disabilities.
Associations of stay-at-home order and face-masking recommendation with trends in daily new cases and deaths of laboratory-confirmed COVID-19 in the United States
Authors: Xu Jie , Hussain Sabiha , Lu Guanzhu , Zheng Kai , Wei Shi , Bao Wei , Zhang Lanjing 
Published: May 2020

AbstractBackground and objectivesPublic health interventions were associated with reduction in coronavirus disease 2019 (COVID-19) transmission in China, but their impacts on COVID-19 epidemiology in other countries are unclear. We examined the associations of stay-at-home order (SAHO) and face-masking recommendation with epidemiology of laboratory-confirmed COVID-19 in the United States.MethodsIn this quasi-experimental study, we modeled the temporal trends in daily new cases and deaths of COVID-19, and COVID-19 time-varying reproduction numbers (Rt) in the United States between March 1 and April 20, 2020, and conducted simulation studies.ResultsThe number and proportion of U.S. residents under SAHO increased between March 19 and April 7, and plateaued at 29,0829,980 and 88.6%, respectively. Trends in COVID-19 daily cases and Rt reduced after March 23 (P<0.001) and further reduced on April 3 (P<0.001), which was associated with implementation of SAHO by 10 states on March 23, and face-masking recommendation on April 3, respectively. The estimates of Rt eventually fell below/around 1.0 on April 13. Similar turning points were identified in the trends of daily deaths with a lag time. Early implementation and early-removal of SAHO would be associated with significantly reduced and increased daily new cases and deaths, respectively.ConclusionsThere were 2 turning points of COVID-19 daily new cases or deaths in the U.S., which appeared to associate with implementation of SAHO and the CDC’s face-masking recommendation. These findings may inform decision-making of lifting SAHO and face-masking recommendation.
The Histopathological Diagnosis of Adenocarcinoma & Squamous Cells Carcinoma of Lungs by Artificial intelligence: A comparative study of convolutional neural networks
Authors: Abbas Mohammad Ali , Bukhari Syed Usama Khalid , Syed Asmara , Shah Syed Sajid Hussain 
Published: May 2020

AbstractIntroductionMalignant tumors of the lung are the most important cause of morbidity and mortality due to cancer all over the world. A rising trend in the incidence of lung cancer has been observed. Histopathological diagnosis of lung cancer is a vital component of patient care. The use of artificial intelligence in the histopathological diagnosis of lung cancer may be a very useful technology in the near future.AimThe aim of the present research project is to determine the effectiveness of convolutional neural networks for the diagnosis of squamous cell carcinoma and adenocarcinoma of the lung by evaluating the digital pathology images of these cancers.Materials & MethodsA total of 15000 digital images of histopathological slides were acquired from the LC2500 dataset. The digital pathology images from lungs are comprised of three classes; class I contains 5000 images of benign lung tissue, class II contains 5,000 images of squamous cell carcinoma of lungs while Class III contains 5,000 images of adenocarcinoma of lungs. Six state of the art off the shelf convolutional neural network architectures, VGG-19, Alex Net, ResNet: ResNet-18, ResNet-34, ResNet-50, and ResNet-101, are used to assess the data, in this comparison study. The dataset was divided into a train set, 55% of the entire data, validation set 20%, and 25% into the test data set.ResultsA number of off the shelf pre-trained (on ImageNet data set) convolutional neural networks are used to classify the histopathological slides into three classes, benign lung tissue, squamous cell carcinoma-lung and adenocarcinoma - lung. The F-1 scores of AlexNet, VGG-19, ResNet-18, ResNet-34, ResNet-50 and ResNet-101, on the test dataset show the result of 0.973, 0.997, 0.986, 0.992, 0.999 and 0.999 respectively.DiscussionThe diagnostic accuracy of more 97% has been achieved for the diagnosis of squamous cell carcinoma and adenocarcinoma of the lungs in the present study. A similar finding has been reported in other studies for the diagnosis of metastasis of breast carcinoma in lymph nodes, basal cell carcinoma, and prostatic cancer.ConclusionThe development of algorithms for the recognition of a specific pattern of the particular malignant tumor by analyzing the digital images will reduce the chance of human errors and improve the efficiency of the laboratory for the rapid and accurate diagnosis of cancer.
Predicting re-emergence times of dengue epidemics at low reproductive numbers: DENV1 in Rio de Janeiro, 1986-1990
Authors: Subramanian Rahul , Romeo-Aznar Victoria , Ionides Edward , Codeço Claudia T. , Pascual Mercedes 
Published: May 2020

AbstractPredicting arbovirus re-emergence remains challenging in regions with limited off-season transmission and intermittent epidemics. Current mathematical models treat the depletion and replenishment of susceptible (non-immune) hosts as the principal drivers of re-emergence, based on established understanding of highly transmissible childhood diseases with frequent epidemics. We extend an analytical approach to determine the number of ‘skip’ years preceding re-emergence for diseases with continuous seasonal transmission, population growth and under-reporting. Re-emergence times are shown to be highly sensitive to small changes in lowR0(secondary cases produced from a primary infection in a fully susceptible population). We then fit a stochastic SIR (Susceptible-Infected-Recovered) model to observed case data for the emergence of dengue serotype DENV1 in Rio de Janeiro. This aggregated city-level model substantially over-estimates observed re-emergence times either in terms of skips or outbreak probability under forward simulation. The inability of susceptible depletion and replenishment to explain re-emergence under ‘well-mixed’ conditions at a city-wide scale demonstrates a key limitation of SIR aggregated models including those applied to other arboviruses. The predictive uncertainty and high skip sensitivity to epidemiological parameters suggest a need to investigate the relevant spatial scales of susceptible depletion and the scaling of microscale transmission dynamics to formulate simpler models that apply at coarse resolutions.
Validation of a commercially available SARS-CoV-2 serological Immunoassay
Authors: Meyer Benjamin , Torriani Giulia , Yerly Sabine , Mazza Lena , Calame Adrien , Arm-Vernez Isabelle , Zimmer Gert , Agoritsas Thomas , Stirnemann Jérôme , Spechbach Hervé , Guessous Idris , Stringhini Silvia , Pugin Jérôme , Roux-Lombard Pascale , Fontao Lionel , Siegrist Claire-Anne , Eckerle Isabella , Vuilleumier Nicolas , Kaiser Laurent ,   for the Geneva Center for Emerging Viral Diseases
Published: May 2020

AbstractObjectivesTo validate the diagnostic accuracy of a Euroimmun SARS-CoV-2 IgG and IgA immunoassay for COVID-19.MethodsIn this unmatched (1:1) case-control validation study, we used sera of 181 laboratory-confirmed SARS-CoV-2 cases and 176 controls collected before SARS-CoV-2 emergence. Diagnostic accuracy of the immunoassay was assessed against a whole spike protein-based recombinant immunofluorescence assay (rIFA) by receiver operating characteristic (ROC) analyses. Discrepant cases between ELISA and rIFA were further tested by pseudo-neutralization assay.ResultsCOVID-19 patients were more likely to be male and older than controls, and 50.3% were hospitalized. ROC curve analyses indicated that IgG and IgA had high diagnostic accuracies with AUCs of 0.992 (95% Confidence Interval [95%CI]: 0.986-0.996) and 0.977 (95%CI: 0.963-0.990), respectively. IgG assays outperformed IgA assays (p=0.008). Taking an assessed 15% inter-assay imprecision into account, an optimized IgG ratio cut-off > 1.5 displayed a 100% specificity (95%CI: 98–100) and a 100% positive predictive value (95%CI: 97-100). A 0.5 cut-off displayed a 97% sensitivity (95%CI: 93–99) and a 97% negative predictive value (95%CI: 93–99). Substituting these thresholds for the manufacturer’s, improved assay performance, leaving 12% of IgG ratios indeterminate between 0.5-1.5.ConclusionsThe Euroimmun assay displays a nearly optimal diagnostic accuracy using IgG against SARS-CoV-2 in patient samples, with no obvious gains from IgA serology. The optimized cut-offs are fit for rule-in and rule-out purposes, allowing determination of whether individuals in our study population have been exposed to SARS-CoV-2 or not. IgG serology should however not be considered as a surrogate of protection at this stage.
Voluntary Cyclical Distancing: A potential alternative to constant level mandatory social distancing, relying on an ‘infection weather report’
Authors: Goldman Daniel 
Published: May 2020

1AbstractCOVID-19 has significantly changed our daily lives. Stay-at-home orders and forced closings of all non-essential businesses has had a significant impact on our economy. While it is important to ensure that the healthcare system is not overwhelmed, there are many questions that remain about the efficacy of extreme social distancing, and whether there are alternatives to mandatory lockdowns. This paper analyzes the utility of various levels of social distancing, and suggests an alternative approach using voluntary distancing informed by an infectious load index or ‘infection weather report.’
COVID-19 Pandemic Prediction for Hungary; a Hybrid Machine Learning Approach
Authors: Pinter Gergo , Felde Imre , Mosavi Amir , Ghamisi Pedram , Gloaguen Richard 
Published: May 2020

AbstractSeveral epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.
“Changes in soluble CD163 indicate monocyte involvement in cognitive deficits in Parkinson’s disease”
Authors: Nissen Sara K. , Ferreira Sara A. , Schulte Claudia , Shrivastava Kalpana , Hennig Dorle , Graversen Jonas , Maetzler Walter , Berg Daniela , Panhelainen Anne , Møller Holger J. , Brockmann Kathrin , Romero-Ramos Marina 
Published: May 2020

ABSTRACTParkinson’s disease is a neurodegenerative disorder with a significant immune component. Numerous studies have reported alterations on immune biomarkers in CSF and serum that associate with symptoms in PD patients. However, it is unclear, which specific immune cells are responsible for the changes in those biomarkers; since most of these cytokines or chemokines, can be produced by a variety of immune cells, or even neurons or glia cells in the brain. Here, we investigate a monocyte/macrophage-specific biomarker: sCD163, the soluble form of the receptor CD163. Our data from tow cohorts show that the CSF-sCD163 increases as the disease progresses, together with a correlated increase in PD-specific as well as neurodegeneration-associated disease biomarkers: alpha-synuclein, tau, and phosphorylated-Tau. Moreover, CSF-sCD163 levels were inversely correlated to the cognitive scores MMSE and MOCA, with higher sCD163 indicating lower cognitive capacity. sCD163 was also increased in serum, although only in female PD patients, suggesting a gender distinctive monocyte-related immune response. CSF-sCD163 also correlated with molecules associated with endothelial cells, tissue infiltration, and activation of B cells and T cells in the PD patients. This suggests activation of both the adaptive and the innate immune system along with recruitment of lymphocytes and monocytes to sites of inflammation in the brain. In serum, sCD163 was associated with pro-inflammatory cytokines and acute phase proteins, suggesting a relation to chronic systemic inflammation. Interestingly, our in vitro study suggests that sCD 163 might enhance alpha-synuclein uptake by myeloid cells without direct binding, thus participating in the clearance of alpha-synuclein. Accordingly, our data supports sCD163 as a potential cognition-related biomarker in PD and corroborates a role for monocytes both in peripheral and brain immune responses that could have direct consequences in the handling of alpha-synuclein.
Differential Levels of Telomeric Oxidized Bases and TERRA Transcripts in Childhood Autism
Authors: Eftekhar Mohammad , Panahi Yasin , Moghaddam Fahimeh Salasar , Eskandari Mohammad Reza , Pezeshk Hamid , Pedram Mehrdad 
Published: May 2020

AbstractThe underlying molecular mechanisms responsible for the etiology of autism and its sex-biased prevalence remain largely elusive. Abnormally shortened telomeres have recently been associated with autism. We have previously shown that children with non-syndromic autism exhibit a sexually dimorphic pattern of relative telomere length (RTL). Only male children with autism have significantly shorter RTLs than the healthy controls and paired siblings. Autistic females have substantially longer RTLs than autistic males. Aberrantly high levels of oxidative stress plays a fundamental role in the pathophysiology of autism, and telomeres are thought to be susceptible to oxidative damage due to their high guanine-repeat content. Employing a quantitative PCR (qPCR)-based method, telomeric oxidized base lesions were measured using genomic DNA extracted from saliva samples, and levels of telomeric RNA transcripts know as TERRA were evaluated using reverse transcriptase qPCR technique. Our data show that the autistic children exhibit substantially higher levels of oxidative base lesions at their telomeres than the healthy controls and paired siblings. Intriguingly, despite having significantly longer RTLs, female children with autism have even higher levels of telomeric oxidized bases than their male counterparts. Furthermore, despite having significantly shorter RTLs, the male children with autism exhibit lower levels of TERRA expression from the short arms of chromosomes 17 and X/P compared to their individually-matched healthy controls. These findings open a fresh angle into autism. Abnormal TL and high levels of telomeric oxidized bases may serve as biomarkers for childhood autism.