bluuebunny/crossref_metadata_2025_split huggingface dataset:
Renal Involvement in Patients with COVID-19 Pneumonia and Outcomes After Stem Cell Nebulization
Authors: Torres Zambrano Gina M. , Villegas Valverde Carlos A. , Hernández Antonio Bencomo , Hadi Lobna Abdel , Rivero Rene Antonio , Carmenate Yendry Ventura 
Published: December 2020

ABSTRACTBackgroundThe COVID-19 pandemic presented an unprecedented challenge to identify effective drugs for prevention and treatment.ObjectiveTo characterize acute renal injury (AKI) in patients with COVID-19 and their relation with clinical outcomes within the framework of the SENTAD COVID clinical trial at the Abu Dhabi Stem Cells Center.MethodsAbu Dhabi Stem Cell Center (ADSCC) proposed a prospective clinical trial nebulization treatment with autologous stem cells (Non-Hematopoietic Peripheral Blood Stem Cells (NHPBSC)), at Abu Dhabi hospitals.Participants20 treated patients were compared with 23 not treated patients. Both groups received COVID 19 standard treatment.OutcomesAfter the results were collected, this study was created to determine the impact of the disease on the renal function and the efficacy of the therapy on patient’s outcomes.ResultsOne third of the critical patients studied suffered kidney failure. Patients in the treated group showed a favorable tendency to improve in contrast to those in the control group. Less patients from group A suffered from sepsis in comparison with the group B (25% vs 65%), HR=0.38, (95% Confidence Interval: 0.16 – 0.86), *p=0.0212. These results suggested a NNT=2.5. An improvement in lymphocyte count, CRP, and shorter hospital stay after treatment was evidenced, which led to less superinfection and sepsis in the treated group.ConclusionsThe proposed anti-inflammatory effect of the stem cells, offers a great promise for managing the illness, emerging as a crucial adjuvant tool in promoting healing and early recovery in severe COVID-19 infections and other supportive treatments.ARTICLE SUMMARYOur study had several strengths and limitation:
It was a randomized trial.The treatment showed a positive result, providing evidence that this intervention is effective in routine practice.We found fewer complications related to prolonged hospital stay in the treated group.The is the small number of participants.It was carried out in 4 different hospitals, each with different criteria for the selection of the initial empirical antimicrobials, which can cause multiple resistant germs.
Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months
Authors: Peck Fleming C. , Gabard-Durnam Laurel J. , Wilkinson Carol L. , Bosl William , Tager-Flusberg Helen , Nelson Charles A. 
Published: December 2020

AbstractEarly identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved outcomes. Use of electroencephalography (EEG) in infants has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment in ASD, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly within the first postnatal year, so altered neural substrates either during or after the first year may serve as early, accurate indicators of later autism diagnosis. Using longitudinal EEG data collected during a passive phoneme task in infants with high familial risk for ASD, we compared predictive accuracy at 6-months (during phoneme learning) versus 12-months (after phoneme learning). Samples at both ages were matched in size and diagnoses (n=14 with later ASD; n= 40 without ASD). Using Pearson correlation feature selection and support vector machine with radial basis function classifier, 100% predictive diagnostic accuracy was observed at both ages. However, predictive features selected at the two ages differed and came from different scalp locations. We also report that performance across multiple machine learning algorithms was highly variable and declined when the 12-month sample size and behavioral heterogeneity was increased. These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes in order to develop clinically relevant classification algorithms.
SARS-CoV-2 shifting transmission dynamics and hidden reservoirs limited the efficacy of public health interventions in Italy
Authors: Giovanetti Marta , Cella Eleonora , Benedetti Francesca , Magalis Brittany Rife , Fonseca Vagner , Fabris Silvia , Campisi Giovanni , Ciccozzi Alessandra , Angeletti Silvia , Borsetti Alessandra , Tambone Vittoradolfo , Sagnelli Caterina , Pascarella Stefano , Riva Alberto , Ceccarelli Giancarlo , Marcello Alessandro , Azarian Taj , Wilkinson Eduan , de Oliveira Tulio , Junior Alcantara Luiz Carlos , Cauda Roberto , Caruso Arnaldo , Dean Natalie E , Browne Cameron , Lourenco Jose , Salemi Marco , Zella Davide , Ciccozzi Massimo 
Published: December 2020

AbstractWe investigated SARS-CoV-2 transmission dynamics in Italy, one of the countries hit hardest by the pandemic, using phylodynamic analysis of viral genetic and epidemiological data. We observed the co-circulation of at least 13 different SARS-CoV-2 lineages over time, which were linked to multiple importations and characterized by large transmission clusters concomitant with a high number of infections. Subsequent implementation of a three-phase nationwide lockdown strategy greatly reduced infection numbers and hospitalizations. Yet we present evidence of sustained viral spread among sporadic clusters acting as “hidden reservoirs” during summer 2020. Mathematical modelling shows that increased mobility among residents eventually catalyzed the coalescence of such clusters, thus driving up the number of infections and initiating a new epidemic wave. Our results suggest that the efficacy of public health interventions is, ultimately, limited by the size and structure of epidemic reservoirs, which may warrant prioritization during vaccine deployment.
Antigen-based rapid diagnostic testing or alternatives for diagnosis of symptomatic COVID-19: A simulation-based net benefit analysis
Authors: Kendall Emily A , Arinaminpathy Nimalan , Sacks Jilian A , Manabe Yukari C , Dittrich Sabine , Schumacher Samuel G , Dowdy David W 
Published: December 2020

AbstractBackgroundSARS-CoV-2 antigen-detection rapid diagnostic tests (Ag-RDTs) can diagnose COVID-19 rapidly and at low cost, but their lower sensitivity than nucleic acid amplification testing (NAAT) has limited clinical adoption.MethodsWe compared Ag-RDT, NAAT, and clinical judgment alone for diagnosing symptomatic COVID-19. We considered an outpatient setting (10% COVID-19 prevalence among the patients tested, 3-day NAAT turnaround) and a hospital setting (40% prevalence, 24-hour NAAT turnaround). We simulated transmission from cases and contacts and relationships between time, viral burden, transmission, and case detection. We compared diagnostic approaches using a measure of net benefit that incorporated both clinical and public health benefits and harms of intervention.ResultsIn the outpatient setting, we estimated that using Ag-RDT instead of NAAT to test 200 individuals could have a net benefit equivalent to preventing all symptomatic transmission from one person with COVID-19 (one “transmission-equivalent”). In the hospital setting, net benefit analysis favored NAAT, and testing 25 patients with NAAT instead of Ag-RDT achieved one “transmission-equivalent” of incremental benefit. In both settings, Ag-RDT was preferred to NAAT if NAAT turnaround time exceeded two days. Both Ag-RDT and NAAT provided greater net benefit than management based on clinical judgment alone, unless intervention carried minimal harm and was provided equally regardless of diagnostic approach.ConclusionsFor diagnosis of symptomatic COVID-19, the speed of diagnosis with Ag-RDT is likely to outweigh its lower accuracy compared to NAAT wherever NAAT turnaround times are two days or longer. This advantage may be even greater if Ag-RDTs are also less expensive.
Competing Risks in Patients with Primary Prevention Implantable Cardioverters Defibrillators: Global Electrical Heterogeneity and Clinical Outcomes (GEHCO) Study
Authors: Waks Jonathan W. , Haq Kazi T. , Tompkins Christine , Rogers Albert J. , Ehdaie Ashkan , Bender Aron , Minnier Jessica , Dalouk Khidir , Howell Stacey , Peiris Achille , Raitt Merritt , Narayan Sanjiv M. , Chugh Sumeet S. , Tereshchenko Larisa G. 
Published: December 2020

AbstractBackgroundGlobal electrical heterogeneity (GEH) is associated with sudden cardiac death in the general population.ObjectiveTo investigate whether GEH is associated with sustained ventricular tachycardia (VT)/ventricular fibrillation (VF) leading to appropriate implantable cardioverter-defibrillator (ICD) therapies in systolic heart failure (HF) patients with primary prevention ICDs.MethodsWe conducted a multicenter retrospective cohort study. GEH was measured by spatial ventricular gradient (SVG) direction and magnitude, QRS-T angle, and sum absolute QRST integral (SAIQRST) on pre-implant 12-lead ECGs. Survival analysis using cause-specific hazard functions compared the strength of associations with two competing outcomes: sustained VT/VF leading to appropriate ICD therapies and all-cause death without appropriate ICD therapies.ResultsWe analyzed data from 2,668 patients (age 63±12y; 23% female; 78% white; 43% nonischemic cardiomyopathy (NICM); left ventricular ejection fraction 28±11% from 6 academic medical centers). After adjustment for demographic, clinical, device, and traditional ECG characteristics, SVG elevation (Hazard Ratio (HR) per 1 standard deviation (SD) 1.14 (95% CI 1.04-1.25);P=0.004), SVG azimuth (HR 1.12(1.01-1.24);P=0.039); SVG magnitude (HR per 1 SD 0.75 (0.66-0.85);P<0.0001), and QRS-T angle (HR per 1 SD 1.21 (95% CI 1.08-1.36);P=0.001) were associated with appropriate ICD therapies. The SVG azimuth was also associated with a lower competing risk of death (Pdifference=0.007): HR 0.91(0.82-1.02);P=0.095. SAIQRST had different associations in ischemic [HR 1.29(1.04-1.60)] and NICM [HR 0.78(0.62-0.96);Pinteraction=0.022].ConclusionIn patients with primary prevention ICDs, GEH is independently associated with appropriate ICD therapies. The SVG vector points in distinctly different directions in patients with two competing outcomes.Clinical Trial RegistrationURL:www.clinicaltrials.govUnique identifier:NCT03210883.
Investigating the Effectiveness of Breast Cancer Supplemental Screening Considering Radiologists’ Bias
Authors: Molani Sevda , Madadi Mahboubeh , Williams Donna L. 
Published: December 2020

AbstractBreast density is known to increase breast cancer risk and decrease mammography screening sensitivity. Breast density notification laws (enacted in 38 states as of September 2020), require physicians to inform women with high breast density of these potential risks. The laws usually require healthcare providers to notify patients of the possibility of using more sensitive supplemental screening tests (e.g., ultrasound). Since the enactment of the laws, there have been controversial debates over i) their implementations due to the potential radiologists bias in breast density classification of mammogram images and ii) the necessity of supplemental screenings for all patients with high breast density. In this study, we formulate a finite-horizon, discrete-time partially observable Markov chain (POMC) to investigate the effectiveness of supplemental screening and the impact of radiologists’ bias on patients’ outcomes. We consider the conditional probability of eventually detecting breast cancer in early states given that the patient develops breast cancer in her lifetime as the primary and the expected number of supplemental tests as the secondary patient’s outcome. Our results indicate that referring patients to a supplemental test solely based on their breast density may not necessarily improve their health outcomes and other risk factors need to be considered when making such referrals. Additionally, average-skilled radiologists’ performances are shown to be comparable with the performance of a perfect radiologist (i.e., 100% accuracy in breast density classification). However, a significant bias in breast density classification (i.e., consistent upgrading or downgrading of breast density classes) can negatively impact a patient’s health outcomes.
Immune-Based Prediction of COVID-19 Severity and Chronicity Decoded Using Machine Learning
Authors: Patterson Bruce K , Guevara-Coto Jose , Yogendra Ram , Francisco Edgar , Long Emily , Pise Amruta , Rodrigues Hallison , Parikh Purvi , Mora Javier , Mora-Rodríguez Rodrigo A 
Published: December 2020

ABSTRACTIndividuals with systemic symptoms long after COVID-19 has cleared represent approximately ~10% of all COVID-19 infected individuals. Here we present a bioinformatics approach to predict and model the phases of COVID so that effective treatment strategies can be devised and monitored. We investigated 144 individuals including normal individuals and patients spanning the COVID-19 disease continuum. We collected plasma and isolated PBMCs from 29 normal individuals, 26 individuals with mild-moderate COVID-19, 25 individuals with severe COVID-19, and 64 individuals with Chronic COVID-19 symptoms. Immune subset profiling and a 14-plex cytokine panel were run on all patients. Data was analyzed using machine learning methods to predict and distinguish the groups from each other.Using a multi-class deep neural network classifier to better fit our prediction model, we recapitulated a 100% precision, 100% recall and F1 score of 1 on the test set. Moreover, a first score specific for the chronic COVID-19 patients was defined asS1 = (IFN-γ + IL-2)/ CCL4-MIP-1β. Second, a score specific for the severe COVID-19 patients was defined asS2 = (10*IL-10 + IL-6) - (IL-2 + IL-8). Severe cases are characterized by excessive inflammation and dysregulated T cell activation, recruitment, and counteracting activities. While chronic patients are characterized by a profile able to induce the activation of effector T cells with pro-inflammatory properties and the capacity of generating an effective immune response to eliminate the virus but without the proper recruitment signals to attract activated T cells.SummaryImmunologic Modeling of Severity and Chronicity of COVID-19
Colorectal cancer screening within colonoscopy capacity constraints: can FIT-based programmes save more lives by trading-off more sensitive test cut-offs against longer screening intervals?
Authors: McFerran Ethna , O’Mahony James F. , Naber Steffie , Sharp Linda , Zauber Ann G. , Lansdorp-Vogelaar Iris , Kee Frank 
Published: December 2020

ABSTRACTIntroductionColorectal cancer (CRC) prevention programmes using faecal immunochemical testing (FIT) as the primary screen typically rely on colonoscopy for secondary and surveillance testing. Colonoscopy capacity is an important constraint, limiting the number of primary tests offered. Many European programmes lack sufficient colonoscopy capacity to provide optimal screening intensity regarding screening age ranges, intervals and FIT cut-offs. It is currently unclear how to optimise programmes within colonoscopy capacity constraints.DesignThe MISCAN-Colon microsimulation model was used to determine if more effective CRC screening programmes can be identified within existing colonoscopy capacity. The model assessed 525 strategies of varying screening intervals, age ranges and FIT cut-offs, including previously unevaluated 4 and 5 year screening intervals. These strategies were compared with policy decisions taken in Ireland to provide CRC screening within available colonoscopy capacity. Outcomes estimated net costs, quality-adjusted-life-years and required colonoscopy numbers. The optimal strategies within finite colonoscopy capacity constraints were identified.ResultsCombining a reduced FIT cut-off of 10 µg Hb/g, an extended screening interval of 4 years and an age range of 60-72 years requires 6% fewer colonoscopies, reduces net costs by 23% while preventing 15% more CRC deaths and saving 16% more QALYs relative to current policy.ConclusionPreviously overlooked longer screening intervals may balance optimal cancer prevention with finite colonoscopy capacity constraints. Simple changes to screening configurations could save lives, reduce costs, and relieve colonoscopy capacity pressures. These findings are directly relevant to CRC screening programmes across Europe that employ FIT-based testing and face colonoscopy capacity constraints.
Interventions to prevent neonatal healthcare-associated infections in low-resource settings: a systematic review
Authors: Fitzgerald Felicity C. , Zingg Walter , Chimhini Gwendoline , Chimhuya Simbarashe , Wittmann Stefanie , Brotherton Helen , Olaru Ioana D. , Neal Samuel R. , Russell Neal , Araujo da Silva André Ricardo , Sharland Mike , Seale Anna C , Cotton Mark F , Coffin Susan , Dramowski Angela 
Published: December 2020

AbstractBackgroundClinically suspected and laboratory-confirmed bloodstream infections are frequent causes of morbidity and mortality during neonatal care. The most effective infection prevention and control (IPC) interventions for neonates in low-and-middle-income countries (LMIC) are unknown.AimTo identify effective interventions in the prevention of hospital-acquired bloodstream infections in LMIC neonatal units.MethodsMedline, PUBMED, The Cochrane Database of Systematic Reviews, EMBASE, and PsychInfo (January 2003 – October 2020) were searched to identify studies reporting single or bundled interventions for prevention of bloodstream infections in LMIC neonatal units.ResultsOur initial search identified 5206 articles; following application of filters, 27 publications met the inclusion and ICROMS assessment criteria and were summarised in the final analysis. No studies were carried out in low-income countries, only one in sub-Saharan Africa and just two in multiple countries. Of the 18 single intervention studies, most targeted skin (n=4) and gastrointestinal mucosal integrity (n=5). Whereas emollient therapy and lactoferrin achieved significant reductions in proven neonatal infection, glutamine and mixed probiotics showed no benefit. Chlorhexidine gluconate for cord care and kangaroo mother care reduced infection in individual single-centre studies. Of the nine studies evaluating bundles, most focused on prevention of device-associated infections and achieved significant reductions in catheter- and ventilator-associated infections.ConclusionThere is a limited evidence-base for the effectiveness of IPC interventions in LMIC neonatal units; bundled interventions targeting device-associated infections were most effective. More multi-site studies with robust study designs are needed to inform IPC intervention strategies in low-resource neonatal units.Sources of SupportFF is supported by the Academy of Medical Sciences, the funders of the Starter Grant for Clinical Lecturers scheme and UCL Great Ormond Street NIHR Biomedical Research Centre. AD is supported by the Fogarty International Center of the National Institutes of Health, Emerging Global Leader Award Number K43-TW010682.
Framework for enhancing the estimation of model parameters for data with a high level of uncertainty
Authors: Libotte Gustavo B. , dos Anjos Lucas , Almeida Regina C. , Malta Sandra M. C. , Silva Renato S. 
Published: December 2020

AbstractReliable data is essential to obtain adequate simulations for forecasting the dynamics of epidemics. In this context, several political, economic, and social factors may cause inconsistencies in the reported data, which reflect the capacity for realistic simulations and predictions. In the case of COVID-19, for example, such uncertainties are mainly motivated by large-scale underreporting of cases due to reduced testing capacity in some locations. In order to mitigate the effects of noise in the data used to estimate parameters of models, we propose strategies capable of improving the ability to predict the spread of the diseases. Using a compartmental model in a COVID-19 study case, we show that the regularization of data by means of Gaussian Process Regression can reduce the variability of successive forecasts, improving predictive ability. We also present the advantages of adopting parameters of compartmental models that vary over time, in detriment to the usual approach with constant values.
Assessing Global Covid-19 Cases Data through Compositional Data Analysis(CoDa)
Authors: Braga Luis P.V. , Feigenbaum Dina 
Published: December 2020

AbstractBackgroundCovid-19 cases data pose an enormous challenge to any analysis. The evaluation of such a global pandemic requires matching reports that follow different procedures and even overcoming some countries’ censorship that restricts publications.MethodsThis work proposes a methodology that could assist future studies. Compositional Data Analysis (CoDa) is proposed as the proper approach as Covid-19 cases data is compositional in nature. Under this methodology, for each country three attributes were selected: cumulative number of deaths (D); cumulative number of recovered patients(R); present number of patients (A).ResultsAfter the operation called closure, with c=1, a ternary diagram and Log-Ratio plots, as well as, compositional statistics are presented. Cluster analysis is then applied, splitting the countries into discrete groups.ConclusionsThis methodology can also be applied to other data sets such as countries, cities, provinces or districts in order to help authorities and governmental agencies to improve their actions to fight against a pandemic.
Spaced training enhances memory and prefrontal ensemble stability in mice
Authors: Glas Annet , Hübener Mark , Bonhoeffer Tobias , Goltstein Pieter M. 
Published: December 2020

SummaryMemory is substantially improved when learning is distributed over time, an effect called “spacing effect”. So far it has not been studied how spaced learning affects neuronal ensembles presumably underlying memory. In the present study, we investigate whether trial spacing increases the stability or size of neuronal ensembles. Mice were trained in the “everyday memory” task, an appetitive, naturalistic, delayed matching-to-place task. Spacing trials by 60 minutes produced more robust memories than training with shorter or longer intervals. c-Fos labeling and chemogenetic inactivation established the necessity of the dorsomedial prefrontal cortex (dmPFC) for successful memory storage.In vivocalcium imaging of excitatory dmPFC neurons revealed that longer trial spacing increased the similarity of the population activity pattern on subsequent encoding trials and upon retrieval. Conversely, trial spacing did not affect the size of the total neuronal ensemble or the size of subpopulations dedicated to specific task-related behaviors and events. Thus, spaced learning promotes reactivation of prefrontal neuronal ensembles processing episodic-like memories.