bluuebunny/crossref_metadata_2025_split huggingface dataset:
Survival analysis in breast cancer using proteomic data from four independent datasets
Authors: Ősz Ágnes , Lánczky András , Győrffy Balázs 
Published: December 2020

ABSTRACTBreast cancer clinical treatment selection is based on the immunohistochemical determination of four proteins: ESR1, PGR, HER2, and MKI67. Our aim was to correlate immunohistochemical results to proteome-level technologies in measuring the expression of these markers. We also aimed to integrate available proteome-level breast cancer datasets to identify and validate new prognostic biomarker candidates.We identified protein studies involving breast cancer patient cohorts with published survival and proteomic information. Immunohistochemistry and proteomic technologies were compared using the Mann-Whitney test. Receiver operating characteristics (ROC) curves were generated to validate discriminative power. Cox regression and Kaplan-Meier survival analysiss were calculated to assess prognostic power. The false discovery rate was computed to correct for multiple hypothesis testing.The complete database contains protein expression data and survival information from four independent cohorts for 1,229 breast cancer patients. In all four studies combined, a total of 7,342 unique proteins were identified, and 1,417 of these were identified in at least three datasets. ESR1, PGR, HER2 protein expression levels determined by RPPA or LC-MS/MS methods showed a significant correlation with the levels determined by immunohistochemistry (p<0.0001). PGR and ESR1 levels showed a moderate correlation (correlation coefficient=0.17, p=0.0399). A panel of candidate proteins, including apoptosis-related proteins (BCL2,), adhesion markers (CDH1, CLDN3, CLDN7) and basal markers (cytokeratins), were validated as prognostic biomarkers. We expanded our established web tool to validate survival-associated biomarkers to include the proteomic datasets analyzed in this study (https://kmplot.com/analysis/).Large proteomic studies now provide sufficient data enabling the validation and ranking of new protein biomarkers.
Mental health problems in the general population during and after the first lockdown phase due to the SARS-Cov-2 pandemic: Rapid review of multi-wave studies
Authors: Richter Dirk , Riedel-Heller Steffi , Zuercher Simeon 
Published: December 2020

AbstractAimsThe SARS-Cov-2 pandemic and the lockdown response are assumed to have increased mental health problems in general populations compared to pre-pandemic times. The aim of this paper is to review studies on the course of mental health problems during and after the first lockdown phase.MethodsWe conducted a rapid review of multi-wave studies in general populations with time points during and after the first lockdown phase. Repeated cross-sectional and longitudinal studies that utilized validated instruments were included. The main outcome was whether indicators of mental health problems have changed during and after the first lockdown phase. The study was registered with PROSPERO No. CRD42020218640.Results23 studies with 56 indicators were included in the qualitative review. Studies that reported data from pre-pandemic assessments through lockdown indicated an increase in mental health problems. During lockdown no uniform trend could be identified. After lockdown mental health problems decreased slightly.ConclusionsAs mental health care utilization indicators and data on suicides do not suggest an increase in demand during the first lockdown phase, we regard the increase in mental health problems as general distress that is to be expected during a global health crisis. Several methodological, pandemic-related, response-related and health policy-related factors need to be considered when trying to gain a broader perspective on the impact of the first wave of the pandemic and the first phase of lockdown on general populations’ mental health.
Novel Approach for Monte Carlo Simulation of the new COVID-19 Spread Dynamics
Authors: Maltezos Stavros , Georgakopoulou Angelika 
Published: December 2020

AbstractA Monte Carlo simulation in a novel approach is used for studying the problem of the outbreak and spread dynamics of the new COVID-19 pandemic in this work. In particular, our goal was to generate epidemiological data based on natural mechanism of transmission of this disease assuming random interactions of a large-finite number of individuals in very short distance ranges. In the simulation we also take into account the stochastic character of the individuals in a finite population and given densities of people. On the other hand, we include in the simulation the appropriate statistical distributions for the parameters characterizing this disease. An important outcome of our work, besides the generated epidemic curves, is the methodology of determining of the effective reproductive number during the main part of the new daily cases of the epidemic. Since this quantity constitutes a fundamental parameter of the SIR-based epidemic models, we also studied how it is affected by small variations of the incubation time and the crucial distance distributions, and furthermore, by the degree of quarantine measures. In addition, we compare our qualitative results with those of selected real epidemiological data.
The impact of the COVID-19 pandemic on mental health and well-being of people living with a long-term physical health condition: a qualitative study
Authors: Fisher A , Roberts A , McKinlay A.R. , Fancourt D , Burton A 
Published: December 2020

AbstractBackgroundThe COVID-19 pandemic and associated restrictions caused major global disruption. Individuals with long-term physical health conditions (LTCs) are at higher risk of severe illness and often subject to the strictest pandemic guidance, so may be disproportionally affected. The aim of this study was to qualitatively explore how living with a LTC during the COVID-19 pandemic affected people’s mental health and wellbeing.Sample and methods32 participants, mean age 57 (SD 13) years, 66% female and 72% white British, who reported having LTCS (most commonly cancer, respiratory conditions or cardiovascular diseases), participated in telephone/video call interviews based on a semi-structured topic guide. Key themes and subthemes were determined using deductive and inductive thematic analysis.ResultsThere were four overarching themes specific to living with a LTC. These were 1) high levels of fear and anxiety related to perceived consequences of catching COVID-19, 2) impact of shielding/isolation on mental health and wellbeing, 3) experience of healthcare during the pandemic and 4) anxiety created by uncertainty about the future. Fourteen subthemes were identified, including concerns about accessing essential supplies and the importance of social support. Individuals who lived alone and were advised to shield could be profoundly negatively affected.ConclusionsThis study found that there were a number of aspects of living with a LTC during the pandemic that had a significant impact on mental health and well-being. There should be focus on how best to provide practical and social support to people with LTCs during a pandemic, particularly if they have to shield or isolate.
Reduced frequency of perforin-positive CD8+ T cells in menstrual effluent of endometriosis patients compared to healthy controls
Authors: Schmitz Timo , Hoffmann Verena , Olliges Elisabeth , Bobinger Alina , Popovici Roxana , Nößner Elfriede , Meissner Karin 
Published: December 2020

AbstractBackgroundEndometriosis is widespread among women in reproductive age and quite commonly reduces life quality of those affected by symptoms like dysmenorrhea, dyspareunia or infertility. The scientific literature indicates many immunological changes like reduced cytotoxicity of natural killer cells or altered concentrations of cytokines and cell adhesion molecules. Frequently examined tissues are peripheral blood, endometrial tissue and peritoneal fluid. Yet, knowledge on immunological differences in menstrual effluent (ME) is scarce.Methods12 women with endometriosis and 11 healthy controls were included in this study. ME was collected using menstrual cups and venous blood samples (PB) were taken. Mononuclear cells were obtained from ME (MMC) and PB (PBMC) and analyzed using flow cytometry. Furthermore, concentrations of cell adhesion molecules (ICAM-I and VCAM-I) and cytokines (IL-6, IL-8 and TNF-α) were measured in ME and PB.ResultsCD8+ T cells obtained from ME were significantly less often perforin-positive in women with endometriosis compared to healthy controls. Additionally, plasma ICAM-I concentrations were significantly lower in the endometriosis group. A comparison between MMC and PBMC revealed that MMC contained significantly less T cells and more B cells. The CD4/CD8 ratio was significantly higher in MMC, and Tregs were significantly less frequently in MMC. In ME, T cells and NK cells expressed significantly more CD69. NK cells obtained from ME were predominantly CD56bright/CD16dim and had a lower frequency of perforin+ cells compared to PBMC NK cells. NKp46 was significantly more expressed on NK cells from PBMC.ConclusionCD8+ T cells obtained from the ME were significantly less perforin-positive in endometriosis patients indicating a reduced cytotoxic potential. MMC are distinctively different from PBMC and, thus, seem to be of endometrial origin.
Supervised Image Classification Algorithm Using Representative Spatial Texture Features: Application to COVID-19 Diagnosis Using CT Images
Authors: Belkhatir Zehor , Estépar Raúl San José , Tannenbaum Allen R. 
Published: December 2020

AbstractAlthough there is no universal definition for texture, the concept in various forms is nevertheless widely used and a key element of visual perception to analyze images in different fields. The present work’s main idea relies on the assumption that there exist representative samples, which we refer to as references as well, i.e., “good or bad” samples that represent a given dataset investigated in a particular data analysis problem. These representative samples need to be accounted for when designing predictive models with the aim of improving their performance. In particular, based on a selected subset of texture gray-level co-occurrence matrices (GLCMs) from the training cohort, we propose new representative spatial texture features, which we incorporate into a supervised image classification pipeline. The pipeline relies on the support vector machine (SVM) algorithm along with Bayesian optimization and the Wasserstein metric from optimal mass transport (OMT) theory. The selection of the best, “good and bad,” GLCM references is considered for each classification label and performed during the training phase of the SVM classifier using a Bayesian optimizer. We assume that sample fitness is defined based on closeness (in the sense of the Wasserstein metric) and high correlation (Spearman’s rank sense) with other samples in the same class. Moreover, the newly defined spatial texture features consist of the Wasserstein distance between the optimally selected references and the remaining samples. We assessed the performance of the proposed classification pipeline in diagnosing the corona virus disease 2019 (COVID-19) from computed tomographic (CT) images.
A Stochastic Compartmental Model for COVID-19
Authors: Sonnino Giorgio , Mora Fernando , Nardone Pasquale 
Published: December 2020

AbstractWe propose two stochastic models for the Coronavirus pandemic. The statistical properties of the models, in particular the correlation functions and the probability density function, have duly been computed. Our models, which generalises a model previously proposed and published in a specialised journal, take into account the adoption of the lockdown measures as well as the crucial role of the hospitals and Health Care Institutes. To accomplish this work we have analysed two scenarios: theSIS-model (Susceptible ⇒ Infectious ⇒ Susceptible) in presence of the lockdown measures and theSIS-model integrated with the action of the hospitals (always in presence of the lockdown measures). We show that in the case of the pureSIS-model, once the lockdown measures are removed, the Coronavirus will start growing again. However, in the second scenario, beyond a certain threshold of the hospital capacities, the Coronavirus is not only kept under control, but its capacity to spread tends to diminish in time. Therefore, the combined effect of the lockdown measures with the action of the hospitals and health Institutes is able to contain and dampen the spread of the SARS-CoV-2 epidemic. This result can be used during a period of time when the massive distribution of delivery of a limited number of vaccines in a given population is not yet feasible. By way of example, we analysed the data for USA and France where the intensities of the noise have been estimated by Statistical Mechanics. In particular, for USA we have analysed two possible hypotheses: USA is still subject to the first wave of infection by and USA is in the second (or third) wave of SARS-CoV-2 infection.The agreement between theoretical predictions and real data confirms the validity of our approach.
Household factors and the risk of severe COVID-like illness early in the US pandemic
Authors: Nash Denis , Qasmieh Saba , Robertson McKaylee , Rane Madhura , Zimba Rebecca , Kulkarni Sarah , Berry Amanda , You William , Mirzayi Chloe , Westmoreland Drew , Parcesepe Angela , Waldron Levi , Kochhar Shivani , Maroko Andrew R , Grov Christian ,   for the CHASING COVID Cohort Study Team
Published: December 2020

AbstractObjectiveTo investigate the role of children in the home and household crowding as risk factors for severe COVID-19 disease.MethodsWe used interview data from 6,831 U.S. adults screened for the Communities, Households and SARS/CoV-2 Epidemiology (CHASING) COVID Cohort Study in April 2020.ResultsIn logistic regression models, the adjusted odds ratio [aOR] of hospitalization due to COVID-19 for having (versus not having) children in the home was 10.5 (95% CI:5.7-19.1) among study participants living in multi-unit dwellings and 2.2 (95% CI:1.2-6.5) among those living in single unit dwellings. Among participants living in multi-unit dwellings, the aOR for COVID-19 hospitalization among participants with more than 4 persons in their household (versus 1 person) was 2.5 (95% CI:1.0-6.1), and 0.8 (95% CI:0.15-4.1) among those living in single unit dwellings.ConclusionEarly in the US SARS-CoV-2 pandemic, certain household exposures likely increased the risk of both SARS-CoV-2 acquisition and the risk of severe COVID-19 disease.
Circulating ACE2-expressing Exosomes Block SARS-CoV-2 Infection as an Innate Antiviral Mechanism
Authors: El-Shennawy Lamiaa , Hoffmann Andrew D. , Dashzeveg Nurmaa K. , Mehl Paul J. , Yu Zihao , Tokars Valerie L. , Nicolaescu Vlad , Ostiguin Carolina , Jia Yuzhi , Li Lin , Furlong Kevin , Mao Chengsheng , Wysocki Jan , Batlle Daniel , Hope Thomas J. , Shen Yang , Luo Yuan , Chae Young , Zhang Hui , Swaminathan Suchitra , Randall Glenn C. , Demonbreun Alexis R , Ison Michael G , Fang Deyu , Liu Huiping 
Published: December 2020

AbstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the coronavirus disease 2019 (COVID-19) with innate and adaptive immune response triggered in such patients by viral antigens. Both convalescent plasma and engineered high affinity human monoclonal antibodies have shown therapeutic potential to treat COVID-19. Whether additional antiviral soluble factors exist in peripheral blood remain understudied. Herein, we detected circulating exosomes that express the SARS-CoV-2 viral entry receptor angiotensin-converting enzyme 2 (ACE2) in plasma of both healthy donors and convalescent COVID-19 patients. We demonstrated that exosomal ACE2 competes with cellular ACE2 for neutralization of SARS-CoV-2 infection. ACE2-expressing (ACE2+) exosomes blocked the binding of the viral spike (S) protein RBD to ACE2+cells in a dose dependent manner, which was 400- to 700-fold more potent than that of vesicle-free recombinant human ACE2 extracellular domain protein (rhACE2). As a consequence, exosomal ACE2 prevented SARS-CoV-2 pseudotype virus tethering and infection of human host cells at a 50-150 fold higher efficacy than rhACE2. A similar antiviral activity of exosomal ACE2 was further demonstrated to block wild-type live SARS-CoV-2 infection. Of note, depletion of ACE2+exosomes from COVID-19 patient plasma impaired the ability to block SARS-CoV-2 RBD binding to host cells. Our data demonstrate that ACE2+exosomes can serve as a decoy therapeutic and a possible innate antiviral mechanism to block SARS-CoV-2 infection.
Quadratic Mutual Information estimation of mouse dLGN receptive fields reveals asymmetry between ON and OFF visual pathways
Authors: Mu Zhiguang , Nikolic Konstantin , Schultz Simon R. 
Published: December 2020

AbstractThe longstanding theory of “parallel processing” predicts that, except for a sign reversal, ON and OFF cells are driven by a similar pre-synaptic circuit and have similar visual field coverage, direction/orientation selectivity, visual acuity and other functional properties. However, recent experimental data challenges this view. Here we present an information theory based receptive field (RF) estimation method - quadratic mutual information (QMI) - applied to multi-electrode array electrophysiological recordings from the mouse dorsal lateral geniculate nucleus (dLGN). This estimation method provides more accurate RF estimates than the commonly used Spike-Triggered Average (STA) method, particularly in the presence of spatially correlated inputs. This improved efficiency allowed a larger number of RFs (285 vs 189 cells) to be extracted from a previously published dataset. Fitting a spatial-temporal Difference-of-Gaussians (ST-DoG) model to the RFs revealed that while the structural RF properties of ON and OFF cells are largely symmetric, there were some asymmetries apparent in the functional properties of ON and OFF visual processing streams - with OFF cells preferring higher spatial and temporal frequencies on average, and showing a greater degree of orientation selectivity.
Easy to interpret Coordinate Based Meta-Analysis of neuroimaging studies: Analysis of Brain Coordinates (ABC)
Authors: Tench CR , Tanasescu R , Constantinescu CS , Auer DP , Cottam WJ 
Published: December 2020

AbstractFunctional MRI and voxel-based morphometry (VBM) are important approaches to testing hypotheses in neuroscience, helping us to understand neurological disease, and brain function and development. However, they are technically challenging with no one optimal generalisable method, and the multiple popular techniques have been shown to produce different results. Furthermore, results may be sensitive to settings, such as smoothing or statistical thresholding, that can be difficult to optimise per hypothesis. It is useful, therefore, to be able to meta-analyse published results from such studies that tested a similar hypothesis potentially using different analysis methods, scanners, and protocols as well as different subjects. Coordinate based meta-analysis (CBMA) offers this using only commonly reported summary results. It is the aim of CBMA to find those results that indicate replicable effects across studies. However, just like the multiple analysis methods offered for neuroimaging, there are now multiple CBMA algorithms each with specific features and empirical parameters/assumptions. Results derived from CBMA are inevitably conditional on the algorithm used, so conclusions are clearer when the analysis approach is easy to understand. With this in mind a new CBMA method (Analysis of Brain Coordinates; ABC) is presented, with the aim of being easy to interpret by eliminating empirical assumptions where possible and by relating statistical thresholding directly to replication of effect.
A Method to Adjust a Prior Distribution in Bayesian Second-level fMRI Analysis
Authors: Han Hyemin 
Published: December 2020

ABSTRACTPrevious research has shown the potential value of Bayesian methods in fMRI (functional magnetic resonance imaging) analysis. For instance, the results from Bayes factor-applied second-level fMRI analysis showed a higher hit rate compared with frequentist second-level fMRI analysis, suggesting greater sensitivity. Although the method reported more positives as a result of the higher sensitivity, it was able to maintain a reasonable level of selectivity in term of the false positive rate. Moreover, employment of the multiple comparison correction method to update the default prior distribution significantly improved the performance of Bayesian second-level fMRI analysis. However, previous studies have utilized the default prior distribution and did not consider the nature of each individual study. Thus, in the present study, a method to adjust the Cauchy prior distribution based on a priori information, which can be acquired from the results of relevant previous studies, was proposed and tested. A Cauchy prior distribution was adjusted based on the contrast, noise strength, and proportion of true positives that were estimated from a meta-analysis of relevant previous studies. In the present study, both the simulated images and real contrast images from two previous studies were used to evaluate the performance of the proposed method. The results showed that the employment of the prior adjustment method resulted in improved performance of Bayesian second-level fMRI analysis.