Preferences for Prenatal Tests for Cystic Fibrosis: A Discrete Choice Experiment to Compare the Views of Adult Patients, Carriers of Cystic Fibrosis and Health Professionals

As new technologies enable the development of non-invasive prenatal diagnosis (NIPD) for cystic fibrosis (CF), research examining stakeholder views is essential for the preparation of implementation strategies. Here, we compare the views of potential service users with those of health professionals who provide counselling for prenatal tests. A questionnaire incorporating a discrete choice experiment examined preferences for key attributes of NIPD and explored views on NIPD for CF. Adult patients (n = 92) and carriers of CF (n = 50) were recruited from one children’s and one adult NHS specialist CF centre. Health professionals (n = 70) were recruited via an e-mail invitation to relevant professional bodies. The key attribute affecting service user testing preferences was no miscarriage risk, while for health professionals, accuracy and early testing were important. The uptake of NIPD by service users was predicted to be high and includes couples that would currently decline invasive testing. Many service users (47%) and health professionals (55.2%) thought the availability of NIPD for CF would increase the pressure to undergo prenatal testing. Most service users (68.5%) thought NIPD for CF should be offered to all pregnant women, whereas more health professionals (68.2%) thought NIPD should be reserved for known carrier couples. The implications for clinical practice are discussed

Effect of COVID-19 Pandemic on Mechanical Thrombectomy for Acute Ischemic Stroke Treatment in United States

There is accumulating data suggesting that the ischemic stroke may be increased in patients with corona virus disease 2019 (COVID-19) due to hyper coagulopathy. An increase in acute ischemic stroke patients who require mechanical thrombectomy is to be expected particularly in regions with high rates of COVID-19

Attention Patterns Detection using Brain Computer Interfaces

The human brain provides a range of functions such as expressing emotions, controlling the rate of breathing, etc., and its study has attracted the interest of scientists for many years. As machine learning models become more sophisticated, and bio-metric data becomes more readily available through new non-invasive technologies, it becomes increasingly possible to gain access to interesting biometric data that could revolutionize Human-Computer Interaction. In this research, we propose a method to assess and quantify human attention levels and their effects on learning. In our study, we employ a brain computer interface (BCI) capable of detecting brain wave activity and displaying the corresponding electroencephalograms (EEG). We train recurrent neural networks (RNNS) to identify the type of activity an individual is performing

Cooperation and Contagion in Web-Based, Networked Public Goods Experiments

A longstanding idea in the literature on human cooperation is that cooperation should be reinforced when conditional cooperators are more likely to interact. In the context of social networks, this idea implies that cooperation should fare better in highly clustered networks such as cliques than in networks with low clustering such as random networks. To test this hypothesis, we conducted a series of web-based experiments, in which 24 individuals played a local public goods game arranged on one of five network topologies that varied between disconnected cliques and a random regular graph. In contrast with previous theoretical work, we found that network topology had no significant effect on average contributions. This result implies either that individuals are not conditional cooperators, or else that cooperation does not benefit from positive reinforcement between connected neighbors. We then tested both of these possibilities in two subsequent series of experiments in which artificial seed players were introduced, making either full or zero contributions. First, we found that although players did generally behave like conditional cooperators, they were as likely to decrease their contributions in response to low contributing neighbors as they were to increase their contributions in response to high contributing neighbors. Second, we found that positive effects of cooperation were contagious only to direct neighbors in the network. In total we report on 113 human subjects experiments, highlighting the speed, flexibility, and cost-effectiveness of web-based experiments over those conducted in physical labs

GyneScan: An Improved Online Paradigm for Screening of Ovarian Cancer via Tissue Characterization

Ovarian cancer is the fifth highest cause of cancer in women and the leading cause of death from gynecological cancers. Accurate diagnosis of ovarian cancer from acquired images is dependent on the expertise and experience of ultrasonographers or physicians, and is therefore, associated with inter observer variabilities. Computer Aided Diagnostic (CAD) techniques use a number of different data mining techniques to automatically predict the presence or absence of cancer, and therefore, are more reliable and accurate. A review of published literature in the field of CAD based ovarian cancer detection indicates that many studies use ultrasound images as the base for analysis. The key objective of this work is to propose an effective adjunct CAD technique called GyneScan for ovarian tumor detection in ultrasound images. In our proposed data mining framework, we extract several texture features based on first order statistics, Gray Level Co-occurrence Matrix and run length matrix. The significant features selected using t-test are then used to train and test several supervised learning based classifiers such as Probabilistic Neural Networks (PNN), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbor (KNN), and Naïve Bayes (NB). We evaluated the developed framework using 1300 benign and 1300 malignant images. Using 11 significant features in KNN/PNN classifiers, we were able to achieve 100% classification accuracy, sensitivity, specificity, and positive predictive value in detecting ovarian tumor. Even though more validation using larger databases would better establish the robustness of our technique, the preliminary results are promising. This technique could be used as a reliable adjunct method to existing imaging modalities to provide a more confident second opinion on the presence/absence of ovarian tumor

Weak Proinsulin Peptide–Major Histocompatibility Complexes Are Targeted in Autoimmune Diabetes in Mice

OBJECTIVE—Weak major histocompatibility complex (MHC) binding of self-peptides has been proposed as a mechanism that may contribute to autoimmunity by allowing for escape of autoreactive T-cells from the thymus. We examined the relationship between the MHC-binding characteristics of a β-cell antigen epitope and T-cell autoreactivity in a model of autoimmune diabetes

Observation and calibration strategies for large-scale multi-beam velocity-resolved mapping of the [CII] emission in the Orion molecular cloud

24 pags., 32 figs., 3 tabs.Context. The [CII] 158 μm far-infrared fine-structure line is one of the dominant cooling lines of the star-forming interstellar medium. Hence [CII] emission originates in and thus can be used to trace a range of ISM processes. Velocity-resolved large-scale mapping of [CII] in star-forming regions provides a unique perspective of the kinematics of these regions and their interactions with the exciting source of radiation. Aims. We explore the scientific applications of large-scale mapping of velocity-resolved [CII] observations. With the [CII] observations, we investigate the effect of stellar feedback on the ISM. We present the details of observation, calibration, and data reduction using a heterodyne array receiver mounted on an airborne observatory. Methods. A 1.15 square degree velocity-resolved map of the Orion molecular cloud centred on the bar region was observed using the German REceiver for Astronomy at Terahertz Frequencies (upGREAT) heterodyne receiver flying on board the Stratospheric Observatory for Infrared Astronomy. The data were acquired using the 14 pixels of the German REceiver for Astronomy at Terahertz Frequencies that were observed in an on-the-fly mapping mode. 2.4 million spectra were taken in total. These spectra were gridded into a three-dimensional cube with a spatial resolution of 14.1 arcseconds and a spectral resolution of 0.3 km s-1. Results. A square-degree [CII] map with a spectral resolution of 0.3 km s-1 is presented. The scientific potential of this data is summarized with discussion of mechanical and radiative stellar feedback, filament tracing using [CII], [CII] opacity effects, [CII] and carbon recombination lines, and [CII] interaction with the large molecular cloud. The data quality and calibration is discussed in detail, and new techniques are presented to mitigate the effects of unavoidable instrument deficiencies (e.g. baseline stability) and thus to improve the data quality. A comparison with a smaller [CII] map taken with the Herschel/Heterodyne Instrument for the Far-Infrared spectrometer is presented. Conclusions. Large-scale [CII] mapping provides new insight into the kinematics of the ISM. The interaction between massive stars and the ISM is probed through [CII] observations. Spectrally resolving the [CII] emission is necessary to probe the microphysics induced by the feedback of massive stars. We show that certain heterodyne instrument data quality issues can be resolved using a spline-based technique, and better data correction routines allow for more efficient observing strategies.This work is based on observations made with the NASA/DLR Stratospheric Observatory for Infrared Astronomy (SOFIA). SOFIA is jointly operated by the Universities Space Research Association, Inc.(USRA), under NASA contract NAS2-97001, and the Deutsches SOFIA Institut (DSI) under DLR contract 50 OK 0901 to the University of Stuttgart. This work is carried out within the Collaborative Research Centre 956, subproject [A4], funded by the Deutsche Forschungsgemeinschaft (DFG) – project ID 184018867. We thank the Spanish MICIU for funding support under grant AYA2017-85111-P

Role of imaging in rare COVID-19 vaccine multiorgan complications

As of September 18th, 2021, global casualties due to COVID-19 infections approach 200 million, several COVID-19 vaccines have been authorized to prevent COVID-19 infection and help mitigate the spread of the virus. Despite the vast majority having safely received vaccination against SARS-COV-2, the rare complications following COVID-19 vaccination have often been life-threatening or fatal. The mechanisms underlying (multi) organ complications are associated with COVID-19, either through direct viral damage or from host immune response (i.e., cytokine storm). The purpose of this manuscript is to review the role of imaging in identifying and elucidating multiorgan complications following SARS-COV-2 vaccination—making clear that, in any case, they represent a minute fraction of those in the general population who have been vaccinated. The authors are both staunch supporters of COVID-19 vaccination and vaccinated themselves as well