Prohormones in the early diagnosis of cardiac syncope

Background--The early detection of cardiac syncope is challenging. We aimed to evaluate the diagnostic value of 4 novel prohormones, quantifying different neurohumoral pathways, possibly involved in the pathophysiological features of cardiac syncope: midregional-pro-A-type natriuretic peptide (MRproANP), C-terminal proendothelin 1, copeptin, and midregionalproadrenomedullin. Methods and Results--We prospectively enrolled unselected patients presenting with syncope to the emergency department (ED) in a diagnostic multicenter study. ED probability of cardiac syncope was quantified by the treating ED physician using a visual analogue scale. Prohormones were measured in a blinded manner. Two independent cardiologists adjudicated the final diagnosis on the basis of all clinical information, including 1-year follow-up. Among 689 patients, cardiac syncope was the adjudicated final diagnosis in 125 (18%). Plasma concentrations of MRproANP, C-terminal proendothelin 1, copeptin, and midregional-proadrenomedullin were all significantly higher in patients with cardiac syncope compared with patients with other causes (P < 0.001). The diagnostic accuracies for cardiac syncope, as quantified by the area under the curve, were 0.80 (95% confidence interval [CI], 0.76-0.84), 0.69 (95% CI, 0.64-0.74), 0.58 (95% CI, 0.52-0.63), and 0.68 (95% CI, 0.63-0.73), respectively. In conjunction with the ED probability (0.86; 95% CI, 0.82-0.90), MRproANP, but not the other prohormone, improved the area under the curve to 0.90 (95% CI, 0.87-0.93), which was significantly higher than for the ED probability alone (P=0.003). An algorithm to rule out cardiac syncope combining an MRproANP level of < 77 pmol/L and an ED probability of < 20% had a sensitivity and a negative predictive value of 99%. Conclusions--The use of MRproANP significantly improves the early detection of cardiac syncope among unselected patients presenting to the ED with syncope

A large annotated medical image dataset for the development and evaluation of segmentation algorithms

Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training

Dissociable Components of Cognitive Control: An Event-Related Potential (ERP) Study of Response Inhibition and Interference Suppression

Background: Cognitive control refers to the ability to selectively attend and respond to task-relevant events while resisting interference from distracting stimuli or prepotent automatic responses. The current study aimed to determine whether interference suppression and response inhibition are separable component processes of cognitive control. Methodology/Principal Findings: Fourteen young adults completed a hybrid Go/Nogo flanker task and continuous EEG data were recorded concurrently. The incongruous flanker condition (that required interference suppression) elicited a more centrally distributed topography with a later N2 peak than the Nogo condition (that required response inhibition). Conclusions/Significance: These results provide evidence for the dissociability of interference suppression and response inhibition, indicating that taxonomy of inhibition is warranted with the integration of research evidence from neuroscience

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts

Warmth and competence perceptions of key protagonists are associated with containment measures during the COVID-19 pandemic: Evidence from 35 countries

It is crucial to understand why people comply with measures to contain viruses and their effects during pandemics. We provide evidence from 35 countries (Ntotal = 12,553) from 6 continents during the COVID-19 pandemic (between 2021 and 2022) obtained via cross-sectional surveys that the social perception of key protagonists on two basic dimensions—warmth and competence—plays a crucial role in shaping pandemic-related behaviors. Firstly, when asked in an open question format, heads of state, physicians, and protest movements were universally identified as key protagonists across countries. Secondly, multiple-group confirmatory factor analyses revealed that warmth and competence perceptions of these and other protagonists differed significantly within and between countries. Thirdly, internal meta-analyses showed that warmth and competence perceptions of heads of state, physicians, and protest movements were associated with support and opposition intentions, containment and prevention behaviors, as well as vaccination uptake. Our results have important implications for designing effective interventions to motivate desirable health outcomes and coping with future health crises and other global challenges.publishedVersio

Measurement of the polar-angle distribution of leptons from W boson decay as a function of the W transverse momentum in proton-antiproton collisions at sqrt{s}=1.8 TeV

We present a measurement of the coefficient alpha_2 of the leptonic polar-angle distribution from W boson decays, as a function of the W transverse momentum. The measurement uses an 80+/-4 pb^{-1} sample of proton-antiproton collisions at sqrt{s}=1.8 TeV collected by the CDF detector and includes data from both the W->e+nu and W->mu+nu decay channels. We fit the W boson transverse mass distribution to a set of templates from a Monte Carlo event generator and detector simulation in several ranges of the W transverse momentum. The measurement agrees with the Standard Model expectation, whereby the ratio of longitudinally to transversely polarized W bosons, in the Collins-Soper W rest frame, increases with the W transverse momentum at a rate of approximately 15% per 10 GeV/c.Comment: 47 pages, 16 figures, submitted to Physical Review

Measurement of the Ratio of b Quark Production Cross Sections in Antiproton-Proton Collisions at 630 GeV and 1800 GeV

We report a measurement of the ratio of the bottom quark production cross section in antiproton-proton collisions at 630 GeV to 1800 GeV using bottom quarks with transverse momenta greater than 10.75 GeV identified through their semileptonic decays and long lifetimes. The measured ratio sigma(630)/sigma(1800) = 0.171 +/- .024 +/- .012 is in good agreement with next-to-leading order (NLO) quantum chromodynamics (QCD)