Background: Thromboelastography (TEG) in venous air embolism (VAE) has been poorly studied. We induced coagulation abnormalities by VAE in a rat model, assessed by TEG with and without mexiletine, a lidocaine analogue local anesthetic.
Methods: Twenty-three Sprague Dawley rats instrumented under isoflurane anesthesia and allowed to recover five days prior to the experiments were randomized into three experimental groups: 1) VAE (n = 6); 2) VAE and mexiletine (n = 9); and 3) normal saline (NS) alone (control group, n = 8). Blood samples were collected at baseline, one hour (h) and 24 h in all groups and analyzed by TEG to record the R, K, angle α and MA parameters.
Results: In Group 1, VAE decreased significantly R at 1 h (31%), K at 1 h (59%) and 24 h (34%); α increased significantly at 1 h (30%) and 24 h (22%). While R returned to baseline values within 24 h, K, MA and α did not. In group-2 (Mexiletine + VAE), K and R decreased at 1 h (48% and 29%, respectively) and at 24 h the changes were non-significant. Angle α increased at 1 h (28%) and remained increased for 24 h (25%). In group 3 (NS), only R was temporarily affected. MA increased significantly at 24 h only in the VAE alone group.
Conclusion: As expected, VAE produced a consistent and significant hypercoagulable response diagnosed/confirmed by TEG. Mexiletine prevented the MA elevation seen with VAE and corrected R and K time at 24 h, whereas angle α remained unchanged. Mexiletine seemed to attenuate the hypercoagulability associated with VAE in this experiment. These results may have potential clinical applications and deserve further investigation.
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http://dx.doi.org/10.28920/dhm47.4.228-232 | DOI Listing |
Diagnostics (Basel)
December 2024
Translational Imaging Centre, Houston Methodist Research Institute, Houston, TX 77030, USA.
Objective: To develop an unsupervised artificial intelligence algorithm for identifying and quantifying the presence of false lumen thrombosis (FL) after Frozen Elephant Trunk (FET) operation in computed tomography angiographic (CTA) images in an interdisciplinary approach.
Methods: CTA datasets were retrospectively collected from eight patients after FET operation for aortic dissection from a single center. Of those, five patients had a residual aortic dissection with partial false lumen thrombosis, and three patients had no false lumen or thrombosis.
Front Neuroinform
December 2024
Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.
Introduction: Modeling multi-channel electroencephalographic (EEG) time-series is a challenging tasks, even for the most recent deep learning approaches. Particularly, in this work, we targeted our efforts to the high-fidelity reconstruction of this type of data, as this is of key relevance for several applications such as classification, anomaly detection, automatic labeling, and brain-computer interfaces.
Methods: We analyzed the most recent works finding that high-fidelity reconstruction is seriously challenged by the complex dynamics of the EEG signals and the large inter-subject variability.
Mach Learn Clin Neuroimaging (2024)
December 2024
Stanford University, Stanford, CA 94305, USA.
Deep learning can help uncover patterns in resting-state functional Magnetic Resonance Imaging (rs-fMRI) associated with psychiatric disorders and personal traits. Yet the problem of interpreting deep learning findings is rarely more evident than in fMRI analyses, as the data is sensitive to scanning effects and inherently difficult to visualize. We propose a simple approach to mitigate these challenges grounded on sparsification and self-supervision.
View Article and Find Full Text PDFComput Biol Med
December 2024
Institute for Imaging, Data and Communications (IDCOM), School of Engineering, University of Edinburgh, Edinburgh, EH9 3FB, UK.
Artifacts are a common problem in physiological time series collected from intensive care units (ICU) and other settings. They affect the quality and reliability of clinical research and patient care. Manual annotation of artifacts is costly and time-consuming, rendering it impractical.
View Article and Find Full Text PDFSci Rep
December 2024
BAOBAB Unit, NeuroSpin center, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
Decoding states of consciousness from brain activity is a central challenge in neuroscience. Dynamic functional connectivity (dFC) allows the study of short-term temporal changes in functional connectivity (FC) between distributed brain areas. By clustering dFC matrices from resting-state fMRI, we previously described "brain patterns" that underlie different functional configurations of the brain at rest.
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