2022 Update of the consensus on the rational use of antithrombotics and thrombolytics in Veterinary Critical Care (CURATIVE) Domain 6: Defining rational use of thrombolytics

Objectives To systematically review available evidence and establish guidelines related to the use of thrombolytics for the management of small animals with suspected or confirmed thrombosis. Design PICO (Population, Intervention, Control, and Outcome) questions were formulated, and worksheets completed as part of a standardized and systematic literature evaluation. The population of interest included dogs and cats (considered separately) and arterial and venous thrombosis. The interventions assessed were the use of thrombolytics, compared to no thrombolytics, with or without anticoagulants or antiplatelet agents. Specific protocols for recombinant tissue plasminogen activator were also evaluated. Outcomes assessed included efficacy and safety. Relevant articles were categorized according to level of evidence, quality, and as to whether they supported, were neutral to, or opposed the PICO questions. Conclusions from the PICO worksheets were used to draft guidelines, which were subsequently refined via Delphi surveys undertaken by the Consensus on the Rational Use of Antithrombotics and Thrombolytics in Veterinary Critical Care (CURATIVE) working group. Results Fourteen PICO questions were developed, generating 14 guidelines. The majority of the literature addressing the PICO questions in dogs is experimental studies (level of evidence 3), thus providing insufficient evidence to determine if thrombolysis improves patient-centered outcomes. In cats, literature was more limited and often neutral to the PICO questions, precluding strong evidence-based recommendations for thrombolytic use. Rather, for both species, suggestions are made regarding considerations for when thrombolytic drugs may be considered, the combination of thrombolytics with anticoagulant or antiplatelet drugs, and the choice of thrombolytic agent. Conclusions Substantial additional research is needed to address the role of thrombolytics for the treatment of arterial and venous thrombosis in dogs and cats. Clinical trials with patient-centered outcomes will be most valuable for addressing knowledge gaps in the field

Assessment of microcirculation variables and endothelial glycocalyx using sidestream dark field videomicroscopy in anesthetized dogs undergoing cardiopulmonary bypass

IntroductionTo evaluate microcirculation and endothelial glycocalyx (eGC) variables using sidestream darkfield (SDF) videomicroscopy in canine cardiopulmonary bypass (CPB).MethodsDogs undergoing CPB for surgical correction of naturally-occurring cardiac disease were prospectively included. Variables collected included patient demographics, underlying cardiac disease, red blood cell flow (Flow), 4-25 μm vessel density (Density), absolute capillary blood volume (CBVabs), relative capillary blood volume (CBVrel) and eGC width assessed by perfused boundary region (PBR). Anesthetized healthy dogs were used as control. Microcirculation and eGC variables were compared at baseline under anesthesia (T0), on CPB prior to cross clamping (T1), after cross clamp removal following surgical correction (T2) and at surgical closure (T3).ResultsTwelve dogs were enrolled, including 10 with a complete dataset. Median Flow was 233.9, 79.9, 164.3, and 136.1 μm/s at T0, T1, T2, and T3, respectively, (p = 1.00). Median Density was 173.3, 118.4, 121.0 and 155.4 mm/mm2 at T0, T1, T2, and T3, respectively, (p = 1.00). Median CBVabs decreased over time: 7.4, 6.6, 4.8 and 4.7 103μm3 at T0, T1, T2, and T3, respectively, (p < 0.01). Median CBVrel increased over time: 1.1, 1.5,1.1, and 1.3 103μm3 at T0, T1, T2, and T3, respectively, (p < 0.001). Median PBR increased over time: 1.8, 2.1, 2.4, 2.1 μm at T0, T1, T2, and T3, respectively, (p < 0.001). Compared to control dogs (n = 8), CPB dogs had lower CBVabs at T0.ConclusionAlterations in eGC thickness and microvascular occur in dogs undergoing CPB for naturally-occurring cardiac disease

Somnotate: a probabilistic sleep stage classifier for studying vigilance state transitions

Electrophysiological recordings from freely behaving animals are a widespread and powerful mode of investigation in sleep research. These recordings generate large amounts of data that require sleep stage annotation (polysomnography), in which the data is parcellated according to three vigilance states: awake, rapid eye movement (REM) sleep, and non-REM (NREM) sleep. Manual and current computational annotation methods ignore intermediate states because the classification features become ambiguous, even though intermediate states contain important information regarding vigilance state dynamics. To address this problem, we have developed "Somnotate"—a probabilistic classifier based on a combination of linear discriminant analysis (LDA) with a hidden Markov model (HMM). First we demonstrate that Somnotate sets new standards in polysomnography, exhibiting annotation accuracies that exceed human experts on mouse electrophysiological data, remarkable robustness to errors in the training data, compatibility with different recording configurations, and an ability to maintain high accuracy during experimental interventions. However, the key feature of Somnotate is that it quantifies and reports the certainty of its annotations. We leverage this feature to reveal that many intermediate vigilance states cluster around state transitions, whereas others correspond to failed attempts to transition. This enables us to show for the first time that the success rates of different types of transition are differentially affected by experimental manipulations and can explain previously observed sleep patterns. Somnotate is open-source and has the potential to both facilitate the study of sleep stage transitions and offer new insights into the mechanisms underlying sleep-wake dynamics

Corrosion behavior of friction stir welded lap joints of AA6061-T6 aluminum alloy

In this work, the corrosion behaviors of friction-stir lap welding of 6061-T6 Al-alloy are studied. The friction-stir lap welding was performed under different welding conditions (rotation speed and welding speed). The corrosion behavior of the parent alloy, the weld nugget zone (WNZ), and the heat affected zone (HAZ) of each welded sample working as an electrode, were investigated by the Tafel polarization test in 3.5 wt. (%) NaCl at ambient temperature. The morphology of the corroded surface of each region was analyzed by scanning electron microscopy together with energy dispersive spectroscopy (SEM-EDS). The results showed that the corrosion resistance of the parent alloy was better than the WNZ and the HAZ in both welding conditions. Localized pit dissolution and intergranular corrosion were the dominant corrosion types observed in the parent alloy, WNZ, and HAZ. The parent alloy, WNZ, and HAZ exhibited similar corrosion potentials (Ecorr) after T6 heat treatment. This treatment had a better effect on the corrosion resistance of the welded regions than the parent alloy

Large Scale Metric Learning for Distance-Based Image Classification on Open Ended Data Sets

International audienceMany real-life large-scale datasets are open-ended and dynamic: new images are continuously added to existing classes, new classes appear over time, and the semantics of existing classes might evolve too. Therefore, we study large-scale image classification methods that can incorporate new classes and training images continuously over time at negligible cost. To this end we consider two distance-based classifiers, the k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers. Since the performance of distance-based classifiers heavily depends on the used distance function, we cast the problem into one of learning a low-rank metric, which is shared across all classes. For the NCM classifier we introduce a new metric learning approach, and we also introduce an extension to allow for richer class representations. Experiments on the ImageNet 2010 challenge dataset, which contains over one million training images of thousand classes, show that, surprisingly, the NCM classifier compares favorably to the more flexible k-NN classifier. Moreover, the NCM performance is comparable to that of linear SVMs which obtain current state-of-the-art performance. Experimentally we study the generalization performance to classes that were not used to learn the metrics. Using a metric learned on 1,000 classes, we show results for the ImageNet-10K dataset which contains 10,000 classes, and obtain performance that is competitive with the current state-of-the-art, while being orders of magnitude faster