Abstract
Systemic vascular inflammation plays multiple maladaptive roles which contribute to the progression and destabilization of atherosclerotic cardiovascular disease (ASCVD). These roles ...include: (i) driving atheroprogression in the clinically stable phase of disease; (ii) inciting atheroma destabilization and precipitating acute coronary syndromes (ACS); and (iii) responding to cardiomyocyte necrosis in myocardial infarction (MI). Despite an evolving understanding of these biologic processes, successful clinical translation into effective therapies has proven challenging. Realizing the promise of targeting inflammation in the prevention and treatment of ASCVD will likely require more individualized approaches, as the degree of inflammation differs among cardiovascular patients. A large body of evidence has accumulated supporting the use of high-sensitivity C-reactive protein (hsCRP) as a clinical measure of inflammation. Appreciating the mechanistic diversity of ACS triggers and the kinetics of hsCRP in MI may resolve purported inconsistencies from prior observational studies. Future clinical trial designs incorporating hsCRP may hold promise to enable individualized approaches. The aim of this Clinical Review is to summarize the current understanding of how inflammation contributes to ASCVD progression, destabilization, and adverse clinical outcomes. We offer forward-looking perspective on what next steps may enable successful clinical translation into effective therapeutic approaches—enabling targeting the right patients with the right therapy at the right time—on the road to more individualized ASCVD care.
There is evidence that transcranial direct current stimulation (tDCS) can improve learning performance. Arguably, this effect is related to long term potentiation (LTP), but the precise biophysical ...mechanisms remain unknown.
We propose that direct current stimulation (DCS) causes small changes in postsynaptic membrane potential during ongoing endogenous synaptic activity. The altered voltage dynamics in the postsynaptic neuron then modify synaptic strength via the machinery of endogenous voltage-dependent Hebbian plasticity. This hypothesis predicts that DCS should exhibit Hebbian properties, namely pathway specificity and associativity.
We studied the effects of DCS applied during the induction of LTP in the CA1 region of rat hippocampal slices and using a biophysical computational model.
DCS enhanced LTP, but only at synapses that were undergoing plasticity, confirming that DCS respects Hebbian pathway specificity. When different synaptic pathways cooperated to produce LTP, DCS enhanced this cooperation, boosting Hebbian associativity. Further slice experiments and computer simulations support a model where polarization of postsynaptic pyramidal neurons drives these plasticity effects through endogenous Hebbian mechanisms. The model is able to reconcile several experimental results by capturing the complex interaction between the induced electric field, neuron morphology, and endogenous neural activity.
These results suggest that tDCS can enhance associative learning. We propose that clinical tDCS should be applied during tasks that induce Hebbian plasticity to harness this phenomenon, and that the effects should be task specific through their interaction with endogenous plasticity mechanisms. Models that incorporate brain state and plasticity mechanisms may help to improve prediction of tDCS outcomes.
•DCS boost LTP and Hebbian associativity, while maintaining pathway specificity.•Effects are consistent with somatic polarization of postsynaptic pyramidal cells.•A computational model captures the dependence on endogenous synaptic activity.•tDCS should be applied during training that induces Hebbian plasticity.•Effects should be specific to trained tasks.
This work updates the methods of Lumpkin and Johnson (2013) to obtain an improved near-surface velocity climatology for the global ocean using observations from undrogued and 15-m drogued Global ...Drifter Program (GDP) drifters. The proposed procedure includes the correction of the slip bias of undrogued drifters, thus recovering about half of the GDP dataset; and a new approach for decomposing Lagrangian data into mean, seasonal and eddy components, which reduces the smoothing of spatial gradients inherent in data binning methods. The sensitivity of the results to method parameters, the method performance relative to other techniques, and the associated estimation errors, are evaluated using statistics calculated for a test dataset consisting of altimeter-derived geostrophic velocities subsampled at the drifter locations, and for the full altimeter-derived geostrophic velocity fields.
It is demonstrated that (1) the correction of drifter slip bias produces statistically similar mean velocities for both drogued and undrogued drifter datasets at most latitudes and reduces differences between their variance estimates, (2) the proposed decomposition method produces pseudo-Eulerian mean fields with magnitudes and horizontal scales closer to time-averaged Eulerian observations than other methods, and (3) standard errors calculated for pseudo-Eulerian quantities underestimate the real errors by a factor of almost two. The improved decomposition method and the inclusion of undrogued drifters in the analysis allows resolving details of the time-mean circulation not well defined in the previous version of the climatology, such as the cross-stream structure of western boundary currents, recirculation cells, and zonally-elongated mid-ocean striations.
•A new global drifter-based climatology of near-surface currents is presented.•Slip correction reduces differences between drogued/undrogued velocity statistics.•Improved decomposition method reduces the smoothing of spatial gradients.•Statistical errors are found to underestimate real errors by a factor of two.•Mesoscale details are resolved, such as the cross-stream profile of narrow currents.
The synergetic effects of recent rising atmospheric CO(2) and temperature are expected to favor tree growth in boreal and temperate forests. However, recent dendrochronological studies have shown ...site-specific unprecedented growth enhancements or declines. The question of whether either of these trends is caused by changes in the atmosphere remains unanswered because dendrochronology alone has not been able to clarify the physiological basis of such trends.
Here we combined standard dendrochronological methods with carbon isotopic analysis to investigate whether atmospheric changes enhanced water use efficiency (WUE) and growth of two deciduous and two coniferous tree species along a 9 degrees latitudinal gradient across temperate and boreal forests in Ontario, Canada. Our results show that although trees have had around 53% increases in WUE over the past century, growth decline (measured as a decrease in basal area increment--BAI) has been the prevalent response in recent decades irrespective of species identity and latitude. Since the 1950s, tree BAI was predominantly negatively correlated with warmer climates and/or positively correlated with precipitation, suggesting warming induced water stress. However, where growth declines were not explained by climate, WUE and BAI were linearly and positively correlated, showing that declines are not always attributable to warming induced stress and additional stressors may exist.
Our results show an unexpected widespread tree growth decline in temperate and boreal forests due to warming induced stress but are also suggestive of additional stressors. Rising atmospheric CO2 levels during the past century resulted in consistent increases in water use efficiency, but this did not prevent growth decline. These findings challenge current predictions of increasing terrestrial carbon stocks under climate change scenarios.
•Esketamine 0.25 mg/kg was noninferior to ketamine 0.5 mg/kg in promoting remission of major depression symptoms 24 h after a single intravenous administration in subjects with treatment-resistant ...depression.•Esketamine and ketamine demonstrated a similar safety pattern and were well tolerated by most participants.•This is the first head-to-head randomized clinical trial comparing racemic ketamine and esketamine in patients with treatment-resistant depression.
Ketamine and its enantiomers have recently been highlighted as one of the most effective therapeutic options in refractory depression. However, racemic ketamine and esketamine have not been directly compared. The aim of this study is to assess the efficacy and safety of esketamine compared to ketamine in patients with treatment-resistant depression (TRD).
This is a randomized, double-blind, active-controlled, bicentre, non-inferiority clinical trial, with two parallel groups. Participants were randomly assigned to a 40-min single intravenous infusion of ketamine 0.5 mg/kg or esketamine 0.25 mg/kg. The primary outcome was the difference in remission rates for depression 24 h following intervention using the Montgomery-Åsberg Depression Rating Scale (MADRS), with a non-inferiority margin of 20%.
63 subjects were included and randomly assigned (29 to receive ketamine and 34 to receive esketamine). At 24 h, 24.1% of participants in the ketamine group and 29.4% of participants in the esketamine group showed remission, with a difference of 5.3% (95% CILB -13.6%), confirming non-inferiority. MADRS scores improved from 33 (SD 9.3) to 16.2 (SD 10.7) in the ketamine group and from 33 (SD 5.3) to 17.5 (SD 12.2) in the esketamine one, with a difference of -5.27% (95% CILB, -13.6). Both groups presented similar mild side effects.
Esketamine was non-inferior to ketamine for TRD 24 h following infusion. Both treatments were effective, safe, and well tolerated.
Registered in Japan Primary Registries Network: UMIN000032355.
Studies have shown that pyrolysis method and temperature are the key factors influencing biochar chemical and physical properties; however, information on the nature of biochar feedstocks is more ...accessible to consumers, making feedstock a better measure for selecting biochars. This study characterizes physical and chemical properties of commercially available biochars and investigates trends in biochar properties related to feedstock material to develop guidelines for biochar use. Twelve biochars were analyzed for physical and chemical properties. Compiled data from this study and from the literature (n = 85) were used to investigate trends in biochar characteristics related to feedstock. Analysis of compiled data reveals that despite clear differences in biochar properties from feedstocks of algae, grass, manure, nutshells, pomace, and wood (hard- and softwoods), characteristic generalizations can be made. Feedstock was a better predictor of biochar ash content and C/N ratio, but surface area was also temperature dependent for wood-derived biochar. Significant differences in ash content (grass and manure > wood) and C/N ratio (softwoods > grass and manure) enabled the first presentation of guidelines for biochar use based on feedstock material.
Transcranial electric stimulation aims to stimulate the brain by applying weak electrical currents at the scalp. However, the magnitude and spatial distribution of electric fields in the human brain ...are unknown. We measured electric potentials intracranially in ten epilepsy patients and estimated electric fields across the entire brain by leveraging calibrated current-flow models. When stimulating at 2 mA, cortical electric fields reach 0.8 V/m, the lower limit of effectiveness in animal studies. When individual whole-head anatomy is considered, the predicted electric field magnitudes correlate with the recorded values in cortical (
= 0.86) and depth (
= 0.88) electrodes. Accurate models require adjustment of tissue conductivity values reported in the literature, but accuracy is not improved when incorporating white matter anisotropy or different skull compartments. This is the first study to validate and calibrate current-flow models with
intracranial recordings in humans, providing a solid foundation to target stimulation and interpret clinical trials.
Aliphatic three- and four-membered rings including cyclopropanes, cyclobutanes, oxetanes, azetidines and bicyclo1.1.1pentanes have been increasingly exploited in medicinal chemistry for their ...beneficial physicochemical properties and applications as functional group bioisosteres. This review provides a historical perspective and comparative up to date overview of commonly applied small rings, exemplifying key principles with recent literature examples. In addition to describing the merits and advantages of each ring system, potential hazards and liabilities are also illustrated and explained, including any significant chemical or metabolic stability and toxicity risks.
Aliphatic small rings including cyclopropanes, cyclobutanes, oxetanes, azetidines and bicyclo1.1.1pentanes have been increasingly exploited in medicinal chemistry. This review summarises judicious successful application and reported limitations of these ring systems.
•An unsupervised approach for fault detection in rotating machinery.•An unsupervised approach for fault classification based on feature importance ranking.•Possibility of performing root cause ...analysis and to be applied in different faults.•A new contribution to Explainable Artificial Intelligence in rotating machinery.•Industrial application with the possibility to change models according to the dataset.
The monitoring of rotating machinery is an essential task in today’s production processes. Currently, several machine learning and deep learning-based modules have achieved excellent results in fault detection and diagnosis. Nevertheless, to further increase user adoption and diffusion of such technologies, users and human experts must be provided with explanations and insights by the modules. Another issue is related, in most cases, with the unavailability of labeled historical data that makes the use of supervised models unfeasible. Therefore, a new approach for fault detection and diagnosis in rotating machinery is here proposed. The methodology consists of three parts: feature extraction, fault detection and fault diagnosis. In the first part, the vibration features in the time and frequency domains are extracted. Secondly, in the fault detection, the presence of fault is verified in an unsupervised manner based on anomaly detection algorithms. The modularity of the methodology allows different algorithms to be implemented. Finally, in fault diagnosis, Shapley Additive Explanations (SHAP), a technique to interpret black-box models, is used. Through the feature importance ranking obtained by the model explainability, the fault diagnosis is performed. Two tools for diagnosis are proposed, namely: unsupervised classification and root cause analysis. The effectiveness of the proposed approach is shown on three datasets containing different mechanical faults in rotating machinery. The study also presents a comparison between models used in machine learning explainability: SHAP and Local Depth-based Feature Importance for the Isolation Forest (Local-DIFFI). Lastly, an analysis of several state-of-art anomaly detection algorithms in rotating machinery is included.
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