Blood–brain barrier (BBB) dysfunction, e.g., increase in BBB permeability, has been reported to contribute to cognitive impairment. However, the effects of anesthesia and surgery on BBB permeability, the underlying mechanisms, and associated cognitive function remain largely to be determined. Here, we assessed the effects of surgery (laparotomy) under 1.4% isoflurane anesthesia (anesthesia/surgery) for 2 h on BBB permeability, levels of junction proteins and cognitive function in both 9- and 18-month-old wild-type mice and 9-month-old interleukin (IL)-6 knockout mice. BBB permeability was determined by dextran tracer (immunohistochemistry imaging and spectrophotometric quantification), and protein levels were measured by Western blot and cognitive function was assessed by using both Morris water maze and Barnes maze. We found that the anesthesia/surgery increased mouse BBB permeability to 10-kDa dextran, but not to 70-kDa dextran, in an IL-6-dependent and age-associated manner. In addition, the anesthesia/surgery induced an age-associated increase in blood IL-6 level. Cognitive impairment was detected in 18-month-old, but not 9-month-old, mice after the anesthesia/surgery. Finally, the anesthesia/surgery decreased the levels of β-catenin and tight junction protein claudin, occludin and ZO-1, but not adherent junction protein VE-cadherin, E-cadherin, and p120-catenin. These data demonstrate that we have established a system to study the effects of perioperative factors, including anesthesia and surgery, on BBB and cognitive function. The results suggest that the anesthesia/surgery might induce an age-associated BBB dysfunction and cognitive impairment in mice. These findings would promote mechanistic studies of postoperative cognitive impairment, including postoperative delirium.
Optical diodes are fundamental elements for optical computing and information processing. Attempts to realize such non-reciprocal propagation of light by breaking the time-reversal symmetry include using indirect interband photonic transitions, the magneto-optical effect, optical nonlinearity or photonic crystals. Alternatively, asymmetric reciprocal transmission of light has been proposed in photonic metamaterial structures for either circularly or linearly polarized waves. Here we employ the recent concept of gradient index metamaterials to demonstrate a waveguide with asymmetric propagation of light, independent of polarization. The device blocks both transverse electric and magnetic polarized modes in one direction but transmits them in the other for a broadband spectrum. Unlike previous works using chiral properties of metamaterials, our device is based on the principle of momentum symmetry breaking at interfaces with phase discontinuities. Experiments in the microwave region verify our findings, which may pave the way to feasible passive optical diodes.
There is a dearth of robust methods to estimate the causal effects of multiple treatments when the outcome is binary. This paper uses two unique sets of simulations to propose and evaluate the use of Bayesian additive regression trees in such settings. First, we compare Bayesian additive regression trees to several approaches that have been proposed for continuous outcomes, including inverse probability of treatment weighting, targeted maximum likelihood estimator, vector matching, and regression adjustment. Results suggest that under conditions of non-linearity and non-additivity of both the treatment assignment and outcome generating mechanisms, Bayesian additive regression trees, targeted maximum likelihood estimator, and inverse probability of treatment weighting using generalized boosted models provide better bias reduction and smaller root mean squared error. Bayesian additive regression trees and targeted maximum likelihood estimator provide more consistent 95% confidence interval coverage and better large-sample convergence property. Second, we supply Bayesian additive regression trees with a strategy to identify a common support region for retaining inferential units and for avoiding extrapolating over areas of the covariate space where common support does not exist. Bayesian additive regression trees retain more inferential units than the generalized propensity score-based strategy, and shows lower bias, compared to targeted maximum likelihood estimator or generalized boosted model, in a variety of scenarios differing by the degree of covariate overlap. A case study examining the effects of three surgical approaches for non-small cell lung cancer demonstrates the methods.
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