2015
DOI: 10.1016/j.brainres.2015.01.049
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Hydralazine administration activates sympathetic preganglionic neurons whose activity mobilizes glucose and increases cardiovascular function

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Cited by 3 publications
(5 citation statements)
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“…In V‐HeFT II (the Vasodilator‐Heart Failure Trial), treatment with ISDN+hydral increased norepinephrine levels . More recently, hydralazine has been shown to activate preganglionic sympathetic neurons . Our findings of increased preload (suggesting volume retention), adverse interstitial remodeling, and reduced 6MW distances may be consistent with sympathetic nervous system activation .…”
Section: Discussionsupporting
confidence: 84%
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“…In V‐HeFT II (the Vasodilator‐Heart Failure Trial), treatment with ISDN+hydral increased norepinephrine levels . More recently, hydralazine has been shown to activate preganglionic sympathetic neurons . Our findings of increased preload (suggesting volume retention), adverse interstitial remodeling, and reduced 6MW distances may be consistent with sympathetic nervous system activation .…”
Section: Discussionsupporting
confidence: 84%
“…44 More recently, hydralazine has been shown to activate preganglionic sympathetic neurons. 45 Our findings of increased preload (suggesting volume retention), adverse interstitial remodeling, and reduced 6MW distances may be consistent with sympathetic nervous system activation. [46][47][48] Other manifestations of sympathetic activation, such as tachycardia, could have been masked by the high utilization of concomitant cardiovascular medications, such as b-blockers.…”
Section: Discussionsupporting
confidence: 77%
“…="Inflection Point" reg [1,6]="Count of c-Fos-positive neurons" reg [1,7]="Background percentage threshold (%)" reg [2,6]=mod$coefficients[1]/2 reg[2, 7]=-mod$coefficients [2]/mod$coefficients [3] export(reg, file = "Result_Non_Linear_Regression.xlsx", overwrite = TRUE) ## tgc2[,8]=mod$coefficients [1]/(1+exp(-(mod$coefficients [2]+mod$coefficients[3] * x))) colnames(tgc2) [8]="est" # Gr?fico com todos os grupos ggplot(tgc, aes(x=Threshold, y=X, colour=Animals)) + labs(x="Background percentage threshold (%)") + labs(y="Average number of the core count of c-Fos-positive neurons") + geom_errorbar(aes(ymin=X-se, ymax=X+se), colour="black", width=1) + theme_bw() + xlim(min(tgc2$Threshold)-2, max(tgc2$Threshold)+2) + geom_point(aes(shape=Animals),size=3, colour="black") + geom_line(data = tgc2, aes(x = Threshold, y = est), size =1.5, color="red") + geom_segment(aes(x = reg [2,7], y = 0, xend = reg [2,7], yend = reg [2,6]), size=1, colour="red", linetype = "dashed") + geom_segment(aes(x = min(tgc2$Threshold)-2, y = reg [2,6], xend = reg [2,7], yend = reg [2,6]),size = 1, colour="red", linetype = "dashed") ggsave(filename = "Nonlinear Regression Adjustment.tiff", scale = 2:2, dpi = 300)…”
Section: Discussionmentioning
confidence: 99%
“…Other works have used a threshold limit with a fixed value [8,20] to choose which cores have to be counted and which does not. That is a good strategy, but using the same threshold for all immunofluorescence pictures cannot be so efficient because the value of the background usually has a considerable variation, even if camera's diaphragm is kept at the same frequency during the acquisition process.…”
Section: Discussionmentioning
confidence: 99%
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