The effect of GIRK4 and RGS6 ablation on APD heterogeneity, μ.

(A) Average APD heterogeneity, μ, as a function of BCL in WT, Girk4-/-, and Rgs6-/- hearts. (B-D) The effect of CCh on μ at different BCL in WT, Girk4-/-, and Rgs6-/- hearts. n = 8, 5, 8 for WT, Rgs6-/-, and Girk4-/- respectively. Statistics performed using 1-way ANOVA.</p

The influence of M₂R-I_KACh signaling on in vivo HR and HRV.

<p>Summary of baseline and post-CCh (300nM CCh) in-vivo HR (A) and HRV (B) in WT, <i>Rgs6</i>^<i>-/-</i>, and <i>Girk4</i>^<i>-/-</i> mice. (‘*’ denotes statistical significance of p < 0.05 between baseline and CCh within the same genotype. ‘&’ denotes a statistically significant (p < 0.05) difference for both baseline and CCh when comparing between two genotypes). ‘#’ denotes statistical significance of p < 0.05 between WT and <i>Rgs6</i>^<i>-/-</i> mice post-CCh. ‘$’ denotes statistical significance of p < 0.05 between <i>Girk4</i>^<i>-/-</i> and <i>Rgs6</i>^<i>-/-</i> mice post-CCh. Statistics performed using 1-way ANOVA.) (C) Quantification of the total number of mice that exhibited arrhythmias post CCh. (‘*’ denotes statistical significance of p < 0.05 between <i>Girk4</i>^<i>-/-</i> and <i>Rgs6</i>^<i>-/-</i>. Statistics performed using Fisher’s exact test). (D) Representative examples of ECG data during control and demonstrating episodes of arrhythmia in WT and <i>Rgs6</i>^<i>-/-</i>, and no arrhythmia in <i>Girk4</i>^<i>-/-</i> mice post CCh. n = 8, 8, 6 for WT, <i>Rgs6</i>^<i>-/</i>-, and <i>Girk4</i>^<i>-/-</i>_, respectively.</p

The effect of GIRK4 and RGS6 ablation on APD₈₀.

<p>(A) Change in average APD₈₀ with decreasing BCL in WT, <i>Girk4</i>^<i>-/-</i>, and <i>Rgs6</i>^<i>-/-</i> hearts. (‘#’ denotes statistical significance of p < 0.05 between WT and <i>Girk4</i>^<i>-/-</i>. ‘$’ denotes statistical significance of p < 0.05 between WT and <i>Rgs6</i>^<i>-/-</i>). (B) Representative 2D APD₈₀ maps from WT, <i>Girk4</i>^<i>-/-</i>, and <i>Rgs6</i>^<i>-/-</i> hearts, constructed at BCL = 120 ms both at baseline and post-CCh injection. Representative action potential traces are shown at baseline (top panel, pixels marked by *) and post-CCh (bottom panel, pixels marked by Δ). (C-E) The effect of CCh on APD₈₀ at decreasing BCL in WT, <i>Girk4</i>^<i>-/-</i>, and <i>Rgs6</i>^<i>-/-</i> hearts. (‘*’ denotes statistical significance of p < 0.05 between baseline and 300nM CCh; ‘&’ denotes statistical significance of p < 0.05 between baseline and 3uM CCh). n = 8, 5, 8 for WT, <i>Rgs6</i>^<i>-/-</i>, and <i>Girk4</i>^<i>-/-</i>_, respectively. All statistics performed using 1-way ANOVA.</p

DataSheet_1_A web-based nomogram model for predicting the overall survival of hepatocellular carcinoma patients with external beam radiation therapy: A population study based on SEER database and a Chinese cohort.zip

BackgroundExternal beam radiation therapy (EBRT) for hepatocellular carcinoma (HCC) is rarely used in clinical practice. This study aims to develop and validate a prognostic nomogram model to predict overall survival (OS) in HCC patients treated with EBRT.MethodWe extracted eligible data of HCC patients between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Those patients were randomly divided into a training cohort (n=1004) and an internal validation cohort (n=429), and an external validation cohort composed of a Chinese cohort (n=95). A nomogram was established based on the independent prognostic variables identified from univariate and multivariate Cox regression analyses. The effective performance of the nomogram was evaluated using the concordance index (C-index), receiver operating characteristic curve (ROC), and calibration curves. The clinical practicability was evaluated using decision curve analysis (DCA).ResultsT stage, N stage, M stage, AFP, tumor size, surgery, and chemotherapy were independent prognostic risk factors that were all included in the nomogram to predict OS in HCC patients with EBRT. In the training cohort, internal validation cohort, and external validation cohort, the C-index of the prediction model was 0.728 (95% confidence interval (CI): 0.716-0.740), 0.725 (95% CI:0.701-0.750), and 0.696 (95% CI:0.629-0.763), respectively. The 6-, 12-,18- and 24- month areas under the curves (AUC) of ROC in the training cohort were 0.835 、0.823 、0.810, and 0.801, respectively; and 0.821 、0.809 、0.813 and 0.804 in the internal validation cohort, respectively; and 0.749 、0.754 、0.791 and 0.798 in the external validation cohort, respectively. The calibration curves indicated that the predicted value of the prediction model performed well. The DCA curves showed better clinical practicability. In addition, based on the nomogram, we established a web-based nomogram to predict the OS of these patients visually.ConclusionBased on the SEER database and an independent external cohort from China, we established and validated a nomogram to predict OS in HCC patients treated with EBRT. In addition, for the first time, a web-based nomogram model can help clinicians judge the prognoses of these patients and make better clinical decisions.</p

The effect of GIRK4 and RGS6 ablation on CV.

<p>(A) Average CV as a function of BCL in WT, <i>Girk4</i>^<i>-/-</i>, and <i>Rgs6</i>^<i>-/-</i> hearts. (B) Representative 2D activation maps from WT, <i>Girk4</i>^<i>-/-</i>, and <i>Rgs6</i>^<i>-/-</i> hearts, constructed at BCL = 150 ms both at baseline and post-CCh injection. (C-E) The effect of CCh on CV at different BCL in WT, <i>Girk4</i>^<i>-/-</i>, and <i>Rgs6</i>^<i>-/-</i>. n = 8, 5, 8 for WT, <i>Rgs6</i>^<i>-/-</i>, and <i>Girk4</i>^<i>-/-</i>_, respectively. Statistics performed using 1-way ANOVA.</p

The effect of GIRK4 and RGS6 ablation on S_max.

<p>Summary of the baseline and post-CCh maximum slopes of APD restitution, S_max, in WT (n = 8), <i>Girk4</i>^<i>-/-</i> (n = 8) and <i>Rgs6</i>^<i>-/-</i> (n = 5) mice. (‘*’ denotes statistical significance of p < 0.05 between post-CCh and baseline for the same genotype. Statistics performed using 1-way ANOVA).</p