A nomogram for predicting the risk of treatment failure of roxadustat in peritoneal dialysis with renal anemia.

The determinants of roxadustat treatment failure in renal anemia remain elusive. This study sought to develop a nomogram for predicting the risk of treatment failure of roxadustat in peritoneal dialysis (PD) with renal anemia. A retrospective cohort analysis from January 1, 2019, to January 31, 2023, included 204 PD patients with renal anemia, stratified by attainment group (Hb ≥ 110 g/L, n = 103) or non-attainment (Hb < 110 g/L, n = 101) within 1 year treatment. Univariate and multivariate Cox proportional hazards regressions were employed to ascertain predictive factors and construct the nomogram. Nomogram efficacy was evaluated via C-index, time-dependent ROC, calibration plots, and decision curve analysis, with internal validation via tenfold cross-validation and 1000 bootstrap resampling iterations. The study identified PD duration, serum transferrin, cardiovascular comorbidities, and stains as significant predictors. The nomogram demonstrated moderate discrimination at 6 months (AUC: 0.717) and enhanced predictive accuracy at 12 months (AUC: 0.741). The predicted and actual risk probabilities were concordant, with clinical net benefits observed at six-month (8 to 53%) and twelve-month (27 to 84%) risk thresholds. This nomogram is a valuable tool for effectively predicting non-attainment risk and facilitating personalized management of renal anemia in PD patients treated with roxadustat.

Investigating the therapeutic effects and mechanisms of Roxadustat on peritoneal fibrosis Based on the TGF-ß/Smad pathway.

Peritoneal fibrosis (PF) is particularly common in individuals undergoing peritoneal dialysis (PD). Fibrosis of the parenchymal tissue typically progresses slowly. Therefore, preventing and reducing the advancement of fibrosis is crucial for effective patient treatment. Roxadustat is a hypoxia-inducible factor prolyl hydroxylase inhibitor (HIF-PHI), primarily used to treat and improve renal anemia. Recent studies have found that HIF-1α possesses antioxidant activity and exerts a certain protective effect in ischemic heart disease and spinal cord injury, while it can also delay the progression of pulmonary and renal fibrosis. This study establishes the mice model through intraperitoneal injection of 4.25 % peritoneal dialysate fluid (PDF) and explores the therapeutic effects of Roxadustat by inducing TGF-ß1-mediated epithelial-mesenchymal transition (EMT) in Met-5A cells. The aim is to investigate the protective role and mechanisms of Roxadustat against PD-related PF. We observed thicker peritoneal tissue and reduced permeability in animals with PD-related PF samples. This was accompanied by heightened inflammation, which Roxadustat alleviated by lowering the levels of inflammatory cytokines (IL-6, TNF-α). Furthermore, Roxadustat inhibited EMT in PF mice and TGF-ß1-induced Met-5A cells, as evidenced by decreased expression of fibrotic markers, such as fibronectin, collagen I, and α-SMA, alongside an elevation in the expression of the epithelial marker, E-cadherin. Roxadustat also significantly decreased the expression of TGF-ß1 and the phosphorylation of p-Smad2 and p-Smad3. In conclusion, Roxadustat ameliorates peritoneal fibrosis through the TGF-ß/Smad pathway.

PROFILE-KERNEL LIKELIHOOD INFERENCE WITH DIVERGING NUMBER OF PARAMETERS.

The generalized varying coefficient partially linear model with growing number of predictors arises in many contemporary scientific endeavor. In this paper we set foot on both theoretical and practical sides of profile likelihood estimation and inference. When the number of parameters grows with sample size, the existence and asymptotic normality of the profile likelihood estimator are established under some regularity conditions. Profile likelihood ratio inference for the growing number of parameters is proposed and Wilk's phenomenon is demonstrated. A new algorithm, called the accelerated profile-kernel algorithm, for computing profile-kernel estimator is proposed and investigated. Simulation studies show that the resulting estimates are as efficient as the fully iterative profile-kernel estimates. For moderate sample sizes, our proposed procedure saves much computational time over the fully iterative profile-kernel one and gives stabler estimates. A set of real data is analyzed using our proposed algorithm.