BACKGROUND: Chronic kidney disease (CKD) poses a major global health challenge, often foreshadowing poor patient outcomes. The C-reactive protein to albumin ratio (CAR) serves as a pivotal biomarker, demonstrating a strong correlation with adverse outcomes in cardiovascular disease (CVD). This study sought to examine the correlation between CAR and the risk of all-cause and cardiovascular mortality in patients with CKD stages 3-5. METHODS: This study utilized data of CKD patients from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2010, with follow-up to December 31, 2019. The optimal CAR cutoff value was identified utilizing the method of maximally selected rank statistics. Multivariable Cox proportional hazards regression model, restricted cubic splines (RCS) model, and subgroup analysis were employed to assess the association between CAR and mortality among CKD patients. RESULTS: During a median (with interquartile range) follow-up period of 115 (112,117) months among 2,841 CKD individuals, 1,893 deaths were observed, including 692 deaths due to CVD events. Based on the RCS analysis, a non-linear correlation was observed between CAR and mortality. Using 0.3 as the optimal CAR cutoff value, the cohort was divided into high and low groups. In the fully adjusted model, CKD patients with high CAR values exhibited an elevated risk of all-cause mortality (hazard ratio [HR] 1.53, 95% confidence interval [CI] 1.28-1.83, P < 0.001) and cardiovascular mortality (HR 1.48, 95% CI 1.08-2.02, P = 0.014). Compared to the population aged >65 years (HR 1.32, 95% CI 0.99-1.76, P = 0.064), the risk of cardiovascular mortality was significantly higher in those aged ≤65 years (HR 2.19, 95% CI 1.18-4.09, P = 0.014) with elevated CAR levels. CONCLUSIONS A notable correlation exists between the elevation of CAR and increased all-cause and cardiovascular mortality, suggesting its potential as an independent indicator for evaluating the prognosis of patients with CKD stages 3-5.
Humans ; Renal Insufficiency, Chronic/epidemiology* ; Cardiovascular Diseases/blood* ; Male ; Female ; Middle Aged ; C-Reactive Protein/metabolism* ; Aged ; Biomarkers/blood* ; Nutrition Surveys ; Adult ; United States/epidemiology* ; Serum Albumin/analysis*

Population bioequivalence (PBE) is a statistical approach recommended by FDA to evaluate the consistency of particle size distribution of numerous nanoparticle products.However, when particle size distribution (PSD) profile exhibits multiple peaks or other complex distributions, the traditional descriptors D50 and SPAN are no longer suitable to describe PSD.Earth mover''s distance (EMD) is a new statistical metric for assessing the difference between distributions.In this study, we used EMD to measure the discrepancy between PSD and then PBE was applied to perform statistical test to establish equivalence.Our results showed that the proposed method can effectively reject an unequivalent product and pass an equivalent product, thus indicating its helpfulness in guiding the optimization of formulation and preparation process.

According to the frequency overlapping of intrinsic mode function (IMF) based on the temporal and spatial filtering of empirical mode decomposition (EMD), which will lead to the question of useful signals and noises filtered together, we proposed a method that numbers of IMF is determined by energy estimate, temporal and spatial filtering combing wavelet threshold and EMD integrating wavelet local signal characteristics of time and scale domain. This method not only used multi-resolution wavelet transform features, but also combined the EMD and Hilbert decomposition of the adaptive spectral analysis of instantaneous frequency and significance of the relationship between energy, so as to solve the problem of useful signal being weakened. With MIT/BIH ECG database standard data subjects, experimental results showed it was an effective method of data processing for handling this type of physiological signals under strong noise.
Algorithms ; Artifacts ; Electrocardiography ; methods ; Humans ; Signal Processing, Computer-Assisted ; Wavelet Analysis