Cardiovascular calcification in chronic kidney disease: Risk factors and effect of α-keto acid tablets

Purpose: To investigate the effect of α-keto acid tablets, and risk factors for cardiovascular calcification in patients with chronic kidney disease (CKD).Methods: A total of 128 CKD patients were enrolled in this study. They were randomly assigned to study and control groups, each with 64 patients. Control patients received symptomatic treatment, while the study group patients received α-keto acid tablets plus. Indices of cardiovascular calcification, blood lipids and mineral metabolism were determined in the 2 groups of patients and compared. Risk factors for cardiovascular calcification were also analyzed.Results: After treatment, the two groups had decreased CACS scores and reduced serum FGF-23levels, with lower values in patients in the study group. Levels of Klotho and fetuin-A were significantly elevated after treatment, with higher values observed in study group patients. The degree of cardiovascular calcification was markedly lower in study group than that in controls. There was no significant difference in blood Ca level between the control and study groups before and after treatment. Logistic multivariate analysis demonstrated that hyperlipidemia, hyperphosphatemia, hypercalcemia, hypertension and diabetes put patients at risk for cardiovascular calcification.Conclusion: Compound α-keto acid tablets delay cardiovascular calcification in patients with CKD, and alleviate symptoms of related risk factors for cardiovascular calcification

A New Road Damage Detection Baseline with Attention Learning

Automated detection of road damage (ADRD) is a challenging topic in road maintenance. It focuses on automatically detecting road damage and assessing severity by deep learning. Because of the sparse distribution of characteristic pixels, it is more challenging than object detection. Although some public datasets provide a database for the development of ADRD, their amounts of data and the standard of classification cannot meet network training and feature learning. With the aim of solving this problem, this work publishes a new road damage dataset named CNRDD, which is labeled according to the latest evaluation standard for highway technical conditions in China (JTG5210-2018). The dataset is collected by professional onboard cameras and is manually labeled in eight categories with three different degrees (mild, moderate and severe), which can effectively help promote research of automated detection of road damage. At the same time, a novel baseline with attention fusion and normalization is proposed to evaluate and analyze the published dataset. It explicitly leverages edge detection cues to guide attention for salient regions and suppresses the weights of non-salient features by attention normalization, which can alleviate the interference of sparse pixel distribution on damage detection. Experimental results demonstrate that the proposed baseline significantly outperforms most existing methods on the existing RDD2020 dataset and the newly released CNRDD dataset. Further, the CNRDD dataset is proved more robust, as its high damage density and professional classification are more conducive to promote the development of ADRD

A New Road Damage Detection Baseline with Attention Learning

Automated detection of road damage (ADRD) is a challenging topic in road maintenance. It focuses on automatically detecting road damage and assessing severity by deep learning. Because of the sparse distribution of characteristic pixels, it is more challenging than object detection. Although some public datasets provide a database for the development of ADRD, their amounts of data and the standard of classification cannot meet network training and feature learning. With the aim of solving this problem, this work publishes a new road damage dataset named CNRDD, which is labeled according to the latest evaluation standard for highway technical conditions in China (JTG5210-2018). The dataset is collected by professional onboard cameras and is manually labeled in eight categories with three different degrees (mild, moderate and severe), which can effectively help promote research of automated detection of road damage. At the same time, a novel baseline with attention fusion and normalization is proposed to evaluate and analyze the published dataset. It explicitly leverages edge detection cues to guide attention for salient regions and suppresses the weights of non-salient features by attention normalization, which can alleviate the interference of sparse pixel distribution on damage detection. Experimental results demonstrate that the proposed baseline significantly outperforms most existing methods on the existing RDD2020 dataset and the newly released CNRDD dataset. Further, the CNRDD dataset is proved more robust, as its high damage density and professional classification are more conducive to promote the development of ADRD

Based on the fully adjusted model, the relationship between DII and higher CKD stage.

The solid red line represents the smooth fitting curve between variables, and the blue band represents the 95% CI of the fitting.</p

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PurposeThe number of CKD patients is on the rise worldwide, and diet has become an essential aspect influencing the treatment and prognosis of CKD. However, limited research has explored the association of the Dietary Inflammatory Index (DII) with CKD progression and the essential kidney function indicator, eGFR, in CKD patients. This study aimed to analyze the association between DII and CKD progression and eGFR in the US CKD population using data from the National Health and Nutrition Examination Survey (NHANES).MethodsThis study utilized data obtained from the National Health and Nutrition Examination Survey (NHANES) spanning from 2007 to 2018, with a total sample size of 2,488 individuals. Study used multiple imputation, based on 5 replications and a chained equation approach method in the R MI procedure, to account for missing data. Weighted multiple logistic regression was used to analyze the relationship between DII and the risk of higher CKD stage and a weighted multiple regression analysis was used to assess the relationship between DII and eGFR. Weighted Generalized Additive Models and smoothed curve fitting were applied to detect potential non-linear relationships in this association.ResultsIn all three models, it was found that DII was positively associated with the risk of higher CKD stage (P P P for trend ConclusionsOur study results indicate that an increase in DII is associated with an increased risk of higher CKD stage and a decrease in eGFR in all three models. In the fully adjusted model, the risk of higher CKD stage increased by 26% and the eGFR decreased by 1.29 ml/min/1.73 m2 for each unit increase in DII. This finding suggests that in patients with CKD in the US, improved diet and lower DII values may help slow the decline in eGFR and delay the progression of CKD.</div

Based on the fully adjusted model, the relationship between DII and eGFR.

The solid red line represents the smooth fitting curve between variables, and the blue band represents the 95% CI of the fitting.</p

Flowchart for selecting analyzed participants.

CKD, chronic kidney disease; DII, Dietary Inflammatory Index; eGFR, estimated glomerular filtration rate; NHANES, National Health and Nutrition Examination Survey.</p

Multivariate weighted logistic regression model reveals the associations between DII and higher CKD stage.

Multivariate weighted logistic regression model reveals the associations between DII and higher CKD stage.</p

Multivariate weighted regression model analysis reveals the associations between DII and eGFR.

Multivariate weighted regression model analysis reveals the associations between DII and eGFR.</p

The stratified analysis of the association between DII and eGFR.

The stratified analysis of the association between DII and eGFR.</p