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Asymmetric dimethylarginine (ADMA) is an endogenous inhibitor of nitric oxide synthase and plays a significant role in the pathogenesis of arteriovenous fistula (AVF) dysfunction. The aim of this study is to evaluate the effect of far-infrared (FIR) therapy on the maturation and patency of newly-created AVFs in patients with advanced diabetic kidney disease (DKD) as well as the concurrent change in plasma ADMA. The study enrolled 144 participants with advanced DKD where 101 patients were randomly allocated to the FIR therapy group (N = 50) and control group (N = 51). Patients receiving FIR therapy had a decreased AVF failure rate within 12 months (16% versus 35.3%; p = 0.027); decreased incremental change of ADMA concentration at the 3rd and 12th month; increased AVF blood flow at the 1st, 3rd, and 12th month; increased 3-month physiologic maturation rate (88% versus 68.6%; p = 0.034); increased 1-year unassisted AVF patency rate (84% versus 64.7%; p = 0.017); and increased clinical AVF maturation rate within 12 months (84% versus 62.7%; p = 0.029) compared to the control group. The study demonstrates that FIR therapy can reduce the incremental changes in plasma ADMA concentration, which may be associated with the improvement of AVF prognosis in patients with advanced DKD.

Plasma galectin-3 (Gal-3) is associated with organ fibrosis, but whether urinary Gal-3 is a potential biomarker of kidney disease progression has never been explored. Between 2018 and 2021, we prospectively enrolled 280 patients who underwent renal biopsy and were divided into three groups based on their urinary Gal-3 levels (

Sepsis may lead to kidney function decline in patients with chronic kidney disease (CKD), and the deleterious effect may persist in patients who survive sepsis. We used a machine learning approach to predict the risk of end-stage renal disease (ESRD) in sepsis survivors. A total of 11,661 sepsis survivors were identified from a single-center database of 112,628 CKD patients between 2010 and 2018. During a median follow-up of 3.5 years, a total of 1366 (11.7%) sepsis survivors developed ESRD after hospital discharge. We adopted the random forest, extra trees, extreme gradient boosting, light gradient boosting machine (LGBM), and gradient boosting decision tree (GBDT) algorithms to predict the risk of ESRD development among these patients. GBDT yielded the highest area under the receiver operating characteristic curve of 0.879, followed by LGBM (0.868), and extra trees (0.865). The GBDT model revealed the strong effect of estimated glomerular filtration rates 2 at discharge in predicting ESRD development. In addition, hemoglobin and proteinuria were also essential predictors. Based on a large-scale dataset, we established a machine learning model computing the risk for ESRD occurrence among sepsis survivors with CKD. External validation is required to evaluate the generalizability of this model.

Dialysis membranes were traditionally classified according to their material compositions (i.e., as cellulosic or synthetic) and on the basis of the new concept of the sieving coefficient (determined by the molecular weight retention onset and molecular weight cut-off). The advantages of synthetic polymer membranes over cellulose membranes are also described on the basis of their physical, chemical, and structural properties. Innovations of dialysis membrane in recent years include the development of medium cutoff membranes; graphene oxide membranes; mixed-matrix membranes; bioartificial kidneys; and membranes modified with vitamin E, lipoic acid, and neutrophil elastase inhibitors. The current state of research on these membranes, their effects on clinical outcomes, the advantages and disadvantages of their use, and their potential for clinical use are outlined and described.

Sepsis survivors have a higher risk of long-term complications. Acute kidney injury (AKI) may still be common among sepsis survivors after discharge from sepsis. Therefore, our study utilized an artificial-intelligence-based machine learning approach to predict future risks of rehospitalization with AKI between 1 January 2008 and 31 December 2018. We included a total of 23,761 patients aged ≥ 20 years who were admitted due to sepsis and survived to discharge. We adopted a machine learning method by using models based on logistic regression, random forest, extra tree classifier, gradient boosting decision tree (GBDT), extreme gradient boosting, and light gradient boosting machine (LGBM). The LGBM model exhibited the highest area under the receiver operating characteristic curves (AUCs) of 0.816 to predict rehospitalization with AKI in sepsis survivors and followed by the GBDT model with AUCs of 0.813. The top five most important features in the LGBM model were C-reactive protein, white blood cell counts, use of inotropes, blood urea nitrogen and use of diuretics. We established machine learning models for the prediction of the risk of rehospitalization with AKI in sepsis survivors, and the machine learning model may set the stage for the broader use of clinical features in healthcare.

Polyacrylonitrile (AN69) filter membranes adsorb cytokines during continuous venovenous hemofiltration (CVVH). Although high-volume hemofiltration has shown limited benefits, the dose-effect relationship in CVVH with AN69 membranes on severe sepsis remains undetermined. This multi-centered study enrolled 266 patients with sepsis-induced multiorgan dysfunction syndrome (MODS) who underwent CVVH with AN69 membranes between 2014 and 2015. We investigated the effects of ultrafiltration rates (UFR) on mortality. We categorized patients that were treated with UFR of 20–25 mL/kg/h as the standard UFR group (n = 124) and those that were treated with a UFR >25 mL/kg/h as the high UFR group (n = 142). Among the patient characteristics, the baseline estimated glomerular filtration rates (eGFR) 2, hemoglobin levels p = 0.005). We concluded that in patients with sepsis-induced MODS, SOFA scores ≥15 predicted a poor rate of survival. High UFR setting >25 mL/kg/h in CVVH with AN69 membranes may reduce the mortality risk in these high-risk patients.

Galectin-3 reportedly participates in the inflammatory process that causes insulin resistance in the target tissues. However, the role of high plasma galectin-3 levels as an indicator of protein-energy wasting (PEW) in patients undergoing maintenance hemodialysis remains unclear. This study included 240 hemodialysis patients (64.5 [55.3−74.0] years, 35.8% women) from a tertiary medical center. A baseline assessment of demographic and clinical data, biochemical parameters, and body composition was conducted. Plasma galectin-3 and other biomarkers were measured using a multiplex bead-based immunoassay. Participants were then divided into two subgroups depending on the median value of plasma galectin-3. Malnutrition was identified using the geriatric nutritional risk index (GNRI) and the criteria of the International Society of Renal Nutrition and Metabolism. Independent risk factors for elevated plasma galectin-3 and malnutrition were identified by multivariate logistic regression. The high galectin-3 group was more likely to be older, have lower lean tissue mass and GNRI scores, be diagnosed with PEW, dialyze through a tunneled catheter, and have higher circulating IL-6, TNF-α, and MCP-1 concentrations than the low galectin-3 group. After multivariate adjustment, only low mean arterial pressure, dialyzing with tunneled cuffed catheters, and elevated systemic inflammatory markers correlated with high galectin-3 levels. Plasma galectin-3 concentrations also increased significantly in hemodialysis patients with PEW. However, compared with other commonly used nutritional indicators, galectin-3 did not show superiority in predicting PEW. Although the plasma galectin-3 levels correlated with PEW severity, this correlation disappeared after adjustment for potential confounding variables (OR, 1.000; 95% CI, 0.999–1.001). In conclusion, plasma galectin-3 is a valuable biomarker for systemic inflammation but is less prominent for PEW in patients with maintenance hemodialysis. Further identification of novel biomarkers is required to detect patients at risk for malnutrition and implement appropriate interventions.

Protein-energy wasting (PEW) is associated with adverse outcomes in hemodialysis patients. This study compares the simplified creatinine index (SCI) and circulating inflammatory markers as nutritional screening tools for hemodialysis patients. Maintenance hemodialysis patients (230 total patients, 34.8% women, 64.0 ± 14.3 years old) from a tertiary medical center were assessed for demographic data, body composition analysis, biochemistry tests, and circulating inflammatory biomarkers. The SCI was calculated using Canaud’s formula. Reduced fat-free mass index (FFMI), a surrogate of lean body mass, was identified according to the European Society for Clinical Nutrition and Metabolism guidelines. Nutritional status was assessed by the geriatric nutritional risk index (GNRI) and International Society of Renal Nutrition and Metabolism (ISRNM) criteria. Multivariate logistic regression revealed independent risk factors for low FFMI and malnutrition. Of the patients, 47.4% had low FFMI. Patients with a reduction in FFMI tended to be older females with lower body mass index, SCI, and GNRI scores but significantly higher levels of interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α), and IL-8. SCI was found to be an independent predictor for reduced FFMI (OR 0.57, 95% CI 0.40–0.81) and presence of PEW according to ISRNM criteria (OR 0.38, 95% CI 0.21–0.68). Although a positive association between systemic inflammatory markers and low FFMI was observed, this association disappeared in multivariate analysis. Moreover, the inflammatory markers examined in this study were not associated with malnutrition after adjusting for potential confounders. Compared with markers of systemic inflammation, SCI achieved better performance in assessing the nutritional status of hemodialysis patients.