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Introduction: Both hypoxia and fibroblast growth factor-23 (FGF-23) are key factors in ischemia-reperfusion (I/R)-induced acute kidney injury (AKI). This study aimed to explore the relationship between hypoxia and FGF-23 in AKI. Methods: An I/R-AKI animal model was established using male BALB/c mice. HK-2 cells, a part of the human proximal tubular epithelial cell line, were subjected to hypoxia/reoxygenation (H/R). qPCR was used to measure FGF-23 and HIF1α, and ELISA was used to measure inflammatory and oxidative stress cytokines. Western blotting was used to measure the phosphorylation of extracellular signal-regulated kinase (ERK) level. Results: In I/R mice, the levels of interleukin-6 (IL-6), tumor necrosis factor (TNF-α), malondialdehyde (MDA), and the phosphorylation of ERK (p-ERK) were increased, whereas the levels of interleukin-10 (IL-10), superoxide dismutase (SOD), glutathione peroxidase (GPx), and klotho were decreased, compared to the sham-operated mice. Silencing the FGF-23 expression in I/R mice normalized the levels of IL-6, IL-10, TNF-α, MDA, SOD, GPx, and p-ERK. In HK-2 cells, hypoxia-reperfusion (H/R) elevated the levels of IL-6, TNF-α, MDA, and p-ERK, but reduced IL-10, SOD, GPx, and klotho levels. Hypoxia induced apoptosis in HK-2 cells, but silencing of FGF-23 expression blocked the effects of hypoxia on cell apoptosis, pro-inflammatory factor levels, oxidative stress response, and p-ERK levels. Conclusion: FGF-23 is a key molecule in AKI, and hypoxia plays a crucial role in AKI by inducing cell apoptosis; however, its role is regulated by FGF-23. FGF-23 affects oxidative stress and the inflammatory response of kidney tissues by activating the ERK/mitogen-activated protein kinase (MAPK) signaling pathway.

Abstract Background Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus (SLE) with poor treatment outcomes. The role and underlying mechanisms of ferroptosis in LN remain largely unknown. We aimed to explore ferroptosis-related molecular subtypes and assess their prognostic value in LN patients. Methods Molecular subtypes were classified on the basis of differentially expressed ferroptosis-related genes (FRGs) via the Consensus ClusterPlus package. The enriched functions and pathways, immune infiltrating levels, immune scores, and immune checkpoints were compared between the subgroups. A scoring algorithm based on the subtype-specific feature genes identified by artificial neural network machine learning, referred to as the NeuraLN, was established, and its immunological features, clinical value, and predictive value were evaluated in patients with LN. Finally, immunohistochemical analysis was performed to validate the expression and role of feature genes in glomerular tissues from LN patients and controls. Results A total of 10 differentially expressed FRGs were identified, most of which showed significant correlation. Based on the 10 FRGs, LN patients were classified into two ferroptosis subtypes, which exhibited significant differences in immune cell abundances, immune scores, and immune checkpoint expression. A NeuraLN-related protective model was established based on nine subtype-specific genes, and it exhibited a robustly predictive value in LN. The nomogram and calibration curves demonstrated the clinical benefits of the protective model. The high-NeuraLN group was closely associated with immune activation. Clinical specimens demonstrated the alterations of ALB, BHMT, GAMT, GSTA1, and HAO2 were in accordance with bioinformatics analysis results, GSTA1 and BHMT were negatively correlated with the severity of LN. Conclusion The classification of ferroptosis subtypes and the establishment of a protective model may form a foundation for the personalized treatment of LN patients.

Introduction The study presented here aimed to establish a predictive model for heart failure (HF) and all-cause mortality in peritoneal dialysis (PD) patients with machine learning (ML) algorithm.Methods We retrospectively included 1006 patients who initiated PD from 2010 to 2016. XGBoost, random forest (RF), and AdaBoost were used to train models for assessing risk for 1-year and 5-year HF hospitalization and mortality. The performance was validated using fivefold cross-validation. The optimal ML algorithm was used to construct the models to predictive the risk of the HF and all-cause mortality. The prediction performance of ML methods and Cox regression was compared.Results Over a median follow-up of 49 months. Two hundred and ninety-eight patients developed HF required hospitalization; 199 patients died during the follow-up. The RF model (AUC = 0.853) was the best performing model for predicting HF, and the XGBoost model (AUC = 0.871) was the best model for predicting mortality. Baseline moderate or severe renal disease, systolic blood pressure (SBP), body mass index (BMI), age, Charlson Comorbidity Index (CCI) score were strongly associated with HF hospitalization, whereas age, CCI score, creatinine, age, high-density lipoprotein cholesterol (HDL-C), total cholesterol, baseline estimated glomerular filtration rate (eGFR) were the most significant predictors of mortality. For all the above endpoints, the ML models demonstrated better discrimination than Cox regression.Conclusions We developed and validated a novel method to predict the risk factors of HF and all-cause mortality that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among PD patients.

Posterior reversible encephalopathy syndrome (PRES) is a rare clinical disease, which has been seen in patients with systemic lupus erythematosus (SLE). Its main manifestations are seizure, headache and other neurological symptoms. While the condition is reversible, if not treated in time, there can be risks of cerebral haemorrhage. We report here the case of a young patient with SLE who developed PRES after receiving the immunosuppressant, mycophenolate mofetil. Neurological symptoms, signs, or changes in a patient’s condition that cannot be explained by lupus, should alert physicians to the possibility of the drug causing PRES, and prompt discontinuation should ensue.

Abstract Background Peritoneal catheter related infection is one of the main complications and the major cause of technical failure of peritoneal dialysis (PD) treatment. However, PD catheter tunnel infection can be difficult to diagnosis and resolve. We presented a rare case in which a granuloma formation after repeated episodes of peritoneal dialysis catheter–related infection. Case presentation A 53-year-old female patient with kidney failure due to chronic glomerulonephritis treated with peritoneal dialysis for 7 years. The patient had repeated exit-site and tunnel inflammation, and repeated suboptimal courses of antibiotics. She switched to hemodialysis after 6 years in a local hospital without the peritoneal dialysis catheter being removed. The patient complained of an abdominal wall mass that lasted for several months. She was admitted to the Department of surgery to undergo mass resection. The resected tissue of the abdominal wall mass was sent for pathological examination. The result showed foreign body granuloma with necrosis and abscess formation. After the surgery, the infection did not recur. Conclusions The following key points can be learned from this case: 1. It is important to strengthen patient follow-up. 2.The PD catheter should be removed as early as possible in patients who do not need long-term PD, especially in patients with a history of exit-site and tunnel infections. 3. For patients presenting abnormal subcutaneous mass, attention should be paid to the possibility of the granuloma formation of infected Dacron cuffs of the PD catheter. If catheter infection occurs repeatedly, catheter removal and debridement should be considered.

Chronic kidney disease (CKD) is a global public health problem, and cardiovascular disease is the most common cause of death in patients with CKD. The incidence and prevalence of cardiovascular events during the early stages of CKD increases significantly with a decline in renal function. More than 50% of dialysis patients die from cardiovascular disease, including coronary heart disease, heart failure, arrhythmia, and sudden cardiac death. Therefore, developing effective methods to control risk factors and improve prognosis is the primary focus during the diagnosis and treatment of CKD. For example, the SPRINT study demonstrated that CKD drugs are effective in reducing cardiovascular and cerebrovascular events by controlling blood pressure. Uncontrolled blood pressure not only increases the risk of these events but also accelerates the progression of CKD. A co-crystal complex of sacubitril, which is a neprilysin inhibitor, and valsartan, which is an angiotensin receptor blockade, has the potential to be widely used against CKD. Sacubitril inhibits neprilysin, which further reduces the degradation of natriuretic peptides and enhances the beneficial effects of the natriuretic peptide system. In contrast, valsartan alone can block the angiotensin II-1 (AT1) receptor and therefore inhibit the renin–angiotensin–aldosterone system. These two components can act synergistically to relax blood vessels, prevent and reverse cardiovascular remodeling, and promote natriuresis. Recent studies have repeatedly confirmed that the first and so far the only angiotensin receptor–neprilysin inhibitor (ARNI) sacubitril/valsartan can reduce blood pressure more effectively than renin–angiotensin system inhibitors and improve the prognosis of heart failure in patients with CKD. Here, we propose clinical recommendations based on an expert consensus to guide ARNI-based therapeutics and reduce the occurrence of cardiovascular events in patients with CKD.

Methylation is one of the most extensive modifications of biological macromolecules and affects cell-fate determination, development, aging, and cancer. Several methylation modifications, including 5-methylcytosine and N6-methyladenosine, play an essential role in many cancers. However, little is known about the relationship between methylation and the prognosis of clear cell renal cell carcinoma (ccRCC). Here, we established a methylation-regulating genes prognostic signature (MRGPS) to predict the prognoses of ccRCC patients. We obtained ccRCC samples from The Cancer Genome Atlas and identified methylation-regulatingd genes (MRGs) from the Gene Set Enrichment Analysis database. We also determined differentially expressed genes (DEGs) and performed cluster analysis to identify candidate genes. Subsequently, we established and validated an MRGPS to predict the overall survival of ccRCC patients. This was also verified in 15 ccRCC samples collected from the Fujian Provincial Hospital via quantitative real-time transcription (qRT-PCR). While 95 MRGs were differentially expressed (DEGs1) between tumor and normal tissues, 17 MRGs were differentially expressed (DEGs2) between cluster 1 and 2. Notably, 13 genes common among DEGs1 and DEGs2 were identified as hub genes. In fact, we established three genes (NOP2, NSUN6, and TET2) to be an MRGPS based on their multivariate Cox regression analysis coefficients (p < 0.05). A receiver operating characteristic curve analysis confirmed this MRGPS to have a good prognostic performance. Moreover, the MRGPS was associated with characteristics of the tumor immune microenvironment and responses to inhibitor checkpoint inhibitors. Data from “IMvigor 210” demonstrated that patients with a low MRGPS would benefit more from atelozumab (p < 0.05). Furthermore, a multivariate analysis revealed that MRGPS was an independent risk factor associated with ccRCC prognosis (p < 0.05). Notably, a nomogram constructed by combining with clinical characteristics (age, grade, stage, and MRGPS risk score) to predict the overall survival of a ccRCC patient had a favorable predictive value. Eventually, our qRT-PCR results showed that tumor tissues had higher NOP2 and NSUN6 expression levels and lower TET2 expression than normal tissues of ccRCC samples. While the proposed MRGPS comprising NOP2, NSUN6, and TET2 can be an alternative prognostic biomarker for ccRCC patients, it is a promising index for personalized ICI treatments against ccRCC.