A fast and efficient liquid chromatography-tandem mass spectrometry method for measuring l- and d-amino acids in the urine of patients with immunoglobulin A nephropathy.

Immunoglobulin nephropathy (IgAN) stands as the most prevalent primary glomerular nephropathy globally, typically diagnosed through an invasive renal biopsy. Emerging research suggests the significant involvement of chiral amino acids in kidney disease progression. This study introduces a nonderivative LC-tandem mass spectrometry approach, offering efficient separation outcomes within 15 min for identifying chiral amino acids in human urine samples. Subsequently, using this method, the analysis of l- and d-amino acids in the urine of both patients with IgAN and healthy individuals was conducted. Fourteen d-amino acids and 20 l-amino acids were identified in the urine samples obtained from 17 patients with IgAN and 21 healthy individuals. The results indicated notable variances in the concentrations of both l- and d-amino acids between the IgAN and healthy control groups. In contrast to the healthy group, the IgAN group exhibited higher mean urine concentrations of most l-amino acids and lower concentrations of d-amino acids. Furthermore, correlations between amino acids and clinical markers were investigated. These results propose a novel method for monitoring trace amino acids in urine samples and introduce a new concept for potential markers of IgAN.

Capsaicin alleviates doxorubicin-induced acute myocardial injury by regulating iron homeostasis and PI3K-Akt signaling pathway.

BACKGROUND: Capsaicin (CAP), a frequently occurring alkaloid component found in spicy peppers, has demonstrated therapeutic potential against tumors, metabolic disease, and cardiovascular disorders. Doxorubicin (DOX), a widely used anthracycline drug in chemotherapy, is notorious for its cardiotoxicity. This study aimed to investigate the potential of CAP in mitigating DOX toxicity in mouse hearts and H9C2 cells, as well as to explore the underlying mechanisms. METHODS: In our study, we conducted experiments on both mice and H9C2 cells. The mice were divided into four groups and treated with different substances: normal saline, CAP, DOX and CAP+DOX. We evaluated the induction of ferroptosis by DOX and the remission of ferroptosis by CAP using various methods, including echocardiography, Hematoxylin and Eosin (H&E) staining, Masson's trichrome staining, and determination of ferroptosis metabolites, genes and proteins. Additionally, we employed RNA-seq to identify the inhibitory effect of CAP on DOX-induced myocardial apoptosis, which was further confirmed through western blotting. Similar approaches were applied to H9C2 cells, yielding reliable results. RESULTS: Our study demonstrated that treatment with CAP improved the survival rate of DOX-treated mice and reduced myocardial injury. Mechanistically, CAP downregulated transferrin (Trf) and upregulated solute carrier family 40 member 1 (SLC40A1), which helped maintain iron levels in the cells and prevent ferroptosis. Furthermore, CAP inhibited DOX-induced apoptosis by modulating the phosphoinositide 3-kinase (PI3K)- protein kinase B (Akt) signaling pathway. Specifically, CAP activated the PI3K-Akt pathway and regulated downstream BCL2 and BAX to mitigate DOX-induced apoptosis. Therefore, our results suggest that CAP effectively alleviates acute myocardial injury induced by DOX. CONCLUSION: Our findings demonstrate that CAP has the potential to alleviate DOX-induced ferroptosis by regulating iron homeostasis. Additionally, it can inhibit DOX-induced apoptosis by activating PI3K-Akt signaling pathway.

Using Bayesian network model with MMHC algorithm to detect risk factors for stroke.

Stroke is a major chronic non-communicable disease with high incidence, high mortality, and high recurrence. To comprehensively digest its risk factors and take some relevant measures to lower its prevalence is of great significance. This study aimed to employ Bayesian Network (BN) model with Max-Min Hill-Climbing (MMHC) algorithm to explore the risk factors for stroke. From April 2019 to November 2019, Shanxi Provincial People's Hospital conducted opportunistic screening for stroke in ten rural areas in Shanxi Province. First, we employed propensity score matching (PSM) for class balancing for stroke. Afterwards, we used Chi-square testing and Logistic regression model to conduct a preliminary analysis of risk factors for stroke. Statistically significant variables were incorporated into BN model construction. BN structure learning was achieved using MMHC algorithm, and its parameter learning was achieved with Maximum Likelihood Estimation. After PSM, 748 non-stroke cases and 748 stroke cases were included in this study. BN was built with 10 nodes and 12 directed edges. The results suggested that age, fasting plasma glucose, systolic blood pressure, and family history of stroke constitute direct risk factors for stroke, whereas sex, educational levels, high density lipoprotein cholesterol, diastolic blood pressure, and urinary albumin-to-creatinine ratio represent indirect risk factors for stroke. BN model with MMHC algorithm not only allows for a complicated network relationship between risk factors and stroke, but also could achieve stroke risk prediction through Bayesian reasoning, outshining traditional Logistic regression model. This study suggests that BN model boasts great prospects in risk factor detection for stroke.