Iron metabolism and chronic inflammation in IgA nephropathy.

IgA nephropathy (IgAN), an immune-mediated chronic inflammatory kidney disease, is the most common primary glomerular disease in Asia, especially in China and Japan. The pathogenesis of IgAN is complex, and the main cause of IgAN is explained by the 'multiple hit' theory, which states that the deposition of immune complexes in renal mesangial cells induces chronic inflammation that leads to kidney damage. Chronic inflammation is associated with iron metabolism, which also plays an essential role in the pathogenesis, progression, diagnosis and prognosis of IgAN. Overall, this review aimed to explore the application of iron metabolism in IgAN by systematically elaborating the relationship between iron metabolism and chronic inflammation in IgAN to speculate on the possible diagnostic and therapeutic significance of iron metabolism indicators in IgAN.

Prognostic value of low-density lipoprotein cholesterol in IgA nephropathy and establishment of nomogram model.

Background: Dyslipidemia is closely related to kidney disease. We aimed to investigate the relationship between low-density lipoprotein cholesterol (LDL-C) and prognosis of IgA nephropathy (IgAN) and build a nomogram prognostic model. Methods: 519 IgAN patients with 61 months median follow-up were enrolled and divided into two groups based on the cut-off value of baseline LDL-C (2.60 mmol/L): the high group (n=253) and the low group (n=266). Renal survival was assessed by Kaplan⁃Meier (KM) survival curve. Risk factors were identified by COX regression analysis. The area under the receiver operating characteristic (ROC) curves (AUC), concordance index (C-index), and calibration curves were applied to evaluate the nomogram model. Results: KM survival curve analysis showed that the high LDL-C group had worse renal survival than the low LDL-C group (χ2 = 8.555, p=0.003). After adjusting for confounding factors, Cox regression analysis showed the baseline LDL-C level was an independent risk factor of end-stage renal disease (ESRD) in IgAN (HR=3.135, 95% CI 1.240~7.926, p =0.016). LDL-C, segmental sclerosis, tubular atrophy/interstitial fibrosis, the prevalence of cardiovascular disease, 24-hour proteinuria were identified and entered into the nomogram models, with AUC of 0.864, 0.827, and 0.792 respectively to predict the 5-, 8-, and 10-year risk of ESRD in IgAN. The C-index of this prediction model was respectively 0.862, 0.838, and 0.800 and was well-calibrated. Conclusion: Elevated LDL-C level is a predictive factor for the prognosis of IgAN. We developed a nomogram model that can predict the risk of ESRD in IgAN by using LDL-C ≥ 2.60 mmol/L.

Ag Nanoparticles with Ultrathin Au Shell-Based Lateral Flow Immunoassay for Colorimetric and SERS Dual-Mode Detection of SARS-CoV-2 IgG.

Immunoglobulin detection is essential for diagnosing progression of SARS-CoV-2 infection, for which SARS-CoV-2 IgG is one of the most important indexes. In this paper, Ag nanoparticles with ultrathin Au shells (∼2 nm) embedded with 4-mercaptobenzoic acid (MBA) (AgMBA@Au) were manufactured via a ligand-assisted epitaxial growth method and integrated into lateral flow immunoassay (LFIA) for colorimetric and SERS dual-mode detection of SARS-CoV-2 IgG. AgMBA@Au possessed not only the surface chemistry advantages of Au but also the superior optical characteristics of Ag. Moreover, the nanogap between the Ag core and the Au shell also greatly enhanced the Raman signal. After being modified with anti-human antibodies, AgMBA@Au recognized and combined with SARS-CoV-2 IgG, which was captured by the SARS-CoV-2 spike protein on the T line. Qualitative analysis was achieved by visually observing the color of the T line, and quantitative analysis was conducted by measuring the SERS signal with a sensitivity four orders of magnitude higher (detection limit: 0.22 pg/mL). The intra-assay and inter-assay variation coefficients were 7.7 and 10.3%, respectively, and other proteins at concentrations of 10 to 20 times higher than those of SARS-CoV-2 IgG could hardly produce distinguishable signals, confirming good reproducibility and specificity. Finally, this method was used to detect 107 clinical serum samples. The results agreed well with those obtained from enzyme-linked immunosorbent assay kits and were significantly better than those of the colloidal gold test strips. Therefore, this dual-mode LFIA has great potential in clinical practical applications and can be used to screen and trace the early immune response of SARS-CoV-2.

Cu2O induced Au nanochains for highly sensitive dual-mode detection of hydrogen sulfide.

Colorimetric and chemoresistive gas sensing methods have aroused great interest in H2S monitoring due to their unique merits of naked-eye readout, and highly sensitive and rapid detection. However, combining these two methods for gas detection, especially utilizing one material as their common sensing material is a grand challenge because they are inconsistent in sensing mechanism. Taking advantage of the strong chemical affinity of Cu2O for H2S and the excellent performance of localized surface plasmon resonance (LSPR) of Au nanoparticles (NPs) in the visible regions and its ability as a noble metal to enhance gas sensing property, the Cu2O-Au nanochains (NCs) were prepared for dual-mode detection of H2S gas. The Cu2O-Au chemoresistive gas sensor shows a 5-fold higher response than Cu2O sensor at room temperature with a low detection limit of 10 ppb. Such good performance is attributed to the spillover effect and catalytic activity of Au NPs, and the enhanced H2S adsorption after Au loading as revealed by density functional theory calculation. Test strips containing Cu2O-Au produced for gaseous H2S detection show superior color gradient changes (blue, yellow, and brown). Finally, the practicability of the method was validated by real-time monitoring H2S released from cell culture.