Accumulating evidence indicates the occurrence and development of diabetic complications relates to not only constant high plasma glucose, but also glucose fluctuations which affect various kinds of molecular mechanisms in various target cells and tissues. In this review, we detail reactive oxygen species and their potentially damaging effects upon glucose fluctuations and resultant downstream regulation of protein signaling pathways, including protein kinase C, protein kinase B, nuclear factor-κB, and the mitogen-activated protein kinase signaling pathway. A deeper understanding of glucose-fluctuation-related molecular mechanisms in the development of diabetic complications may enable more potential target therapies in future.

Quercetin has attracted more attention in recent years due to its protective role against ischemia/reperfusion injury. Quercetin can alleviate oxidative stress injury through the inhibition of NADPH oxidase and xanthine oxidase, blockage of the Fenton reaction, and scavenging of reactive oxygen species. Quercetin can also exert anti-inflammatory and anti-apoptotic effects by reducing the response to inflammatory factors and inhibiting cell apoptosis. Moreover, it can induce vasodilation effects through the inhibition of endothelin-1 receptors, the enhancement of NO stimulation and the activation of the large-conductance calcium-activated potassium channels. Finally, Quercetin can also antagonize the calcium overload. These multifaceted activities of Quercetin make it a potential therapeutic alternative for the treatment of ischemia/reperfusion injury.

Background: Mesenchymal stem cells (MSCs) have been widely applied to treat various inflammatory diseases. Inflammatory cytokines can induce both apoptosis and autophagy in MSCs. However, whether autophagy plays a pro- or con-apoptosis effect on MSCs in an inflammatory microenvironment has not been clarified. Methods: We inhibited autophagy by constructing MSCs with lentivirus containing small hairpin RNA to knockdown Beclin-1 and applied these MSCs to a model of sepsis to evaluate therapeutic effect of MSCs. Results: Here we show that inhibition of autophagy in MSCs increases the survival rate of septic mice more than control MSCs, and autophagy promotes apoptosis of MSCs during application to septic mice. Further study demonstrated that autophagy aggravated tumor necrosis factor alpha plus interferon gamma-induced apoptosis of MSCs. Mechanically, autophagy inhibits the expression of the pro-survival gene Bcl-2 via suppressing reactive oxygen species/mitogen-activated protein kinase 1/3 pathway. Conclusions: Our findings indicate that an inflammatory microenvironment-induced autophagy promotes apoptosis of MSCs. Therefore, modulation of autophagy in MSCs would provide a novel approach to improve MSC survival during immunotherapy.

The most critical obstacle preventing lithium–sulfur batteries (LSBs) from practical application is the shuttle effect. Herein, we present a zirconium based metal–organic framework (MOF) composite separator which effectively blocks polysulfides from shuttling without hampering the migration of lithium ions owing to the sieving effect of the MOFs. Moreover, owing to the high Brunauer–Emmett–Teller (BET) surface area of 1418 m² g⁻¹ and chemical interactions with zirconium and oxygen on the MOFs, the polysulfides can also be physically and chemically adsorbed on the surface of the MOFs, which further blocks the polysulfides from shuttling through the intergranular channels of the MOF particles. In addition, the wettability of the composite separator with the electrolyte is enhanced owing to the polar functional groups of –COOH and –OH on the MOFs. The battery with a composite separator delivers an initial discharge capacity of 1239 mA h g⁻¹ at 0.2C and 1147.4 mA h g⁻¹ at 0.5C. The reversible capacity is maintained at 964.1 mA h g⁻¹ with an average fading of only 0.08% per cycle at 0.5C. The capacity at the 2C rate with the composite separator is 955.8 mA h g⁻¹, which is far more than that of the pristine separator, which is 289.2 mA h g⁻¹.

We aimed to screen out biomarkers for atrial fibrillation (AF) based on machine learning methods and evaluate the degree of immune infiltration in AF patients in detail. Two datasets (GSE41177 and GSE79768) related to AF were downloaded from Gene expression omnibus (GEO) database and merged for further analysis. Differentially expressed genes (DEGs) were screened out using “limma” package in R software. Candidate biomarkers for AF were identified using machine learning methods of the LASSO regression algorithm and SVM-RFE algorithm. Receiver operating characteristic (ROC) curve was employed to assess the diagnostic effectiveness of biomarkers, which was further validated in another independent validation dataset of GSE14975. Moreover, we used CIBERSORT to study the proportion of infiltrating immune cells in each sample, and the Spearman method was used to explore the correlation between biomarkers and immune cells. 129 DEGs were identified, and CYBB, CXCR2, and S100A4 were identified as key biomarkers of AF using LASSO regression and SVM-RFE algorithm. Both in the training dataset and the validation dataset, CYBB, CXCR2, and S100A4 showed favorable diagnostic effectiveness. Immune infiltration analysis indicated that, compared with sinus rhythm (SR), the atrial samples of patients with AF contained a higher T cells gamma delta, neutrophils and mast cells resting, whereas T cells follicular helper were relatively lower. Correlation analysis demonstrated that CYBB, CXCR2, and S100A4 were significantly correlated with the infiltrating immune cells. In conclusion, this study suggested that CYBB, CXCR2, and S100A4 are key biomarkers of AF correlated with infiltrating immune cells, and infiltrating immune cells play pivotal roles in AF.