Characterization of the m6A regulators' landscape highlights the clinical significance of acute myocardial infarction.

Objective: Acute myocardial infarction (AMI) is a severe cardiovascular disease that threatens human life and health globally. N6-methyladenosine (m6A) governs the fate of RNAs via m6A regulators. Nevertheless, how m6A regulators affect AMI remains to be deciphered. To solve this issue, an integrative analysis of m6A regulators in AMI was conducted. Methods: We acquired transcriptome profiles (GSE59867, GSE48060) of peripheral blood samples from AMI patients and healthy controls. Key m6A regulators were used for LASSO, and consensus clustering was conducted. Next, the m6A score was also computed. Immune cell infiltration, ferroptosis, and oxidative stress were evaluated. In-vitro and in-vivo experiments were conducted to verify the role of the m6A regulator ALKBH5 in AMI. Results: Most m6A regulators presented notable expression alterations in circulating cells of AMI patients versus those of controls. Based on key m6A regulators, we established a gene signature and a nomogram for AMI diagnosis and risk prediction. AMI patients were classified into three m6A clusters or gene clusters, respectively, and each cluster possessed the unique properties of m6A modification, immune cell infiltration, ferroptosis, and oxidative stress. Finally, the m6A score was utilized to quantify m6A modification patterns. Therapeutic targeting of ALKBH5 greatly alleviated apoptosis and intracellular ROS in H/R-induced H9C2 cells and NRCMs. Conclusion: Altogether, our findings highlight the clinical significance of m6A regulators in the diagnosis and risk prediction of AMI and indicate the critical roles of m6A modification in the regulation of immune cell infiltration, ferroptosis, and oxidative stress.

Rapid diagnosis of membranous nephropathy based on serum and urine Raman spectroscopy combined with deep learning methods.

Membranous nephropathy is the main cause of nephrotic syndrome, which has an insidious onset and may progress to end-stage renal disease with a high mortality rate, such as renal failure and uremia. At present, the diagnosis of membranous nephropathy mainly relies on the clinical manifestations of patients and pathological examination of kidney biopsy, which are expensive, time-consuming, and have certain chance and other disadvantages. Therefore, there is an urgent need to find a rapid, accurate and non-invasive diagnostic technique for the diagnosis of membranous nephropathy. In this study, Raman spectra of serum and urine were combined with deep learning methods to diagnose membranous nephropathy. After baseline correction and smoothing of the data, Gaussian white noise of different decibels was added to the training set for data amplification, and the amplified data were imported into ResNet, AlexNet and GoogleNet models to obtain the evaluation results of the models for membranous nephropathy. The experimental results showed that the three deep learning models achieved an accuracy of 1 for the classification of serum data of patients with membranous nephropathy and control group, and the discrimination of urine data was above 0.85, among which AlexNet was the best classification model for both samples. The above experimental results illustrate the great potential of serum- and urine-based Raman spectroscopy combined with deep learning methods for rapid and accurate identification of patients with membranous nephropathy.