Exploring causal effects and potential mediating mechanisms of genetically linked environmental senses with intracerebral hemorrhage.

The occurrence mechanism of intracerebral hemorrhage remains unclear. Several recent studies have highlighted the close relationship between environmental senses and intracerebral hemorrhage, but the mechanisms of causal mediation are inconclusive. We aimed to investigate the causal relationships and potential mechanisms between environmental senses and intracerebral hemorrhage. Multiple Mendelian randomization methods were used to identify a causal relationship between environmental senses and intracerebral hemorrhage. Gut microbiota and brain imaging phenotypes were used to find possible mediators. Enrichment and molecular interaction analyses were used to identify potential mediators and molecular targets. No causal relationship between temperature and visual perception with intracerebral hemorrhage was found, whereas long-term noise was identified as a risk factor for intracerebral hemorrhage (OR 2.95, 95% CI: 1.25 to 6.93, PIVW = 0.01). The gut microbiota belonging to the class Negativicutes and the order Selenomonadales and the brain image-derived phenotypes ICA100 node 54, edge 803, edge 1149, and edge 1323 played mediating roles. "Regulation of signaling and function in synaptic organization" is the primary biological pathway of noise-induced intracerebral hemorrhage, and ARHGAP22 may be the critical gene. This study emphasized the importance of environmental noise in the prevention, disease management, and underlying biological mechanisms of intracerebral hemorrhage.

A consensual machine-learning-assisted QSAR model for effective bioactivity prediction of xanthine oxidase inhibitors using molecular fingerprints.

Xanthine oxidase inhibitors (XOIs) have been widely studied due to the promising potential as safe and effective therapeutics in hyperuricemia and gout. Currently, available XOI molecules have been developed from different experiments but they are with the wide structure diversity and significant varying bioactivities. So it is of great practical significance to present a consensual QSAR model for effective bioactivity prediction of XOIs based on a systematic compiling of these XOIs across different experiments. In this work, 249 XOIs belonging to 16 scaffolds were collected and were integrated into a consensual dataset by introducing the concept of IC50 values relative to allopurinol (RIC50). Here, extended connectivity fingerprints (ECFPs) were employed to represent XOI molecules. By performing effective feature selection by machine-learning method, 54 crucial fingerprints were indicated to be valuable for predicting the inhibitory potency (IP) of XOIs. The optimal predictor yields the promising performance by different cross-validation tests. Besides, an external validation of 43 XOIs and a case study on febuxostat also provide satisfactory results, indicating the powerful generalization of our predictor. Here, the predictor was interpreted by shapely additive explanation (SHAP) method which revealed several important substructures by mapping the featured fingerprints to molecular structures. Then, 15 new molecules were designed and predicted by our predictor to show superior IP than febuxostat. Finally, molecular docking simulation was performed to gain a deep insight into molecular binding mode with xanthine oxidase (XO) enzyme, showing that molecules with selenazole moiety, cyano group and isopropyl group tended to yield higher IP. The absorption, distribution, metabolism, excretion and toxicity (ADMET) prediction results further enhanced the potential of these novel XOIs as drug candidates. Overall, this work presents a QSAR model for accurate prediction of IP of XOIs, and is expected to provide new insights for further structure-guided design of novel XOIs.

Combining bioinformatics and machine learning algorithms to identify and analyze shared biomarkers and pathways in COVID-19 convalescence and diabetes mellitus.

Background: Most patients who had coronavirus disease 2019 (COVID-19) fully recovered, but many others experienced acute sequelae or persistent symptoms. It is possible that acute COVID-19 recovery is just the beginning of a chronic condition. Even after COVID-19 recovery, it may lead to the exacerbation of hyperglycemia process or a new onset of diabetes mellitus (DM). In this study, we used a combination of bioinformatics and machine learning algorithms to investigate shared pathways and biomarkers in DM and COVID-19 convalescence. Methods: Gene transcriptome datasets of COVID-19 convalescence and diabetes mellitus from Gene Expression Omnibus (GEO) were integrated using bioinformatics methods and differentially expressed genes (DEGs) were found using the R programme. These genes were also subjected to Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to find potential pathways. The hub DEGs genes were then identified by combining protein-protein interaction (PPI) networks and machine learning algorithms. And transcription factors (TFs) and miRNAs were predicted for DM after COVID-19 convalescence. In addition, the inflammatory and immune status of diabetes after COVID-19 convalescence was assessed by single-sample gene set enrichment analysis (ssGSEA). Results: In this study, we developed genetic diagnostic models for 6 core DEGs beteen type 1 DM (T1DM) and COVID-19 convalescence and 2 core DEGs between type 2 DM (T2DM) and COVID-19 convalescence and demonstrated statistically significant differences (p<0.05) and diagnostic validity in the validation set. Analysis of immune cell infiltration suggests that a variety of immune cells may be involved in the development of DM after COVID-19 convalescence. Conclusion: We identified a genetic diagnostic model for COVID-19 convalescence and DM containing 8 core DEGs and constructed a nomogram for the diagnosis of COVID-19 convalescence DM.