Prognostic significance of factor XIIIA promoter methylation status in aneurysmal subarachnoid haemorrhage (aSAH)

Differentiation between descending thoracic aortic diseases using machine learning and plasma proteomic signatures

BACKGROUND

Descending thoracic aortic aneurysms and dissections can go undetected until severe and catastrophic, and few clinical indices exist to screen for aneurysms or predict risk of dissection.

METHODS

This study generated a plasma proteomic dataset from 75 patients with descending type B dissection (Type B) and 62 patients with descending thoracic aortic aneurysm (DTAA). Standard statistical approaches were compared to supervised machine learning (ML) algorithms to distinguish Type B from DTAA cases. Quantitatively similar proteins were clustered based on linkage distance from hierarchical clustering and ML models were trained with uncorrelated protein lists across various linkage distances with hyperparameter optimization using fivefold cross validation. Permutation importance (PI) was used for ranking the most important predictor proteins of ML classification between disease states and the proteins among the top 10 PI protein groups were submitted for pathway analysis.

RESULTS

Of the 1,549 peptides and 198 proteins used in this study, no peptides and only one protein, hemopexin (HPX), were significantly different at an adjusted p < 0.01 between Type B and DTAA cases. The highest performing model on the training set (Support Vector Classifier) and its corresponding linkage distance (0.5) were used for evaluation of the test set, yielding a precision-recall area under the curve of 0.7 to classify between Type B from DTAA cases. The five proteins with the highest PI scores were immunoglobulin heavy variable 6-1 (IGHV6-1), lecithin-cholesterol acyltransferase (LCAT), coagulation factor 12 (F12), HPX, and immunoglobulin heavy variable 4-4 (IGHV4-4). All proteins from the top 10 most important groups generated the following significantly enriched pathways in the plasma of Type B versus DTAA patients: complement activation, humoral immune response, and blood coagulation.

CONCLUSIONS

We conclude that ML may be useful in differentiating the plasma proteome of highly similar disease states that would otherwise not be distinguishable using statistics, and, in such cases, ML may enable prioritizing important proteins for model prediction.

Optimal Course of Statins for Patients With Aneurysmal Subarachnoid Hemorrhage: Is Longer Treatment Better? A Meta-Analysis of Randomized Controlled Trials

Statins are used in clinical practice to prevent from complications such as cerebral vasospasm (CVS) after aneurysmal subarachnoid hemorrhage (aSAH). However, the efficacy and safety of statins are still controversial due to insufficient evidence from randomized controlled trials and inconsistent results of the existing studies. This meta-analysis aimed to systematically review the latest evidence on the time window and complications of statins in aSAH. The randomized controlled trials in the databases of The Cochrane Library, PubMed, Web of Science, Embase, CNKI, and Wanfang from January 2005 to April 2021 were searched and analyzed systematically. Data analysis was performed using Stata version 16.0. The fixed-effects model (M-H method) with effect size risk ratio (RR) was used for subgroups with homogeneity, and the random-effects model (D-L method) with effect size odds ratio (OR) was used for subgroups with heterogeneity. The primary outcomes were poor neurological prognosis and all-cause mortality, and the secondary outcomes were cerebral vasospasm (CVS) and statin-related complications. This study was registered with PROSPERO (International Prospective Register of Systematic Reviews; CRD42021247376). Nine studies comprising 1,464 patients were included. The Jadad score of the patients was 5–7. Meta-analysis showed that poor neurological prognosis was reduced in patients who took oral statins for 14 days (RR, 0.73 [0.55–0.97]; I² = 0%). Surprisingly, the continuous use of statins for 21 days had no significant effect on neurological prognosis (RR, 1.04 [0.89–1.23]; I² = 17%). Statins reduced CVS (OR, 0.51 [0.36–0.71]; I² = 0%) but increased bacteremia (OR, 1.38 [1.01–1.89]; I² = 0%). In conclusion, a short treatment course of statins over 2 weeks may improve neurological prognosis. Statins were associated with reduced CVS. Based on the pathophysiological characteristics of CVS and the evaluation of prognosis, 2 weeks could be the optimal time window for statin treatment in aSAH, although bacteremia may increase.

Studies on the Mechanism of Gegen Qinlian Decoction in Treating Diabetes Mellitus Based on Network Pharmacology

Diabetes mellitus (DM) is a chronic disease that is very common and seriously threatens patient health. Gegen Qinlian decoction (GQD) has long been applied clinically, but its mechanism in pharmacology has not been extensively and systematically studied. A GQD protein interaction network and diabetes protein interaction network were constructed based on the methods of system biology. Functional module analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Gene Ontology (GO) enrichment analysis were carried out on the 2 networks. The hub nodes were filtered by comparative analysis. The topological parameters, interactions, and biological functions of the 2 networks were analyzed in multiple ways. By applying GEO-based external datasets to verify the results of our analysis that the Gene Set Enrichment Analysis (GSEA) displayed metabolic pathways in which hub genes played roles in regulating different expression states. Molecular docking is used to verify the effective components that can be combined with hub nodes. By comparing the 2 networks, 24 hub targets were filtered. There were 7 complex relationships between the networks. The results showed 4 topological parameters of the 24 selected hub targets that were much higher than the median values, suggesting that these hub targets show specific involvement in the network. The hub genes were verified in the GEO database, and these genes were closely related to the biological processes involved in glucose metabolism. Molecular docking results showed that 5,7,2', 6'-tetrahydroxyflavone, magnograndiolide, gancaonin I, isoglycyrol, gancaonin A, worenine, and glyzaglabrin produced the strongest binding effect with 10 hub nodes. This compound–target mode of interaction may be the main mechanism of action of GQD. This study reflected the synergistic characteristics of multiple targets and multiple pathways of traditional Chinese medicine and discussed the mechanism of GQD in the treatment of DM at the molecular pharmacological level.