Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients.

BACKGROUND: Acute myocardial infarction (AMI) has two clinical characteristics: high missed diagnosis and dysfunction of leukocytes. Transcriptional RNA on leukocytes is closely related to the course evolution of AMI patients. We hypothesized that transcriptional RNA in leukocytes might provide potential diagnostic value for AMI. Integration machine learning (IML) was first used to explore AMI discrimination genes. The following clinical study was performed to validate the results. METHODS: A total of four AMI microarrays (derived from the Gene Expression Omnibus) were included in bioanalysis (220 sample size). Then, the clinical validation was finished with 20 AMI and 20 stable coronary artery disease patients (SCAD). At a ratio of 5:2, GSE59867 was included in the training set, while GSE60993, GSE62646, and GSE48060 were included in the testing set. IML was explicitly proposed in this research, which is composed of six machine learning algorithms, including support vector machine (SVM), neural network (NN), random forest (RF), gradient boosting machine (GBM), decision trees (DT), and least absolute shrinkage and selection operator (LASSO). IML had two functions in this research: filtered optimized variables and predicted the categorized value. Finally, The RNA of the recruited patients was analyzed to verify the results of IML. RESULTS: Thirty-nine differentially expressed genes (DEGs) were identified between controls and AMI individuals from the training sets. Among the thirty-nine DEGs, IML was used to process the predicted classification model and identify potential candidate genes with overall normalized weights > 1. Finally, two genes (AQP9 and SOCS3) show their diagnosis value with the area under the curve (AUC) > 0.9 in both the training and testing sets. The clinical study verified the significance of AQP9 and SOCS3. Notably, more stenotic coronary arteries or severe Killip classification indicated higher levels of these two genes, especially SOCS3. These two genes correlated with two immune cell types, monocytes and neutrophils. CONCLUSION: AQP9 and SOCS3 in leukocytes may be conducive to identifying AMI patients with SCAD patients. AQP9 and SOCS3 are closely associated with monocytes and neutrophils, which might contribute to advancing AMI diagnosis and shed light on novel genetic markers. Multiple clinical characteristics, multicenter, and large-sample relevant trials are still needed to confirm its clinical value.

Highly Water-Stable and Efficient Hydrogen-Producing Heterostructure Synthesized from Mn0.5Cd0.5S and a Zeolitic Imidazolate Framework ZIF-8 via Ligand and Cation Exchange.

Developing highly water-stable zeolitic imidazolate frameworks (ZIFs) for visible-light-driven photocatalytic hydrolysis is important and challenging. Herein, the Type II heterojunction catalyst Mn0.5Cd0.5S@ZIF-8 and its derivatives (including MCS@ZIF-8-Mn, MCS@ZIF-8-Br, and MCS@ZIF-8-MB) were successfully constructed using a facile strategy. Through dual postsynthetic ligand and cation exchange (PSE) treatments of Mn(Ac)2·4H2O and 4-bromo-1H-imidazole for ZIF-8, the hydrogen production efficiency of the MCS@ZIF-8-MB heterojunction catalyst can reach 5.450 mmol·g-1·h-1 and remain at 97.11% after 9 h of the stability test. Construction of heterojunctions can effectively improve the hydrogen production performance of Mn0.5Cd0.5S while maintaining excellent water stability. X-ray photoelectron spectroscopy results show that upon successful construction of the MCS@ZIF-8-MB heterojunction an interface forms between the surface of MCS and ZIF-8-MB, effectively weakening the photocorrosion of MCS. Density functional theory calculations also indicate that the induction of Mn can increase the electronic states of p and d orbitals near the Fermi level of ZIF-8, suggesting that Mn(II) attracts more electrons than Zn(II). This provides more powerful theoretical evidence for the electron cloud shift from the electron donor S2- to Mn2+.

Metal Mesh and Narrow Band Gap Mn0.5Cd0.5S Photocatalyst Cooperation for Efficient Hydrogen Production.

A novel co-catalyst system under visible-light irradiation was constructed using high-purity metal and alloy mesh and a Mn0.5Cd0.5S photocatalyst with a narrow band gap (1.91 eV) prepared by hydrothermal synthesis. The hydrogen production rate of Mn0.5Cd0.5S changed from 2.21 to 6.63 mmol·(g·h)-1 with the amount of thioacetamide, which was used as the sulphur source. The introduction of Ag, Mo, Ni, Cu, and Cu-Ni alloy meshes efficiently improved the H2 production rate of the co-catalyst system, especially for the Ni mesh. The improvement can reach an approximately six times greater production, with the highest H2 production rate being 37.65 mmol·(g·h)-1. The results showed that some bulk non-noble metal meshes can act as good or better than some noble metal nanoparticles deposited on the main photocatalyst for H2 evolution due to the promotion of photoinduced electron transfer, increase in redox reaction sites, and prevention of the recombination of carriers.

Multiple-model machine learning identifies potential functional genes in dilated cardiomyopathy.

Introduction: Machine learning (ML) has gained intensive popularity in various fields, such as disease diagnosis in healthcare. However, it has limitation for single algorithm to explore the diagnosing value of dilated cardiomyopathy (DCM). We aim to develop a novel overall normalized sum weight of multiple-model MLs to assess the diagnosing value in DCM. Methods: Gene expression data were selected from previously published databases (six sets of eligible microarrays, 386 samples) with eligible criteria. Two sets of microarrays were used as training; the others were studied in the testing sets (ratio 5:1). Totally, we identified 20 differently expressed genes (DEGs) between DCM and control individuals (7 upregulated and 13 down-regulated). Results: We developed six classification ML methods to identify potential candidate genes based on their overall weights. Three genes, serine proteinase inhibitor A3 (SERPINA3), frizzled-related proteins (FRPs) 3 (FRZB), and ficolin 3 (FCN3) were finally identified as the receiver operating characteristic (ROC). Interestingly, we found all three genes correlated considerably with plasma cells. Importantly, not only in training sets but also testing sets, the areas under the curve (AUCs) for SERPINA3, FRZB, and FCN3 were greater than 0.88. The ROC of SERPINA3 was significantly high (0.940 in training and 0.918 in testing sets), indicating it is a potentially functional gene in DCM. Especially, the plasma levels in DCM patients of SERPINA3, FCN, and FRZB were significant compared with healthy control. Discussion: SERPINA3, FRZB, and FCN3 might be potential diagnosis targets for DCM, Further verification work could be implemented.