Objective: The cell cycle-related proteins cyclin B1 (CCNB1) and cyclin B2 (CCNB2) are potentially involved in the underlying mechanisms of psoriasis. The present study aimed to explore this possibility using bioinformatics approaches. Methods: CCNB1 and CCNB2 protein levels were evaluated in 14 psoriasis patients and five healthy controls by enzyme-linked immunosorbent assays, and their mRNA levels were evaluated using data from four publicly available datasets (GSE53552, GSE41664, GSE14905, and GSE13355). Comparison of high- and low-expressing groups were performed to reveal CCNB1- and CCNB2-related differentially expressed genes, which were then assessed based on gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Correlation analyses between CCNB1 and CCNB2 levels and immune infiltration, as well as typical targets of psoriasis, were also performed. Results: Overall, 12 CCNB1 and CCNB2 common immune-related targets potentially involved in psoriasis were identified. These could regulate the cell cycle of through multiple pathways. In addition, CCNB1 and CCNB2 were found to potentially support the release of key molecular targets of psoriasis through the regulation of mast cell activation and macrophage polarization. Conclusions: These findings suggest that CCNB1 and CCNB2 may represent valuable molecular biomarkers of psoriasis, contributing to its onset and progression.

The aim of the study was to investigate whether bivalirudin versus unfractionated heparin (UFH) reduces infarct size (IS) for primary percutaneous coronary intervention (PPCI) in large acute myocardial infarction (AMI). This multicentre open-label trial randomised 78 patients undergoing PPCI for large AMI to bivalirudin or UFH. The primary endpoint was IS, assessed by cardiac magnetic resonance (CMR) five days after PPCI. Secondary endpoints included index of microcirculatory resistance (IMR), CMR-assessed microvascular obstruction (MVO) and ejection fraction, and biomarkers for thrombin activity and cell injury. No difference was observed in mean IS at five days (25.0±19.7 g for bivalirudin vs. 27.1±20.7 g for UFH; p=0.75). Early MVO was numerically lower with bivalirudin (5.3±5.8 g vs. 7.7±6.3 g; p=0.17), with no significant difference in ejection fraction at 90 days (54.6±12.0% vs. 49.1±12.1%; p=0.11). In the biomarkers, thrombin-antithrombin complexes were reduced by 4.8 ug/L over the first day for bivalirudin versus an increase of 1.9 ug/L in the heparin arm (p=0.0003). Acute IMR was lower (43.5±21.6 vs. 68.7±35.8 mmHg×s, respectively; p=0.014). In a planned interim analysis, an approximate 11% reduction in IS was observed with bivalirudin; the trial was discontinued for futility. This study did not achieve its primary endpoint of significant infarct size reduction in PPCI by prolonged bivalirudin infusion compared to UFH, even though complete thrombin inhibition was achieved in the acute phase, with a lower myocardial microcirculation resistance at the end of the procedure.

Psoriasis is an independent risk factor for coronary heart disease. It is important for predicting the complications of coronary heart disease in patients with psoriasis. In this study, related cases were collected from the case system of Qingdao University, and commonly used laboratory indicators were extracted. Artificial neural network (ANN) and logistics regression analysis were used to learn to distinguish psoriasis patients, coronary heart disease patients, and psoriasis patients with coronary heart disease. We identified independent risk factors for coronary heart disease in psoriasis patients that exacerbate coronary heart disease symptoms in patients with psoriasis. Analysis shows that the accuracy of the ANN model was higher than 79%. It was determined that age, chlorinated, phosphorus, magnesium, low-density lipoprotein, triglycerides, high density lipoprotein and total cholesterol are independent risk factors for coronary heart disease in patients with psoriasis. Similarly, gender, age, chlorinated, magnesium, triglycerides, and high density lipoprotein are risk factors that exacerbate coronary heart disease symptoms in patients with psoriasis. The presented approach is a valuable tool for identifying psoriasis patients, coronary heart disease patients, and psoriasis patients with coronary heart disease. It can also serve as a support tool clinicians in the diagnostic process, by providing an outstanding support in the diagnostics prevention of coronary heart disease in psoriasis.

Introduction. The purpose of this study is to use deep learning and machine learning to learn and classify patients with cutaneous melanoma with different prognoses and to explore the application value of deep learning in the prognosis of cutaneous melanoma patients. Methods. In deep learning, VGG‐19 is selected as the network architecture and learning model for learning and classification. In machine learning, deep features are extracted through the VGG‐19 network architecture, and the support vector machine (SVM) model is selected for learning and classification. Compare and explore the application value of deep learning and machine learning in predicting the prognosis of patients with cutaneous melanoma. Result. According to receiver operating characteristic (ROC) curves and area under the curve (AUC), the average accuracy of deep learning is higher than that of machine learning, and even the lowest accuracy is better than that of machine learning. Conclusion. As the number of learning increases, the accuracy of machine learning and deep learning will increase, but in the same number of cutaneous melanoma patient pathology maps, the accuracy of deep learning will be higher. This study provides new ideas and theories for computational pathology in predicting the prognosis of patients with cutaneous melanoma.

Background: Psoriasis is a recurrent, chronic, inflammation- and immune-mediated skin disease with multiple causative factors. However, the genetic markers associated with recurrence have not yet been fully elucidated. Accordingly, in this study, we aimed to identify markers associated with the recurrence of psoriasis. Methods: We analyzed differentially expressed genes to determine which targets were associated with the recurrence of psoriasis and used these data to construct a protein-protein interaction network using Cytoscape software. The results were then validated by analysis of core targets using Gene Expression Omnibus (GEO) datasets and clinical samples. Functional enrichment analysis was used to explore the potential mechanisms mediating the recurrence of psoriasis. Results: We screened out six core targets that played important roles in recurrence of psoriasis, and validation of GEO datasets and clinical samples showed that the expression levels of five core targets were higher in patients with psoriasis than in healthy individuals. Functional enrichment analysis revealed that the cell cycle and oocyte meiosis signaling pathways were involved in the recurrence of psoriasis. Conclusion: Our findings provided insights into the mechanisms mediating the onset and recurrence of psoriasis.