Background: Sepsis is a life-threatening syndrome eliciting highly heterogeneous host responses. Current prognostic evaluation methods used in clinical practice are characterized by an inadequate effectiveness in predicting sepsis mortality. Rapid identification of patients with high mortality risk is urgently needed. The phenotyping of patients will assistant invaluably in tailoring treatments.Methods: Machine learning and deep learning technology are used to characterize the patients’ phenotype and determine the sepsis severity. The database used in this study is MIMIC-III and MIMIC-IV (‘Medical information Mart for intensive care’) which is a large, public, and freely available database. The K-means clustering is used to classify the sepsis phenotype. Convolutional neural network (CNN) was used to predict the 28-day survival rate based on 35 blood test variables of the sepsis patients, whereas a double coefficient quadratic multivariate fitting function (DCQMFF) is utilized to predict the 28-day survival rate with only 11 features of sepsis patients.Results: The patients were grouped into four clusters with a clear survival nomogram. The first cluster (C_1) was characterized by low white blood cell count, low neutrophil, and the highest lymphocyte proportion. C_2 obtained the lowest Sequential Organ Failure Assessment (SOFA) score and the highest survival rate. C_3 was characterized by significantly prolonged PTT, high SIC, and a higher proportion of patients using heparin than the patients in other clusters. The early mortality rate of patients in C_3 was high but with a better long-term survival rate than that in C_4. C_4 contained septic coagulation patients with the worst prognosis, characterized by slightly prolonged partial thromboplastin time (PTT), significantly prolonged prothrombin time (PT), and high septic coagulation disease score (SIC). The survival rate prediction accuracy of CNN and DCQMFF models reached 92% and 82%, respectively. The models were tested on an external dataset (MIMIC-IV) and achieved good performance. A DCQMFF-based application platform was established for fast prediction of the 28-day survival rate.Conclusion: CNN and DCQMFF accurately predicted the sepsis patients’ survival, while K-means successfully identified the phenotype groups. The distinct phenotypes associated with survival, and significant features correlated with mortality were identified. The findings suggest that sepsis patients with abnormal coagulation had poor outcomes, abnormal coagulation increase mortality during sepsis. The anticoagulation effects of appropriate heparin sodium treatment may improve extensive micro thrombosis-caused organ failure.

Background/Aims: To investigate the regulation of LaCl₃ on lipopolysaccharides (LPS)-induced pro-inflammatory cytokines and adhesion molecules in human umbilical vein endothelial cells (HUVECs). Methods: Primary cultured HUVECs were pretreated with 2.5 µM LaCl₃ for 30 min followed by 1 µg/ml LPS for 2 h. Pro-inflammatory cytokine and adhesion molecule expressions were determined by real-time RT-PCR and ELISA. NF-κB/p65 nuclear translocation was examined by immunofluorescence and immuno-blot, and its DNA-binding activity was measured by chemiluminescence. Recruitment of NF-κB/p65, Jmjd3, and H3K27me3 to gene promoter regions was determined by ChIP-qPCR. Results: LaCl₃ exhibited no cytotoxic effects to primary HUVECs at concentrations ≤ 50 µM. LPS-mediated TNF-α, IL-1β, IL-6, MMP-9, and ICAM-1 production, nuclear translocation, and DNA-binding activity of NF-κB/p65, as well as Jmjd3 expression, were all reduced significantly by LaCl₃. Furthermore, LaCl₃ treatment significantly impaired LPS-induced enrichment of NF-κB/p65 to the promoter regions of TNF-α, MMP-9, IL-1β, ICAM-1, and IL-6; and of Jmjd3 to the promoter regions of TNF-α, MMP-9, IL-1β, and IL-6. H3K27me3 abundance in the promoter regions of TNF-α and ICAM-1 increased significantly in following LaCl₃ treatment. Conclusion: LaCl₃ inhibits pro-inflammatory cytokine and adhesion molecule expressions induced by LPS in HUVECs. NF-κB and histone demethylase Jmjd3 are involved in this effect.

Background: The intriguing connection between selenium and cancer resembles a captivating puzzle that keeps researchers engaged and curious. While selenium has shown promise in reducing cancer risks through supplementation, its interaction with epigenetics in cervical cancer remains a fascinating yet largely unexplored realm. Unraveling the intricacies of selenium's role and its interaction with epigenetic factors could unlock valuable insights in the battle against this complex disease. Result: Selenium has shown remarkable inhibitory effects on cervical cancer cells in various ways. In in vitro studies, it effectively inhibits the proliferation, migration, and invasion of cervical cancer cells, while promoting apoptosis. Selenium also demonstrates significant inhibitory effects on human cervical cancer-derived organoids. Furthermore, in an in vivo study, the administration of selenium dioxide solution effectively suppresses the growth of cervical cancer tumors in mice. One of the mechanisms behind selenium's inhibitory effects is its ability to inhibit histone demethylases, specifically JMJD3 and UTX. This inhibition is observed both in vitro and in vivo. Notably, when JMJD3 and UTX are inhibited with GSK-J4, similar biological effects are observed in both in vitro and in vivo models, effectively inhibiting organoid models derived from cervical cancer patients. Inhibiting JMJD3 and UTX also induces G2/M phase arrest, promotes cellular apoptosis, and reverses epithelial-mesenchymal transition (EMT). ChIP-qPCR analysis confirms that JMJD3 and UTX inhibition increases the recruitment of a specific histone modification, H3K27me3, to the transcription start sites (TSS) of target genes in cervical cancer cells (HeLa and SiHa cells). Furthermore, the expressions of JMJD3 and UTX are found to be significantly higher in cervical cancer tissues compared to adjacent normal cervical tissues, suggesting their potential as therapeutic targets. Conclusions: Our study highlights the significant inhibitory effects of selenium on the growth, migration, and invasion of cervical cancer cells, promoting apoptosis and displaying promising potential as a therapeutic agent. We identified the histone demethylases JMJD3 and UTX as specific targets of selenium, and their inhibition replicates the observed effects on cancer cell behavior. These findings suggest that JMJD3 and UTX could be valuable targets for selenium-based treatments of cervical cancer.