Pyroptosis, an inflammatory form of programmed cell death, is the initiating event of sepsis and results in immune imbalance by releasing IL-1β and IL-18 in the early stages. Studies show that enhancing autophagy via genetic manipulation can inhibit pyroptosis and prolong the survival of a sepsis animal model, indicating a possible therapeutic strategy against sepsis. However, almost no study so far has achieved pyroptosis inhibition via pharmacological autophagy induction in a sepsis disease model. To this end, we established an in vitro sepsis model by stimulating primary human umbilical vein endothelial cells (HUVECs) with lipopolysaccharide (LPS), and analyzed the effect of the autophagy agonist rapamycin (RAPA) on pyroptosis. Phorbol 12-myristate 13-acetate- (PMA-) activated human THP-1 cells were used as the positive control. LPS significantly increased the levels of the pyroptotic protein Gasdermin D (GSDMD), cysteinyl aspartate-specific proteinase 1 (caspase-1), secreted LDH, IL-1β, and IL-18. RAPA treatment downregulated the above factors and enhanced autophagy in the LPS-stimulated HUVECs and THP-1 cells. This study shows that RAPA abrogates LPS-mediated increase in IL-1β and IL-18 by inhibiting pyroptosis and enhancing autophagy.Sanhuang Xiexin Decoction (SXD) is commonly used to treat type 2 diabetes mellitus (T2DM) in clinical practice of traditional Chinese medicine (TCM). In order to elucidate the specific analysis mechanisms of SXD for T2DM, the method of network pharmacology was applied to this article. First, the effective ingredients of SXD were obtained and their targets were identified based on the TCMSP database. The T2DM-related targets screened from the GEO database were also collected by comparing the differential expressed genes between T2DM patients and healthy individuals. Then, the common targets in SXD-treated T2DM were obtained by intersecting the putative targets of SXD and the differential expressed genes of T2DM. And the protein-protein interaction (PPI) network was established using the above common targets to screen key genes through protein interactions. Meanwhile, these common targets were used for GO and KEGG analyses to further elucidate how they exert antidiabetic effects. Finally, a gene pathway network and VEGFA were the key genes for SXD against T2DM. Based on the network pharmacology approach, we identified key genes and pathways related to the prognosis and pathogenesis of T2DM and also provided a feasible method for further studying the chemical basis and pharmacology of SXD. To explore multiscale integrated analysis methods in identifying key regulators of esophageal cancer (ESCA). We downloaded the ESCA dataset from The Cancer Genome Atlas (TCGA) database, which contained RNA-seq data, miRNA-seq data, methylation data, and clinical phenotype information. https://www.selleckchem.com/products/ABT-869.html Then, we combined ESCA-related genes from the NCBI-GENE and OMIM databases and RNA-seq dataset from TCGA to analyze differentially expressed genes (DEGs). Meanwhile, differentially expressed miRNAs (DEmiRNAs) and genes with differential methylation levels were identified. The pivot-module pairs were established using the RAID v2.0 database and TRRUST v2 database. Next, the multifactor-regulated functional network was constructed based on the above information. Additionally, gene corresponding targeted drug information was obtained from the DrugBank database. Moreover, we further screened regulators by assessing their diagnostic value and prognostic value, especially the value of distinguishing patients at TNM I stage from normal patients. In addition, the external database from the Gene Expression Omnibus (GEO) database was used for validation. Lastly, gene set enrichment analysis (GSEA) was performed to explore the potential biological functions of key regulators. Our study indicated that CXCL8, CYP2C8, and E2F1 had excellent diagnostic and prognostic values, which may be potential regulators of ESCA. At the same time, the good early diagnosis ability of the three regulators also provided new insights for the diagnosis and early treatment of ESCA patients. We develop a multiscale integrated analysis and suggest that CXCL8, CYP2C8, and E2F1 are promising regulators with good diagnostic and prognostic values in ESCA. We develop a multiscale integrated analysis and suggest that CXCL8, CYP2C8, and E2F1 are promising regulators with good diagnostic and prognostic values in ESCA. To explore the relationship between elevated serum C-reactive protein (CRP) level and postoperative delirium (POD). 206 patients scheduled to receive cervical or lumbar vertebra surgery under general anesthesia for more than 2 hours in a single medical center were observed and analyzed. Patients' serum CRP, delirious status (using the confusion assessment method (CAM)), and delirious score (using the memorial delirium assessment scale (MDAS)) were examined before surgery and 1-2 days after surgery. The association of a serum CRP elevation value from before to after surgery (D-CRP) with delirium occurrence within 2 days after surgery was assessed with a binary logistic regression model, while the association of D-CRP with the postoperative delirious score was assessed with a linear regression model. The effect of D-CRP on predicting delirium occurrence was evaluated with the area under the receiver operating characteristic (ROC) curve (AUC). D-CRP was significantly positively associated with postoperative delirium occurrence (OR = 1.047, 95%CI = 1.013, 1.082), and D-CRP was also significantly linearly associated with the postoperative delirious score ( = 0.014, 95%CI = 0.006, 0.023). AUC of ROC was 0.711 ( = 0.014), suggesting that D-CRP had moderate efficacy on predicting postoperative delirium occurrence ( < 0.05). Elevated serum CRP after surgery may be a risk factor for and a predictor of postoperative delirium. Elevated serum CRP after surgery may be a risk factor for and a predictor of postoperative delirium. COVID-19 first broke out in China and spread rapidly over the world. To describe the CT features of COVID-19 pneumonia and to share our experience at initial diagnoses. . Data from 53 patients (31 men, 22 women; mean age, 53 years; age range, 16-83 years) with confirmed COVID-19 pneumonia were collected. Their complete clinical data was reviewed, and their CT features were recorded and analyzed. The average time between onset of illness and the initial CT scan was six days (range, 1-42 days). A total of 399 segments were involved and distributed bilaterally (left lung 186 segments [46.6%], right lung 213 segments [53.4%]) and peripherally (38 [71.7%] patients). Multiple lobes (45 [84.9%]) and bilateral lower lobes (left lower lobe 104 [26.1%], right lower lobe 107 [26.8%], and total 211 [52.9%]) were the most commonly involved. Ground-glass opacity with consolidation (24 [45.3%]) and pure ground-glass opacity (28 [52.8%]) were the main findings. The other findings were crazy-paving (14 [26.4%]), bronchiectasis (12 [22.