No abstract

Imatinib (IM) is highly effective in treatment of chronic myeloid leukemia (CML) but does not eliminate minimal residual disease (MRD), which remains a potential source of relapse. IM treatment effectively inhibits BCR-ABL kinase activity in CML cells, suggesting that additional kinase-independent mechanisms contribute to the presence of MRD. Bone marrow (BM) microenvironment protecting CML cells from IM treatment was investigated. Culturing CML cell line K562 in human stromal cell line HS-5-derived conditioned medium significantly inhibited apoptosis induced by IM, which was soluble factor-mediated drug resistance (SFM-DR). The BM stroma-derived soluble factors could enhance the resistance of K562 cells to IM by increasing Stat3 phosphorylation on tyrosine 705 and subsequently increasing the expression of anti-apoptotic proteins and P-glycoprotein (P-gp) in K562 cells. Furthermore, the reversal effect of oroxylin A, a naturally monoflavonoid isolated from the root of Scutellaria baicalensis Georgi, in K562 cells within the SFM-DR model was detected. After treatment of weakly toxic concentration of oroxylin A, the apoptosis of K562 cells induced by IM was increased dramatically through suppressing Stat3 pathway. In addition, the in vivo study showed that oroxylin A potentiates the inhibitory effects of IM on leukemia development by suppressing Stat3 pathway in the K562 xenograft model. In conclusion, IM-induced resistance in K562 cells within the SFM-DR model correlated with increasing Stat3 signaling and upregulating P-gp expression through Stat3 pathway. Additionally, oroxylin A improved the sensitivity of K562 cells to IM in SFM-DR model and in vivo, and the underlying mechanism attributed to the suppression of Stat3 pathway, which suggested oroxylin A might be a promising agent for treatment designed to eradicate MRD in CML patients.

Ischaemia-reperfusion (IR) injury is a major issue in cardiac transplantation. Inflammatory processes play a major role in myocardial IR injury. Lipocalin-2 (Lcn2), which is also known as neutrophil gelatinase-associated lipocalin, has multiple functions that include the regulation of cell death/survival, cell migration/invasion, cell differentiation and iron delivery. In our study, the hearts of C57BL/6 mice were flushed with and stored in cold Bretschneider solution for 8 h and then transplanted into a syngeneic recipient. We found that Lcn2 neutralization decreased the recruitment of neutrophils and macrophages. Troponin T (TnT) production, 24 h after myocardial IR injury, was reduced through anti-Lcn2 antibody administration. The cardiac output at 60 mmHg of afterload pressure was significantly increased in hearts administrated with antiLcn2 antibody administration (anti-Lcn-2: 58.9 AE 5.62 ml/min; control: 25.8 AE 4.1 ml/min; P < 0.05). Anti-Lcn2 antibody treatment suppressed M1 marker (IL-12, IL-23 and iNOS) expression but increased M2 marker (IL-10, Arg1 and Mrc1) expression. Furthermore, in our vitro and vivo experiments, we found that anti-Lcn2 antibody treatment failed to induce M1-related gene expression in response to LPS and that Lcn2 neutralization enhanced the expression of M2-related genes following IL-4 treatment. In conclusion, Lcn2 promotes M1 polarization, and Lcn2 neutralization attenuates cardiac IR injury.

Background and purpose Stroke‐associated pneumonia (SAP) is a common, severe but preventable complication after acute ischaemic stroke (AIS). Early identification of patients at high risk of SAP is especially necessary. However, previous prediction models have not been widely used in clinical practice. Thus, we aimed to develop a model to predict SAP in Chinese AIS patients using machine learning (ML) methods. Methods Acute ischaemic stroke patients were prospectively collected at the National Advanced Stroke Center of Nanjing First Hospital (China) between September 2016 and November 2019, and the data were randomly subdivided into a training set and a testing set. With the training set, five ML models (logistic regression with regulation, support vector machine, random forest classifier, extreme gradient boosting (XGBoost) and fully connected deep neural network) were developed. These models were assessed by the area under the curve of receiver operating characteristic on the testing set. Our models were also compared with pre‐stroke Independence (modified Rankin Scale), Sex, Age, National Institutes of Health Stroke Scale (ISAN) and Pneumonia Prediction (PNA) scores. Results A total of 3160 AIS patients were eventually included in this retrospective study. Among the five ML models, the XGBoost model performed best. The area under the curve of the XGBoost model on the testing set was 0.841 (sensitivity, 81.0%; specificity, 73.3%). It also achieved significantly better performance than ISAN and PNA scores. Conclusions Our study demonstrated that the XGBoost model with six common variables can predict SAP in Chinese AIS patients more optimally than ISAN and PNA scores.