Human umbilical cord mesenchymal stem cells alleviate fatty liver ischemia-reperfusion injury by activating autophagy through upregulation of IFNγ.

Liver ischemia-reperfusion injury (IRI) is an important factor affecting the prognosis of liver transplantation, and extended criteria donors (e.g., steatosis donor livers) are considered to be more sensitive to ischemia-reperfusion injury in liver transplantation. Currently, the application of human umbilical cord mesenchymal stem cells (hMSCs) has great promise in the treatment of various injuries in the liver. This study aimed to investigate the therapeutic role and mechanism of hMSCs in fatty liver IRI. After more than 8 weeks of high-fat chow feeding, we constructed a fatty liver mouse model and established ischemic injury of about 70% of the liver. Six hours after IRI, liver injury was significantly alleviated in hMSCs-treated mice, and the expression levels of liver enzyme, inflammatory factor TNF-α, and apoptotic proteins were significantly lower than those of the control group, which were also significant in pathological sections. Transcriptomics analysis showed that IFNγ was significantly upregulated in the hMSCs group. Mechanistically, IFNγ, which activates the MAPK pathway, is a potent agonist that promotes the occurrence of autophagy in hepatocytes to exert a protective function, which was confirmed by in vitro experiments. In summary, hMSCs treatment could slow down IRI in fatty liver by activating autophagy through upregulation of IFNγ, and this effect was partly direct.

Development of VER-50589 analogs as novel Hsp90 inhibitors.

As an important target for tumor therapy, heat shock protein 90 has attracted tremendous attention. Through structure analysis, we rationally designed three analogs of VER-50589 which is a known and potent Hsp90 inhibitor. Target inhibitory activity result showed that one compound dubbed as 12-1 exhibited strong inhibitory activity against Hsp90 with an IC50 value of 9 nM. In tumor cell viability experiment, compound 12-1 robustly repressed the proliferation against six human tumor cells with IC50 values all in nanomolar range scoring over VER-50589 and geldanamycin. 12-1 was able to induce apoptosis of tumor cells and arrest the tumor cell cycle in G0/G1 phase. Meanwhile, western blot results showed that 12-1 could significantly downregulated the expression of two Hsp90 client proteins CDK4 and HER2. Finally, molecular dynamic simulation showed that compound 12-1 could fit well with ATP binding site on N-terminal of Hsp90.

Identification of a Novel Gene Signature Based on Kinesin Family Members to Predict Prognosis in Glioma.

Background and Objectives: Extensive research indicates that the kinesin superfamily (KIFs) regulates tumor progression. Nonetheless, the potential prognostic and therapeutic role of KIFs in glioma has been limited. Materials and Methods: Four independent cohorts from The Cancer Genome Atlas (TCGA) database and the Chinese Glioma Genome Atlas (CGGA) database were generated into a large combination cohort for identification of the prognostic signature. Following that, systematic analyses of multi-omics data were performed to determine the differences between the two groups. In addition, IDH1 was selected for the differential expression analysis. Results: The signature consists of five KIFs (KIF4A, KIF26A, KIF1A, KIF13A, and KIF13B) that were successfully identified. Receiver operating characteristic (ROC) curves indicated the signature had a suitable performance in prognosis prediction with the promising predictive area under the ROC curve (AUC) values. We then explored the genomic features differences, including immune features and tumor mutation status between high- and low-risk groups, from which we found that patients in the high-risk group had a higher level of immune checkpoint modules, and IDH1 was identified mutated more frequently in the low-risk group. Results of gene set enrichment analysis (GSEA) analysis showed that the E2F target, mitotic spindle, EMT, G2M checkpoint, and TNFa signaling were significantly activated in high-risk patients, partially explaining the differential prognosis between the two groups. Moreover, we also verified the five signature genes in the Human Protein Atlas (HPA) database. Conclusion: According to this study, we were able to classify glioma patients based on KIFs in a novel way. More importantly, the discovered KIFs-based signature and related characteristics may serve as a candidate for stratification indicators in the future for gliomas.

Design, synthesis and antitumor activity evaluation of novel HDAC inhibitors with tetrahydrobenzothiazole as the skeleton.

HDACs as important targets for cancer therapy have attracted extensive attentions. In this work, a series of sixteen hydroxamic acid based HDAC inhibitors were designed and synthesized with 4,5,6,7-tetrahydrobenzothiazole as the structural core. Majority of them exhibited potent inhibitory activities against HDACs and one leading compound 6h was dug out. 6h was proven to be a pan-HDAC inhibitor and displayed high cytotoxicity against seven human cancer cell lines with IC50 values in low micromolar range. 6h could arrest cell cycle in G2/M phase and induce apoptosis in A549 cells. Moreover, compound 6h exhibited remarkable anti-migration and anti-angiogenesis activities. At the same time, 6h was able to elevate the expression of acetylated α-tubulin and acetylated histone H3 in a dose-dependent manner. Docking simulation revealed that 6h fitted well into the active sites of HDAC2 and 6. Finally, compound 6h also exerted potent antitumor effects in an A549 zebrafish xenograft model. Our study demonstrated that compound 6h was a promising candidate for further preclinical studies.

Prediction model of pressure injury occurrence in diabetic patients during ICU hospitalization--XGBoost machine learning model can be interpreted based on SHAP.

BACKGROUND: The occurrence of pressure injury in patients with diabetes during ICU hospitalization can result in severe complications, including infections and non-healing wounds. AIMS: The aim of this study was to predict the occurrence of pressure injury in ICU patients with diabetes using machine learning models. STUDY DESIGN: In this study, LASSO regression was used for feature screening, XGBoost was employed for machine learning model construction, ROC curve analysis, calibration curve analysis, clinical decision curve analysis, sensitivity, specificity, accuracy, and F1 score were used for evaluating the model's performance. RESULTS: Out of the 503 ICU patients with diabetes included in the study, pressure injury developed in 170 cases, resulting in an incidence rate of 33.8 %. The XGBoost model had a higher AUC for predicting pressure injury in patients with diabetes during ICU hospitalization (train: 0.896, 95 %CI: 0.863 to 0.929; test: 0.835, 95 % CI: 0.761-0.908). The importance of SHAP variables in the model from high to low was: 'Days in ICU', 'Mechanical Ventilation', 'Neutrophil Count', 'Consciousness', 'Glucose', and 'Warming Blanket'. CONCLUSION: The XGBoost machine learning model we constructed has shown high performance in predicting the occurrence of pressure injury in ICU patients with diabetes. Additionally, the SHAP method enables the interpretation of the results provided by the machine learning model. RELEVANCE TO CLINICAL PRACTICE: Improve the ability to predict the early occurrence of pressure injury in diabetic patients in the ICU. This will enable clinicians to intervene early and reduce the occurrence of complications.