BackgroundThe goal of this study was to observe the effect of the apoptosis of Kupffer cells (KCs) selectively induced by zoledronate liposomes following the hepatic ischemia-reperfusion injury (IRI) in the rat liver transplantation model and to explore its mechanisms.Material/MethodsThe rat liver transplantation model was established using the improved Kamada method. Male Sprague Dawley rats were randomly divided into 3 groups: no liver transplantation or drug treatment (Group A); donor rats were injected with 1 mL normal saline through the tail vein for 3 continuous days before transplantation, and the donor liver was preserved in cold for 2 hours (Group B); donor rats were injected with 1 mL zoledronate liposomes (0.001 mg/mL) through the tail vein for 3 continuous days before transplantation, and the donor liver was preserved in cold for 2 hours (Group C). At 24 hours after transplantation, the receiving rats were sacrificed for sampling.ResultsCompared with Group C and Group A, the bile secretion flow was dramatically decreased in Group B, whereas the serum liver function index [alanine aminotransferase (ALT), glutamate aminotransferase (AST), and γ-glutamyl transpeptidase (γ-GT)] was significantly increased (P<0.01), and the pathological injury area was obviously increased. Compared with Group B, the levels of serum interleukin1 (IL-1), tumor necrosis factor-α (TNF-α), and the apoptotic index in Group C were significantly decreased (P<0.05), and Suzuki scores of congestion, vacuolar degeneration, and necrosis were all reduced (P<0.05).ConclusionsThe apoptosis of KCs selectively induced by zoledronate liposomes inhibited the inflammatory cascade reaction induced by KC activation and reduced the release of cytokines and decreased the extent of IRI in the liver transplantation in animal model.
To establish a method for the preparation of zoledronate liposome and to observe its effect on inducing the apoptosis of rat liver Kupffer cells. Methods: Zoledronate was encapsulated in liposomes, and then the entrapment rate was detected on a spectrophotometer. The prepared Zoledronate liposome (0.01 mg/mL) was injected into the tail vein of SD rats. Three days later, the number of Kupffer cells (CD68 positive) in rat liver tissue was detected by immunohistochemistry. Flow cytometry was used to detect the apoptosis rate of the isolated liver Kupffer cell cultured in vitro. Results: The entrapment rate of Zoledronate was 43.4±7.8%. Immunohistochemistry revealed that the number of Kupffer cells was 19.3±2.1 in PBS group and 5.5±1.7 in Zoledronate liposome group, with a significant difference (P<0.05). The apoptosis rate of Kupffer cells was 4.1±0.8% in PBS group, while it was 9±2.2% and 23.3±5.9% in Zoledronate liposomes groups with different concentrations of Zoledronate liposome (P<0.05). Conclusions: Zoledronate liposomes can effectively induce the apoptosis of Kupffer cells in vivo and in vitro, and the apoptosis rate is related to the concentration of Zoledronate liposome. To establish a rat liver Kupffer cell apoptosis model can provide a new means for further study on Kupffer cell function.
Background: According to the global cancer burden data released in 2020, breast cancer (BC) has become the most common cancer in the world. Similar to those of other cancers, the present methods used in clinic for diagnosing early BC are invasive, inaccurate, and insensitive. Hence, new non-invasive methods capable of early diagnosis are needed. Methods: We applied next-generation sequencing and analyzed the messenger RNA (mRNA) profiles of plasma extracellular vesicles (EVs) derived from 14 BC patients and 6 patients with benign breast lesions.We used 3 regression models, namely support vector machine (SVM), linear discriminate analysis (LDA), and logistic regression (LR), to develop classifiers for use in making predictive BC diagnoses; and used 259 plasma samples, including those obtained from 144 patients with BC, 72 patients with benign breast lesions, and 43 healthy women, which were divided into training groups and validation groups to verify their performances as classifiers by quantitative reverse transcription polymerase chain reaction (RT-qPCR). The area under the curve (AUC) and accuracy, sensitivity, and specificity of the classifiers were cross-validated with the leave-1-out cross-validation (LOOCV) method. Results: Among all combinations assessed with the 3 different regression models, an 8-mRNA combination, named EXOB mRNA , exhibited high performance [accuracy =71.9% and AUC =0.718, 95% confidence interval (CI): 0.652 to 0.784] in the training cohort after LOOCV was performed, showing the largest AUC in the SVM model. The mRNAs in EXOB mRNA were HLA-DRB1, HAVCR1, ENPEP, TIMP1, CD36, MARCKS, DAB2, and CXCL14. In the validation cohort, the AUC of EXOB mRNA was 0.737 (95% CI: 0.636 to 0.837). In addition, gene function and pathway analyses revealed that different levels of gene expression were associated with cancer. Conclusions: We developed a high-performing predictive classifiers including 8 mRNAs from plasma extracellular vesicles for diagnosing breast cancer.
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