This study explored the effects of a classical Chinese medicine formula- Xiao-Chai-Hu Tang(XCHT) on the model mice with D-galactosamine -induced liver injury. Sixty male imprinting control region (ICR) mice were used in the present study, and they were separated randomly into 6 groups: a normal control group (Group A, n=10), a model control (Group B, n=10), a positive control (Group C, n=10), a low dose of XCHT group (Group D, n=10), a medium dose of XCHT group (Group E, n=10), and a high dose of XCHT group (Group F, n=10). ELISA was used to detect the IL-6 and TNF-α levels in the serum. Real-time PCR was performed to assess the expression of FasmRNA, Fas-LmRNA, Bcl-2mRNA of the liver tissues. Western blotting was used to detect the Bax protein expression of the liver tissues. The serum IL-6 and TNF-α levels of Group B were significantly higher than the other groups (P<0.05). The expression of Fas mRNA, Fas-LmRNA, and Bax protein of the liver tissues of Group B were significantly higher than those of the other groups (P<0.05). The expression of Bcl-2 mRNA of the liver tissues of Group B was significantly lower than other groups (P<0.05). Both of XCHT and biphenyl dicarboxylate significantly decreased the serum IL-6 and TNF-α levels and FasmRNA, FasLmRNA, Bax protein expression and increased the Bcl-2 mRNA expression of the liver tissues of model mice (P<0.05). It may be through decreasing the serum IL-6 and TNF-α levels and FasmRNA, FasLmRNA, Bax protein expression and increasing the Bcl-2 mRNA expression of the liver tissues that XCHT significantly relieved the D-galactosamine -induced liver injury.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3746662 | PMC |
http://dx.doi.org/10.4314/ajtcam.v9i3.16 | DOI Listing |
Background: Cerebrospinal fluid rhinorrhoea (CSFR) is a common complication following endonasal skull base surgery, a technique that is fundamental to the treatment of pituitary adenomas and many other skull base tumours. The CRANIAL study explored CSFR incidence and related risk factors, particularly skull base repair techniques, a multicentre prospective observational study. We sought to use machine learning to leverage this complex multicentre dataset for CSFR prediction and risk factor analysis.
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