Objective To explore the role and clinical significance of GSTM 3 ( glutathione S-trans-ferase mu 3) expression in prostate cancer (PCa). Methods We had used the two-dimensional fluores-cence difference gel electrophoresis ( 2D-DIGE) and mass spectral analysis to further verify the microarray data of mRNA expression profiling discovered .GSTM3 mRNA level was detected by Rael-time Quantitative PCR ( RT-QPCR) in 28 pairs of prostate cancer tissue and benign tissue .The relationship of GSTM 3 level with the serum PSA level and the clinical feature of PCa were analyzed . Results In 2D-DIGE study, we found that the expression of GSTM 3 protein in adjacent tissues was significantly higher than that in PCa tis-sues (P<0.05).RT-QPCR results showed that GSTM3 in adjacent tissues (8.12±0.51) was significantly higher than that in PCa tissues (7.18±0.54) (P<0.05).There was no significant difference of GSTM3 ex-pression in different serum PSA packets ( P>0.05) and prostate cancer clinical pathological parameters ( P>0.05). Conclusions GSTM3 expression is down-regulated in PCa tissues, and we may identify PCa by detecting the GSTM 3 expression .

Objective:To identify the risk factors for poor prognosis following interventional treatment in the patients with postherpetic neuralgia (PHN) and construct a predictive model.Methods:The medical records from patients with PHN undergoing interventional therapy at the First Affiliated Hospital of Soochow University from March 2020 to August 2023 were retrospectively collected, including basic characteristics, past medical and surgical history, symptoms, medication therapy, clinical pain score, neutrophil/lymphocyte ratio (NLR) before interventional treatment and interventional treatment methods. Logistic regression analysis was used to identify the risk factors associated with poor prognosis following interventional treatment in PHN patients, and a nomogram predictive model for poor prognosis was constructed. The discrimination and calibration of the nomogram predictive model were evaluated using the C-index and Hosmer-Lemeshow test. Calibration curves and clinical decision curves were drawn to further verify the accuracy of the predictive model.Results:The results of the multivariate logistic regression analysis show that increasing age, prolonged disease duration, elevated NLR, use of immunosuppressants and use of pulsed radiofrequency were independent risk factors for poor prognosis following intervention treatment in PHN patients ( P<0.05). The nomogram predictive model for poor prognosis following PHN interventional treatment constructed based on these factors had a C-index of 0.844. Calibration curves showed good consistency between predicted probability of poor prognosis and actual incidence of poor prognosis. Clinical decision curves indicated that the predictive model provided good accuracy and net benefit. Conclusions:Increasing age, prolonged disease course, elevated NLR, use of immunosuppressants and use of pulsed radiofrequency are independent risk factors for poor prognosis following interventional treatment in the patients with PHN. The nomogram predictive model based on these factors can effectively predict the occurrence of poor prognosis in PHN patients undergoing interventional treatment.