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Background: Choosing the appropriate treatment early and predicting the efficacy of neoadjuvant chemotherapy (NAC) for locally advanced breast cancer patients are of particular importance for clinicians. Developing and validating a multiparametric model for predicting NAC would be very meaningful for clinical practice. Methods: This study included 91 patients with locally advanced breast cancer treated from 2016 to 2020. The correlation between multiparametric characteristics and the efficacy of NAC was examined. The data were randomly divided into training and validation sets. A least absolute shrinkage and selection operator (LASSO) regression analysis was used for the variable screening. A multivariable logistic regression analysis was used to construct the model. Calibration and decision curves were used to assess the performance of the established model. Results: Lymph node metastasis, the first standard apparent diffusion coefficient (ADC) at the baseline, the change in the standard ADC at the first follow-up, the change in tumor volume at the first follow-up, and the clinical stage of the tumor at the baseline were selected for inclusion in the model. In the receiver operating characteristic (ROC) analysis, the areas under the curve (AUCs) were 0.984 [95% confidence interval (CI): 0.958-1] and 0.815 (95% CI: 0.509-1) for the primary and validation cohorts, respectively. The utility of the established model was confirmed by calibration and decision curves, and a nomogram was obtained. Conclusions: A multiparametric model based on clinical-pathological-magnetic resonance imaging (MRI) features was established to predict the effect of NAC in patients with locally advanced breast cancer.
RESUMO
PURPOSE: Neuroinflammation runs through the whole process of nervous system diseases and brain injury. Inflammasomes are thought to be especially relevant to immune homeostasis, and their dysregulation contributes to pyroptosis. The natural compound Ginsenoside Rg1 has been shown to possess anti-inflammatory effects; however, its underlying mechanisms are not entirely clear. Therefore, this study was undertaken to investigate the role and mechanisms of Rg1 in regulating the production of inflammasomes and pyroptosis of microglia in vivo and in vitro. METHODS: BV-2 microglial cells were pretreated with Rg1, stattic and interleukin-6 (IL-6), and then stimulated with lipopolysaccharide (LPS) (2µg/mL). Hoechst staining and Annexin V-FITC/PI assay were then carried out. The expression levels of cleaved-caspase-1, pro-caspase-1, interleukin-1ß (IL-1ß), mature-IL-1ß, gasdermin D (GSDMD), activated NH(2)-terminal fragment of GSDMD (GSDMD-N), NOD-, LRR- and pyrin domain-containing 3 (NLRP3), apoptosis-associated speck-like protein containing a CARD (ASC), absent in melanoma 2 (AIM2), signal transducer and activator of transcription 3 (STAT3) and phosphorylated STAT3 in BV-2 were detected by Western blotting. Additionally, immunofluorescence staining was used to determine the expression of NLRP3 and p-STAT3 in postnatal rat brain and BV-2 microglia subjected to LPS stimulation and Rg1 pretreatment. The targets of transcription factor STAT3 were predicted by hTFtarget and chromatin immunoprecipitation (ChIP) was used to confirm the interaction between STAT3 and AIM2. RESULTS: We showed here that Rg1 effectively inhibited the expression of inflammasomes and microglia pyroptosis induced by LPS. The targets predicted data of Rg1 from Swiss target prediction database showed STAT3 had the highest thresholds of probability score. Rg1 can regulate the phosphorylation of STAT3, which binds to the promoter region of inflammasome AIM2. CONCLUSION: It is suggested that STAT3 signaling can initiate the transcription activity of AIM2. Rg1 regulates microglia pyroptosis in neuroinflammation induced by LPS through targeting STAT3 signaling.
