Allicin Overcomes Doxorubicin Resistance of Breast Cancer Cells by Targeting the Nrf2 Pathway.

Breast cancer (BC) is a lethal disorder that threatens the life safety of the majority of females globally, with rising morbidity and mortality year by year. Doxorubicin is a cytotoxic anthracycline antibiotic that is widely used as one of the first-line chemotherapy agents for patients with BC. However, the efficacy of doxorubicin in the clinic is largely limited by its serious side effects and acquired drug resistance. Allicin (diallyl thiosulfinate), as the major component and key active compound present in freshly crushed garlic, has shown potential effects in suppressing chemotherapy resistance in various cancers. Our research aimed to explore the relationship between allicin and doxorubicin resistance in BC. To generate doxorubicin-resistant BC cell lines (MCF-7/DOX and MDA-MB-231/DOX), doxorubicin-sensitive parental cell lines MCF-7 and MDA-MB-231 were continuously exposed to stepwise increased concentrations of doxorubicin over a period of 6 months. CCK-8, colony formation, flow cytometry, RT-qPCR, and western blotting assays were performed to investigate the effects of allicin and/or doxorubicin treatment on the viability, proliferation and apoptosis and the expression of Nrf2, HO-1, phosphate AKT and AKT in doxorubicin-resistant BC cells. Our results showed that combined treatment of allicin with doxorubicin exhibited better effects on inhibiting the proliferation and enhancing the apoptosis of doxorubicin-resistant BC cells than treatment with allicin or doxorubicin alone. Mechanistically, allicin suppressed the levels of Nrf2, HO-1, and phosphate AKT in doxorubicin-resistant BC cells. Collectively, allicin improves the doxorubicin sensitivity of BC cells by inactivating the Nrf2/HO-1 signaling pathway.

Intelligent Recognition Technology for the Segmentation of Traffic Indication Images Concerning Different Pavement Materials.

Traffic indication is an important part of the road environment, providing information about road conditions, restrictions, prohibitions, warnings, and the current status related to the flow of the traffic and other navigational aspects. The shape, color, and pictogram of a traffic indication are encoded into the visual characteristics of traffic signs. Not paying attention to these traffic signs could lead directly or indirectly to traffic accidents. In this article, the support traffic indication vector recognition (STIVR) method is proposed to classify the best signal detection to avoid traffic congestion and accidents. The proposed STIVR recognizes the traffic indication system automatically, reduces occurrences of traffic accidents, and helps drivers move safely on different pavement materials. Besides, the adaptive median filter (AMF) algorithm is used to pre-process and protect the traffic indication images without obscuring them. Thus, it indicates the edge of the non-smoothed nasty ferment from the service. In the detection of traffic events, indication images are enhanced, pre-treated, and divided according to symbols and their characteristics such as color, shape, or both. The output becomes a segmented image, including the available space identified as a road sign. The experimental results show that the proposed method functions well; achieves a sufficiently higher process speed and better segmentation of traffic indications and more accuracy in recognition of the objects. For example, the proposed method reaches a higher sensitivity performance of 96%.

A novel prognostic model associated with the overall survival in patients with breast cancer based on lipid metabolism-related long noncoding RNAs.

BACKGROUND: Lipid metabolism is closely related to the occurrence and development of breast cancer. Our purpose was to establish a novel model based on lipid metabolism-related long noncoding RNAs (lncRNAs) and evaluate the potential clinical value in predicting prognosis for patients suffering from breast cancer. METHODS: RNA data and clinical information for breast cancer were obtained from the cancer genome atlas (TCGA) database. Lipid metabolism-related lncRNAs were identified via the criteria of correlation coefficient |R2 | > 0.4 and p < 0.001, and prognostic lncRNAs were identified to establish model through Cox regression analysis. The training set and validation set were established to certify the feasibility, and all samples were separated into high-risk group or low-risk group. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were conducted to evaluate the potential biological functions, and the immune infiltration levels were explored through Cibersortx database. RESULTS: A total of 14 lncRNAs were identified as protective genes (AC022150.4, AC061992.1, AC090948.3, AC092794.1, AC107464.3, AL021707.8, AL451085.2, AL606834.2, FLJ42351, LINC00926, LINC01871, TNFRSF14-AS1, U73166.1 and USP30-AS1) with HRs < 1 while 10 lncRNAs (AC022150.2, AC090948.1, AC243960.1, AL021707.6, ITGB2-AS1, OTUD6B-AS1, SP2-AS1, TOLLIP-AS1, Z68871.1 and ZNF337-AS1) were associated with increased risk with HRs >1. A total of 24 prognostic lncRNAs were selected to construct the model. The patients in low-risk group were associated with better prognosis in both training set (p < 0.001) and validation set (p < 0.001). The univariate and multivariate Cox regression analyses revealed that risk score was an independent prognostic factors in both training set (p < 0.001) and validation set (p < 0.001). GO and GSEA analyses revealed that these lncRNAs were related to metabolism-related signal pathway and immune cells signal pathway. Risk score was negatively correlated with B cells (r = -0.097, p = 0.002), NK cells (r = -0.097, p = 0.002), Plasma cells (r = -0.111, p = 3.329e-04), T-cells CD4 (r = -0.064, p = 0.039) and T-cells CD8 (r = -0.322, p = 2.357e-26) and positively correlated with Dendritic cells (r = 0.077, p = 0.013) and Monocytes (r = 0.228, p = 1.107e-13). CONCLUSION: The prognostic model based on lipid metabolism lncRNAs possessed an important value in survival prediction of breast cancer patients.

Comparison of Survival Outcomes Among Patients With Breast Cancer With Distant vs Ipsilateral Supraclavicular Lymph Node Metastases.

Importance: There is a lack of studies exploring whether the survival of patients with distant lymph node metastases (DLNM) is different from that of patients with ipsilateral supraclavicular lymph node metastases (ISLM) and other stage IV breast cancer. Objective: To assess the survival of patients with DLNM from breast cancer vs ISLM and other stage IV breast cancer. Design, Setting, and Participants: This cohort study included 2033 patients diagnosed with breast cancer between January 1, 2010, and December 31, 2014, from the Surveillance, Epidemiology and End Results registries database. Three groups of patients were included: (1) patients with ISLM without any distant metastasis, (2) patients with DLNM, and (3) patients with distant metastases (DLNM excluded). Patients younger than 18 years or older than 100 years were excluded. The data were analyzed in February 2020. Exposures: Surgery for primary tumor, surgery for distant lymph nodes, and radiotherapy. Main Outcomes and Measures: Overall survival (OS) and breast cancer-specific survival (BCSS). Results: Of the 2033 women (mean [SD] age, 62.03 [14.62] years [range, 23.00-99.00 years]; 1510 White participants [74.3%]) with breast cancer included in the study, 346 patients (17.0%) had DLNM, 212 (10.4%) had ISLM, and 1475 (72.6%) had distant metastases (DLNM excluded). The 3-year BCSS rates were 63.24% for ISLM, 64.54% for DLNM, and 41.20% for distant metastases. The 3-year OS rates were 53.46% for ISLM, 62.67% for DLNM, and 38.21% for distant metastases. Compared with patients with ISLM, patients with DLNM showed similar BCSS (hazard ratio [HR], 0.81; 95% CI, 0.52-1.25; P = .34) and OS (HR, 0.73; 95% CI, 0.51-1.05; P = .09), whereas patients with distant metastases showed significantly poorer BCSS (HR, 1.99; 95% CI, 1.43-2.78; P < .001) and OS (HR, 1.79; 95% CI, 1.35-2.38; P < .001). Of the 346 patients with DLNM, primary surgery (HR, 0.21; 95% CI, 0.12-0.39; P < .001) and radiotherapy (HR, 0.46; 95% CI, 0.25-0.87; P = .02) were significantly associated with improved OS. Conclusions and Relevance: The results of this cohort study suggest that DLNM of breast cancer, with similar survival to N3c disease (indicating metastases to the ipsilateral supraclavicular lymph nodes), might be a regional disease, and reassessment of the role of lymph node metastases in breast cancer may be necessary. Given these findings, aggressive locoregional therapies for this disease are recommended, although future studies are still needed to confirm these results.

[Effect of extracelluar organic matter and natural organic matter on coagulation].

Influence of EOM and NOM on removal of algae and turbidity was investigated. The result showed that EOM had both beneficial and harmful effects on coagulation, it hindered the charge neutrality of the flocculant. Zeta potential of algae decreased from -40.6 mV to -14.7 mV, only when the modified chitosan was added above 0.2 mg x L(-1). But it became a coagulant aid when it combined with flocculant. The experiment indicated that turbidity removal would reach the peak efficiency (96%) with appropriate concentration of EOM (5.18 mg x L(-1)), therefore EOM would enhance the removal efficiency. NOM had the more negative effect on coagulation, the optimum removal efficiency of algae and turbidity decreased by 11% and 18% separately. Besides, the optimum dosage of modified chitosan increased from 0.35 mg x L(-1) and 0.1 mg x L(-1) to 0.7 mg x L(-1) and 0.3 mg x L(-1) respectively. So it is the key point to take advantage of EOM and remove the NOM in practice, as a result the flocculant loading will be decreased, the removal efficiency will be improved.