PurposeWe aimed to investigate the relationship between pretreatment neutrophil-to-lymphocyte ratio (NLR)/platelet-to-lymphocyte ratio (PLR) and the estimation of hormone-receptor-negative (HR−) breast cancer patients’ survival in a Chinese cohort.Patients and methodsOf 434 consecutive HR− nonmetastatic breast cancer patients treated between 2004 and 2010 in the Affiliated Hospital of Academy of Military Medical Sciences, 318 eligible cases with complete data were included in the present study. Kaplan–Meier analysis was performed to determine the overall survival (OS) and disease-free survival (DFS). Univariate and multivariate Cox proportional hazards models were used to test the usefulness of NLR and PLR.ResultsUnivariate analysis indicated that both elevated NLR and PLR (both P<0.001) were associated with poor OS. The utility of NLR remained in the multivariate analysis (P<0.001), but not PLR (P=0.104). The analysis results for DFS were almost the same as OS. Subgroup analysis revealed a significant association between increased NLR and PLR (P<0.001 and P=0.011) and poor survival in triple-negative breast cancer. However, for human epidermal growth factor receptor 2-positive breast cancer, only NLR was significantly associated with OS in the multivariate analysis (P=0.001).ConclusionThe present study indicates that both increased NLR and PLR are associated with poor survival in HR−breast cancer patients. Meanwhile, NLR is independently correlated with OS and DFS, but PLR is not.

Radiotherapy plays an important role in the treatment of non-small cell lung cancer. Accurate segmentation of the gross target volume is very important for successful radiotherapy delivery. Deep learning techniques can obtain fast and accurate segmentation, which is independent of experts’ experience and saves time compared with manual delineation. In this paper, we introduce a modified version of ResNet and apply it to segment the gross target volume in computed tomography images of patients with non-small cell lung cancer. Normalization was applied to reduce the differences among images and data augmentation techniques were employed to further enrich the data of the training set. Two different residual convolutional blocks were used to efficiently extract the deep features of the computed tomography images, and the features from all levels of the ResNet were merged into a single output. This simple design achieved a fusion of deep semantic features and shallow appearance features to generate dense pixel outputs. The test loss tended to be stable after 50 training epochs, and the segmentation took 21 ms per computed tomography image. The average evaluation metrics were: Dice similarity coefficient, 0.73; Jaccard similarity coefficient, 0.68; true positive rate, 0.71; and false positive rate, 0.0012. Those results were better than those of U-Net, which was used as a benchmark. The modified ResNet directly extracted multi-scale context features from original input images. Thus, the proposed automatic segmentation method can quickly segment the gross target volume in non-small cell lung cancer cases and be applied to improve consistency in contouring.

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At present, synthetic dyes have been widely used in several industries such as textiles, rubber, paper, plastic, and leather tanning. The dyes in effluents are among the most aggressive pollutions of all the industrial sectors and can lead to severe water contamination as well as resource waste without proper treatment and recovery. In the present work, magnetic poly­(aspartic acid)-poly­(acrylic acid) hydrogels (PAsp-PAA/Fe3O4) with semi-interpenetrating networks are successfully prepared. Their physicochemical properties are systematically characterized using scanning electron microscopy, Fourier transform infrared spectroscopy, the Brunauer–Emmett–Teller method, thermogravimetric analysis, and vibrating sample magnetometry. Organic dye methylene blue (MB) and neutral red (NR) adsorption onto the PAsp-PAA/Fe3O4 hydrogel are studied for the first time, and the maximum adsorption capacities of MB and NR are calculated by using the Langmuir model as 357.14 and 370.37 mg·g–1, respectively. The adsorption kinetics can be well described by a pseudo-second-order kinetic model. Furthermore, reusability tests indicate that the hydrogel has good reproducibility. In conclusion, the results demonstrate that the PAsp-PAA/Fe3O4 hydrogel can used as an excellent adsorbent material for removing dye pollutants from wastewater.

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