Analysis of 116 cases of rectal cancer treated by transanal local excision

BACKGROUND: The purpose of this research was to evaluate the therapeutic effects and prognostic factors of transanal local excision (TAE) for rectal cancer. METHODS: We retrospectively analyzed 116 cases that underwent TAE for rectal cancer from 1995 to 2008. A Cox regression analysis was used to analyze prognostic factors. RESULTS: The survival times for the patients were from 14 to 160.5 months (median time, 58.5 months). The 5-year and 10-year overall survival rates were 72% and 53%, respectively. In all 16 cases experienced local recurrence (13.8%). Pathological type, recurrence or metastasis, and depth of infiltration (T stage) were the prognostic factors according to the univariate analysis, and the latter two were independent factors affecting patient prognosis. For patients with T1 stage who underwent adjuvant radiotherapy, there was no local recurrence; for those in T2 stage, the local recurrence rate was 14.6%. In addition, there was no difference between the patients who received radiotherapy and those who did not (T1: P = 0.260, T2: P = 0.262 for survival rate and T1: P = 0.480, T2: P = 0.560 for recurrence). CONCLUSIONS: The result of TAE for rectal cancer is satisfactory for T1 stage tumors, but it is not suitable for T2 stage tumors

ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentation

Breast cancer is one of the common cancers that endanger the health of women globally. Accurate target lesion segmentation is essential for early clinical intervention and postoperative follow-up. Recently, many convolutional neural networks (CNNs) have been proposed to segment breast tumors from ultrasound images. However, the complex ultrasound pattern and the variable tumor shape and size bring challenges to the accurate segmentation of the breast lesion. Motivated by the selective kernel convolution, we introduce an enhanced selective kernel convolution for breast tumor segmentation, which integrates multiple feature map region representations and adaptively recalibrates the weights of these feature map regions from the channel and spatial dimensions. This region recalibration strategy enables the network to focus more on high-contributing region features and mitigate the perturbation of less useful regions. Finally, the enhanced selective kernel convolution is integrated into U-net with deep supervision constraints to adaptively capture the robust representation of breast tumors. Extensive experiments with twelve state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets demonstrate that our method has a more competitive segmentation performance in breast ultrasound images.Comment: 12 pages, 8 figure

A Possible Role of HMGB1 in DNA Demethylation in CD4 + T Cells from Patients with Systemic Lupus Erythematosus

The aberrant activity of CD4 + T cells in patients with systemic lupus erythematosus (SLE) is associated with DNA hypomethylation of the regulatory regions in CD11a and CD70 genes. Our previous studies demonstrated that Gadd45a contributes to the development of SLE by promoting DNA demethylation in CD4 + T cells. In this study, we identified proteins that bind to Gadd45a in CD4 + T cells during SLE flare by using the method of co-immunoprecipitation and mass spectrometry, High mobility group box protein 1 (HMGB1) is one of identified proteins. Furthermore, gene and protein expression of HMGB1 was significantly increased in SLE CD4 + T cells compared to controls, and HMGB1 mRNA was correlated with CD11a and CD70 mRNA. A significant, positive correlation was found between HMGB1 mRNA and SLEDAI for SLE patients. Our data demonstrate that HMGB1 binds to Gadd45a and may be involved in DNA demethylation in CD4 + T cells during lupus flare