Cancer stem cells (CSCs) are subpopulations of tumor masses with unique abilities in self-renewal, stemness maintenance, drug resistance, and the promotion of cancer recurrence. Recent studies have suggested that breast CSCs play essential roles in chemoresistance. Therefore, new agents that selectively target such cells are urgently required. Reactive oxygen species (ROS)-producing enzymes are the reason for an elevated tumor oxidant status. The nuclear factor erythroid 2-related factor 2 (Nrf2) is a transcriptional factor, which upon detecting cellular oxidative stress, binds to the promoter region of antioxidant genes. By triggering a cytoprotective response, Nrf2 maintains cellular redox status. Cripto-1 participates in the self-renewal of CSCs. Herein, luteolin, a flavonoid found in Taraxacum officinale extract, was determined to inhibit the expressions of stemness-related transcriptional factors, the ATP-binding cassette transporter G2 (ABCG2), CD44, aldehyde dehydrogenase 1 activity as well as the sphere formation properties of breast CSCs. Furthermore, luteolin suppressed the protein expressions of Nrf2, heme oxygenase 1 (HO-1), and Cripto-1 which have been determined to contribute critically to CSC features. The combination of luteolin and the chemotherapeutic drug, Taxol, resulted in enhanced cytotoxicity to breast cancer cells. These findings suggest that luteolin treatment significantly attenuated the hallmarks of breast cancer stemness by downregulating Nrf2-mediated expressions. Luteolin constitutes a potential agent for use in cancer stemness-targeted breast cancer treatments.

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Globally, the incidence rate for breast cancer ranks first. Treatment for early-stage breast cancer is highly cost effective. Five-year survival rate for stage 0–2 breast cancer exceeds 90%. Screening mammography has been acknowledged as the most reliable way to diagnose breast cancer at an early stage. Taiwan government has been urging women without any symptoms, aged between 45 and 69, to have a screening mammogram bi-yearly. This brings about a large workload for radiologists. In light of this, this paper presents a deep neural network (DNN)-based model as an efficient and reliable tool to assist radiologists with mammographic interpretation. For the first time in the literature, mammograms are completely classified into BI-RADS categories 0, 1, 2, 3, 4A, 4B, 4C and 5. The proposed model was trained using block-based images segmented from a mammogram dataset of our own. A block-based image was applied to the model as an input, and a BI-RADS category was predicted as an output. At the end of this paper, the outperformance of this work is demonstrated by an overall accuracy of 94.22%, an average sensitivity of 95.31%, an average specificity of 99.15% and an area under curve (AUC) of 0.9723. When applied to breast cancer screening for Asian women who are more likely to have dense breasts, this model is expected to give a higher accuracy than others in the literature, since it was trained using mammograms taken from Taiwanese women.

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BACKGROUNDColonic lipomas are rare, slow-growing benign tumors. Colonic lipomas are generally asymptomatic and are found incidentally. Although cases of cecal lipoma have been sporadically reported in the literature, the disease has not been systematically reviewed.CASE SUMMARYWe present a 44-year-old man who underwent a routine physical check-up during which colonoscopic examination revealed an asymptomatic 1.5-cm cecal mass at the appendiceal orifice. Laparoscopic exploration was performed that also demonstrated a congested and erythematous appendix. En bloc resection of both the cecum and vermiform appendix was performed because of the suspicion of malignancy. Histopathological examination revealed a cecal lipoma composed of mature adipose tissue, and the appendix showed subclinical inflammation. Our procedures and findings were discussed, along with relevant English literature that was retrieved from the PubMed database from 2000 to 2017. Twenty-six cases, including ours, were reported. Consistent with the findings of the literature, it is difficult to obtain a definitive diagnosis by colonoscopic biopsy.CONCLUSIONSurgery remains the treatment of choice for this condition. Intraoperative frozen pathological sectioning helped the surgeon decide the extent of surgery, and radical surgery was avoided. Excision of benign lesions occupying the appendiceal orifice may be indicated for the prevention of later development of acute appendicitis. The prognosis is generally good, with only one of the 26 reported patients complicated with acute appendicitis, who subsequently succumbed due to severe comorbidities and sepsis.

Sarcopenia, characterized by a decline of skeletal muscle mass, has emerged as an important prognostic factor for cancer patients. Trunk computed tomography (CT) is a commonly used modality for assessment of cancer disease extent and treatment outcome. CT images can also be used to analyze the skeletal muscle mass filtered by the appropriate range of Hounsfield scale. However, a manual depiction of skeletal muscle in CT scan images for assessing skeletal muscle mass is labor-intensive and unrealistic in clinical practice. In this paper, we propose a novel U-Net based segmentation system for CT scan of paravertebral muscles in the third and fourth lumbar spines. Since the number of training samples is limited (i.e., 1024 CT images only), it is well-known that the performance of the deep learning approach is restricted due to overfitting. A data augmentation strategy to enlarge the diversity of the training set to boost the performance further is employed. On the other hand, we also discuss how the number of features in our U-Net affects the performance of the semantic segmentation. The efficacies of the proposed methodology based on w/ and w/o data augmentation and different feature maps are compared in the experiments. We show that the Jaccard score is approximately 95.0% based on the proposed data augmentation method with only 16 feature maps used in U-Net. The stability and efficiency of the proposed U-Net are verified in the experiments in a cross-validation manner.

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