SATB2 shows different profiles between appendiceal adenocarcinomas ex goblet cell carcinoids and appendiceal/colorectal conventional adenocarcinomas: An immunohistochemical study with comparison to CDX2

Background: Special AT-rich sequence-binding protein 2 (SATB2) is a novel marker for colorectal adenocarcinomas but little is known about its expression in appendiceal adenocarcinomas. We aim to investigate SATB2 in these tumors and colorectal adenocarcinomas with comparison to CDX2. Methods: Immunohistochemical stains for SATB2 and CDX2 were performed in 49 appendiceal adenocarcinomas (23 conventional, 26 adenocarcinoma ex goblet cell carcinoids (AdexGCCs)) and 57 colorectal adenocarcinomas. Their expression was correlated with tumor differentiation and growth patterns. Results: SATB2 staining was positive in 26/26 (100%) appendiceal AdexGCCs and 15/23 (65%) appendiceal conventional adenocarcinomas (P = 0.001). Their mean percentage of SATB2-positive cells was 93% and 34%, respectively (P \u3c 0.0001). CDX2 staining was seen in 26/26 (100%) AdexGCCs and 22/23 (96%) appendiceal conventional adenocarcinomas (P = 0.4694). SATB2 and CDX2 showed similar staining in AdexGCCs but CDX2 labeled more tumor cells than SATB2 in conventional adenocarcinomas (mean 84% vs. 34%, P \u3c 0.0001). SATB2 and CDX2 staining was seen in 82% (47/57) and 96% (55/57) colorectal adenocarcinomas, respectively (P = 0.01). The mean percentage of cells positive for SATB2 and CDX2 was 48% and 91%, respectively (P \u3c 0.00001). Decreased SATB2 immunoreactivity was associated with non-glandular differentiation particularly signet ring cells in colorectal (P = 0.001) and appendiceal conventional adenocarcinomas (P = 0.04) but not in appendiceal AdexGCCs. Conclusions: SATB2 is a highly sensitive marker for appendiceal AdexGCCs with similar sensitivity as CDX2. In colorectal and appendiceal conventional adenocarcinomas, SATB2 is not as sensitive as CDX2 and its immunoreactivity is dependent on tumor differentiation

A Randomized Pilot Study of Atractylenolide I on Gastric Cancer Cachexia Patients

We determined the therapeutic efficacy of atractylenolide I (ATR), extracted from largehead atractylodes rhizome, in managing gastric cancer cachexia (GCC), and interpreted its probable pharmacological mechanism via investigating tumor necrosis factor alpha (TNF-α), interleukin-1 (IL-1), interleukin-6 (IL-6) and proteolysis-inducing factor (PIF). This was a randomized but not-blinded pilot. The study group (n = 11) received 1.32 g per day of atractylenolide I (ATR) and the control group (n = 11) received 3.6 g per day of fish-oil-enriched nutritional supplementation (FOE) for 7 weeks. Conservative therapy was similar in both groups. Clinical [appetite, body weight, mid-arm muscle circumference (MAMC), Karnofsky performance status (KPS) status], biomarker (TNF-α, IL-1, IL-6 and PIF) were evaluated in the basal state, at the third and seventh weeks. To analyze changes of cytokines, an immumohistochemistry technique was adopted. Base line characteristics were similar in both groups. Effects on MAMC and body weight increase, TNF-α increase and IL-1 decreases of serum level were significant in both groups (P < 0.05). ATR was significantly more effective than FOE in improving appetite and KPS status, and decreasing PIF positive rate (P < 0.05). Slight nausea (3/11) and dry mouth (1/11) were shown in intervention groups but did not interrupt treatment. These preliminary findings suggest that ATR might be beneficial in alleviating symptoms, in modulating cytokine and in inhibiting PIF proteolysis of gastric cancer cachexia. Further research using a randomized controlled design is necessary to confirm these pilot study findings

Sirtuin 1 and Autophagy Attenuate Cisplatin-Induced Hair Cell Death in the Mouse Cochlea and Zebrafish Lateral Line

Cisplatin-induced ototoxicity is one of the major adverse effects in cisplatin chemotherapy, and hearing protective approaches are unavailable in clinical practice. Recent work unveiled a critical role of autophagy in cell survival in various types of hearing loss. Since the excessive activation of autophagy can contribute to apoptotic cell death, whether the activation of autophagy increases or decreases the rate of cell death in CDDP ototoxicity is still being debated. In this study, we showed that CDDP induced activation of autophagy in the auditory cell HEI-OC1 at the early stage. We then used rapamycin, an autophagy activator, to increase the autophagy activity, and found that the cell death significantly decreased after CDDP injury. In contrast, treatment with the autophagy inhibitor 3-methyladenine (3-MA) significantly increased cell death. In accordance with in vitro results, rapamycin alleviated CDDP-induced death of hair cells in zebrafish lateral line and cochlear hair cells in mice. Notably, we found that CDDP-induced increase of Sirtuin 1 (SIRT1) in the HEI-OC1 cells modulated the autophagy function. The specific SIRT1 activator SRT1720 could successfully protect against CDDP-induced cell loss in HEI-OC1 cells, zebrafish lateral line, and mice cochlea. These findings suggest that SIRT1 and autophagy activation can be suggested as potential therapeutic strategies for the treatment of CDDP-induced ototoxicity

Clustering Product Features for Opinion Mining

In sentiment analysis of product reviews, one important problem is to produce a summary of opinions based on product features/attributes (also called aspects). However, for the same feature, people can express it with many different words or phrases. To produce a useful summary, these words and phrases, which are domain synonyms, need to be grouped under the same feature group. Although several methods have been proposed to extract product features from reviews, limited work has been done on clustering or grouping of synonym features. This paper focuses on this task. Classic methods for solving this problem are based on unsupervised learning using some forms of distributional similarity. However, we found that these methods do not do well. We then model it as a semi-supervised learning problem. Lexical characteristics of the problem are exploited to automatically identify some labeled examples. Empirical evaluation shows that the proposed method outperforms existing state-of-the-art methods by a large margin