Low-grade myofibroblastic sarcoma of the liver misdiagnosed as cystadenoma:A case report

BACKGROUND Low-grade myofibroblastic sarcoma(LGMS)is a rare malignant tumor.It has no specific clinical manifestations and commonly occurs in the head and neck,extremities and other body parts,with the liver not as its predisposing site.CASE SUMMARY We report a case report of a 58-year-old man with right upper abdominal pain for 11 d.Contrast-enhanced computed tomography(CECT),CE magnetic resonance imaging and CE ultrasound(US)all showed a cystic-solid mass in the right liver.As the initial clinical diagnosis was hepatic cystadenoma,surgical resection was performed,and the postoperative pathology indicated hepatic LGMS.The 3-mo follow-up showed favorable recovery of the patient.However,at 7-mo follow-up,two-dimensional US and CECT showed a suspected metastatic lesion in the rightmiddle abdomen.CONCLUSION Hepatic MS is particularly rare and easily misdiagnosed,more cases will contribute to the understanding and the diagnosis accuracy.

Ester-Derivatized Indoles as Fluorescent and Infrared Probes for Hydration Environments

Tryptophan derivatives have long been used as site-specific biological probes. 4-Cyanotrypto- phan emits in the visible region and is the smallest blue fluorescent amino acid probe for bio- logical applications. Other indole or tryptophan analogs may emit at even longer wavelengths than 4-cyanotryptophan. We performed FTIR, UV-Vis, and steady-state and time-resolved fluorescence spectroscopy on six ester-derivatized indoles in different solvents. Methyl indole- 4-carboxylate emits at 450 nrn with a long fluorescence lifetirne, and is a promising candidate for a fluorescent probe. The ester-derivatized indoles could be used as spectroscopic probes to study local protein environments. Our measurements provide a guide for choosing esterderivatized indoles to use in practice and data for computational modeling of the effect of substitution on the electronic transitions of indole.

CSLabel： An Approach for Labelling Mobile App Reviews

Mobile apps （applications） have become a popular form of software, and the app reviews by users have become an important feedback resource. Users may raise some issues in their reviews when they use apps, such as a functional bug, a network lag, or a request for a feature. Understanding these issues can help developers to focus on users＇ concerns, and help users to evaluate similar apps for download or purchase. However, we do not know which types of issues are raised in a review. Moreover, the amount of user reviews is huge and the nature of the reviews＇ text is unstructured and informal. In this paper, we analyze 3 902 user reviews from 11 mobile apps in a Chinese app store -- 360 Mobile Assistant, and uncover 17 issue types. Then, we propose an approach CSLabel that can label user reviews based on the raised issue types. CSLabel uses a cost-sensitive learning method to mitigate the effects of the imbalanced data, and optimizes the setting of the support vector machine （SVM） classifier＇s kernel function. Results show that CSLabel can correctly label reviews with the precision of 66.5%, the recall of 69.8%, and the F1 measure of 69.8%. In comparison with the state-of-the-art approach, CSLabel improves the precision by 14%, the recall by 30%, the F1 measure by 22%. Finally, we apply our approach to two real scenarios： 1） we provide an overview of 1 076 786 user reviews from 1 100 apps in the 360 Mobile Assistant and 2） we find that some issue types have a negative correlation with users＇ evaluation of apps.