A New Chinese Medicine Intestine Formula Greatly Improves the Effect of Aminosalicylate on Ulcerative Colitis

Ulcerative colitis (UC) is a chronic lifelong inflammatory disorder of the colon. Current medical treatment of UC relies predominantly on the use of traditional drugs, including aminosalicylates, corticosteroids, and immunosuppressants, which failed to effectively control this disease’s progression and produced various side effects. Here, we report a new Chinese medicine intestine formula (CIF) which greatly improved the effect of mesalazine, an aminosalicylate, on UC. In the present study, 60 patients with chronic UC were treated with oral mesalazine alone or in combination with CIF enema. The combination of mesalazine and CIF greatly and significantly improved the clinical symptoms and colon mucosal condition and improved the Mayo Clinic Disease Activity Index and health-related quality of life, when compared to mesalazine alone. In particular, the addition of CIF further decreased serum levels of tumor necrosis factor-alpha and hypersensitivity C-reactive protein but in contrast increased interleukin-4. Thus, the results demonstrate the beneficial role of CIF in UC treatment, which may be mediated by the regulation of inflammation

ELM-Based Active Learning via Asymmetric Samplers: Constructing a Multi-Class Text Corpus for Emotion Classification

A high-quality annotated text corpus is vital when training a deep learning model. However, it is insurmountable to acquire absolute abundant label-balanced data because of the huge labor and time costs needed in the labeling stage. To alleviate this situation, a novel active learning (AL) method is proposed in this paper, which is designed to scratch samples to construct multi-class and multi-label Chinese emotional text corpora. This work shrewdly leverages the superiorities, i.e., less learning time and generating parameters randomly possessed by extreme learning machines (ELMs), to initially measure textual emotion features. In addition, we designed a novel combined query strategy called an asymmetric sampler (which simultaneously considers uncertainty and representativeness) to verify and extract ideal samples. Furthermore, this model progressively modulates state-of-the-art prescriptions through cross-entropy, Kullback–Leibler, and Earth Mover’s distance. Finally, through stepwise-assessing the experimental results, the updated corpora present more enriched label distributions and have a higher weight of correlative emotional information. Likewise, in emotion classification experiments by ELM, the precision, recall, and F1 scores obtained 7.17%, 6.31%, and 6.71% improvements, respectively. Extensive emotion classification experiments were conducted by two widely used classifiers—SVM and LR—and their results also prove our method’s effectiveness in scratch emotional texts through comparisons

Integration of Multi-Branch GCNs Enhancing Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) is a complex and challenging task in Natural Language Processing (NLP). It aims to extract the triplet of aspect term, opinion term, and their associated sentiment polarity, which is a more fine-grained study in Aspect Based Sentiment Analysis. Furthermore, there have been a large number of approaches being proposed to handle this relevant task. However, existing methods for ASTE suffer from powerless interactions between different sources of textual features, and they usually exert an equal impact on each type of feature, which is quite unreasonable while building contextual representation. Therefore, in this paper, we propose a novel Multi-Branch GCN (MBGCN)-based ASTE model to solve this problem. Specifically, our model first generates the enhanced semantic features via the structure-biased BERT, which takes the position of tokens into the transformation of self-attention. Then, a biaffine attention module is utilized to further obtain the specific semantic feature maps. In addition, to enhance the dependency among words in the sentence, four types of linguistic relations are defined, namely part-of-speech combination, syntactic dependency type, tree-based distance, and relative position distance of each word pair, which are further embedded as adjacent matrices. Then, the widely used Graph Convolutional Network (GCN) module is utilized to complete the work of integrating the semantic feature and linguistic feature, which is operated on four types of dependency relations repeatedly. Additionally, an effective refining strategy is employed to detect whether word pairs match or not, which is conducted after the operation of each branch GCN. At last, a shallow interaction layer is designed to achieve the final textual representation by fusing the four branch features with different weights. To validate the effectiveness of MBGCNs, extensive experiments have been conducted on four public and available datasets. Furthermore, the results demonstrate the effectiveness and robustness of MBGCNs, which obviously outperform state-of-the-art approaches

Tunica albuginea allograft: a new model of LaPeyronie′s disease with penile curvature and subtunical ossification

The pathophysiology of LaPeyronie's disease (PD) is considered to be multifactorial, involving genetic predisposition, trauma, inflammation and altered wound healing. However, these factors have not yet been validated using animal models. In this study, we have presented a new model obtained by tunica albuginea allograft. A total of 40, 16-week-old male rats were used. Of these, 8 rats served as controls and underwent a 10 × 2-mm-wide tunical excision with subsequent autografting, whereas the remaining 32 underwent the same excision with grafting of the defect with another rat's tunica. Morphological and functional testing was performed at 1, 3, 7 and 12 weeks after grafting. Intracavernous pressure, the degree of penile curvature and elastic fiber length were evaluated for comparison between the allograft and control groups. The tissues were obtained for histological examination. The penile curvature was significantly greater in the allografted rats as compared with the control rats. The erectile function was maintained in all rats, except in those assessed at 12 weeks. The elastin fiber length was decreased in the allografted tunica as compared to control. SMAD2 expression was detected in the inner part of the allograft, and both collagen-II- and osteocalcin-positive cells were also noted. Tunica albuginea (TA) allograft in rats is an excellent model of PD. The persistence of curvature beyond 12 weeks and the presence of ossification in the inner layer of the TA were similar to those observed in men with PD. Validation studies using this animal model would aid understanding of the PD pathophysiology for effective therapeutic interventions