Combined treatment with basalt stone and placenta extract to improve the chronic pain and scar after breast cancer surgery: a case report.

BACKGROUND: The changes in body image caused by breast deformities and postoperative pain have a detrimental influence on the physical and mental health of patients with breast cancer. The postoperative quality of life (QOL) of these patients reduces significantly owing to the changes in the breast, an organ unique to women, that occur following breast cancer surgery. CASE PRESENTATION: This case report presents the case of a Asian woman in her early 40 s with postoperative hypertrophic scarring and contraction of the scar following mastectomy; the patient presented with decreased range of motion of the upper arm, hyperpigmentation from radiation burns, changes in breast shape, and chronic pain. The patient received a combination therapy comprising Basalt Stone Treatment and the application of horse placenta extract. As a result of a total of eight sessions conducted once every two weeks, the patient's pain and scar improved. No adverse events were observed after the therapy. CONCLUSION: Combination therapy with Basalt Stone Treatment and horse placenta extract improved the chronic pain and scar after breast cancer surgery.

A hashtag recommendation system for twitter data streams.

BACKGROUND: Twitter has evolved into a powerful communication and information sharing tool used by millions of people around the world to post what is happening now. A hashtag, a keyword prefixed with a hash symbol (#), is a feature in Twitter to organize tweets and facilitate effective search among a massive volume of data. In this paper, we propose an automatic hashtag recommendation system that helps users find new hashtags related to their interests on-demand. METHODS: For hashtag ranking, we propose the Hashtag Frequency-Inverse Hashtag Ubiquity (HF-IHU) ranking scheme, which is a variation of the well-known TF-IDF, that considers hashtag relevancy, as well as data sparseness which is one of the key challenges in analyzing microblog data. Our system is built on top of Hadoop, a leading platform for distributed computing, to provide scalable performance using Map-Reduce. Experiments on a large Twitter data set demonstrate that our method successfully yields relevant hashtags for user's interest and that recommendations are more stable and reliable than ranking tags based on tweet content similarity. RESULTS AND CONCLUSIONS: Our results show that HF-IHU can achieve over 30 % hashtag recall when asked to identify the top 10 relevant hashtags for a particular tweet. Furthermore, our method out-performs kNN, k-popularity, and Naïve Bayes by 69, 54, and 17 %, respectively, on recall of the top 200 hashtags.