Molecular Subtypes of Breast Cancers from Myanmar Women: A Study of 91 Cases at Two Pathology Centers

BACKGROUND: Breast cancer is the most common cancer in Myanmar women. Revealing the hormonal receptor status, human epidermal growth factor receptor 2 (HER2) and Ki-67 expression is useful for estimating patient prognosis as well as determination of treatment strategy. However, immunohistochemical features and classification of molecular subtypes in breast cancers from Myanmar remain unknown. METHODS: The clinicopathological features of 91 breast cancers from Myanmar women were examined. Immunohistochemistry was performed on tissue specimens with antibodies to estrogen receptor (ER), progesterone receptor (PgR), HER2, Ki-67, cytokeratin (CK)5/6 and CK14. Immunohistochemistry-based molecular subtyping was conducted. RESULTS: Breast cancers in Myanmar women were relatively large, high grade with frequent metastatic lymph nodes. Of the 91 patients, tumors with ER positive, PgR positive, and HER2 positive were 57.1%, 37.4%, and 28.6%, respectively. The most prevalent subtype was luminal B (HER2-) (39.6%), followed by HER2 (22.0%), triple negative (TN)-basal-like (12.1%), luminal A (11.0%), TN-null (8.8%) and luminal B (HER2+) (6.6%). The mean Ki-67 expression of 91 cases was 33.9% (33.9% ± 19.2%) and the median was 28% (range; 4%-90%). The mean Ki-67 expression of luminal A, luminal B, HER2 and TN-basal-like/ null was 7%, 30%, 40%, and 57%/43%, respectively. A higher Ki-67 expression significantly correlated with a higher grade, larger size and higher stage of malignancy. CONCLUSIONS: We, for the first time, investigated the histopathological features of breast cancers from Myanmar women. Myanmar breast cancers appeared to be aggressive in nature, as evidenced by high frequency of poor-prognosis subtypes with high level of Ki-67 expression

Smartphone Gait Authentication: Recognizing Smartphone Users based on their Gait

Gait recognition using smartphone motion sensors such as accelerometers and gyroscopes is relatively underdeveloped compared to those using machine vision. This project explored the various state of the art neural networks-based approaches for accelerometer and gyroscope-based gait analysis and evaluated them. CNN and LSTM neural networks architectures proposed in prior work are replicated to achieve similar results on a gait dataset gathered in the wild. Prior work focused deep learning models for gait recognition on data gathered in controlled user studies

Forex Trading Systems: An Algorithmic Approach to Technical Trading

In financial trading, emotion can often obstruct clear decision making. The goal of this project is to build a system which can overcome this by trading foreign currencies autonomously. Three systems were created: two relying on neural networks, and a third on pattern recognition of candlestick charts. A fourth system has been designed to allocate funds to the others using utility theory. Though the algorithms were not profitable, a powerful interface was built, connecting Python scripts to MetaTrader 4 for trading