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Chuqian Lei,Ciqiu Yang,Bin Xia,Fei Ji,Yi Zhang,Hongfei Gao,Qianqian Xiong,Yufeng Lin,Xiaosheng Zhuang,Liulu Zhang,Teng Zhu,Minyi Cheng,Mei Yang,Kun Wang 한국유방암학회 2020 Journal of breast cancer Vol.23 No.1
Purpose: Tau is a microtubule-associated protein that can be found in both normal and abnormal breast cells. Whether the expression of Tau protein can predict the response to neoadjuvant chemotherapy (NACT) is still unclear. In this study, we assessed the role of Tau protein expression in predicting a pathological complete response (pCR) to NACT for different subtypes of breast cancer. Methods: Four hundred and sixty-eight eligible patients were retrospectively recruited in our study. The relationship between clinicopathologic factors, including Tau protein expression, and pCR in different subtypes was evaluated using logistic regression analysis. Correlation between Tau and disease-free survival (DFS) and overall survival (OS) was performed using Kaplan–Meier analysis. Results: The expression of Tau protein was negatively correlated with pCR, especially in triple-negative breast cancer (TNBC). No significant difference was observed in the luminal human epidermal growth factor receptor-2 (HER2)-negative subtype and HER2-positive subtype. Patients with pCR were associated with better DFS and OS (p < 0.05). However, Tau protein expression had no association with either DFS or OS (p > 0.05). Conclusion: Tau protein expression can predict pCR before NACT in TNBC, but there was no correlation between Tau expression and DFS or OS.
Dan Jeric Arcega Rustia,Chien Erh Lin,Jui-Yung Chung,Yi-Ji Zhuang,Ju-Chun Hsu,Ta-Te Lin 한국응용곤충학회 2020 Journal of Asia-Pacific Entomology Vol.23 No.1
This work presents an automated insect pest counting and environmental condition monitoring system using integrated camera modules and an embedded system as the sensor node in a wireless sensor network. The sensor node can be used to simultaneously acquire images of sticky paper traps and measure temperature, humidity, and light intensity levels in a greenhouse. An image processing algorithm was applied to automatically detect and count insect pests on an insect sticky trap with 93% average temporal detection accuracy compared with manual counting. The integrated monitoring system was implemented with multiple sensor nodes in a greenhouse and experiments were performed to test the system’s performance. Experimental results show that the automatic counting of the monitoring system is comparable with manual counting, and the insect pest count information can be continuously and effectively recorded. Information on insect pest concentrations were further analyzed temporally and spatially with environmental factors. Analyses of experimental data reveal that the normalized hourly increase in the insect pest count appears to be associated with the change in light intensity, temperature, and relative humidity. With the proposed system, laborious manual counting can be circumvented and timely assessment of insect pest and environmental information can be achieved. The system also offers an efficient tool for long-term insect pest behavior observations, as well as for practical applications in integrated pest management (IPM).