In this study, HER2 RNA aptamers were conjugated to mertansine (DM1) and the anti-cancer effectiveness of the conjugate was evaluated in HER2-overexpressing breast cancer models. The conjugate of HER2 aptamer and anticancer drug DM1 (aptamer-drug conjugate, ApDC) was prepared and analyzed using HPLC and mass spectrometry. The cell-binding affinity and cytotoxicity of the conjugate were determined using confocal microscopy and WST-1 assay. The in vivo anti-tumoral efficacy of ApDC was also evaluated in mice carrying BT-474 breast tumors overexpressing HER2. The synthesized HER2-specific RNA aptamers were able to specifically and efficiently bind to HER-positive BT-474 breast cancer cells, but not to HER2-negative MDA-MB-231 breast cancer cells. Also, the HER2-specific ApDC showed strong toxicity to the target cells, BT-474, but not to MDA-MB-231 cells. According to the in vivo analyses drawn from the mouse xenografts of BT-747 tumor, the ApDC was able to more effectively inhibit the tumor growth. Compared to the control group, the mice treated with the ApDC showed a significant reduction of tumor growth. Besides, any significant body weight losses or hepatic toxicities were monitored in the ApDC-treated mice. This research suggests the HER2 aptamer-DM1 conjugate as a target-specific anti-cancer modality and provides experimental evidence supporting its enhanced effectiveness for HER2-overexpressing target tumors. This type of aptamer-conjugated anticancer drug would be utilized as a platform structure for the development of versatile targeted high-performance anticancer drugs by adopting the easy deformability and high affinity of aptamers.

As the volume of spatial data has rapidly increased over the last several decades, there is a growing concern about missing and incomplete observations that may result in biased conclusions. Several recent studies have reported that machine learning techniques can more efficiently address this limitation in emerging data sets than conventional interpolation approaches, such as inverse distance weighting and kriging. However, most existing studies focus on data from environmental sciences; so, further evaluations are required to assess their strengths and limitations for socioeconomic data, such as house price data. In this study, we conducted a comparative analysis of four commonly used methods: neural networks, random forests, inverse distance weighting, and kriging. We applied these methods to the real estate transaction data of Seoul, South Korea, and demonstrated how the values of the houses at which no transactions are recorded could be predicted. Our empirical analysis suggested that the neural networks and random forests can provide a more accurate estimation than the interpolation counterparts. Of the two machine learning techniques, the results from a random forest model were slightly better than those from a neural network model. However, the neural network appeared to be more sensitive to the amount of training data, implying that it has the potential to outperform the other methods when there are sufficient data available for training.