-PURPOSE: Triple negative breast cancers (estrogen, progesterone and human epidermal growth factor 2 (HER2) receptor-negative) are among the most aggressive forms of cancers with limited treatment options. Doxorubicin is one of the agents found in many of the current cancer treatment protocols, although its use is limited by dose-dependent cardiotoxicity. This work investigates one of the ways to suppress cancer growth by inhibiting tumor cell ability to remove acid accumulated during its metabolism by proton pump inhibitor esomeprazole (a drug with extensive clinical use) which could serve as an addition to doxorubicin therapy. METHODS: In this work, we have investigated growth suppression of triple-negative breast cancer cells MDA-MB-468 by esomeprazole and doxorubicin by trypan blue exclusion assay.
For indoor localisation, a challenge in data-driven localisation is to ensure sufficient data to train the prediction model to produce a good accuracy. However, for WiFi-based data collection, human effort is still required to capture a large amount of data as the representation Received Signal Strength (RSS) could easily be affected by obstacles and other factors. In this paper, we propose an extendGAN+ pipeline that leverages up-sampling with the Dirichlet distribution to improve location prediction accuracy with small sample sizes, applies transferred WGAN-GP for synthetic data generation, and ensures data quality with a filtering module. The results highlight the effectiveness of the proposed data augmentation method not only by localisation performance but also showcase the variety of RSS patterns it could produce. Benchmarking against the baseline methods such as fingerprint, random forest, and its base dataset with localisation models, extendGAN+ shows improvements of up to 23.47%, 25.35%, and 18.88% respectively. Furthermore, compared to existing GAN+ methods, it reduces training time by a factor of four due to transfer learning and improves performance by 10.13%.
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