The H19 long non-coding RNA is involved in the development of tamoxifen resistance in breast cancer. However, the relationship between H19 and the metastatic potential and treatment options for tamoxifen-resistant (TAMR) breast cancer is not completely understood. Curcumin inhibits cellular proliferation, migration and invasiveness in several cancer types, including pancreatic cancer, breast cancer and chronic myeloid leukemia. The present study aimed to investigate the role of H19 in MCF-7/TAMR cell epithelial-mesenchymal transition (EMT), migration and invasiveness, and to assess the ability of curcumin to inhibit H19-mediated effects. Reverse transcription-quantitative PCR and western blot analysis were conducted to detect the gene or protein expression. Cell Counting Kit-8, wound healing and Transwell invasion assays were performed to estimate the capabilities of cell viability, invasion and migration. H19 overexpression enhanced MCF-7/TAMR cell EMT, invasion and migration by upregulating Snail. Furthermore, curcumin notably decreased the expression levels of epithelial marker E-cadherin and markedly increased the expression levels of mesenchymal marker N-cadherin in MCF-7/TAMR cells compared with the control group. In addition, following treatment with curcumin for 48 h, H19 expression was decreased in a dose-dependent manner. Moreover, curcumin treatment for 48 h significantly attenuated H19-induced alterations in N-cadherin and E-cadherin expression levels. Curcumin also prevented H19-induced invasion and migration. The present study indicated that H19 may serve as a promoting factor of EMT, invasion and migration in MCF-7/TAMR cells, suggesting that curcumin may prevent H19-associated metastasis. Therefore, curcumin may serve as a promising therapeutic drug for patients with TAMR breast cancer.
While reliable drought prediction is fundamental for drought mitigation and water resources management, it is still a challenge to develop robust drought prediction models due to complex local hydro‐climatic conditions and various predictors. Sea surface temperature (SST) is considered as the fundamental predictor to develop drought prediction models. However, traditional models usually extract SST signals from one or several specific sea zones within a given time span, which limits full use of SST signals for drought prediction. Here, we introduce a new meteorological drought prediction approach by using the antecedent SST fluctuation pattern (ASFP) and machine learning techniques (e.g., support vector regression (SVR), random forest (RF), and extreme learning machine (ELM)). Three models (i.e., ASFP‐SVR, ASFP‐ELM, and ASFP‐RF) are developed for ensemble, probability, and deterministic drought predictions. The Colorado, Danube, Orange, and Pearl River basins with frequent droughts over different continents are selected, as the cases, where standardized precipitation evapotranspiration index (SPEI) are predicted at the 1° × 1° resolution with 1‐ and 3‐month lead times. Results show that the ASFP‐ELM model can effectively predict space‐time evolutions of drought events with satisfactory skills, outperforming the ASFP‐SVR and ASFP‐RF models. Our study has potential to provide a reliable tool for drought prediction, which further supports the development of drought early warning systems.
Landslide susceptibility mapping is very important for landslide risk evaluation and land use planning. Toward this end, this paper presents a case study in Ningqiang County, Shanxi Province, China. Slope units were selected as the basic mapping units. A traditional statistical certainty factor model (CF), a machine learning support vector machine model (SVM) and random forest model (RF), along with a hybrid CF-SVM model and a CF-RF model were applied to analyze landslide susceptibility. Firstly, 10 landslide conditioning factors were selected, namely slope-angle, altitude, slope aspect, degree of relief, lithology, distance to rivers, distance to faults, distance to roads, average annual rainfall and normalized difference vegetation index. The 23,169 slope units were generated from a Digital Elevation Model and the corresponding 10 conditioning factor layers were produced from both geological and geographical data. Then, landslide susceptibility mapping was carried out using the five models, respectively. Next, the landslide density (LD), frequency ratio (FR), the area under the curve (AUC) and other indicators were used to validate the rationality, performance and accuracy of the models. The results showed that the susceptibility maps produced from the different models were all reasonable. In each map, the LD and FR were greatest in the zones classed as having very high landslide susceptibility, followed by the high, moderate, low and very low landslide susceptibility classes, respectively. From the comparison of the different maps and ROC curves, the RF model based on slope units was the most appropriate for landslide susceptibility mapping in the study area. It was also found that the combination of weaker learner model (CF model here) with a stronger learner model (SVM and RF model here) can impact the applicability of the stronger model.
Airborne hyperspectral images are used for crop identification with a high classification accuracy because of their high spectral resolution, spatial resolution, and signal-to-noise ratio (SNR). However, the tradeoffs between the three core parameters of a hyperspectral imager (SNR, spatial resolution, and spectral resolution) should be considered for designing an efficient imaging system. Only a few reported studies on the analysis of the impact of SNR on identification accuracy are available. Further, the tradeoffs and mutual interactions among these parameters are rarely considered. In this empirical study, our aim was to understand the relationship among the core parameters and their effects on crop identification accuracy by analyzing the tradeoffs and mutual interactions among these parameters. We analyzed the hyperspectral images of a typical plain agricultural area in Xiongan, China, acquired by the newly developed sensor airborne multimodular imaging spectrometer
Background Metastatic prostate cancer is initially sensitive to androgen receptor inhibition, but eventually becomes metastatic castration-resistant prostate cancer (mCRPC). Olaparib has longer progression-free survival and better measures of response and patient-reported end points than either enzalutamide or abiraterone. In the present study, 2 Markov models were established to analyze the cost utility of olaparib in treating mCRPC from the perspectives of health services in China and the United States. Methods Markov models were established to simulate the progress of mCRPC in China and the United States. The state transition probabilities and clinical data were extracted from the PROfound trial. The cost data were estimated from local pricing, the relevant literature and expert consultancy. The health outcomes are expressed by quality-adjusted life years (QALYs). All costs and incremental cost-effectiveness ratios (ICERs) are presented in US dollars. One-way deterministic sensitivity analysis and probabilistic sensitivity analysis were performed to assess the uncertainty of the models. Results Based on the Chinese Markov model, the base case ICER for olaparib versus the control group was ¥392,727.87, with incremental costs of ¥93,673.23 and an incremental QALY of 0.23, indicating that it was not cost effective from the aspect of the Chinese healthcare system. However, as shown by the American Markov model, olaparib was dominant versus the control group, with a cost saving of $69,675.20 and a gain of 0.23 QALYs. One-way deterministic sensitivity analysis and probabilistic sensitivity analyses showed that the modeling results were not significantly affected by the model parameters. Conclusions Olaparib treatment in patients with mCRPC is not cost effective in China, but it is cost saving in the United States.
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