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Background: Since thermo-chemotherapy was suggested as an effective treatment for gastric cancer, we aimed to evaluate the effects of hyperthermia combined with cisplatin (DDP) on the inhibition of human gastric cancer drug-resistant cells in vitro and explore its possible mechanisms.

Methods: SGC-7901/DDP cells were cultured and divided into control, cisplatin, hyperthermia, and hyperthermia combined with cispla- tin groups. Hyperthermia was done at 42°C, 44°C, 46°C, 48°C, and 50°C for 12 h, 24 h, 36 h; 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl- 2H-tetrazolium bromide (MTT) assay detected the proliferation of SGC-7901/DDP at different time and temperature, and the apoptotic rate of SGC-7901/DDP cells was evaluated by using Annexin staining assay. High-throughput Chromatin immunoprecipitation (ChIP)- seq was applied to test long non-coding RNA expression in SGC-7901/DDP cells. Then, real-time fluorescence quantitative polymerase chain reaction was used to verify the expression of long non-coding RNA in all groups.

Results: Double staining showed that hyperthermia combined with cisplatin increased the rate of early apoptosis of SGC-7901/DDP cells. Long non-coding RNA high-throughput ChIP-seq showed a significantly larger amount of long non-coding RNAs and mRNAs in the cells treated with hyperthermia combined cisplatin group in comparison with the control group. We observed that the upregulated mRNAs and long non-coding RNAs were highly related to immune system response and CD95 signaling pathway in nucleus, and down- regulated mRNAs and long non-coding RNA were highly related to Mammalian target of rapamycin (mTOR) and Tumor necrosis factor (TNF) receptor signaling pathway in cytoplasm.

Conclusion: Hyperthermia combined with cisplatin reversed the expression of a large number of mRNAs and long non-coding RNAs in human gastric cancer drug-resistant cells. The molecular mechanism of inhibiting the proliferation of human gastric cancer drug- resistant cells may be related to the upregulation of long non-coding RNAs and mRNAs contributed in CD95, mTOR, and TNF receptor signaling pathway.

During the last few years, Unmanned Aerial Vehicles (UAVs) technologies are widely used to improve agriculture productivity while reducing drudgery, inspection time, and crop management cost. Moreover, they are able to cover large areas in a matter of a few minutes. Due to the impressive technological advancement, UAV-based remote sensing technologies are increasingly used to collect valuable data that could be used to achieve many precision agriculture applications, including crop/plant classification. In order to process these data accurately, we need powerful tools and algorithms such as Deep Learning approaches. Recently, Convolutional Neural Network (CNN) has emerged as a powerful tool for image processing tasks achieving remarkable results making it the state-of-the-art technique for vision applications. In the present study, we reviewed the recent CNN-based methods applied to the UAV-based remote sensing image analysis for crop/plant classification to help researchers and farmers to decide what algorithms they should use accordingly to their studied crops and the used hardware. Fusing different UAV-based data and deep learning approaches have emerged as a powerful tool to classify different crop types accurately. The readers of the present review could acquire the most challenging issues facing researchers to classify different crop types from UAV imagery and their potential solutions to improve the performance of deep learning-based algorithms.