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Objective: This study aimed to predict the targets and signaling pathways affected by Tengli Kangliu Decoction (TKD) in the treatment of colorectal cancer (CRC) precursor lesions and to determine TKDs mechanism of action based on previous experimental results using network pharmacology techniques and methods., Methods: Using the traditional Chinese medicine systems pharmacology database (TCMSP) and UniProt database, the active ingredients and potential targets of TKD were identified. Human colorectal adenoma (CRA) targets were analyzed using the GeneCards database, the Online mendelian inheritance in man (OMIM) database, and the NCBI database. The common targets of drug-disease interactions were input into the String database to construct a protein-protein interaction (PPI) network. These data were then used to construct the network diagram. Gene ontology (GO) function analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis were performed on the target genes. Finally, the component-disease-pathway-target network file was imported into Cytoscape 3.8.0 and used to construct the pathway network diagram., Results: Compounds with a drug-likeness (DL) score ≥ 0.18 and an oral bioavailability (OB) ≥ 30% were selected as the active constituents of TKD. Two hundred eighty eight chemical constituents were screened and 305 chemical drug targets were predicted. After further screening, 1942 disease-related targets, which are hypothesized to be the main chemical components of TKD, were obtained. When comparing the targets of action and CRA treatment targets, 172 common targets were identified. Using GO enrichment analysis of common targets of drug diseases, 2550 biological processes (BP) were predicted, 164 items of which were related to molecular functioning (MF), and 67 items related to cell composition. KEGG pathway analysis was performed on the common targets of drug diseases, and a total of 178 signaling pathways were enriched., Conclusion: Using network pharmacology research, this study reports on the synergistic effect of the multiple components of TKD on the multi-target, and multiple pathways of colorectal precancerous lesions. These findings lay a theoretical foundation for further colorectal precancerous lesions research., Competing Interests: The authors have no conflict of interests to disclose., (Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.)

Gas injection and water injection are common and effective methods to improve oil recovery. To ensure its production effect, it is necessary to simulate the oilfield production process. However, traditional composition simulation runs a large number of calculations and takes a long time. Through the analysis of relevant data, we found that production is affected by many factors and has a strong sequential character. Therefore, this paper proposes a deep learning model for reservoir production prediction based on stacked long short-term memory network (LSTM). It is applied to other well patterns with a short production time and a few samples in the same oilfield block by transfer learning. The model achieves an effective combination with the actual reservoir production process. At the same time, it uses the knowledge learned from the well pattern with sufficient historical data to assist in the establishment of the model of the well pattern with limited data. This can obtain accurate prediction results and save the model training time, thus getting more effective application effects than composition simulation. This paper verifies the effectiveness of the proposed method through the data and multiple different injection combinations of the Tarim oilfield., Competing Interests: The authors declare no competing financial interest., (© 2021 The Authors. Published by American Chemical Society.)