Academic literature on the topic 'Fang she sheng wu xue'

Abstract AT-rich interaction domain 1A (ARID1A) functions as a tumor suppressor gene, and its loss or inactivation is a common occurrence in various human malignancies, including around 30% of gastric cancers (GC). This study aimed to identify potential therapeutic targets for GC cells with ARID1A deficiency. After robust screening from a chemical library consisting of 551 diverse protein kinase inhibitors, we identified the AKT inhibitor AZD5363 as being the most potent lead compound in inhibiting viability of ARID1A−/− cells. A synthetic lethality between loss of ARID1A expression and AKT inhibition by AZD5363 was validated in both GC cell model system and xenograft model. Mechanistically, AZD5363 treatment induced pyroptotic cell death in ARID1A-deficient GC cells through activation of the caspase-3/GSDME pathway. Furthermore, ARID1A occupied the AKT gene promoter and regulated its transcription negatively, thus the GC cells deficient in ARID1A showed increased expression and phosphorylation of AKT. Our study demonstrates a novel synthetic lethality interaction and unique mechanism between ARID1A loss and AKT inhibition, which may provide a therapeutic and mechanistic rationale for targeted therapy on patients with ARID1A-defective GC who are most likely to be beneficial to AZD5363 treatment. Citation Format: Menghan Fang, Youfen Lin, Chaorong Xue, Kaiqin Sheng, Zegeng Guo, Yuting Han, Hanbin Lin, Yuecheng Wu, Stephen B. Howell, Xu Lin, Xinjian Lin. Targeting ARID1A-mutated gastric cancer cells with synthetic lethal approaches [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3221.

Underwater acoustic signature identification has been employed as a technique for detecting underwater vehicles, such as in anti-submarine warfare or harbour security systems. The underwater sound channel, however, has interference due to spatial variations in topography or sea state conditions and temporal variations in water column properties, which cause multipath and scattering in acoustic propagation. Thus, acoustic data quality control can be very challenging. One of challenges for an identification system is how to recognise the same target signature from measurements under different temporal and spatial settings. This paper deals with the above challenges by establishing an identification system composed of feature extraction, classification algorithms, and feature selection with two approaches to recognise the target signature of underwater radiated noise from a research vessel, Ocean Researcher III, with a bottom mounted hydrophone in five cruises in 2016 and 2017. The fundamental frequency and its power spectral density are known as significant features for classification. In feature extraction, we extract the features before deciding which is more significant from the two aforementioned features. The first approach utilises Polynomial Regression (PR) classifiers and feature selection by Taguchi method and analysis of variance under a different combination of factors and levels. The second approach utilises Radial Basis Function Neural Network (RBFNN) selecting the optimised parameters of classifier via genetic algorithm. The real-time classifier of PR model is robust and superior to the RBFNN model in this paper. This suggests that the Automatic Identification System for Vehicles using Acoustic Signature developed here can be carried out by utilising harmonic frequency features extracted from unmasking the frequency bandwidth for ship noises and proves that feature extraction is appropriate for our targets. 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