Aspirin inhibits proliferation of gastric cancer cells via IL 6/STAT3 signaling pathway

Purpose: To study the effect of aspirin on the proliferation and apoptosis of gastric cancer cells, and its key molecular mechanism of action. Methods: Gastric cancer SGC7901 cells were treated with aspirin at concentrations of 0, 1, 2 and 4 mmol/L. Cell proliferation was measured using cell counting kit (CCK)-8 assay, while messenger ribonucleic acid (mRNA) expressions of interleukin (IL)-6, B-cell lymphoma 2 (Bcl-2) and Bcl-2 associated X protein (Bax) were assessed by reverse transcription-polymerase chain reaction (RT-PCR). Cell apoptosis was determined by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL). Furthermore, the protein expression levels of the signal transducer and activator of transcription 3 (STAT3), phosphorylated STAT3 (p-STAT3), Bcl-2 and Bax were evaluated by Western blotting. Results: Compared with control group, 1, 2 and 4 mmol/L aspirin groups showed lower cell proliferation, and decreased mRNA expressions of Bcl-2 and Bax and IL-6 release at 24, 48 and 72 h (p < 0.05). Cell apoptosis in the aspirin groups was higher than in the control group. Also, compared with the control group, 1 mmol/L aspirin group did not exhibit significant changes in the expressions of STAT3 and p-STAT3 at 72 h. On the other hand, the 2 mmol/L aspirin group at 72 h and the 4 mmol/L aspirin group exhibited significant increases in the expressions of STAT3 and p-STAT3 (p < 0.05). Furthermore, the levels of Bcl-2 and Bax declined in the aspirin groups when compared with the control group (p < 0.05). Conclusion: Aspirin inhibits the proliferation of gastric cancer SGC7901 cells, and induces their apoptosis in vitro in IL-6/STAT3 signaling pathway. The results of the current study may provide new insight into the treatment of gastric cancer

The Research on Exogenous Problems of Farmers’ Piritual and Cultural Education in China

The author studied and analyzed the exogenous problems of the farmers’ spiritual and cultural education, and found out: In today’s China, the exogenous problems of the farmers’ spiritual and cultural education mainly reflected in the separation of spiritual and cultural education is from social environment, political system, economic development, and cultural concepts etc. Then the author put forward to the countermeasures and suggestions aimed at optimizing the allocation of famers’ spiritual and cultural educations resources, environment and evaluation system construction and so on

ReAct: Synergizing Reasoning and Acting in Language Models

While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. In this paper, we explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it to interface with external sources, such as knowledge bases or environments, to gather additional information. We apply our approach, named ReAct, to a diverse set of language and decision making tasks and demonstrate its effectiveness over state-of-the-art baselines, as well as improved human interpretability and trustworthiness over methods without reasoning or acting components. Concretely, on question answering (HotpotQA) and fact verification (Fever), ReAct overcomes issues of hallucination and error propagation prevalent in chain-of-thought reasoning by interacting with a simple Wikipedia API, and generates human-like task-solving trajectories that are more interpretable than baselines without reasoning traces. On two interactive decision making benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and reinforcement learning methods by an absolute success rate of 34% and 10% respectively, while being prompted with only one or two in-context examples

Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving. ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices. Our experiments show that ToT significantly enhances language models' problem-solving abilities on three novel tasks requiring non-trivial planning or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of tasks, our method achieved a success rate of 74%. Code repo with all prompts: https://github.com/princeton-nlp/tree-of-thought-llm.Comment: NeurIPS 2023 camera ready version. Code repo with all prompts: https://github.com/princeton-nlp/tree-of-thought-ll

Mode and Logic of Token Transaction Supervision

This paper mainly studies the attitude and supervision mode of various countries towards digital tokens. The strength and methods of supervision in various countries are different. There are cross-border transaction tokens and other chaotic situations. It is difficult to supervise and protect the interests of investors, which is not conducive to the healthy development of financial economy in various countries. In addition, with the development of globalization, digital tokens are going to the world and need to circulate among countries. Therefore, a recognized regulatory principle is needed to ensure the healthy development of digital finance worldwide. With the goal of single independent law, with the help of "regulatory sandbox" and "white list", we explore regulatory principles

Beyond the Stereotype of Tolerance: Diversified Milieu and Contextual Difference

We explore whether there are value preferences of creative workers in addition to tolerance and how these value preferences vary among different occupation categories and countries. We use a dataset of 1968 and 1076 observations in China and the U.S., respectively, from the World Values Survey dataset (2017–2020, wave 7) (WVS 7), with a Structure Equation Modelling (SEM) and Multinomial Logit Model (MLM) at the micro level. The findings reveal that (1) the Chinese sample is more likely to have a balanced preference of tolerance towards migrants, religions, and homosexuality, while the American sample’s preference of tolerance is much more likely to be interpreted as accepting homosexuality only; (2) the American sample also shows preferences towards responsibility, technology, work style, and political actions, while a preference for happiness and political actions is identified in the Chinese sample; and (3) with a higher level of creativity, the difference regarding understanding of tolerance is more likely to be highlighted between China and the U.S. This study provides a quite unconventional perspective for understanding the composition of preferences and, to a certain extent, reconciles the inconsistency between the theoretical advocacy of building up a selected milieu and the reality of creative workers’ blended value mix