17 research outputs found

    Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review

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    This paper delves into the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs). Prompt engineering is the process of structuring input text for LLMs and is a technique integral to optimizing the efficacy of LLMs. This survey elucidates foundational principles of prompt engineering, such as role-prompting, one-shot, and few-shot prompting, as well as more advanced methodologies such as the chain-of-thought and tree-of-thoughts prompting. The paper sheds light on how external assistance in the form of plugins can assist in this task, and reduce machine hallucination by retrieving external knowledge. We subsequently delineate prospective directions in prompt engineering research, emphasizing the need for a deeper understanding of structures and the role of agents in Artificial Intelligence-Generated Content (AIGC) tools. We discuss how to assess the efficacy of prompt methods from different perspectives and using different methods. Finally, we gather information about the application of prompt engineering in such fields as education and programming, showing its transformative potential. This comprehensive survey aims to serve as a friendly guide for anyone venturing through the big world of LLMs and prompt engineering

    Acetylshikonin from Zicao Prevents Obesity in Rats on a High-Fat Diet by Inhibiting Lipid Accumulation and Inducing Lipolysis.

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    Various drugs have been developed to treat obesity, but these have undesirable secondary effects, and an efficient but non-toxic anti-obesity drug from natural sources is desired. This study investigated the anti-obesity effects and mechanisms of action of acetylshikonin (AS)-which is used in traditional Chinese medicine-in rats on a high-fat diet (HFD). Rats were fed a normal diet or an HFD; the latter group was received no treatment or were treated with 100, 300, or 900 mg/kg AS extract by intragastric administration for 6 weeks. In addition, 3T3-L1 adipocytes were treated with AS and the effects on adipogenesis and lipolysis were evaluated by western blot analysis of adipogenic transcription factors and lipid-metabolizing enzyme levels and the phosphorylation status of protein kinase (PK) A and hormone-sensitive lipase (HSL). AS prevented HFD-induced obesity including reduction in body weight, white adipose tissue content, liver mass, and serum triglyceride and free fatty acid levels in rats. It also suppressed the expression of adipogenic differentiation transcription factors and decreased the expression of the adipocyte-specific proteins HSL and adipose triglyceride lipase (ATGL). Furthermore, AS treatment induced lipolysis, leading to the release of glycerol and increased in PKA and HSL phosphorylation. These findings demonstrate that AS has anti-obesity effects in a rat model and may be a safe treatment for obesity in humans

    AS induces lipolysis in mature adipocytes by activating the phosphorylation and activity of PKA and HSL.

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    <p>Fully differentiated 3T3-L1 adipocytes were incubated in the absence and presence of various concentrations of AS (0, 0.5, 1.5, and 4.5 μM) for 3 h. Immunoblots are representative of seven independent experiments; β-actin served as a loading control. *P < 0.05, **P < 0.01, ***P < 0.001 vs. untreated adipocytes at the same time point.</p

    AS inhibits 3T3-L1 preadipocyte differentiation and fat accumulation.

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    <p>(<b>A</b>) Cell viability was assessed with the MTT assay24 h after AS treatment. (<b>B</b>) Relative lipid content. (<b>C</b>) Inhibition of fat droplet formation by AS. 3T3-L1 cells were seeded and induced to differentiate for 8 days with or without AS in 6-well plates, then stained with Oil Red O and imaged by light microscopy (200×). Values are expressed as mean ± SEM. Significant differences were observed between the untreated and differentiated control cells. *P < 0.05, **P < 0.01, **P < 0.001 vs. AS-treated cells; <sup>#</sup>P < 0.05, <sup>##</sup>P < 0.01, <sup>###</sup>P < 0.001 vs. differentiated control cells.</p

    AS inhibits PPARγ, C/EBPα, HSL, and ATGL expression in adipocytes.

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    <p>Protein levels were assessed by western blotting in 3T3-L1 cells cultured for 0, 2, 4, and 7 days in the presence or absence of 1.5 μM AS during adipogenesis. Immunoblots are representative of five independent experiments; β-actin served as a loading control. *P < 0.05, **P < 0.01, ***P < 0.001 vs. untreated adipocytes at the same time point.</p

    AS extract reduces adipose tissue weight, white adipocyte size and liver accumulation in obese rats.

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    <p>Coefficients of (<b>A</b>) epididymal adipose tissue and (<b>B</b>) liver were calculated after rats had fasted for 12 h at the end of experiment according to the following equation: coefficient = tissue (g/rat)/weight (g/rat). (<b>C</b>) Epididymal adipose tissue and (<b>D</b>) liver were stained with hematoxylin and eosin for histopathological examination. Images were acquired at 400× magnification on a light microscopy. Values are expressed as mean ± SE (n = 6). Significant differences were observed between normal and HFD groups. *P < 0.05, **P < 0.01, ***P < 0.001 vs. normal group; <sup>#</sup>P < 0.05, <sup>##</sup>P < 0.01, <sup>###</sup>P < 0.001 vs. model group.</p

    AS extract reduces HFD-induced obesity in rats.

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    <p>Rats were randomly divided into five groups: normal control, HFD model, and HFD with AS extract (100, 300, or 900 mg/kg). The extract was intragastric administered for 6 weeks. (<b>A</b>) Food intake per rat was recorded six times a week. (<b>B</b>) Food efficiency ratio was calculated as body weight gain divided by food intake. (<b>C</b>) Change in body weight was measured six times a week. (<b>D</b>) Body weight gain measured in a given week was subtracted from the weight in the previous week. Values are expressed as mean ± SE (n = 30). Significant differences were observed between normal and HFD groups. *P < 0.05, **P < 0.01, ***P < 0.001 vs. normal group; <sup>#</sup>P < 0.05, <sup>##</sup>P < 0.01, <sup>###</sup>P < 0.001 vs. model group.</p
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