Nutrient deprivation induces the Warburg effect through ROS/AMPK-dependent activation of pyruvate dehydrogenase kinase

AbstractThe Warburg effect is known to be crucial for cancer cells to acquire energy. Nutrient deficiencies are an important phenomenon in solid tumors, but the effect on cancer cell metabolism is not yet clear. In this study, we demonstrate that starvation of HeLa cells by incubation with Hank's buffered salt solution (HBSS) induced cell apoptosis, which was accompanied by the induction of reactive oxygen species (ROS) production and AMP-activated protein kinase (AMPK) phosphorylation. Notably, HBSS starvation increased lactate production, cytoplasmic pyruvate content and decreased oxygen consumption, but failed to change the lactate dehydrogenase (LDH) activity or the glucose uptake. We found that HBSS starvation rapidly induced pyruvate dehydrogenase kinase (PDK) activation and pyruvate dehydrogenase (PDH) phosphorylation, both of which were inhibited by compound C (an AMPK inhibitor), NAC (a ROS scavenger), and the dominant negative mutant of AMPK. Our data further revealed the involvement of ROS production in AMPK activation. Moreover, DCA (a PDK inhibitor), NAC, and compound C all significantly decreased HBSS starvation-induced lactate production accompanied by enhancement of HBSS starvation-induced cell apoptosis. Not only in HeLa cells, HBSS-induced lactate production and PDH phosphorylation were also observed in CL1.5, A431 and human umbilical vein endothelial cells. Taken together, we for the first time demonstrated that a low-nutrient condition drives cancer cells to utilize glycolysis to produce ATP, and this increases the Warburg effect through a novel mechanism involving ROS/AMPK-dependent activation of PDK. Such an event contributes to protecting cells from apoptosis upon nutrient deprivation

High-resolution computed tomography illustrating pulmonary lymphangitic carcinomatosis in a patient with advanced pancreatic cancer: a case report

We present a case of advanced pancreatic cancer with diffuse pulmonary interstitial infiltrates and dyspnea in a 61-year-old Asian Taiwanese female. Although surgical lung biopsy is the diagnostic gold standard in most interstitial lung disease, it frequently leads to complications in sick patients. Based on the overall clinico-radiologic correlation, a diagnosis of pulmonary lymphangitic carcinomatosis was supported by the characteristic findings in high-resolution computed tomography

Increased risk of endometriosis in patients with endometritis — a nationwide cohort study involving 84,150 individuals

Objectives: To evaluate the incidence of endometriosis among endometritis patients and its association with confoundingcomorbidities.Material and methods: A population-based, retrospective cohort study of women aged between 20 to 55 years, who werenewly diagnosed with endometritis between 2000 to 2013. A total of 16,830 endometritis patients and 67,230 non-endometritisindividuals were enrolled by accessing data from the National Health Insurance Research Database of Taiwan.The comorbidities accessed were uterine leiomyoma, rheumatoid arthritis, ovarian cancer, infertility and allergic diseases.Results: The mean follow-up period was 9.15 years for the non-endometritis cohort and 9.13 years for the endometritiscohort. There were significantly higher percentages of uterine leiomyoma, rheumatoid arthritis, infertility, ovarian cancerand allergic diseases in the endometritis cohort than in the non-endometritis cohort. Patients with endometritis hada 1.5-fold increased risk of their condition advancing to endometriosis (HR 1.58, 95% CI 1.48–1.68).Conclusions: Our results suggest that patients with endometritis exhibited a positive correlation in developing endometriosis

AI-Generated Content (AIGC): A Survey

To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace manual content generation by generating content based on user-inputted keywords or requirements. The development of large model algorithms has significantly strengthened the capabilities of AIGC, which makes AIGC products a promising generative tool and adds convenience to our lives. As an upstream technology, AIGC has unlimited potential to support different downstream applications. It is important to analyze AIGC's current capabilities and shortcomings to understand how it can be best utilized in future applications. Therefore, this paper provides an extensive overview of AIGC, covering its definition, essential conditions, cutting-edge capabilities, and advanced features. Moreover, it discusses the benefits of large-scale pre-trained models and the industrial chain of AIGC. Furthermore, the article explores the distinctions between auxiliary generation and automatic generation within AIGC, providing examples of text generation. The paper also examines the potential integration of AIGC with the Metaverse. Lastly, the article highlights existing issues and suggests some future directions for application.Comment: Preprint. 14 figures, 4 table