Quinolinone inhibits proliferation of gastric cancer cells and induces their apoptosis via down-regulation of the expression of pro-oncogene c-Myc

Purpose: To determine the anti-proliferative potential of quinolinone against gastric cancer cells, and the underlying mechanism of action.Methods: Quinolinone-mediated proliferative changes were measured using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay, while its effect on apoptosis was determined by flow cytometry. Transwell and wound healing assays were used for the determination of the effect of quinolinone on cell invasion and migration. The effect of quinolinone on protein expression levels were assayed with western blotting.Results: Quinolinone caused reduction in gastric cancer cell viability, but it had no effect on normal (GES-1) cells. Treatment with 8 μM quinolinone reduced the viability of SNU-5 and SGC-7901 cells to 32 and 27 %, respectively. Moreover, 8 μM quinolinone induced 67.90 and 71.54 % apoptosis in SNU-5 and SGC-7901 cells, respectively. Quinolinone significantly increased the population of cells in G1 phase, and suppressed migration potential (p < 0.05). Furthermore, in quinolinone-treated cells, the expression levels of p-PI3K, c-Myc and p-AKT were much lower than those in untreated cells (p < 0.05). Quinolinone also downregulated the expressions of MMP-2 and MMP-9, while it upregulated p21 expression in SNU-5 and SGC-7901 cells.Conclusion: Quinolinone suppresses the growth of SNU-5 and SGC-7901 gastric cancer cells via cell cycle arrest, induction of apoptosis and downregulation of the expressions of c-Myc and metalloproteinases. Thus, quinolinone may be developed as a potential drug candidate for the treatment of gastric cancer. Keywords: Gastric cancer, Apoptosis, Metalloproteinases, Phosphorylatio

Risk Factors for Death in Patients with Atrial Fibrillation

With the aging society and increasing risk factors for cardiovascular diseases, the incidence of atrial fbrillation is gradually in-creasing, seriously afecting human health. Previous studies have shown that hypertension, diabetes, heart failure, chronic kidney disease, etc, increase the risk of death of patients with atrial fbrillation. Research has shown that cardiac pathological remodeling is the fundamental pathophysiological mechanism for atrial fbrillation, which is closely related to thromboembolism and death in patients with atrial fbrillation. At present, there is limited research on the risk factors for increased mortality in patients with atrial fbrillation. Therefore, this article will review the related factors that increase the risk of death in patients with atrial fbrillation

Discrimination of Colon Cancer Stem Cells Using Noncanonical Amino Acid

Cancer stem cells (CSCs) may be responsible for tumor recurrence. Metabolic labelling of newly synthesized proteins with non-canonical amino acids allows us to discriminate CSCs in mixed populations due to the quiescent nature of these cells

Imagine, Initialize, and Explore: An Effective Exploration Method in Multi-Agent Reinforcement Learning

Effective exploration is crucial to discovering optimal strategies for multi-agent reinforcement learning (MARL) in complex coordination tasks. Existing methods mainly utilize intrinsic rewards to enable committed exploration or use role-based learning for decomposing joint action spaces instead of directly conducting a collective search in the entire action-observation space. However, they often face challenges obtaining specific joint action sequences to reach successful states in long-horizon tasks. To address this limitation, we propose Imagine, Initialize, and Explore (IIE), a novel method that offers a promising solution for efficient multi-agent exploration in complex scenarios. IIE employs a transformer model to imagine how the agents reach a critical state that can influence each other's transition functions. Then, we initialize the environment at this state using a simulator before the exploration phase. We formulate the imagination as a sequence modeling problem, where the states, observations, prompts, actions, and rewards are predicted autoregressively. The prompt consists of timestep-to-go, return-to-go, influence value, and one-shot demonstration, specifying the desired state and trajectory as well as guiding the action generation. By initializing agents at the critical states, IIE significantly increases the likelihood of discovering potentially important under-explored regions. Despite its simplicity, empirical results demonstrate that our method outperforms multi-agent exploration baselines on the StarCraft Multi-Agent Challenge (SMAC) and SMACv2 environments. Particularly, IIE shows improved performance in the sparse-reward SMAC tasks and produces more effective curricula over the initialized states than other generative methods, such as CVAE-GAN and diffusion models.Comment: The 38th Annual AAAI Conference on Artificial Intelligenc

Applying Back Propagation Algorithm and Analytic Hierarchy Process to Environment Assessment

This paper designs a new and scientific environmental quality assessment method, and takes Saihan dam as an example to explore the environmental improvement degree to the local and Beijing areas. AHP method is used to assign values to each weight 7 primary indicators and 21 secondary indicators were used to establish an environmental quality assessment model. The conclusion shows that after the establishment of Saihan dam, the local environmental quality has been improved by 7 times, and the environmental quality in Beijing has been improved by 13%. Then the future environmental index is predicted. Finally the Spearson correlation coefficient is analyzed, and it is proved that correlation is 99% when the back-propagation algorithm is used to test and prove that the error is little

Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends

Learning binary classifiers from positive and unlabeled data (PUL) is vital in many real-world applications, especially when verifying negative examples is difficult. Despite the impressive empirical performance of recent PUL methods, challenges like accumulated errors and increased estimation bias persist due to the absence of negative labels. In this paper, we unveil an intriguing yet long-overlooked observation in PUL: \textit{resampling the positive data in each training iteration to ensure a balanced distribution between positive and unlabeled examples results in strong early-stage performance. Furthermore, predictive trends for positive and negative classes display distinctly different patterns.} Specifically, the scores (output probability) of unlabeled negative examples consistently decrease, while those of unlabeled positive examples show largely chaotic trends. Instead of focusing on classification within individual time frames, we innovatively adopt a holistic approach, interpreting the scores of each example as a temporal point process (TPP). This reformulates the core problem of PUL as recognizing trends in these scores. We then propose a novel TPP-inspired measure for trend detection and prove its asymptotic unbiasedness in predicting changes. Notably, our method accomplishes PUL without requiring additional parameter tuning or prior assumptions, offering an alternative perspective for tackling this problem. Extensive experiments verify the superiority of our method, particularly in a highly imbalanced real-world setting, where it achieves improvements of up to $11.3\%$ in key metrics. The code is available at \href{https://github.com/wxr99/HolisticPU}{https://github.com/wxr99/HolisticPU}.Comment: 25 page

Breast Tumour Initiating Cell Fate Is Regulated by Microenvironmental Cues from an Extracellular Matrix

Cancer stem cells, also known as tumour-initiating cells (TICs), are identified as highly tumorigenic population within tumours and hypothesized to be main regulators in tumour growth, metastasis and relapse. Evidence also suggests that a tumour microenvironment plays a critical role in the development and progression of cancer, by constantly modulating cell–matrix interactions. Scientists have tried to characterize and identify the TIC population but the actual combination of extracellular components in deciphering the fate of TICs has not been explored. The basic unanswered question is the phenotypic stability of this TIC population in a tissue extracellular matrix setting. The in vivo complexity makes it difficult to identify parameters in a diverse milieu that affect TICs behaviour. Herein we studied how the TIC population would respond when subjected to a unique microenvironment composed of different extracellularproteins. The TIC-enriched population isolated from a Her2/neu-induced mouse mammary tumour was cultured on collagen, fibronectin and laminin coated substrates for one to two weeks. Our observations indicate that a laminin substrate can maintain the majority of the self-renewing and tumorigenic TIC population, whereas collagen induced a more differentiated phenotype of the cells. Also interestingly, fibronectin substrates dictated an invasive phenotype of TICs as evidenced from the EMT-related gene expression pattern. The results of this study signify that the microenvironmental cues play a considerable role in tumour relapse and progression by altering the cancer stem cell behaviour and thus this knowledge could be used to design novel cancer therapeutics

Spatio-Temporal Patterns and Impacts of Sediment Variations in Downstream of the Three Gorges Dam on the Yangtze River, China

Spanning the Yangtze River of China, the Three Gorges Dam (TGD) has received considerable concern worldwide with its potential impacts on the downstream side of the dam. This work investigated the spatio-temporal variations of suspended sediment concentration (SSC) at the downstream section of Yichang-to-Chenglingji from 2002 to 2015. A random forest model was developed to estimate SSC using MODIS ground reflectance products, and the spatio-temporal distributions of SSC were retrieved with this model to investigate the characteristics of water-silt variation. Our results revealed that, relatively, SSC before 2003 was evenly distributed in the downstream Yangtze River, while this spatial distribution pattern changed ce 2003 when the dam started storing water. Temporally, the SSC demonstrated a W-shaped curve of seasonal variation as one peak occurred in September and two troughs in March and November, and showed a significantly decreasing trend after three-stage impoundment. After official operation of the TGD in 2009, the SSC was reduced by over 40% than before 2003. Spatially, the most significant changes occurred in the upper Jingjiang section, where the SSC dropped by 45%. During all stages of impoundment, the water impoundment to 135 m in 2003 had the most significant impact on suspended sediment. The decreased SSC has led to emerging risks of bank failure, aggravated erosion of water front and aggressive down-cutting erosion along the downstream of the dam, as well as other ecological and environmental issues that require urgent attention by the government

Unlocking the Power of Open Set : A New Perspective for Open-Set Noisy Label Learning

Learning from noisy data has attracted much attention, where most methods focus on closed-set label noise. However, a more common scenario in the real world is the presence of both open-set and closed-set noise. Existing methods typically identify and handle these two types of label noise separately by designing a specific strategy for each type. However, in many real-world scenarios, it would be challenging to identify open-set examples, especially when the dataset has been severely corrupted. Unlike the previous works, we explore how models behave when faced with open-set examples, and find that \emph{a part of open-set examples gradually get integrated into certain known classes}, which is beneficial for the separation among known classes. Motivated by the phenomenon, we propose a novel two-step contrastive learning method CECL (Class Expansion Contrastive Learning) which aims to deal with both types of label noise by exploiting the useful information of open-set examples. Specifically, we incorporate some open-set examples into closed-set classes to enhance performance while treating others as delimiters to improve representative ability. Extensive experiments on synthetic and real-world datasets with diverse label noise demonstrate the effectiveness of CECL

NMI inhibits cancer stem cell traits by downregulating hTERT in breast cancer.

N-myc and STAT interactor (NMI) has been proved to bind to different transcription factors to regulate a variety of signaling mechanisms including DNA damage, cell cycle and epithelial-mesenchymal transition. However, the role of NMI in the regulation of cancer stem cells (CSCs) remains poorly understood. In this study, we investigated the regulation of NMI on CSCs traits in breast cancer and uncovered the underlying molecular mechanisms. We found that NMI was lowly expressed in breast cancer stem cells (BCSCs)-enriched populations. Knockdown of NMI promoted CSCs traits while its overexpression inhibited CSCs traits, including the expression of CSC-related markers, the number of CD44+CD24- cell populations and the ability of mammospheres formation. We also found that NMI-mediated regulation of BCSCs traits was at least partially realized through the modulation of hTERT signaling. NMI knockdown upregulated hTERT expression while its overexpression downregulated hTERT in breast cancer cells, and the changes in CSCs traits and cell invasion ability mediated by NMI were rescued by hTERT. The in vivo study also validated that NMI knockdown promoted breast cancer growth by upregulating hTERT signaling in a mouse model. Moreover, further analyses for the clinical samples demonstrated that NMI expression was negatively correlated with hTERT expression and the low NMI/high hTERT expression was associated with the worse status of clinical TNM stages in breast cancer patients. Furthermore, we demonstrated that the interaction of YY1 protein with NMI and its involvement in NMI-mediated transcriptional regulation of hTERT in breast cancer cells. Collectively, our results provide new insights into understanding the regulatory mechanism of CSCs and suggest that the NMI-YY1-hTERT signaling axis may be a potential therapeutic target for breast cancers