Metformin Inhibited Growth, Invasion and Metastasis of Esophageal Squamous Cell Carcinoma in Vitro and in Vivo

Background/Aims: This study aimed at investigating the effects of metformin on the growth and metastasis of esophageal squamous cell carcinoma (ESCC) in vitro and in vivo. Methods: Two human ESCC cell lines EC9706 and Eca109 were selected and challenged with metformin in this study. Western blot assay was performed to detect th level of Bcl-2, Bax and Caspase-3. Scratch wound assay, transwell assay and Millicell invasion assay were used to assay the invasion and migration of EC9706 and Eca109 cells. Nude mice tumor models were used to assay the growth and lung metastasis of ESCC cells after metformin treatment. The plasma glucose level was also assayed. Results: We found that metformin significantly inhibited proliferation and induced apoptosis of both ESCC cell lines in a dose- and time-dependent manner, and the expression of Bcl-2 was down-regulated and Bax and Caspase-3 were up-regulated. Metformin significantly inhibited the invasion and migration of EC9706 and Eca109 cells (p < 0.05). mRNA and protein levels of MMP-2 and MMP-9 decreased significantly upon treatment with metformin of 10mM for 12, 24 and 48h in a time-dependent manner (p < 0.05). In line with in vitro results, in vivo experiments demonstrated that metformin inhibited tumorigenicity, inhibited lung metastasis and down-regulated the expression of MMP-2 and MMP-9. Moreover, we showed that metformin treatment did not cause significant alteration in liver and renal functions and plasma glucose level. Conclusion: Our study for the first time demonstrated the anti-invasive and anti-metastatic effects of metformin on human ESCC cells both in vitro and in vivo, which might be associated with the down-regulation of MMP-2 and MMP-9. As a whole, our results indicate the potential of metformin to be developed as a chemotherapeutic agent for patients with ESCC and might stimulate future studies on this area

Positive and negative intergroup contact and willingness to engage in intergroup interactions among high-status (Han) and low-status (Uyghur) group members in China: The moderating role of social dominance orientation

The present study investigated whether the associations of positive and negative intergroup contact with behavioral intentions (intentions to have contact with the outgroup in the future) are moderated by social dominance orientation (SDO), by considering the perspective of both high- and low-status group members in the context of China. Participants were 325 Han (high-status) and 373 Uyghur (low-status) members, who completed a self-report questionnaire. Results indicated that positive contact was associated with more positive behavioral intentions among high-SDO high-status group members, whereas SDO did not moderate the association between positive contact and behavioral intentions among low-status group members. In addition, negative contact was associated with lower behavioral intentions among high-SDO high-status group members, and among low-SDO low-status group members. This study suggests that attention should be placed simultaneously on positive and negative contact and on individual difference variables relevant to social ideologies, such as SDO

AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?

Can we better anticipate an actor's future actions (e.g. mix eggs) by knowing what commonly happens after his/her current action (e.g. crack eggs)? What if we also know the longer-term goal of the actor (e.g. making egg fried rice)? The long-term action anticipation (LTA) task aims to predict an actor's future behavior from video observations in the form of verb and noun sequences, and it is crucial for human-machine interaction. We propose to formulate the LTA task from two perspectives: a bottom-up approach that predicts the next actions autoregressively by modeling temporal dynamics; and a top-down approach that infers the goal of the actor and plans the needed procedure to accomplish the goal. We hypothesize that large language models (LLMs), which have been pretrained on procedure text data (e.g. recipes, how-tos), have the potential to help LTA from both perspectives. It can help provide the prior knowledge on the possible next actions, and infer the goal given the observed part of a procedure, respectively. To leverage the LLMs, we propose a two-stage framework, AntGPT. It first recognizes the actions already performed in the observed videos and then asks an LLM to predict the future actions via conditioned generation, or to infer the goal and plan the whole procedure by chain-of-thought prompting. Empirical results on the Ego4D LTA v1 and v2 benchmarks, EPIC-Kitchens-55, as well as EGTEA GAZE+ demonstrate the effectiveness of our proposed approach. AntGPT achieves state-of-the-art performance on all above benchmarks, and can successfully infer the goal and thus perform goal-conditioned "counterfactual" prediction via qualitative analysis. Code and model will be released at https://brown-palm.github.io/AntGP

Estimation of Canopy Height from a Multi-SINC Model in Mediterranean Forest with Single-baseline TanDEM-X InSAR Data

TanDEM-X interferometric synthetic aperture radar (InSAR) data have demonstrated promising advantages and potential in recent years for the inversion of forest height. InSAR coherence becomes the primary input feature when a precise digital terrain model (DTM) is unavailable, but the relationship between InSAR coherence and forest height remains uncertain because of the complexity of forest scenes. In this paper, a method for retrieving canopy height in Mediterranean forests, characterised by short and sparse trees, using a single-pass bistatic TanDEM-X InSAR dataset is proposed. To improve the accuracy of forest height inversion from the uncertain correlation between InSAR coherence and canopy height, we begin by using the established SINC model with two semi-empirical parameters and then expand the single curve into a collection of three curves, forming the Multi-SINC model. To determine the optimal relationship (curve) between TanDEM-X InSAR coherence and canopy height, the problem is shifted from parameter inversion to classification. To solve the problem, we used optical remote sensing data, a small amount of LiDAR data, and TanDEM-X InSAR data in combination with machine learning for classification. As a proof-of-concept, we conducted forest height retrieval at two study sites in Spain with complex terrain and diverse forest types. The results were verified by comparing them with LiDAR product forest height, which demonstrated improved performance (RMSE = 2.49 m and 1.7 m) compared to the SeEm-SINC model (RMSE = 3.28 m and 2.36 m).This work was funded by the National Key Research and Development Program of China (No. 2022YFB3902605), the National Natural Science Foundation of China (No. 42227801), the Natural Science Foundation for Excellent Young Scholars of Hunan Province (No. 2023JJ20061), the Spanish Ministry of Science and Innovation (State Agency of Research, AEI), and the European Funds for Regional Development under Project PID2020-117303GB-C22/AEI/10.13039/501100011033

Mutual Information-Based Brain Network Analysis in Post-stroke Patients With Different Levels of Depression

Post-stroke depression (PSD) is the most common stroke-related emotional disorder, and it severely affects the recovery process. However, more than half cases are not correctly diagnosed. This study was designed to develop a new method to assess PSD using EEG signal to analyze the specificity of PSD patients' brain network. We have 107 subjects attended in this study (72 stabilized stroke survivors and 35 non-depressed healthy subjects). A Hamilton Depression Rating Scale (HDRS) score was determined for all subjects before EEG data collection. According to HDRS score, the 72 patients were divided into 3 groups: post-stroke non-depression (PSND), post-stroke mild depression (PSMD) and post-stroke depression (PSD). Mutual information (MI)-based graph theory was used to analyze brain network connectivity. Statistical analysis of brain network characteristics was made with a threshold of 10–30% of the strongest MIs. The results showed significant weakened interhemispheric connections and lower clustering coefficient in post-stroke depressed patients compared to those in healthy controls. Stroke patients showed a decreasing trend in the connection between the parietal-occipital and the frontal area as the severity of the depression increased. PSD subjects showed abnormal brain network connectivity and network features based on EEG, suggesting that MI-based brain network may have the potential to assess the severity of depression post stroke