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Macrophages are crucial cells in the human body’s innate immunity and are engaged in a variety of non-inflammatory reactions. Macrophages can develop into two kinds when stimulated by distinct internal environments: pro-inflammatory M1-like macrophages and anti-inflammatory M2-type macrophages. During inflammation, the two kinds of macrophages are activated alternatively, and maintaining a reasonably steady ratio is critical for maintaining homeostasis in vivo . M1 macrophages can induce inflammation, but M2 macrophages suppress it. The imbalance between the two kinds of macrophages will have a significant impact on the illness process. As a result, there are an increasing number of research being conducted on relieving or curing illnesses by altering the amount of macrophages. This review summarizes the role of macrophage polarization in various inflammatory diseases, including autoimmune diseases (RA, EAE, MS, AIH, IBD, CD), allergic diseases (allergic rhinitis, allergic dermatitis, allergic asthma), atherosclerosis, obesity and type 2 diabetes, metabolic homeostasis, and the compounds or drugs that have been discovered or applied to the treatment of these diseases by targeting macrophage polarization.

Multimodal learning analytics (MMLA), which has become increasingly popular, can help provide an accurate understanding of learning processes. However, it is still unclear how multimodal data is integrated into MMLA. By following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this paper systematically surveys 346 articles on MMLA published during the past three years. For this purpose, we first present a conceptual model for reviewing these articles from three dimensions: data types, learning indicators, and data fusion. Based on this model, we then answer the following questions: 1. What types of data and learning indicators are used in MMLA, together with their relationships; and 2. What are the classifications of the data fusion methods in MMLA. Finally, we point out the key stages in data fusion and the future research direction in MMLA. Our main findings from this review are (a) The data in MMLA are classified into digital data, physical data, physiological data, psychometric data, and environment data; (b) The learning indicators are behavior, cognition, emotion, collaboration, and engagement; (c) The relationships between multimodal data and learning indicators are one-to-one, one-to-any, and many-to-one. The complex relationships between multimodal data and learning indicators are the key for data fusion; (d) The main data fusion methods in MMLA are many-to-one, many-to-many and multiple validations among multimodal data; and (e) Multimodal data fusion can be characterized by the multimodality of data, multi-dimension of indicators, and diversity of methods.

Background Cancer-associated fibroblasts (CAFs) and vasculogenic mimicry (VM) play important roles in the occurrence and development of tumors. However, the relationship between CAFs and VM formation, especially in gallbladder cancer (GBC) has not been clarified. In this study, we investigated whether gallbladder CAFs (GCAFs) can promote VM formation and tumor growth and explored the underlying molecular mechanism. Methods A co-culture system of human GBC cells and fibroblasts or HUVECs was established. VM formation, proliferation, invasion, migration, tube formation assays, CD 31 -PAS double staining, optic/electron microscopy and tumor xenograft assay were used to detect VM formation and malignant phenotypes of 3-D co-culture matrices in vitro, as well as the VM formation and tumor growth of xenografts in vivo, respectively. Microarray analysis was used to analyze gene expression profile in GCAFs/NFs and VM (+)/VM (−) in vitro. QRT-PCR, western blotting, IHC and CIF were used to detected NOX4 expression in GCAFs/NFs, 3-D culture/co-culture matrices in vitro, the xenografts in vivo and human gallbladder tissue/stroma samples. The correlation between NOX4 expression and clinicopathological and prognostic factors of GBC patients was analyzed. And, the underlying molecular mechanism of GCAFs promoting VM formation and tumor growth in GBC was explored. Results GCAFs promote VM formation and tumor growth in GBC; and the finding was confirmed by facts that GCAFs induced proliferation, invasion, migration and tube formation of GBC cells in vitro, and promoted VM formation and tumor growth of xenografts in vivo. NOX4 is highly expressed in GBC and its stroma, which is the key gene for VM formation, and is correlated with tumor aggression and survival of GBC patients. The GBC patients with high NOX4 expression in tumor cells and stroma have a poor prognosis. The underlying molecular mechanism may be related to the upregulation of NOX4 expression through paracrine IL-6 mediated IL-6/JAK/STAT3 signaling pathway. Conclusions GCAFs promote VM formation and tumor growth in GBC via upregulating NOX4 expression through the activation of IL-6-JAK-STAT3 signal pathway. NOX4, as a VM-related gene in GBC, is overexpressed in GBC cells and GCAFs, which is related to aggression and unfavorable prognosis of GBC patients.

Norcantharidin (NCTD) is a demethylated derivative of cantharidin, which is an anticancer active ingredient of traditional Chinese medicine, and is currently used clinically as a routine anti-cancer drug in China. Clarifying the anticancer effect and molecular mechanism of NCTD is critical for its clinical application. Here, we summarized the physiological, chemical, pharmacokinetic characteristics and clinical applications of NCTD. Besides, we mainly focus on its potential multi-target anticancer activities and underlying mechanisms, and discuss the problems existing in clinical application and scientific research of NCTD, so as to provide a potential anticancer therapeutic agent for human malignant tumors.

This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.

Digital technology plays a unique role in the cultivation of students’ creative thinking, which helps them solve poorly structured problems with effective and original solutions. This study applied collaborative learning in a digital-technology-supported artificial intelligence (AI) course and aimed to explore the impact of collaborative learning on fourth-grade students’ creative thinking. According to whether a leadership role was assigned by a teacher and a final consensus was built in the group, four patterns of collaborative learning were designed for comparison in order to determine which pattern was more effective for the promotion of students’ creative thinking. In total, 37 fourth-grade students taking part in the study were divided into four groups, and each group adapted one of four patterns of collaborative learning. The Torrance Creative Thinking Test (TTCT-Figure) was used to test the pre- and post-creative thinking of the four groups of students. A paired-sample t-test was used to analyze the pre- and post-tests of students’ creative thinking to verify whether all four patterns of collaborative learning could improve the students’ creative thinking. One-way ANOVA was used to analyze the post-test results of the four groups’ creative thinking to determine the differences in the creative thinking of the four groups of students. The results indicated that the patterns of collaborative learning used by G1, G3, and G4 were effective in improving students’ creative thinking, but the pattern for G2 was not. Moreover, there were significant differences in the cultivation of students’ creative thinking via AI courses among these four patterns of collaborative learning. The G4 students, who had an assigned leadership role and consensus building, showed the greatest improvement in creative thinking. In particular, without an assigned leadership role and consensus building, students’ flexibility of creative thinking would be improved to a greater extent. Teachers can adapt the findings of this study in order to consciously train team leaders in the collaborative learning process and guide them to reach a consensus to achieve the goal of fostering creative thinking in digital-technology-supported courses. To be specific, teachers should let students participate in group collaborative learning in a free way to cultivate their flexibility.

In this paper, machine learning techniques and risk factor analyses are applied to a marine hazard potential map of the Taiwan Strait. The waters surrounding Taiwan are characterized by dense maritime traffic, including commercial cargo transportation and fishing operations. Marine accidents caused by severe weather conditions are frequently reported, leading to irreversible loss of life and property. To mitigate these risks, this study utilizes the XGBoost machine learning model in conjunction with oceanic parameters and historical accident statistics to map the risk potential distribution of maritime accidents across the Taiwan Strait on a monthly basis. To address the challenge of limited historical accident data, this research employs a TVAE (Tabular Variational Autoencoder) to generate synthetic maritime accident data. The quality of such synthetic data is evaluated by comparing the similarity of probability distributions between the original and synthetic datasets. The resulting risk potential maps indicate that risk levels are significantly higher during the winter and lower during the summer. Furthermore, the SHAP (SHapley Additive exPlanations) model is applied to analyze key risk factors, identifying wave height as the primary driver, followed by meridional (north–south) wind speed and the primary spatial modes of wave height. These findings are validated using the National Ocean Database and Sharing System (NODASS) data, providing a comprehensive explanation of the underlying physical mechanisms. This study has successfully utilized the XGBoost machine learning model together with the TVAE generative technique to develop monthly marine hazard potential distribution maps for the Taiwan Strait. The novel research flowchart employed in this study can be applied to many other marine problems.

Gemcitabine (GEM), as an anti-metabolic nucleoside analog, has been shown to have anticancer effects in various tumors, but its chemotherapy resistance is still an important factor leading to poor prognosis of cancer patient. A large number of studies in recent years have shown that autophagy plays an important role in the chemotherapy sensitivity of many tumors, including pancreatic, non-small cell lung, and bladder cancer. However, whether GEM causes autophagy in gallbladder cancer (GBC) and whether it is related to chemotherapy resistance is unknown. In the present study, we demonstrated that GEM induced apoptosis and protective autophagy in GBC cells, which may be related to the AKT/mTOR signaling pathway, and GEM in combination with autophagy inhibitor chloroquine can strengthen the cytotoxic effect of GEM on GBC in vitro and in vivo. These findings showed that both autophagy and AKT/mTOR signals were engaged in GBC cell death evoked by GEM, GBC patients might benefit from this new treatment strategy, and molecular targeted treatment in combination with autophagy inhibitors shows promise as a treatment improvement.

In online learning, students’ learning data such as time and logs are commonly used to predict the student’s learning performance. In a hybrid context, learning activities occur both online and offline. Thus, how to integrate online and offline learning data effectively for an accurate learning performance prediction becomes very challenging. This paper proposes a “prediction and alert” model for students’ learning performance in a hybrid learning context. The model is developed and evaluated through analyzing the 16-week (one semester) attributes of English learning data of 50 students in the eighth grade. Six significant variables were determined as learning performance attributes, namely, qualified rate, excellent rate, scores, number of practice sessions, practice time, and completion. The proposed model was put into actual practice through four months of application and modification, in which a sample of 50 middle school students participated. The model shows the feasibility and effectiveness of data analysis for hybrid learning. It can support students’ continuous online and offline learning more effectively.

In this paper, we treat optimization problems as a kind of reinforcement learning problems regarding an optimization procedure for searching an optimal solution as a reinforcement learning procedure for finding the best policy to maximize the expected rewards. This viewpoint motivated us to propose a Q -learning-based swarm optimization (QSO) algorithm. The proposed QSO algorithm is a population-based optimization algorithm which integrates the essential properties of Q -learning and particle swarm optimization. The optimization procedure of the QSO algorithm proceeds as each individual imitates the behavior of the global best one in the swarm. The best individual is chosen based on its accumulated performance instead of its momentary performance at each evaluation. Two data sets including a set of benchmark functions and a real-world problem—the economic dispatch (ED) problem for power systems—were used to test the performance of the proposed QSO algorithm. The simulation results on the benchmark functions show that the proposed QSO algorithm is comparable to or even outperforms several existing optimization algorithms. As for the ED problem, the proposed QSO algorithm has found solutions better than all previously found solutions.