Tumor-associated macrophages in colorectal cancer metastasis: molecular insights and translational perspectives.

Metastasis is the leading cause of high mortality in colorectal cancer (CRC), which is not only driven by changes occurring within the tumor cells, but is also influenced by the dynamic interaction between cancer cells and components in the tumor microenvironment (TME). Currently, the exploration of TME remodeling and its impact on CRC metastasis has attracted increasing attention owing to its potential to uncover novel therapeutic avenues. Noteworthy, emerging studies suggested that tumor-associated macrophages (TAMs) within the TME played important roles in CRC metastasis by secreting a variety of cytokines, chemokines, growth factors and proteases. Moreover, TAMs are often associated with poor prognosis and drug resistance, making them promising targets for CRC therapy. Given the prognostic and clinical value of TAMs, this review provides an updated overview on the origin, polarization and function of TAMs, and discusses the mechanisms by which TAMs promote the metastatic cascade of CRC. Potential TAM-targeting techniques for personalized theranostics of metastatic CRC are emphasized. Finally, future perspectives and challenges for translational applications of TAMs in CRC development and metastasis are proposed to help develop novel TAM-based strategies for CRC precision medicine and holistic healthcare.

Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.

Drug discovery plays a critical role in advancing human health by developing new medications and treatments to combat diseases. How to accelerate the pace and reduce the costs of new drug discovery has long been a key concern for the pharmaceutical industry. Fortunately, by leveraging advanced algorithms, computational power and biological big data, artificial intelligence (AI) technology, especially machine learning (ML), holds the promise of making the hunt for new drugs more efficient. Recently, the Transformer-based models that have achieved revolutionary breakthroughs in natural language processing have sparked a new era of their applications in drug discovery. Herein, we introduce the latest applications of ML in drug discovery, highlight the potential of advanced Transformer-based ML models, and discuss the future prospects and challenges in the field.

Small variation induces a big difference: the effect of polymerization kinetics of graphitic carbon nitride on its photocatalytic activity.

Graphitic carbon nitride (g-CN) has emerged as a promising visible-light-responsive photocatalyst, and its activity is highly sensitive to synthesis conditions. In this work, we attempt to correlate the photocatalytic activity of g-CN with its production yield, which is kinetically determined by the specific condensation process. Bulk g-CN samples were synthesized by the conventional condensation procedure, but in static air and flowing air, respectively. The one synthesized in static air showed a lower production yield with an increased specific surface area and preferential surface chemical states, corresponding to a significantly improved activity for photocatalytic hydrogen evolution (PHE) and dye degradation. We further synthesized a series of g-CN samples by merely changing the synthetic atmosphere, the ramping rate, and the loading amount of the precursor, and the difference in their PHE performance was found to be as high as 7.05 times. The notable changes in their production yields as well as the photocatalytic activities have been discussed from the point of view of polymerization reaction kinetics, and the self-generated NH3 atmosphere plays a crucial role.