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Hepatocellular carcinoma (HCC) is one of the leading causes of cancer deaths worldwide. Significantly activated uridine nucleotide and fatty acid metabolism in HCC cells promote malignant proliferation and immune evasion. Herein, it is demonstrated that the tripartite motif 65 (TRIM65) E3 ubiquitin‐protein ligase, O‐GlcNAcylated via O‐GlcNAcylation transferase, is highly expressed in HCC and facilitated metabolic remodeling to promote the accumulation of products related to uracil metabolism and palmitic acid, driving the progression of HCC. Mechanistically, it is showed that TRIM65 mediates ubiquitylation at the K44 residue of neurofibromatosis type 2 (NF2), the key protein upstream of classical Hippo signaling. Accelerated NF2 degradation inhibits yes‐associated protein 1 phosphorylation, inducing aberrant activation of related metabolic enzyme transcription, and orchestrating metabolic and immune advantages. In conclusion, these results reveal a critical role for the TRIM family molecule TRIM65 in supporting HCC cell survival and highlight the therapeutic potential of targeting its E3 ligase activity to alter the regulation of proteasomal degradation. [ABSTRACT FROM AUTHOR]

Accurate and efficient molecular recognition plays a crucial role in the fields of molecular detection and diagnostics. Conventional trial‐and‐error‐based molecular recognition approaches have always been challenged in distinguishing minimal differences between targets and non‐targets, such as single nucleotide polymorphisms (SNPs) of oligonucleotides. To address these challenges, here, a novel concept of dynamic addressing analysis is proposed. In this concept, by dissecting the regions of the target and creating a corresponding recognizer, it is possible to eliminate the inaccuracy and inefficiency of recognition. To achieve this concept, a Dynamic Addressing Molecular Robot (DAMR), a DNA‐based dynamic addressing device is developed which is capable of dynamically locating targets. DAMR is designed to first bind to the conserved region of the target while addressing the specific region dynamically until accurate recognition is achieved. DAMR has provided an approach for analyzing low‐resolution targets and has been used for analyzing SNP of miR‐196a2 in both cell and serum samples, which has opened new avenues for effective and efficient molecular recognition. [ABSTRACT FROM AUTHOR]

When players are dissatisfied with their actual payoffs, they will change the actuality by learning strategy of neighbors. The more effort players put in, the more likely they are to succeed in learning. Inspired by this, this paper proposes a two-stage strategy update rule based on learning cost. The players first decide whether to learn strategy according to the updating willingness. If the players imitate the strategy of neighbors, they need to pay the learning cost. Results show that for the well-mixed population, if the updating willingness is homogeneous and remains unchanged, reducing the updating willingness or increasing the learning cost can extend the life cycle of cooperators. If the updating willingness is heterogeneous and dynamically adjusted based on the difference between the actual payoff and the expected payoff, increasing aspiration value and learning cost promotes cooperation. For the structured population, if the updating willingness is homogeneous and remains unchanged, the moderate learning cost is beneficial for cooperators to resist the temptation of defection, and reducing updating willingness makes the system maintain cooperation within a larger parameter range. If the updating willingness is heterogeneous and dynamically adjusted, the larger learning cost and the appropriate aspiration value promote cooperation. This study highlights the complex dynamics of cooperation in paid strategy learning, contributing to the theory of cooperation in the evolutionary game. [ABSTRACT FROM AUTHOR]

Functional imaging (FI) techniques have revolutionized tumor imaging by providing information on specific tumor functions, such as glycometabolism. However, tumor cells lack unique molecular characteristics at the molecular level and metabolic pathways, resulting in limited metabolic differences compared to normal cells and increased background signals from FI. To address this limitation, we developed a novel imaging technique termed proximity‐enhanced functional imaging (PEFI) for accurate visualization of tumors. By using "two adjacent chemically labeled glycoproteins" as output signals, we significantly enhance the metabolic differences between tumor and normal cells by PEFI, thereby reducing the background signals for analysis and improving the accuracy of tumor functional imaging. Our results demonstrate that PEFI can accurately identify tumors at the cellular, tissue, and animal level, and has potential value in clinical identification and analysis of tumor cells and tissues, as well as in the guidance of clinical tumor resection surgery. [ABSTRACT FROM AUTHOR]