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Abstract Background More than 90% of colorectal cancer (CRC) arises from advanced adenomas (AA) and gut microbes are closely associated with the initiation and progression of both AA and CRC. Objective To analyze the characteristic microbes in AA. Methods Fecal samples were collected from 92 AA and 184 negative control (NC). Illumina HiSeq X sequencing platform was used for high-throughput sequencing of microbial populations. The sequencing results were annotated and compared with NCBI RefSeq database to find the microbial characteristics of AA. R-vegan package was used to analyze α diversity and β diversity. α diversity included box diagram, and β diversity included Principal Component Analysis (PCA), principal co-ordinates analysis (PCoA), and non-metric multidimensional scaling (NMDS). The AA risk prediction models were constructed based on six kinds of machine learning algorithms. In addition, unsupervised clustering methods were used to classify bacteria and viruses. Finally, the characteristics of bacteria and viruses in different subtypes were analyzed. Results The abundance of Prevotella sp900557255, Alistipes putredinis, and Megamonas funiformis were higher in AA, while the abundance of Lilyvirus, Felixounavirus, and Drulisvirus were also higher in AA. The Catboost based model for predicting the risk of AA has the highest accuracy (bacteria test set: 87.27%; virus test set: 83.33%). In addition, 4 subtypes (B1V1, B1V2, B2V1, and B2V2) were distinguished based on the abundance of gut bacteria and enteroviruses (EVs). Escherichia coli D, Prevotella sp900557255, CAG-180 sp000432435, Phocaeicola plebeiuA, Teseptimavirus, Svunavirus, Felixounavirus, and Jiaodavirus are the characteristic bacteria and viruses of 4 subtypes. The results of Catboost model indicated that the accuracy of prediction improved after incorporating subtypes. The accuracy of discovery sets was 100%, 96.34%, 100%, and 98.46% in 4 subtypes, respectively. Conclusion Prevotella sp900557255 and Felixounavirus have high value in early warning of AA. As promising non-invasive biomarkers, gut microbes can become potential diagnostic targets for AA, and the accuracy of predicting AA can be improved by typing.

Cancer is a leading cause of human death worldwide, and the modulation of the metabolic properties of T cells employed in cancer immunotherapy holds great promise for combating cancer. As a crucial factor, energy metabolism influences the activation, proliferation, and function of T cells, and thus metabolic reprogramming of T cells is a unique research perspective in cancer immunology. Special conditions within the tumor microenvironment and high-energy demands lead to alterations in the energy metabolism of T cells. In-depth research on the reprogramming of energy metabolism in T cells can reveal the mechanisms underlying tumor immune tolerance and provide important clues for the development of new tumor immunotherapy strategies as well. Therefore, the study of T cell energy metabolism has important clinical significance and potential applications. In the study, the current achievements in the reprogramming of T cell energy metabolism were reviewed. Then, the influencing factors associated with T cell energy metabolism were introduced. In addition, T cell energy metabolism in cancer immunotherapy was summarized, which highlighted its potential significance in enhancing T cell function and therapeutic outcomes. In summary, energy exhaustion of T cells leads to functional exhaustion, thus resulting in immune evasion by cancer cells. A better understanding of reprogramming of T cell energy metabolism may enable immunotherapy to combat cancer and holds promise for optimizing and enhancing existing therapeutic approaches.

Abstract In recent years, bacteria have gained considerable attention as a promising drug carrier that is critical in improving the effectiveness and reducing the side effects of anti‐tumor drugs. Drug carriers can be utilised in various forms, including magnetotactic bacteria, bacterial biohybrids, minicells, bacterial ghosts and bacterial spores. Additionally, functionalised and engineered bacteria obtained through gene engineering and surface modification could provide enhanced capabilities for drug delivery. This review summarises the current studies on bacteria‐based drug delivery systems for anti‐tumor therapy and discusses the prospects and challenges of bacteria as drug carriers. Furthermore, our findings aim to provide new directions and guidance for the research on bacteria‐based drug systems.

In the dynamic analysis of the assembly structure,there are three methods to simplify the connection between different components: node coupling,rigid / flexible connections and MPC algorithm. Through comparing the accuracy of the three methods in solid model connections and comes to the conclusion that the simplify accuracy of node coupling and MPC algorithm is higher than the rigid / flexible connection,while MPC algorithm is more convenient and practical. The MPC algorithm is applied to the modal analysis of a gearbox assembly,and a good result is got.

This study aims to examine the co-existing patterns of teachers' satisfaction with personal lives and online teaching, and the factors of these patterns during the pandemic. Self-report questionnaire was administered to 751 primary and secondary school teachers in China during the pandemic. Latent profile analysis and multiple logistic regression were used. We found four patterns: Low satisfaction, High life--Low teaching satisfaction, Low life--Medium teaching satisfaction, and High satisfaction group. Sense of control was more associated with High life--Low teaching satisfaction group. Teaching efficacy and family interference with work were more associated with High satisfaction group. Job burnout and work interference with family were more associated with Low satisfaction and Low life--Medium teaching satisfaction group. The findings indicate the heterogeneity of teachers' satisfaction with personal lives and online teaching during the pandemic. Grade, sense of control, teaching efficacy, job burnout, and work-family conflict are important factors.