Reinvigorating exhausted CD8+ cytotoxic T lymphocytes in the tumor microenvironment and current strategies in cancer immunotherapy.

Immunotherapy has revolutionized the treatment of cancer in recent years and achieved overall success and long-term clinical benefit in patients with a wide variety of cancer types. However, there is still a large proportion of patients exhibiting limited or no responses to immunotherapeutic strategy, some of which were even observed with hyperprogressive disease. One major obstacle restricting the efficacy is that tumor-reactive CD8+ T cells, which are central for tumor control, undergo exhaustion, and lose their ability to eliminate cancer cells after infiltrating into the strongly immunosuppressive tumor microenvironment. Thus, as a potential therapeutic rationale in the development of cancer immunotherapy, targeting or reinvigorating exhausted CD8+ T cells has been attracting much interest. Hitherto, both intrinsic and extrinsic mechanisms that govern CD8+ T-cell exhaustion have been explored. Specifically, the transcriptional and epigenetic landscapes have been depicted utilizing single-cell RNA sequencing or mass cytometry (CyTOF). In addition, cellular metabolism dictating the tumor-infiltrating CD8+ T-cell fate is currently under investigation. A series of clinical trials are being carried out to further establish the current strategies targeting CD8+ T-cell exhaustion. Taken together, despite the proven benefit of immunotherapy in cancer patients, additional efforts are still needed to fully circumvent limitations of exhausted T cells in the treatment. In this review, we will focus on the current cellular and molecular understanding of metabolic changes, epigenetic remodeling, and transcriptional regulation in CD8+ T-cell exhaustion and describe hypothetical treatment approaches based on immunotherapy aiming at reinvigorating exhausted CD8+ T cells.

An intelligent identification and classification system of decoration waste based on deep learning model.

Efficient sorting and recycling of decoration waste are crucial for the industry's transformation, upgrading, and high-quality development. However, decoration waste can contain toxic materials and has greatly varying compositions. The traditional method of manual sorting for decoration waste is inefficient and poses health risks to sorting workers. It is therefore imperative to develop an accurate and efficient intelligent classification method to address these issues. To meet the demand for intelligent identification and classification of decoration waste, this paper applied the deep learning method You Only Look Once X (YOLOX) to the task and proposed an identification and classification framework of decoration waste (YOLOX-DW framework). The proposed framework was validated and compared using a multi-label image dataset of decoration waste, and a robot automatic sorting system was constructed for practical sorting experiments. The research results show that the proposed framework achieved a mean average precision (mAP) of 99.16 % for different components of decoration waste, with a detection speed of 39.23 FPS. Its classification efficiency on the robot sorting experimental platform reached 95.06 %, indicating a high potential for application and promotion. This provides a strategy for the intelligent detection, identification, and classification of decoration waste.