Search

Breast cancer is the most prevalent malignant neoplasm worldwide, necessitating the development of novel therapeutic strategies owing to the limitations posed by conventional treatment modalities. Immunotherapy is an innovative approach that has demonstrated significant efficacy in modulating a patient's innate immune system to combat tumor cells. In the era of precision medicine, adoptive immunotherapy for breast cancer has garnered widespread attention as an emerging treatment strategy, primarily encompassing cellular therapies such as tumor-infiltrating lymphocyte therapy, chimeric antigen receptor T/natural killer/M cell therapy, T cell receptor gene–engineered T cell therapy, lymphokine-activated killer cell therapy, cytokine-induced killer cell therapy, natural killer cell therapy, and γδ T cell therapy, among others. This treatment paradigm is based on the principles of immune memory and antigen specificity, involving the collection, processing, and expansion of the patient's immune cells, followed by their reintroduction into the patient's body to activate the immune system and prevent tumor recurrence and metastasis. Currently, multiple clinical trials are assessing the feasibility, effectiveness, and safety of adoptive immunotherapy in breast cancer. However, this therapeutic approach faces challenges associated with tumor heterogeneity, immune evasion, and treatment safety. This review comprehensively summarizes the latest advancements in adoptive immunotherapy for breast cancer and discusses future research directions and prospects, offering valuable guidance and insights into breast cancer immunotherapy. [ABSTRACT FROM AUTHOR]

Underwater acoustic channels often have to face the interference of impulsive noise, which is usually modeled by α-stable distribution in simulation experiments. To solve the problem of underwater acoustic channel estimation under impulsive noise, this paper proposes a convex combination–variable-step-size least mean p-norm algorithm. The algorithm incorporates a convex combination into the variable-step-size least mean p-norm algorithm and uses the convex combination of different convergence domains provided by changing the parameters of the Gaussian function to further improve the effect after convergence. The simulation results of channel estimation show that the convex combination–variable-step-size least mean p-norm algorithm provides a more stable, robust, and universal solution than the variable-step-size least mean p-norm algorithm. [ABSTRACT FROM AUTHOR]