The continuous development of antineoplastic therapy has significantly reduced the mortality of patients with malignant tumors, but its induced cardiotoxicity has become the primary cause of long‐term death in patients with malignant tumors. However, the pathogenesis of cardiotoxicity of antineoplastic therapy is currently unknown, and practical means of prevention and treatment are lacking in clinical practice. Therefore, how to effectively prevent and treat cardiotoxicity while treating tumors is a major challenge. Animal models are important tools for studying cardiotoxicity in antitumor therapy and are of great importance in elucidating pathophysiological mechanisms and developing and evaluating modality drugs. In this paper, we summarize the existing animal models in antitumor therapeutic cardiotoxicity studies and evaluate the models by observing the macroscopic signs, echocardiography, and pathological morphology of the animals, aiming to provide a reference for subsequent experimental development and clinical application.

In real-world applications, users express different behaviors when they interact with different items, including implicit click/like interactions, and explicit comments/reviews interactions. Nevertheless, almost all recommender works are focused on how to describe user preferences by the implicit click/like interactions, to find the synergy of people. For the content-based explicit comments/reviews interactions, some works attempt to utilize them to mine the semantic knowledge to enhance recommender models. However, they still neglect the following two points: (1) The content semantic is a universal world knowledge; how do we extract the multi-aspect semantic information to empower different domains? (2) The user/item ID feature is a fundamental element for recommender models; how do we align the ID and content semantic feature space? In this paper, we propose a 'plugin' semantic knowledge transferring method LoID, which includes two major components: (1) LoRA-based large language model pretraining to extract multi-aspect semantic information; (2) ID-based contrastive objective to align their feature spaces. We conduct extensive experiments with SOTA baselines to demonstrate superiority of our method LoID.