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Cancer immunotherapy effect can be greatly enhanced by other methods to induce immunogenic cell death (ICD), which has profoundly affected immunotherapy as a highly efficient paradigm. However, these treatments have significant limitations, either by causing damage of the immune system or limited to superficial tumors. Sonodynamic therapy (SDT) can induce ICD to promote immunotherapy without affecting the immune system because of its excellent spatiotemporal selectivity and low side effects. Nevertheless, SDT is still limited by low reactive oxygen species yield and the complex tumor microenvironment. Recently, some emerging SDT‐based nanomedicines have made numerous attractive and encouraging achievements in the field of cancer immunotherapy due to high immunotherapeutic efficiency. However, this cross‐cutting field of research is still far from being widely explored due to huge professional barriers. Herein, the characteristics of the tumor immune microenvironment and the mechanisms of ICD are firstly systematically summarized. Subsequently, the therapeutic mechanism of SDT is fully summarized, and the advantages and limitations of SDT are discussed. The representative advances of SDT‐based nanomedicines for cancer immunotherapy are further highlighted. Finally, the application prospects and challenges of SDT‐based immunotherapy in future clinical translation are discussed.

Many factors such as trauma and COVID-19 cause acute kidney injury (AKI). Late AKI have a very high incidence and mortality rate. Early diagnosis of AKI provides a critical therapeutic time window for AKI treatment to prevent progression to chronic renal failure. However, the current clinical detection based on creatinine and urine output isn't effective in diagnosing early AKI. In recent years, the early diagnosis of AKI has made great progress with the advancement of information technology, nanotechnology, and biomedicine. These emerging methods are mainly divided into two aspects: First, predicting AKI through models construct by machine learning; Second, early diagnosis of AKI through detection of newly-discovered early biomarkers. Currently, these methods have shown great potential and become an attractive tool for the early diagnosis of AKI. Therefore, it is very important to discuss and summarize these methods for the early diagnosis of AKI. In this review, we first systematically summarize the application of machine learning in AKI prediction algorithms and specific scenarios. In addition, we introduce the key role of early biomarkers in the progress of AKI, and then comprehensively summarize the application of emerging detection technologies for early AKI. Finally, we discuss current challenges and prospects of machine learning and biomarker detection. The review is expected to provide new insights for early diagnosis of AKI, and provided important inspiration for the design of early diagnosis of other major diseases.

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