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Radiodynamic therapy (RDT), as an emerging cancer treatment method, has attracted attention due to its remarkable therapeutic efficacy using low-dose, high-energy radiation (such as X-rays) and has shown significant potential in cancer treatment. The RDT system typically consists of scintillators and photosensitizers (PSs). Scintillators absorb X-rays and convert them to visible light, activating nearby PSs to generate cytotoxic reactive oxygen species (ROS). Challenges faced by the two-component strategy, including low loading capacity and inefficient energy transfer, hinder its final effectiveness. In addition, the tumor microenvironment (TME) with hypoxia and immunosuppression limits the efficacy of RDTs. Recent advances introduce one-component RDT systems based on nanomaterials with high-Z metal elements, which effectively inhibit deep-seated tumors. These novel RDT systems exhibit immune enhancement and immune memory, potentially eliminating both primary and metastatic tumors. This review comprehensively analyzes recent advances in the rational construction of RDTs, exploring their mechanisms and application in the treatment of deep-seated tumors. Aimed at providing a practical resource for oncology researchers and practitioners, the review offers new perspectives for potential future directions in RDT research.

Photodynamic therapy (PDT) shows promise in tumor treatment, particularly when combined with nanotechnology. This study examines the impact of deep learning, particularly the Cellpose algorithm, on the comprehension of cancer cell responses to PDT. The Cellpose algorithm enables robust morphological analysis of cancer cells, while logistic growth modelling predicts cellular behavior post-PDT. Rigorous model validation ensures the accuracy of the findings. Cellpose demonstrates significant morphological changes after PDT, affecting cellular proliferation and survival. The reliability of the findings is confirmed by model validation. This deep learning tool enhances our understanding of cancer cell dynamics after PDT. Advanced analytical techniques, such as morphological analysis and growth modeling, provide insights into the effects of PDT on hepatocellular carcinoma (HCC) cells, which could potentially improve cancer treatment efficacy. In summary, the research examines the role of deep learning in optimizing PDT parameters to personalize oncology treatment and improve efficacy.

Luminescence thermometry usually suffer from cellular complexity of the biochemical environment (such as pH and ionic strength), and thus the accuracy and reliability of the determined intracellular temperature are directly affected. Herein, a photoluminescent nanothermometer composed of polymer encapsulated quantum dots (P-QD) has been developed. And the prepared nanothermometer exhibits some advantages: such as non-sensitivity to pH and ionic strength, as well as high detection sensitivity and ultrahigh reversibility. The intracellular temperature was accurately determined under physiological conditions with different pH and ionic strength, and direct measurement of thermogenesis in individual cells has been achieved. [ABSTRACT FROM AUTHOR]

The article presents a study that explores the dynamic real-time imaging of living cell traction force by piezo-phototronic light nano-antenna array. It mentions that the study presents a rapid and ultrahigh-resolution methodology for the fundamental study of cardiomyocyte behavior at the cell or subcellular level.