Simultaneous photothermal and photodynamic therapies (PTT and PDT) hold great promise for noninvasive tumor therapy. However, effective regulation of PTT and PDT with optimal synergistic effects remains a challenge. To date, there are only a few synergistic PTT and PDT methods with suitable collaborative effects due to the rarity of efficient nanoplatforms with good cascading properties. To overcome this limitation, a proof-of-concept of self-cascade PTT and PDT was developed for hypoxia-reversible tumor therapy. With the assembly of dimeric indocyanine green (DICG) with oxygen nanobubbles (O₂-NBs), DICG/O₂-NBs typically exhibit J-aggregates for significant PTT effects, with a high photothermal conversion efficiency of 51.45% under 880 nm light irradiation. Interestingly, the PTT performance of DICG/O₂-NBs can switch J-aggregates of DICG into DICG monomers with efficient O₂ gas liberation, while producing hydroxyl radicals for type I PDT. Additionally, the evolved DICG monomer reacts with the released O₂ to generate plenty of ¹O₂ for efficient type II PDT. With these advantages, the cascaded nanoplatform achieves good tumor targeting and biocompatibility, and thus has high tumor inhibition of 94.26%, with an obvious ability to reverse hypoxia. This work demonstrates the efficiency of self-cascade PTT and PDT nanomedicine for high-performance hypoxia-reversible tumor ablation, thus providing a new approach for the development of cascading phototheranostics in vivo.

Nanomaterials play a crucial role in the biomedical field, and with the rise of the digital era, artificial intelligence (AI) has become a valuable tool in all stages of nanomaterial development, spanning from design to synthesis and characterization. In this review, we explore recent advancements in the field of AI-driven nanomaterials. Firstly, we delve into how AI can be leveraged in material design, utilizing vast databases to develop new materials. Secondly, we discuss intelligent synthesis, where AI algorithms are employed to optimize the synthesis process. Subsequently, we explore how to efficiently extract depth information from nanomaterial characterization results using AI-based methods. Lastly, we offer a glimpse into the future of biomedical nanomaterials, highlighting the potential impact of AI in this rapidly evolving field.

Despite advances in diagnostic and therapeutic technologies for cardiovascular diseases (CVDs), it remains a leading cause of mortality and morbidity worldwide. This underscores the urgency for innovative approaches aiming at early and precise detection and treatment of CVDs to reduce the disease burden. Iron oxide nanoparticles (IONPs), with their unique magnetism and bioproperties, have shown great potential in this regard. In this review, we will begin with a brief overview of the synthesis and properties of IONPs. We will then focus on the latest applications of IONPs in CVDs, including diagnosis and treatment. The use of IONPs in the integration of diagnosis and treatment for CVDs is a promising field, and will be addressed in a separate section. The translational potential and challenges of IONPs will also be discussed. In conclusion, ongoing research and development of IONP-based strategies are highly likely to address current challenges effectively, and offer more personalized and efficient options for the diagnosis and treatment of CVDs.