Deep Learning Insights into the Dynamic Effects of Photodynamic Therapy on Cancer Cells.

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.

Low-dose X-ray radiodynamic therapy solely based on gold nanoclusters for efficient treatment of deep hypoxic solid tumors combined with enhanced antitumor immune response.

Background: Radiodynamic therapy (RDT) is an emerging novel anti-cancer treatment based on the generation of cytotoxic reactive oxygen species (ROS) at the lesion site following the interaction between low-dose X-ray and a photosensitizer (PS) drug. For a classical RDT, scintillator nanomaterials loaded with traditional PSs are generally involved to generate singlet oxygen (1O2). However, this scintillator-mediated strategy generally suffers from insufficient energy transfer efficiency and the hypoxic tumor microenvironment, and finally severely impedes the efficacy of RDT. Methods: Gold nanoclusters were irradiated by low dose of X-ray (called RDT) to investigate the production of ROS, killing efficiency of cell level and living body level, antitumor immune mechanism and biosafety. Results: A novel dihydrolipoic acid coated gold nanoclusters (AuNC@DHLA) RDT, without additional scintillator or photosensitizer assisted, has been developed. In contrast to scintillator-mediated strategy, AuNC@DHLA can directly absorb the X-ray and exhibit excellent radiodynamic performance. More importantly, the radiodynamic mechanism of AuNC@DHLA involves electron-transfer mode resulting in O2 -• and HO•, and excess ROS has been generated even under hypoxic conditions. Highly efficient in vivo treatment of solid tumors had been achieved via only single drug administration and low-dose X-ray radiation. Interestingly, enhanced antitumor immune response was involved, which could be effective against tumor recurrence or metastasis. Negligible systemic toxicity was also observed as a consequence of the ultra-small size of AuNC@DHLA and rapid clearance from body after effective treatment. Conclusions: Highly efficient in vivo treatment of solid tumors had been achieved, enhanced antitumor immune response and negligible systemic toxicity were observed. Our developed strategy will further promote the cancer therapeutic efficiency under low dose X-ray radiation and hypoxic conditions, and bring hope for clinical cancer treatment.

Accession-specific flowering time variation in response to nitrate fluctuation in Arabidopsis thalian a.

Flowering time, a key transition point from vegetative to reproductive growth, is regulated by an intrinsic complex of endogenous and exogenous signals including nutrient status. For hundreds of years, nitrogen has been well known to modulate flowering time, but the molecular genetic basis on how plants adapt to ever-changing nitrogen availability remains not fully explored. Here we explore how Arabidopsis natural variation in flowering time responds to nitrate fluctuation. Upon nitrate availability change, we detect accession- and photoperiod-specific flowering responses, which also feature a accession-specific dependency on growth traits. The flowering time variation correlates well with the expression of floral integrators, SOC1 and FT, in an accession-specific manner. We find that gene expression variation of key hub genes in the photoperiod-circadian-clock (GI), aging (SPLs) and autonomous (FLC) pathways associates with the expression change of these integrators, hence flowering time variation. Our results thus shed light on the molecular genetic mechanisms on regulation of accession- and photoperiod-specific flowering time variation in response to nitrate availability.