Iron and copper: critical executioners of ferroptosis, cuproptosis and other forms of cell death.

Regulated cell death (RCD) is a regulable cell death that involves well-organized signaling cascades and molecular mechanisms. RCD is implicated in fundamental processes such as organ production and tissue remodeling, removing superfluous structures or cells, and regulating cell numbers. Previous studies have not been able to reveal the complete mechanisms, and novel methods of RCD are constantly being proposed. Two metal ions, iron (Fe) and copper (Cu) are essential factors leading to RCDs that not only induce ferroptosis and cuproptosis, respectively but also lead to cell impairment and eventually diverse cell death. This review summarizes the direct and indirect mechanisms by which Fe and Cu impede cell growth and the various forms of RCD mediated by these two metals. Moreover, we aimed to delineate the interrelationships between these RCDs with the distinct pathways of ferroptosis and cuproptosis, shedding light on the complex and intricate mechanisms that govern cellular survival and death. Finally, the prospects outlined in this review suggest a novel approach for investigating cell death, which may involve integrating current therapeutic strategies and offer a promising solution to overcome drug resistance in certain diseases. Video Abstract.

A transformer-based multi-task deep learning model for simultaneous infiltrated brain area identification and segmentation of gliomas.

BACKGROUND: The anatomical infiltrated brain area and the boundaries of gliomas have a significant impact on clinical decision making and available treatment options. Identifying glioma-infiltrated brain areas and delineating the tumor manually is a laborious and time-intensive process. Previous deep learning-based studies have mainly been focused on automatic tumor segmentation or predicting genetic/histological features. However, few studies have specifically addressed the identification of infiltrated brain areas. To bridge this gap, we aim to develop a model that can simultaneously identify infiltrated brain areas and perform accurate segmentation of gliomas. METHODS: We have developed a transformer-based multi-task deep learning model that can perform two tasks simultaneously: identifying infiltrated brain areas segmentation of gliomas. The multi-task model leverages shaped location and boundary information to enhance the performance of both tasks. Our retrospective study involved 354 glioma patients (grades II-IV) with single or multiple brain area infiltrations, which were divided into training (N = 270), validation (N = 30), and independent test (N = 54) sets. We evaluated the predictive performance using the area under the receiver operating characteristic curve (AUC) and Dice scores. RESULTS: Our multi-task model achieved impressive results in the independent test set, with an AUC of 94.95% (95% CI, 91.78-97.58), a sensitivity of 87.67%, a specificity of 87.31%, and accuracy of 87.41%. Specifically, for grade II-IV glioma, the model achieved AUCs of 95.25% (95% CI, 91.09-98.23, 84.38% sensitivity, 89.04% specificity, 87.62% accuracy), 98.26% (95% CI, 95.22-100, 93.75% sensitivity, 98.15% specificity, 97.14% accuracy), and 93.83% (95%CI, 86.57-99.12, 92.00% sensitivity, 85.71% specificity, 87.37% accuracy) respectively for the identification of infiltrated brain areas. Moreover, our model achieved a mean Dice score of 87.60% for the whole tumor segmentation. CONCLUSIONS: Experimental results show that our multi-task model achieved superior performance and outperformed the state-of-the-art methods. The impressive performance demonstrates the potential of our work as an innovative solution for identifying tumor-infiltrated brain areas and suggests that it can be a practical tool for supporting clinical decision making.

The S40 family members delay leaf senescence by promoting cytokinin synthesis.

Leaf senescence is regulated by both endogenous hormones and environmental stimuli in a programmed and concerted way. The members of the S40 family have been reported to play roles in leaf senescence. Here we identified an S40 family member, CiS40-11, from Caragana intermedia. Phylogenetic analysis revealed that the CiS40-11 protein had the highest identity with AtS40-5 (AT1G11700) and AtS40-6 (AT1G61930) of Arabidopsis thaliana. CiS40-11 was highly expressed in leaves and was down-regulated after dark treatment. The subcellular localization analysis showed that CiS40-11 was a cytoplasm-nucleus dual-localized protein. Leaf senescence was delayed in both the CiS40-11 overexpressed A. thaliana and its transiently expressed C. intermedia. Transcriptomic analysis and endogenous hormones assay revealed that CiS40-11 inhibited leaf senescence via promoting the biosynthesis of cytokinins by blocking AtMYB2 expression in the CiS40-11 overexpression lines. Furthermore, overexpression of either AtS40-5 or AtS40-6 showed similar phenotype as the CiS40-11 overexpressing lines, while in the ats40-5a or ats40-6a mutants, the AtMYB2 expression was increased and their leaves exhibited a premature senescence phenotype. These results provide a new molecular mechanism of the S40 family in leaf senescence regulation of plants.