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Zhongxiang Ding

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Aug 2026

Identification of new targets for the protection against radiation-induced brain injury through integrated dual-omics analysis.

BACKGROUND Radiation-induced brain injury (RIBI) is a serious complication of cranial radiotherapy, yet its molecular mechanisms remain unclear. This study aimed to identify novel therapeutic targets for RIBI through integrated dual-omics analysis. METHODS A mouse model of RIBI was established using 15 Gy of whole-brain X-ray irradiation. Behavioral tests and histopathological examinations were performed to validate cognitive dysfunction and neuronal damage. Hippocampal tissues were analyzed via transcriptomics and metabolomics to uncover key molecular changes. RESULTS Transcriptomic analysis identified 29 significantly differentially expressed genes, including upregulated neuroinflammatory genes (Pcsk9, Ifi213) and downregulated neuroprotective factors (Tlx3, Irx1, Irx5), implicating exacerbated neuroinflammatory responses and impaired neurodevelopmental processes. Metabolomic profiling revealed 63 significantly altered metabolites, including elevated DNA oxidative damage markers and depleted branched-chain amino acids (BCAAs), suggesting mitochondrial dysfunction and increased oxidative stress. Integrated analysis highlighted correlations among neuroinflammation, DNA damage, and metabolic dysregulation, pointing to a potential interplay between these pathways. CONCLUSIONS This study demonstrates that RIBI pathogenesis involves synergistic interactions between neuroinflammation, DNA damage, and metabolic dysregulation. Targeting Pcsk9, enhancing DNA repair capacity, or supplementing BCAAs could represent potential neuroprotective strategies, although these correlative findings require functional validation. These findings provide a foundation for future studies on mitigating cognitive decline in patients receiving cranial radiotherapy.

Xue-Jiao Li, Kun Shu, Xiao He et al. · Experimental Neurology · 0 citations

Open access Jul 2026

BrainSeg: a generalized framework for comprehensive multimodal brain tissue segmentation, parcellation, and lesion labeling.

Precise brain segmentation is fundamental for quantitative neuroimaging analysis. However, most existing methods lack generalization across the human lifespan and diverse imaging modalities, limiting their utility for Comprehensive Brain Segmentation (CBS) (i.e., tissue segmentation, parcellation, and lesion labeling). To address this, we propose BrainSeg, a novel unified framework, for CBS by using large-scale datasets spanning the entire lifespan, with adaptability to diverse uni- and multimodal input scenarios without the need for retraining or finetuning. Comprehensive experiments are conducted on lifespan data ranging from 14 gestational weeks to 100 years of age, consisting of 45,998 multimodal scans from 26 datasets, which are further augmented by our proposed synthesis strategy. Systematic validation and in-depth analysis demonstrate that our BrainSeg can achieve state-of-the-art performance across all three core CBS tasks, with the averaged Dice ratios reaching up to 96.94% for tissue segmentation, 94.25% for brain parcellation, and 91.06% for lesion labeling in the internal validations. It maintains similarly high accuracy in external validations, with averaged Dice ratios achieving 94.01% for tissue segmentation, and 91.20% for brain parcellation, underscoring its robustness and generalizability across diverse conditions. In summary, BrainSeg serves as a versatile foundation tool, providing flexible and reliable analysis for large-scale neuroimaging studies.

Shijie Huang, Zifeng Lian, Dengqiang Jia et al. · npj Digital Medicine · 1 citation