UNASSIGNED: Non-invasive stratification of the liver decompensation risk remains unmet in people with compensated cirrhosis. This study aimed to develop a non-invasive tool (NIT) to predict hepatic decompensation.
UNASSIGNED: This retrospective study recruited 689 people with compensated cirrhosis (median age, 54 years; 441 men) from 5 centres from January 2016 to June 2020. Baseline abdominal computed tomography (CT), clinical features, and liver stiffness were collected, and then the first decompensation was registered during the follow-up. The spleen-based model was designed for predicting decompensation based on a deep learning segmentation network to generate the spleen volume and least absolute shrinkage and selection operator (LASSO)-Cox. The spleen-based model was trained on the training cohort of 282 individuals (Institutions I-III) and was validated in 2 external validation cohorts (97 and 310 individuals from Institutions IV and V, respectively) and compared with the conventional serum-based models and the Baveno VII criteria.
UNASSIGNED: The decompensation rate at 3 years was 23%, with a 37.6-month median (IQR 21.1-52.1 months) follow-up. The proposed model showed good performance in predicting decompensation (C-index ≥0.84) and outperformed the serum-based models (C-index comparison test p <0.05) in both the training and validation cohorts. The hazard ratio (HR) for decompensation in individuals with high risk was 7.3 (95% CI 4.2-12.8) in the training and 5.8 (95% CI 3.9-8.6) in the validation (log-rank test, p <0.05) cohorts. The low-risk group had a negligible 3-year decompensation risk (≤1%), and the model had a competitive performance compared with the Baveno VII criteria.
UNASSIGNED: This spleen-based model provides a non-invasive and user-friendly method to help predict decompensation in people with compensated cirrhosis in diverse healthcare settings where liver stiffness is not available.
UNASSIGNED: People with compensated cirrhosis with larger spleen volume would have a higher risk of decompensation. We developed a spleen-based model and validated it in external validation cohorts. The proposed model might help predict hepatic decompensation in people with compensated cirrhosis when invasive tools are unavailable.

Although as a mainstay modal for cancer treatment, the clinical effect of radiotherapy (RT) does not yet meet the need of cancer patients. Developing tumour-preferential radiosensitizers or combining RT with other treatments has been acknowledged highly necessary to enhance the efficacy of RT. The present study reported a multifunctional bioactive small-molecule (designated as IR-83) simultaneously exhibiting tumour-preferential accumulation, near-infrared imaging and radio/photodynamic/photothermal therapeutic effects. IR-83 was designed and synthesized by introducing 2-nitroimidazole as a radiosensitizer into the framework of heptamethine cyanine dyes inherently with tumour-targeting and photosensitizing effects. As results, IR-83 preferentially accumulated in tumours, suppressed tumour growth and metastasis by integrating radio/photodynamic/photothermal multimodal therapies. Mechanism studies showed that IR-83 accumulated in cancer cell mitochondria, induced excessive reactive oxygen species (ROS), and generated high heat after laser irradiation. On one hand, these phenomena led to mitochondrial dysfunction and a sharp decline in oxidative phosphorylation to lessen tissue oxygen consumption. On the other hand, excessive ROS in mitochondria destroyed the balance of antioxidants and oxidative stress balance by down-regulating the intracellular antioxidant system, and subsequently sensitized ionizing radiation-generated irreversible DNA double-strand breaks. Therefore, this study presented a promising radiosensitizer and a new alternative strategy to enhance RT efficacy via mitochondria-targeting multimodal synergistic treatment.