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Background and objective: Due to their aggressive nature and poor prognosis, malignant femoral bone tumors present considerable hurdles. Early treatment commencement is essential for enhancing vital and practical outcomes. In this investigation, deep learning algorithms will be used to analyze magnetic resonance imaging (MRI) data to identify bone tumors that are malignant. Methodology: The study cohort included 44 patients, with ages ranging from 17 to 78 (22 women and 22 males). To categorize T1 and T2 weighted MRI data, this paper presents an improved DenseNet network model for the classification of bone tumor MRI, which is named GHA-DenseNet. Based on the original DenseNet model, the attention module is added to solve the problem that the deep convolutional model can reduce the loss of key features when capturing the location and content information of femoral bone tumor tissue due to the limitation of local receptive field. In addition, the sparse connection mode is used to prune the connection mode of the original model, so as to remove unnecessary and retain more useful fast connection mode, and alleviate the overfitting problem caused by small dataset size and image characteristics. In a clinical model designed to anticipate tumor malignancy, the utilization of T1 and T2 classifier output values, in combination with patient-specific clinical information, was a crucial component. Results: The T1 classifier's accuracy during the training phase was 92.88% whereas the T2 classifier's accuracy was 87.03%. Both classifiers demonstrated accuracy of 95.24% throughout the validation phase. During training and validation, the clinical model's accuracy was 82.17% and 81.51%, respectively. The clinical model's receiver operating characteristic (ROC) curve demonstrated its capacity to separate classes. Conclusions: The proposed method does not require manual segmentation of MRI scans because it makes use of pretrained deep learning classifiers. These algorithms have the ability to predict tumor malignancy and shorten the diagnostic and therapeutic turnaround times. Although the procedure only needs a little amount of radiologists' involvement, more testing on a larger patient cohort is required to confirm its efficacy.

Background: Evaluating clinical outcome prior to concurrent chemoradiotherapy remains challenging for oesophageal squamous cell carcinoma (OSCC) as traditional prognostic markers are assessed at the completion of treatment. Herein, we investigated the potential of using sub-region radiomics as a novel tumour biomarker in predicting overall survival of OSCC patients treated by concurrent chemoradiotherapy. Methods: Independent patient cohorts from two hospitals were included for training (n = 87) and validation (n = 46). Radiomics features were extracted from sub-regions clustered from patients' tumour regions using K-means method. The LASSO regression for ‘Cox’ method was used for feature selection. The survival prediction model was constructed based on the sub-region radiomics features using the Cox proportional hazards model. The clinical and biological significance of radiomics features were assessed by correlation analysis of clinical characteristics and copy number alterations(CNAs) in the validation dataset. Findings: The overall survival prediction model combining with seven sub-regional radiomics features was constructed. The C-indexes of the proposed model were 0.729 (0.656–0.801, 95% CI) and 0.705 (0.628–0.782, 95%CI) in the training and validation cohorts, respectively. The 3-year survival receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.811 (0.670–0.952, 95%CI) in training and 0.805 (0.638–0.973, 95%CI) in validation. The correlation analysis showed a significant correlation between radiomics features and CNAs. Interpretation: The proposed sub-regional radiomics model could predict the overall survival risk for patients with OSCC treated by definitive concurrent chemoradiotherapy. Fund: This work was supported by the Zhejiang Provincial Foundation for Natural Sciences, National Natural Science Foundation of China. Keywords: Oesophageal cancer, Radiomics, Prognostic model, Radiation therapy, Imaging

Summary: Renal tubular epithelial cells injury is one of the most important pathological features in hyperuricemic nephropathy (HN). However, the involvement of gasdermin D (GSDMD)-mediated pyroptosis in HN remains obscure. We found GSDMD was upregulated in the kidney tissue of HN mice, which was accompanied by the loss of renal function, renal tubular fibrosis, and reduced body weight. These changes in HN mice were inhibited by GSDMD knockout. Knockdown of GSDMD inhibited the high uric acid-induced injury in cultured cells (NRK-52E). Mechanistically, co-immunoprecipitation showed that RIG-I exist in a complex with caspase-1. Overexpression of RIG-I induced increased expression of caspase-1 protein and caspase-1 activity. Caspase-1 interference significantly reduced the increase of caspase-1 activity and IL-1β production caused by RIG-I overexpression. Knockdown of RIG-I or caspase-1 decreased high uric acid-induced injury in NRK-52E. This work illustrates that targeting the RIG-I/caspase-1/GSDMD may provide potential therapeutic benefits to HN.