Objective : To explore the utility of a nomogram integrating contrast-enhanced CT radiomics with clinical features in the preoperative prediction of WHO/ISUP grade for clear cell renal cell carcinoma(ccRCC). Methods: A total of 214 patients with pathologically proven ccRCC who underwent enhanced CT scan before surgery were retrospectively included. According to the WHO/ISUP grade system, the cases were classified into low-grade(grades Ⅰ-Ⅱ) and high-grade(grades Ⅲ-Ⅳ), and then randomly divided into training and test set with a ratio of 4 ∶1. Regions of interest were segmented from both unenhanced and three-phase enhanced images, and radiomic features were extracted. Feature selection and dimensionality reduction were performed using Spearman rank correlation coefficients and LASSO regression, followed by the construction of the radiomic model with the KNN algorithm. Clinical and semantic imaging features were selected through univariate and multivariate analyses, and a clinical model was developed using the KNN algorithm. The clinical and radiomics signatures were used to construct a combined model and a nomogram was developed. The ROC curve and delong test were used to evaluate the diagnostic performance of the model, while calibration and decision curve analyses assessed its accuracy and clinical applicability. Results: 8 clinical features and 11 radiomic features were selected. The combined model, integrating these clinical and radiomics signatures, exhibited robust predictive performance with AUC values of 0.887 in the training set and 0.800 in the test set. The calibration curve demonstrated good consistency between the nomogram model and actual outcomes, while decision curve analysis indicated a favorable net benefit for the nomogram. Conclusion The nomogram constructed by combining radiomics and clinical signatures can provide evidence for preoperative prediction of ccRCC grade and guide clinical decision-making.

Objective: To analyze the clinical manifestations and results of etiological examinations of 17 elderly patients with influenza A (H1N1) viral pneumonia, and to understand the clinical features of pneumonia and molecular characteristics of influenza A (H1N1) virus infection in the elderly. Methods: The elderly patients with pneumonia who were hospitalized in the Department of Respiratory Diseases of Nanjing First Hospital from January 2018 to March were enrolled. The cases were confirmed by nucleic acid examination for influenza virus and the clinical data were collected. After the amplification of the whole genome of influenza virus, the high throughput sequencing and bioinformatics analysis were performed. Results: The mean age of the 17 enrolled patients was 73.8±10.8. All of them had at least 1 underlying disease, and 7 cases had co-infection. Respiratory symptoms and fever were the most prominent clinical manifestations. Lesions in both lungs were found in 76.5% of the patients. The result of high throughput sequencing showed that all the viruses were highly homologous to the vaccine strain, and the HA gene belonged to the 6B.1 subgroup. Furthermore, three variations of antigenic locus (H138Y, S74R and S164T in HA) and a drug-resistant variation (H275Y in NA) were detected in the circulating strains. Conclusions Elderly patients with influenza A (H1N1) virus pneumonia often have underlying diseases and are prone to have co-infection. The molecular characteristics of the virus and the variation of key amino acid loci should be closely monitored in order to provide evidence for epidemic prevention and clinical antiviral treatment.