Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning.

Background: Bone metastasis is a common adverse event in kidney cancer, often resulting in poor survival. However, tools for predicting KCBM and assessing survival after KCBM have not performed well. Methods: The study uses machine learning to build models for assessing kidney cancer bone metastasis risk, prognosis, and performance evaluation. We selected 71,414 kidney cancer patients from SEER database between 2010 and 2016. Additionally, 963 patients with kidney cancer from an independent medical center were chosen to validate the performance. In the next step, eight different machine learning methods were applied to develop KCBM diagnosis and prognosis models while the risk factors were identified from univariate and multivariate logistic regression and the prognosis factors were analyzed through Kaplan-Meier survival curve and Cox proportional hazards regression. The performance of the models was compared with current models, including the logistic regression model and the AJCC TNM staging model, applying receiver operating characteristics, decision curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts. Results: Our prognosis model achieved an AUC of 0.8269 (95%CI: 0.8083-0.8425) in the internal validation cohort and 0.9123 (95%CI: 0.8979-0.9261) in the external validation cohort. In addition, we tested the performance of the extreme gradient boosting model through decision curve analysis curve, Precision-Recall curve, and Brier score and two models exhibited excellent performance. Conclusion: Our developed models can accurately predict the risk and prognosis of KCBM and contribute to helping improve decision-making.

Assessment of the phytochemical profile and antioxidant activities of eight kiwi berry (Actinidia arguta (Siebold & Zuccarini) Miquel) varieties in China.

The kiwi berry (Actinidia arguta) is a new product on the market that expanding worldwide acceptance and consumption. This widespread interest has created an increasing demand to identify the nutritional and health benefits of kiwi berry. Many studies are being actively conducted to investigate the composition and health-promoting effects of kiwi berry. In this study, the phytochemical content of free and bound fractions of eight kiwi berry varieties were systematically investigated in order to better understand the potential of this superfood crop. Nine phenolic monomers were identified and quantified by ultrahigh-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry and ultrahigh-performance liquid chromatography-PAD. Antioxidant activity was further determined via peroxyl radical scavenging capacity and cellular antioxidant activity assays. The free extracts had higher phytochemical contents and antioxidant activities than the corresponding bound extracts among the eight kiwi berry varieties. Bivariate Pearson's and multivariate correlation analyses showed that antioxidant activities were most related to the total phenolic, flavonoid, vitamin C, and phenolic acids contents. The results provide a theoretical basis for the selection of kiwi berry varieties and the utilization of functional foods.