A predictive model for recurrence in patients with borderline ovarian tumor based on neural multi-task logistic regression.

Background: Effective management of patients with borderline ovarian tumor (BOT) requires the timely identification of those at a higher risk of recurrence. Artificial neural networks have been successfully used in many areas of clinical event prediction, significantly affecting clinical decisions and practice.

Objective: We developed and validated a novel clinical model based on neural multi-task logistic regression (N-MTLR) for predicting recurrence in patients with BOT who underwent initial surgeries, and compared its prediction performance with that of the Cox regression model.

First case of endometrial cancer after yolk sac tumor in a patient with Li-Fraumeni syndrome.

Background: Li-Fraumeni syndrome (LFS) is a rare autosomal dominant disease with high penetrance caused by a germline variant of TP53 gene. We report the first case of endometrial cancer after yolk sac tumor with LFS.

Case Presentation: The presented female patient underwent right adnexectomy at age 23 because of a yolk sac tumor of the ovary.

PCDD/F removal at low temperatures over vanadium-based catalyst: insight into the superiority of mechanochemical method.

The high toxicity and low volatility of PCDD/Fs prevent detailed study of their catalytic degradation removal characteristics. In this study, 1,2-dichlorobenzene (1,2-DCBz) was initially used as a model to investigate the catalytic characteristics of various vanadium-based catalysts prepared by different methods. Then, the optimized catalyst was used for catalytic degradation of real PCDD/Fs at low temperatures based on a self-made stable source.