Development and validation of a nomogram to predict survival for advanced male breast cancer.

Male breast cancer is a rare disease. Many experiences of male breast cancer were derived of female breast cancer. However, there are huge differences between two groups. We conducted this study to find a reliable prognostic model for advanced male breast cancer. The cohort was selected from the Surveillance, Epidemiology, and End Results database. The enrolled patients were randomly divided into training and validation group. The univariate and multivariate analyses were used for prognostic assessment and a nomogram was built. Calibration curves and concordance index were compiled to determine predictive and discriminatory capacity. The time-dependent receiver operating curves and the decision curve analysis was used to verify the model's ability. Two hundred and eighty individuals were enrolled. The cumulative rates of 1-, 3- and 5-year overall survival (OS) rates were 98.6%, 72% and 57.9%. The C-indexes for OS were 0.835 (95%CI, 0.777-0.893) in the training group and 0.765 (95%CI, 0.668-0.862) in the validation group. The calibration curves confirmed the consistency of the nomogram both in the training and validation group. The time-dependent receiver operating curves and decision curve analysis demonstrated that the nomogram had better prediction capacity than TNM stage system for advanced male breast cancer. The nomogram we built was a reliable and solid predictive model.

Effects of preemptive interferon-α monotherapy in acute leukemia patients with relapse tendency after allogeneic hematopoietic stem cell transplantation: a case-control study.

Interferon-α (IFN-α) inhibits tumor growth and mimics graft-versus-leukemia after allogeneic hematopoietic stem cell transplantation (allo-HSCT). In the current case-control study, we compared treatment responses in acute leukemia patients with relapse tendency post-allo-HSCT receiving preemptive IFN-α after withdrawal of immunosuppressants (n = 31) vs. receiving no IFN-α (n = 67). In the IFN-α group, 25 patients responded to the treatment without progressing to hematological relapse. In the non-IFN-α group, only 22 patients responded to the treatment. The response rate differed significantly (80.6 vs. 32.8%, P < 0.001). The 2-year cumulative incidence of relapse was 31.6 and 61.2% in the IFN-α and the non-IFN groups, respectively (P = 0.006). The 2-year leukemia-free survival and overall survival rate was 57.4 vs. 28.4% (P < 0.001) and 67.6 vs. 32.9% (P = 0.001), respectively. Among the 31 patients in the IFN-α group, 18 patients (58.1%) developed graft-versus-host disease (GVHD): 6 acute and 12 limited chronic GVHD. Patients who developed GVHD had higher treatment response rate than patients without GVHD (88.9 vs. 53.8%, P = 0.022). In conclusion, preemptive IFN-α therapy is a safe and effective treatment to prevent disease progression in high-risk patients with relapse tendency post-allo-HSCT.

Large-scale hydropower system optimization using dynamic programming and object-oriented programming: the case of the Northeast China Power Grid.

This paper examines long-term optimal operation using dynamic programming for a large hydropower system of 10 reservoirs in Northeast China. Besides considering flow and hydraulic head, the optimization explicitly includes time-varying electricity market prices to maximize benefit. Two techniques are used to reduce the 'curse of dimensionality' of dynamic programming with many reservoirs. Discrete differential dynamic programming (DDDP) reduces the search space and computer memory needed. Object-oriented programming (OOP) and the ability to dynamically allocate and release memory with the C++ language greatly reduces the cumulative effect of computer memory for solving multi-dimensional dynamic programming models. The case study shows that the model can reduce the 'curse of dimensionality' and achieve satisfactory results.