1-methylnicotinamide and its structural analog 1,4-dimethylpyridine for the prevention of cancer metastasis

Background: 1-methylnicotinamide (1-MNA), an endogenous metabolite of nicotinamide, has recently gained interest due to its anti-inflammatory and anti-thrombotic activities linked to the COX-2/PGI2 pathway. Given the previously reported anti-metastatic activity of prostacyclin (PGI2), we aimed to assess the effects of 1-MNA and its structurally related analog, 1,4-dimethylpyridine (1,4-DMP), in the prevention of cancer metastasis. Methods: All the studies on the anti-tumor and anti-metastatic activity of 1-MNA and 1,4-DMP were conducted using the model of murine mammary gland cancer (4T1) transplanted either orthotopically or intravenously into female BALB/c mouse. Additionally, the effect of the investigated molecules on cancer cell-induced angiogenesis was estimated using the matrigel plug assay utilizing 4T1 cells as a source of pro-angiogenic factors. Results: Neither 1-MNA nor 1,4-DMP, when given in a monotherapy of metastatic cancer, influenced the growth of 4T1 primary tumors transplanted orthotopically; however, both compounds tended to inhibit 4T1 metastases formation in lungs of mice that were orthotopically or intravenously inoculated with 4T1 or 4T1-luc2-tdTomato cells, respectively. Additionally, while 1-MNA enhanced tumor vasculature formation and markedly increased PGI2 generation, 1,4-DMP did not have such an effect. The anti-metastatic activity of 1-MNA and 1,4-DMP was further confirmed when both agents were applied with a cytostatic drug in a combined treatment of 4T1 murine mammary gland cancer what resulted in up to 80 % diminution of lung metastases formation. Conclusions: The results of the studies presented below indicate that 1-MNA and its structural analog 1,4-DMP prevent metastasis and might be beneficially implemented into the treatment of metastatic breast cancer to ensure a comprehensive strategy of metastasis control

Machine learning models for predicting patients survival after liver transplantation

In our work, we have built models predicting whether a patient will lose an organ after a liver transplant within a specified time horizon. We have used the observations of bilirubin and creatinine in the whole first year after the transplantation to derive predictors, capturing not only their static value but also their variability. Our models indeed have a predictive power that proves the value of incorporating variability of biochemical measurements, and it is the first contribution of our paper. As the second contribution we have identified that full-complexity models such as random forests and gradient boosting lack sufficient interpretability despite having the best predictive power, which is important in medicine. We have found that generalized additive models (GAM) provide the desired interpretability, and their predictive power is closer to the predictions of full-complexity models than to the predictions of simple linear models