Matlab r2015a

Association between clinical data [Fédération Internationale de Gynécologie et d’Obstétrique (FIGO), TNM] and methylation status, corrected for disease status (primary or recurrence), was assessed using binomial logistic regression. The survival and hazard functions were estimated by Kaplan-Meier estimator and the log-rank test, respectively. Analyses were performed in Matlab R2015a (The Mathworks). An alpha level of 0.05 was employed for rejecting the null-hypothesis.

We applied a dynamic programming model developed by Kong and Mondschein¹² to calculate the optimal ages to perform a fixed number of screening Mx in order to minimize the lifetime death rate from BC (software MATLAB R2015a published by MathWorks). The model decides whether or not to perform a screening mammogram every year during a woman's lifetime. The decision balances the immediate benefit of detecting a tumor at an earlier stage (thereby increasing the chances of survival) and the benefit of waiting until the woman is older, when the probability of developing a tumor might be higher. These decisions depend on incidence, false negative rates, and natural progression of the tumor, as a function of age, sensitivity of clinical breast examination, and survival rates at different stages of the tumor. Given an optimal allocation of screening mammograms, the model computes the corresponding expected costs (including the cost of the screening mammograms, direct cost of diagnosis, treatment, follow-up, and false positives), which enables us to perform a cost-effectiveness analysis. The model also offers the flexibility to evaluate the expected lifetime cost and lifetime death rate for any specific screening policy defined by the decision maker.

The sputter deposition technique along with a lift-off process previously described by Chun et al. was used to create M-TFN [15 (link)]. First, five different micro patterns were defined via a conventional photolithography process. Then, 50 µm deep trenches were created by the Deep Reactive Ion Etching (DRIE) technique. Once trenches were formed, both the copper sacrificial layer and silicon dioxide material inhibition layer were deposited for the thin film nitinol sputter deposition process. The deposited thin film nitinol was crystallized at 500 °C for 20 min in a vacuum of less than 1 × 10⁻⁷ Torr [16 (link)]. Finally, the M-TFN was obtained by etching sacrificial and inhibition layers consecutively. Scanning electron microscopy (SEM) (Jeol-JSM 6610 LV, Tokyo, Japan) was used to characterize the geometry and size of the M-TFN. The obtained SEM images were used to calculate the porosity of the fenestrations in M-TFN with the custom-built MATLAB R2015a (MathWorks, Natick, MA, USA) image analysis code.