Cold Atmospheric Plasma Dose Optimization for Neurofibroma Treatments

Problem: Cold atmospheric plasmas (CAPs) have shown promise in cancer treatment. However, their extreme sensitivity to noise and interference from targets and measuring devices makes dose control and optimization challenging.

Approach: We have taken a grid of measurements across a subsection of the CAP parameter space (input voltage and frequency) while measuring the spectrographic data of the jet just above the target. This data indicates the excited species within the jet and reflects the dosage of reactive oxygen and nitrogen species delivered. We used a gaussian process regression model to generalize our findings across the parameter space.

We then applied this method to neurofibroma treatments (a form of neural tissue cancer). We added treatment time as a third parameter and focused on subregions of interest. We used cell proliferation post-treatment as a metric of treatment success and compared healthy neural cells, malignant cancer cells, and benign cancer cells.  Several different optimization algorithms will be compared to find the best method to identify the most effective and selective treatments.

Status: All data has been collected. However, the analysis and optimization of the treatments for neural cells still need to be performed.