While listening to my fellow peers give their chalk talks this week I was struck by how different everyone’s research projects are from one another. Some people are working in Matlab, others are amputating fish fins or watching plant seeds germinate, and still others are analyzing the structure of mantis shrimp claws. Our research projects truly run the gamut, from cell biology to plant biology to computer science. All of the sudden it hit me: research can take on a variety of forms. Ok, so this probably shouldn’t come as a big surprise, but I guess I had just assumed everyone was doing basically the same things that I’ve been doing: cell culture, PCR, gel electrophoresis, DNA sequencing. While its true that some people are doing these things as well, its also true that most people’s daily research routines are far different from mine.
For example, I want to focus on Matt’s research, which I found particularly interesting as well as completely different from my own research. Matt is working in Matlab to create a method for quantifying the pain that patients report having before and after treatment. I think his research is so interesting because it tackles such a tricky and difficult question. How exactly do you quantify pain if the sensation of pain is a subjective experience, unique to the individual? Patients suffering from the same disease may report experiencing pain in different areas of the body. These patients may also report different levels of pain in those areas. So how does the health care provider determine whether their treatment is working for a particular patient? This is the problem Matt’s research is designed to solve.
Before treatment a patient is given a sheet of paper with the outline of a human body printed on it. The patient then shades in the region of the body in which she is experiencing pain and rates the level of her pain on a scale from one to ten. It is Matt’s job to convert these visual representations of a patient’s pain into concrete data. It seems like a simple task until you consider the fact that Matt is working with hundreds of these pain diagrams, all of which must be scanned and ‘translated’ into a language the computer can understand.
As Matt explained the steps he has to take to make the diagrams ‘understandable’ to a computer, I began to understand how difficult and frustrating the process must be. First he has to crop the diagram so that only the shaded regions within the body are registered as areas with pain (sometimes the patient scribbles outside of the lines, which we assume does not mean that the patient is experiencing pain outside of his or her body). Next Matt has to fill in the shaded area completely (eliminating the white spaces within a shaded area so that the entire area is a solid color). Once the area in which the patient reports having pain has been properly defined the computer can then measure what percentage of the entire body is affected by pain. After treatment the patient will be asked to fill out another diagram to report his or her pain. Then the area of pain the patient reported having before treatment will be compared to the area of remaining pain reported after treatment.
I can only imagine that the work that Matt is doing in Matlab to accomplish his intended goal of quantifying a patient’s reported pain is difficult and at times extremely frustrating, especially since he has never worked in Matlab before. It must be easy to feel overwhelmed. I admire his perseverance (Matlab is notoriously hard to learn) and I’m really impressed by how much he’s been able to accomplish in only a month’s worth of work!
