Skip to main content

Alan - Turbulent Flows Ahead



So, change of plans on receiving the human islet cells for live testing. I talked to Charlie and he said that the human islet cell supplier might not be able to have any cells in stock for the foreseeable future. The problem with obtaining human islets is that they’re extremely hard to come by, since they have to be sourced from recently deceased, young, healthy organ donors. It’s understandable that there aren’t many “in stock”. I asked Charlie about subbing in mouse islets since those could be harvested at will, but he explained that each mouse only has a small fraction of the islets that a human does, so we would need to sacrifice 80-100 mice for just this project – an ethical choice that I am happy to avoid having to make. Fingers crossed that I receive them before I leave California though!

After the past two weeks, I successfully fabricated a few more hollow fiber cartridges that were suitable for in vitro tests. I tested them all for water flow rates with this pump setup, and collected pressure drop data for each of them. 

Charlie showed me this cool book on Molecular Transport Phenomena for biomedical engineering purposes, where I found this equation from Poiseuille’s law for modeling the fluid flow through my hollow fibers. 

It turns out that my collected pressure/flow rate data matched the expected values from the calculations, but they were all off by a certain factor (probably just a unit conversion error). Either way, it was super cool to see my fluid flow data correlate with a mathematical model.

Since I won’t be able to test any cells soon, I’m making the most of the little time I have left here by working with Nathan, who’s a mechanical engineer in the Roy lab. There’s a ton of cool design that has gone into developing both the artificial pancreas device as well as the artificial kidney device.

Charlie told me that they’re still seeing a significant amount of clotting inside the outer structures of the pancreas device when they implant it in pig studies, which they think might be from the sharp turns of the ultrafiltrate flow path. After talking with Nathan about the manufacturing and machining challenges of making the outer structures, he gave me some part files for the device which I’ve been able to open with Solidworks and explore using CAD. 

I figured out how to run computational fluid dynamics simulations (CFD) on the flow paths as well – here’s a picture of what the fluid velocities look like (most of the actual device is cut out for confidentiality). The red areas indicate fast-moving ultrafiltrate flow, which prevents proteins from having time to settle and clot. Blue areas indicate stagnant flow, which suggests clotting will occur. Hopefully I’ll have time to engineer some different possible flow path designs which might minimize this stagnation.



Other than that, I’m excited to be working more with the design side of the device and finishing up my time at the Roy Lab!  



Labels:

Comments

  1. TECJuly 28, 2018 at 7:57 AM

    Very cool. And it's a lovely example of a 4th degree function in real life - Q goes as the 4th power of R. These don't come up too often!

    ReplyDelete

Post a Comment

Popular posts from this blog

Kylie Heering, Week 2 at the Goldstein Lab

We started off our week with a congratulatory acai bowl trip to celebrate Preston’s acceptance into a training grant program. Acai bowls in California top Playa Bowls (no question about it). From what I can tell, its a pretty huge honor to be recognized by this grant, but he’s really humble about it. On Monday, Preston and I decided that testing antibodies that have never been tested on prostate epithelial cells before would be a good objective for my first Western blot on my own. We needed to probe for ASCT2, a glutamine transporter, and GLS in order to determine if their corresponding antibodies are functional. Antibodies are crucial for Western blots because they bind to the protein of interest (POI), allowing for us to qualify its expression after imaging. As such, Preston wanted to make sure they worked by probing for ASCT2 and GLS on three different cell lines. Cell lines are commercially purchased human cells that have been immortalized (modified to grow indefinitely) by telome...
Post a Comment
Read more

Alan - First Week at UCSF

Hi Everyone! After arriving in San Francisco last Sunday, I spent this past week settling into the downtown Berkeley apartment that I’ll be sharing with Rohit for the next couple of months, as well as learning my way around the Roy lab at UCSF. First day at the lab was really exciting. Here are a couple pictures of the Mission Bay campus, which was completed just a few years ago. Everything is super new and modern, and there’s still construction for other buildings going on around the campus. Most of the people who work at the Mission Bay campus are either professional researchers or doctors/nurses for the nearby hospital. The graduate students take most of their classes at the original Parnassus campus (where Maya is). I work in Byers Hall, which is connected to Genentech Hall and a short walk down the block from the shuttle stop. There are three other volunteers working for the Roy lab this summer – Kimmai, David, and Pujita, who are all undergrad college students...
1 comment
Read more

Jaewon Oh - Week 7 and 8

Finally done here with my experience and I wish I had more time keep researching so that I have something a little more "finalised" to present. But I guess that's what past EXP kids meant when they said that 8 weeks of research is not enough and I'll have to work with what I've got. To solve the problem of not having enough data points, we used the online TCGA database for raw data that would be used to calculate mutation rates. Mutation rates were calculated through an R coding script that Dr. Cannataro had made. Because the mutation rates were tumor specific, we had to change the proportions that were obtained from the IARC database using data from another database called cBioPortal. Basically we had to multiply the number of times a certain variant was seen in the IARC database by the percentage of tumors that have a tp53 mutation, because our mutation rates are calculated across all tumors in specific cancers (confusing, I know). After graphing the mutatio...
Post a Comment
Read more