Many diseases are very complex, involving many biological pathways. It is very difficult to find a single drug with actions on enough pathways to slow or reverse many diseases. As an alternative there is excitement around using cells to treat disease. Cells can sense their environment and change their response to match.
Mesenchymal stem cells (MSCs) have performed well in many types of disease. I currently work in the Renal Diagnostics and Therapeutics Unit at NIDDK. Before I joined the group they found MSCs were beneficial in sepsis. Treatment had to be soon after injury limiting the clinical usefulness. One potential reason for this was most MSCs got stuck in the lungs. If more cells got to the kidneys they might work better. At the time it was not known how to guide more cells to the kidneys.
Recently, we have worked with Scott Burks and Joe Frank from the Clinical Center at NIH. They are able to use pulsed focused ultrasound to alter tissues to recruit more MSCs. We tested pulsed focused ultrasound in a cisplatin model. Cisplatin is a treatment for some types of cancer but often causes kidney injury. Pulsed focused ultrasound guided MSC treatment reduced damage from cisplatin.
Acute kidney injury (AKI) is a common complication in hospitalized patients. Patients with AKI are more likely to die or have a worse quality of life after leaving the hospital. Early treatment is our best option for preventing these bad outcomes. To start treatment we first need to detect AKI.
Several biomarkers go up when a patient has AKI. Doctors watch the biomarker levels and start treatment when they rise. There is still uncertainty about which biomarker is best.
I am co-author on a recently published article exploring this issue. We compared creatinine and cystatin C in the serum. Cystatin C predicted kidney function better than creatinine. There was no difference in predicting death.
Following a hiatus of a couple of years I have rejoined the competitors on kaggle. The UPenn and Mayo Clinic Seizure Detection Challenge had 8 days to run when I decided to participate. For the time I had available I'm quite pleased with my final score. I finished in 27th place with 0.93558. The metric used was area under the ROC curve, 1.0 is perfect and 0.5 being no better than random.
Prompted by a post from Zac Stewart I decided to give pipelines in scikit-learn a try. The data from the challenge consisted of electroencephalogram recordings from several patients and dogs. These subjects had different numbers of channels in their recordings, so manually implementing the feature extraction would have been very slow and repetitive. Using pipelines made the process incredibly easy and allowed me to make changes quickly.
The features I used were incredibly simple. All the code is in transformers.py - I used variance, median, and the FFT which I pooled into 6 bins. No optimization of hyperparameters was attempted before I ran out of time.
Next time, I'll be looking for a competition with longer to run.
Over the past several months I have been working on a method for measuring fibrosis. I published an article based on this work in Physiological Reports. The journal has started a podcast series and this article was in the second episode. I discussed the article with Physiological Reports editor Tom Kleyman. I embedded the full podcast below and the article is available on the journal website
Fibrosis is an important step in healing an injury. The scar that might form after a cut is an example of normal physiological fibrosis. Unfortunately fibrosis is not always benign. Pathological fibrosis is the deposition of excessive fibrous tissue. This interferes with healing and the function of the organ. Fibrosis is a dominant feature in the histological damage seen in many diseases. Examples include idiopathic pulmonary fibrosis, liver cirrhosis, and Crohn's disease. My interest is in chronic kidney disease.
The advanced stages of kidney disease requires treatment by dialysis or kidney transplantation. Both of these options have many negative consequences. Treatments to slow the development of fibrosis would help many patients.
Accurate measurements of fibrosis are vital in treatment development. The sirius red method in this article is more reproducible and precise. I hope it will contribute to getting better treatment options to the patients that need them.
This Saturday the DC Python group ran a coding meetup. As part of the event I ran an introduction to scientific computing for about 7 people.
After a quick introduction to numpy, matplotlib, pandas and scikit-learn we decided to pick a dataset and apply some machine learning. The dataset we decided to use was from a Kaggle competition looking at the Titanic disaster. This competition had been posted to help the community get started with machine learning so it seemed perfect.