2017 Individual Seed Projects
- 300,000 people in the United States experience a TIA annually with over 10% suffering an ischemic stroke in the subsequent 90 days.
- No effective biomarker currently exists.
- Our approach identifies the immune profile of TIAs utilizing state of the art immune monitoring - Mass Cytometry by Time Of Flight Mass Spectrometry (CyTOF)
- Identification of these blood biomarkers will drastically change the current management of TIAs to prevent future strokes
Theo Kok
(Maastricht University)
Almudena Espín Pérez
(Maastricht University)
This new collaboration between Stanford and Maastricht will identify biomarkers for early prediction of cancer and exposure to food based carcinogens
The first aim will identify predictive cancer markers using computational systems biology methods applied to genomic data from 700 healthy subjects, half of whom developed cancer in later decades of life
The second aim will integrate genomic and epigenetic data from colon samples of people exposed to different levels of carcinogens in their diet, and compare them to colorectal cancer and inflammatory bowel disease
Sensing technologies that provide real-time continuous monitoring of biochemical information non-invasively will enable unprecedented opportunities for understanding and monitoring human health, as well as allowing precise diagnosis and treatment. In this project, our team will design and test a REPEAT sensing geometry that may enable the continuous quantification of specific biochemicals in human sweat.
Audrey Bowden, PhD
Urine provides a wealth of information to monitor personal health status and disease progression.
We propose to prototype a portable device that would allow patients to run the standard “Routine Urinalysis and Microscopy” (RUM) protocol of the clinic in the comfort of their own home.
Our at-home platform would enable more continuous monitoring and tracking of data associated with health status, providing a new opportunity for early disease detection or collecting of markers that may predict disease onset.
Daniel Heywood
Our project aims to measure the impedance spectrum of blood noninvasively by leveraging blood’s ability to transport heat by bulk motion, along with its temperature sensitivity. A small heater can introduce a periodic temperature variation from the skin surface, which manifests as a downstream impedance variation, which in turn can be measured using skin-surface electrodes.
What is the biological correlate of subjective sleep quality? Using a variety of machine learning techniques to decode the recorded sleep of more than 5,000 adults, we will determine whether there is a single, underlying biological signal that corresponds to that feeling of restedness in the morning.
The discovery of such a biological correlate is a critical component in creating a closed-loop system to meaningfully modify an individual’s sleep health.
The VascTrac Research Study aims to clinically evaluate and validate passive smartphone activity biomarkers in patients with cardiovascular disease. Accuracy of mobile devices will be measured in and outside the clinic longitudinally at a granular level never before possible. We are passionate about identifying clinically significant signals in the passive activity which can be used as screening, diagnostic and surveillance tools.
Jiheng Zhao
Goal: To develop a non-invasive blood glucose sensor by measuring the acetone concentration in human breath for diabetic patients.
Such acetone sensors should have the following attributes.
- High sensitivity: sub ppm level
- Fast response: about one minute or less
- Good selectivity: insensitive to other compounds in human breath
Project Goal
Develop injectable in-vivo implant for real-time and continuous biosensing of biomarkers to establish patient-specific baselines and to understand disease dynamics. As proof of concept, we focus on troponin I and II and cytokines IL-10 and TGF-beta for myocardial infarction and atherosclerosis syndrome.
Our Technology
We will integrate highly specific and sensitive aptamer sensors with “batteryless” implants powered by and communicated with ultrasound. The implant will be “injectable” due to its small form factor.
Specific Aims
- Develop aptamer probes for troponin and cytokines
- Design the mm-sized CMOS implant and integrate the aptamer probes
- Perform in vitro and in vivo testing of the implant
An in-house developed high efficient, exosome isolation tool (ExoTiC) will be utilized for exosome and inflammasome isolation from well phenotyped and already bio-banked cohorts available at Stanford University. Exosomal miRNA and inflammasomal protein expression profiles will then be defined.
We will create a blueprint map of exosomal miRNA and inflammasomal protein expression library; for insulin sensitive and resistant individuals before and after exercise (Aim-1), and for insulin sensitive and resistant (non diabetic) vs. diabetic individuals at resting (Aim-2).
Olivia Deitcher, PhD
Stanford University
Fatih Inci
An in-house developed high efficient, exosome isolation tool (ExoTiC) will be utilized for exosome and inflammasome isolation from well phenotyped and already bio-banked cohorts available at Stanford University. Exosomal miRNA and inflammasomal protein expression profiles will then be defined.
We will create a blueprint map of exosomal miRNA and inflammasomal protein expression library for both health and disease stages of diabetic cardiomyopathy.
Byron Reeves, PhD
Nilam Ram, PhD (PSU)
- Much of life is now digitized on personal screens including information about relationships, money, work, play, travel and much more.
- We are recording screenshots of digital personal life from laptops and smartphones, and identifying features related to health outcomes.
- Screenomics is science of studying digital life experiences, exposures and actions through personal screenshots (the screenome) for their impact on thoughts, emotions, behaviors and health.
- The screenshots that make up the screenome are recorded every 5 seconds that devices are turned on.
- The images are encrypted, compressed and transmitted to Stanford servers for analysis.
- We extract text and images from the screenshots and create searchable databases.
- We currently have over 10M screenshots from ~400 people
- We visualize the information obtained in the screenome and analyze the behavioral sequences for content and action patterns.
- Examples above:
- The color bars on the left are 1-week screenomes for 30 people; colors indicate the timing and material viewed.
- The circle on the right shows switching across devices and among multiple types of content.
- Using machine learning and individual dynamic modeling, we search for the screenshot features and screenome actions, and combinations of them, that predict health outcomes.
- Examples above (from left):
- Person searching for information about a suicide bridge.
- Texting about bruises that won’t heal.
- Exhibiting memory problems in communication with a spouse.
- Asking about dark skin on the back of the neck and why it won’t wash off.