We present the creation of wearable devices that measure transcutaneous partial oxygen or carbon dioxide pressure (tcpO2 or tcpCO2) non-invasively. The devices are highly sensitive to the physiological pO2/pCO2, and detects changes in luminescence (lifetime or ratiometric brightness) of mellaporphyrins or HPTS dye molecules embedded within breathable, hydrophobic films. Our first in human measurements reveal the devices are able to detect quick and subtle changes in tcpO2 due to local and systemic changes in blood flow of skin or deeper muscle tissue. Models to extract tissue oxygenation and oxygen consumption rate are explored.
Non-invasive approaches to human data collection have become relevant in athlete performance monitoring and the clinical setting. Historically, these metrics rely on measurements such as blood or tissue oxygen saturation, yet saturation lacks a degree of specificity clinicians and professional trainers desire. To overcome this, we developed a wearable, non-invasive, optical transcutaneous tissue oxygenation sensor that overcomes inefficiencies in specific measurements and the devices that collect those measurements. We report on innovations to the prototype device, including the implementation of more sophisticated quantification methods directly on the device as well as the addition of new sensors to measure other clinical parameters.
We present a wireless, wearable device to measure transcutaneous partial oxygen pressure (tcpO2) non-invasively. The device, tuned to physiological range pO2, detects changes in phosphorescence lifetime and intensity of ultra-bright metalloporphyrins embedded within breathable films. We have implemented machine learning algorithms to improve the accuracy of the measurements against changes in temperature, photobleaching, inter-device/film variations, etc. Our first in human measurements reveal the devices are able to detect quick and subtle changes in tcpO2 due to local and systemic changes in blood flow of skin or deeper muscle tissue. Models to extract tissue oxygenation and oxygen consumption rate are explored.
The onset and progression of dermal inflammation are easy to diagnose, but challenging to quantify, stage, and measure. CARS and SRS imaging are modalities capable of providing insight into the dynamics of structural and drug/fluid concentration changes throughout a time course of tissue imaging. This work displays two main avenues of exploration using dermatitis-induced mouse models. First, we track inflammation development and resolution over a four-day time course, capturing CARS and SRS image data at multiple time points to use in a machine learning (ML) based approach trained to classify the extent of inflammation in the provided images. Second, we treat mice with anti-inflammatory agents to determine whether these agents truly help with inflammation resolution, using our ML-based approach trained on structural and concentration rich images as a proxy for the pharmacodynamic response. We additionally use ML interpretability methods to aid in the justification of our results.
Wearable devices have found widespread application in recent years as consumer electronics for sports and health tracking. A metric of health which is overlooked in currently available technology is the measurement of oxygen in living tissue, a key component in the cellular energy production. We report on the development of an optical wireless wearable prototype for transcutaneous oxygen monitoring based on the phosphorescence emission of a highly breathable oxygen sensing film. The device is truly wearable, weighs under 20 grams,is completely self-contained, requires no external readout electronics and is highly sensitive to oxygen in the physiological range.
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