Mar 18, 2021

In a recent paper published in eNeuro, Jude Frie and colleagues from Ontario, Canada present  a new, opensource, lowcost, and versatile e-cigarette vapor exposure device: OpenVape.  Additionally, the authors also tested the OpenVape device by performing a behavioral study and  a pharmacokinetic study; the data from these studies are also discussed in their paper to  demonstrate OpenVape as a reliable and applicable open-source neuroscience tool.  

OpenVape is a vapor exposure apparatus comprised of an Arduino Uno microcontroller that  relays coded instructions to an H-bridge motor controller; the H-bridge then sends the specific  signals to the motors that regulate the produced vapor sent to exposure chambers. Further, one  major benefit of the OpenVape device is that it can be easily customized for a variety of  vaporizers including those that use nicotine pods or tanks, and even cannabis flower or  concentrate vaporizers. The Arduino code is also customizable, allowing users to choose the  vapor delivery pulse time and pulse interval time specific to their vaporizer device. The code for  the Arduino, as well as detailed instructions for constructing OpenVape and a materials list, are  available at the Khokhar Lab website listed below. Also available on the website is a custom  printed circuit board (PCB), which is plugged into the Arduino to control all logic commands and  replaces the H-bridge and other wired connections. The PCB provided on the website helps  simplify the OV’s construction and decreases cost! Without the PCB, the OV device still only  costs $230 CAD, or roughly $180 USD, to build per apparatus. Another benefit of OV is that  users do not need extensive coding or circuit experience to build or use the device due to the  simplicity of OV’s construction and programming.  

Given the growing prevalence of nicotine vaping and e-cigarette use among adolescents, along  with the fact that many popular e-cigarettes have high nicotine content, it is increasingly  important to conduct research on the effects of nicotine vapor. However, many of the nicotine  vapor exposure devices that are commercially available are extremely expensive and are  restricted to proprietary software and hardware for operation. Many of these aerosol exposure  devices also lack compatibility with new vaping devices. Even alternatives to commercially  available devices are expensive, may not be open source, and are complex. To answer some of  the barriers and complications surrounding previously available aerosol exposure devices,  OpenVape was created. OpenVape is low cost, compatible with a range of vaporizers, is simple  in construction and operation, is customizable, and is open-source! Additionally, all of the OV  components are outside of the exposure chambers, which mitigates the possibilities of animals  harming themselves and device damage.  

Despite the simplicity of OV’s construction and operation, the device is a reliable and relevant  neuroscience tool. Frie and colleagues conducted a conditioned place preference behavioral  study using the current most popular e-cigarette called JUUL. Through the study, Frie and  colleagues found that brief exposure to nicotine vapor from JUUL devices were enough to  produce conditioned place preference in rats. Additionally, they found that nicotine vapor, when  delivered in adolescence, may have greater reward-like properties, which is consistent with  previous studies involving nicotine exposure. The other study conducted by Frie and colleagues  measured plasma nicotine and cotinine concentrations, and found that plasma nicotine and  cotinine levels could be detected even after brief exposure. The plasma nicotine levels seen  with the 8 minute nicotine vapor exposure group is consistent with those found in nicotine  injection studies, and are comparable to levels seen in adult cigarette smokers. These studies  and findings demonstrate that OpenVape is highly reliable, relevant, and applicable in  behavioral studies and pharmacological studies. The huge benefit, though, is that this device is  open source! 

This research tool was created by your colleagues. Please acknowledge the Principal Investigator, cite the article in which the tool was described, and include an RRID in the Materials and Methods of your future publications.  RRID:SCR_021485

Read the publication!

Read more about this project in the eNeuro publication! 

Read the Documentation

Get access to the code from the Khokhar Lab website!

Thanks Olivia!

This project summary is a part of the collection from neuroscience undergraduate students in the Computational Methods course at American University. 

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