OpenVape: An Open-Source E-Cigarette Vapor Exposure Device for Rodents
Bibliographic record
Abstract
The prevalence of "vaping" has recently seen significant increases in North America, especially in adolescents. However, the behavioral correlates of vaping are largely unexplored. The uptake of existing technologies meant for rodent vapor inhalation remains limited because of a lack of affordability and versatility (ability to be used with a variety of vaporizers). The OpenVape (OV) offers an open-source, low-cost solution that can be used in a variety of research contexts. Here, we present a specific use case, combining the OV apparatus with JUUL e-cigarettes. This apparatus consists of Arduino-operated vacuum pumps that deliver vapor directly from e-cigarettes to exposure chambers. The OV is easy to build and customize for any type of vaporizer (e.g., nicotine pod or tank; cannabis flower or concentrates). To test the OV, we performed biochemical verification and behavioral studies. The behavioral test (conditioned place preference, CPP) was conducted using adolescent and adult animals to assess developmental differences in the rewarding effects of nicotine vapor, as previously observed with injected nicotine. These findings demonstrate that even after brief exposures to nicotine vapor, pharmacologically relevant nicotine and cotinine levels could be detected in plasma, and significant CPP was observed, especially in adolescent rats which showed preference at shorter puff delivery durations (lower nicotine doses) compared with adults. Together, these findings suggest that OV provides an affordable, open-source option for preclinical behavioral research into the effects of vaping.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".