Open-source 3D printed manifolds for exposure studies using human airway epithelial cells
Bibliographic record
Abstract
Abstract Rationale Inhalation of airborne stimuli can damage the airway epithelium, increasing the risk of developing respiratory or systemic diseases. In vitro studies using air-liquid interface cell cultures enable controlled investigation of cellular responses to relevant exposures. Commercial in vitro exposure systems provide precise and reproducible dosage but require significant capital investment and are not amenable to customization. Research groups interested in respiratory exposure science may benefit from a more accessible alternative open-source exposure system. We present 3D printed manifolds for applying a range of airborne exposures uniformly across standard, commercially available 6- and 24-well plates with air-liquid interface culture inserts. Methods A simple chamber-style exposure system and the manifolds were evaluated for exposure uniformity via computational fluid dynamics simulations and deposition of nebulized FITC-labelled dextran. The chamber and manifolds were manufactured using a stereolithography 3D printer. Cannabis concentrate vapor was generated from 3 different vaporizers and applied to well plates using the manifold system. Calu-3 cells were cultured on Transwell™ inserts and exposed to whole tobacco smoke or room air. Results The manifolds produced less variation in simulated air velocities and physical deposition of FITC-dextran aerosol deposition across well plates compared to those of the chamber-style exposure system. Distinct doses of cannabis concentrate vapour were delivered to well plates with low variation among wells. Whole tobacco smoke exposure using the manifold system induced functional changes in Calu-3 airway epithelial cell barrier function, cytokine production (IL-6 and IL-8), and cell membrane potential. Conclusions Collectively, our data demonstrate the feasibility and the validity of our open-source 3D printed manifolds for use in studying various respiratory exposures and position our designs as more accessible options in parallel with commercially available systems. All article content is licensed under a Creative Commons Attribution (CC BY-NC 4.0) license ( https://creativecommons.org/licenses/by-nc/4.0/ ).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".