Open-source, three-dimensionally printed manifolds for exposure studies using human airway epithelial cells
Bibliographic record
Abstract
Rationale: exposure systems provide precise and reproducible dosage but require significant investment. Exposure science may benefit from a more accessible customisable open-source exposure system. We present three-dimensionally (3D) printed manifolds for applying a range of exposures uniformly across standard, commercially available 6- and 24-well plates with ALI culture inserts. Methods: simulations and deposition of nebulised fluorescein isothiocyanate (FITC)-labelled dextran. Chamber and manifolds were manufactured using 3D stereolithography printing. Cannabis concentrate vapour was generated from three different vaporisers and applied to well plates using the manifold system. Calu-3 cells and primary HAECs were cultured on Transwell inserts for exposure studies. Results: The manifolds produced less variation in simulations and physical deposition of FITC-dextran aerosol across well plates compared to the chamber system. Distinct doses of cannabis concentrate vapour were delivered to well plates with minimal variation among wells. Whole tobacco smoke exposure using the manifold system induced functional changes in Calu-3 barrier function, cytokine production (interleukin (IL)-6 and IL-8) and cell membrane potential. Cannabis smoke led to reduced primary HAEC barrier function in a dose- and strain-dependent manner. Conclusions: Our data demonstrate the feasibility and the validity of our open-source 3D printed manifolds for use in studying multiple exposures and position our designs as an accessible option in parallel with commercially available systems.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".