Development of a 3D printed device to support long term intestinal culture as an alternative to hyperoxic chamber methods
Bibliographic record
Abstract
BACKGROUND: Most interactions between pathogenic microorganisms and their target host occur on mucosal surfaces of internal organs such as the intestine. In vitro organ culture (IVOC) provides an unique tool for studying host-pathogen interactions in a controlled environment. However, this technique requires a complex laboratory setup and specialized apparatus. In addition, issues arise when anaerobic pathogens are exposed to the hyperoxic environment required for intestinal culture. The objective of this study was to develop an accessible 3D-printed device that would allow manipulation of the gas mixture used to supply the tissue culture media separately from the gas mixture exposed to the mucosal side of explants. RESULTS: = 20) and cultured for 0h, 8h, 18h and 24h using the device. After the culture period, explants were fixed in formalin and H&E stained sections were evaluated for histological defects of the mucosa. At 8h, 66% of samples displayed no histological abnormalities, whereas samples collected at 18h and 24h displayed progressively increasing rates of superficial epithelial erosion and epithelial metaplasia. CONCLUSIONS: The 3D-design reported here allows investigators to setup intestinal culture explants while manipulating the gas media explants are exposed to, to support tissue viability for a minimal of 8h. The amount of media necessary and tissue contamination are potential issues associated with this apparatus.
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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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".