Expression of genes involved in drug metabolism differs between perfusable 3D liver tissue and conventional 2D‐cultured hepatocellular carcinoma cells
Bibliographic record
Abstract
Tubular 3D liver tissue with enhanced capillary-like structures branching from a large main channel is potentially useful for drug discovery because the perfusable main channel and capillary-like structures enable mass transfer into and out from the tissue. Tubular liver tissue is comprised of the hepatocellular carcinoma cell line HepG2, human umbilical vein endothelial cells (HUVECs), and mesenchymal stem cells (MSCs), using a perfusion device functioning as the interface for an external pump. This study aimed to compare the expression of genes involved in drug metabolism between 2D-cultured hepatocellular carcinoma cells and 3D-cultured tubular liver tissue. Gene expression profiles of 2D-cultured cells and tubular liver tissue were compared using RNA sequencing. Multidimensional scaling analysis revealed that culture dimensionality had a more prominent effect on gene expression profiles than perfusion conditions. More specifically, genes involved in drug metabolism such as CYP2D6, CYP2E1, NNMT, and SLC28A1 were slightly upregulated in the 3D cultures, while certain genes such as ALDH1B1, ALDH1A2, and SULT1E1 were downregulated. These results indicate that gene expression profiles are largely influenced by culture dimensionality and are potentially useful to researchers intending to switch from 2D culture to 3D culture of hepatocellular carcinoma or other tissue types.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".