Investigating the mechanisms of peritoneal metastasis in gastric adenocarcinoma using a novel ex vivo peritoneal explant model
Bibliographic record
Abstract
Gastric adenocarcinoma, commonly known as stomach cancer, has a predilection for metastasis to the peritoneum, which portends limited survival. The peritoneal metastatic cascade remains poorly understood, and existing models fail to recapitulate key elements of the interaction between cancer cells and the peritoneal layer. To explore the underlying cellular and molecular mechanisms of peritoneal metastasis, we developed an ex vivo human peritoneal explant model. Fresh peritoneal tissue samples were suspended, mesothelial layer down but without direct contact, above a monolayer of red-fluorescent dye stained AGS human gastric adenocarcinoma cells for 24 h, then washed thoroughly. Implantation of AGS cells within the explanted peritoneum and invasion beyond the mesothelial layer were examined serially using real-time confocal fluorescence microscopy. Histoarchitecture of the explanted peritoneum was preserved over 5 days ex vivo. Both implantation and invasion were suppressed by restoration of functional E-cadherin through stable transfection of AGS cells, demonstrating sensitivity of the model to molecular manipulation. Thus, our ex vivo human peritoneal explant model permits meaningful investigation of the pathways and mechanism that contribute to peritoneal metastasis. The model will facilitate screening of new therapies that target peritoneal dissemination of gastric, ovarian and colorectal cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".