Gastric and intestinal claudin expression at the invasive front of gastric carcinoma
Bibliographic record
Abstract
Like gastric and intestinal mucins, the tight junction proteins called claudins can be used to determine the differentiation of gastric mucosa. We investigated the expression of claudins in gastric cancer and proposed a new claudin-based gastric cancer classification system. The expression of gastric (claudin-18) and intestinal (claudin-3 and claudin-4) claudins in non-neoplastic gastric mucosa (with intestinal metatplasia [IM], 78 cases; without IM, 88 cases) and 94 gastric cancers was analyzed immunohistochemically, as was the expression of gastric (MUC5A and MUC6) and intestinal (CD10 and MUC2) mucins. Heterogeneous expression of claudin-3, claudin-4 and claudin-18 was detected in advanced gastric cancer; however, there was no significant association between the claudins and the clinicopathological parameters. These gastric cancer tissues were also subclassified into claudin-based phenotypes: gastric claudin (G-CLDN), 28 cases (30%); intestinal claudin (I-CLDN), 41 cases (44%); and unclassified claudin (U-CLDN), 25 cases (26%). Interestingly, the U-CLDN gastric cancers had worse malignancy grades, not only in size and invasiveness but also in potential metastatic ability and patient outcome. Although the mucin-based gastric cancer classification was also assessed, no significant correlation was found between mucin production and clinicopathological parameters. These observations suggest that loss of claudin expression may enhance the grade of malignancy of gastric cancer in vivo. Classification of gastric cancers using gastric and intestinal claudins is a good biomarker for assessing the risk of poor prognosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".