Bibliographic record
Abstract
BACKGROUND: This study was aimed at evaluating the correlation between Hepatocyte paraffin 1 (Hep par 1) and colorectal cancer. METHODS: To this end, 50 intestinal biopsies were analyzed including 10 colorectal polyps with low grade dysplasia, 10 with high grade dysplasia, 10 colorectal adenocarcinomas, 10 specimens of normal ileum and 10 of normal colon mucosa. Tissue sections were immunostained for Hep par 1 utilizing a commercial antibody. Normal colonic mucosa did not express Hep par 1. RESULTS: Immunoreactivity for Hep par 1 was detected in 20% of polyps with low grade dysplasia, 50% of polyps with high grade dysplasia and 60% of colorectal carcinomas. Hep par 1 was frequently detected in the deepest areas of adenocarcinomas mainly in infiltrating tumour cells. CONCLUSIONS: Our data show that Hep par 1 immunoreactivity in human colon carcinogenesis is correlated with progression from low grade to high grade dysplasia and adenocarcinoma. In clinical practice, our data show that caution should be taken in utilizing Hep par 1 as the sole tool in differentiating hepatocellular carcinoma from a liver metastasis of colon adenocarcinoma. Our data encourage further investigations into the potential role played by Hep par 1 in gastrointestinal carcinogenesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".