Low Expression of Transforming Growth Factor β in the Epithelium of Barrett’s Esophagus
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to investigate the expression of transforming growth factor β (TGF-β) in the different stages of Barrett's esophagus (BE). METHODS: Paired endoscopic esophageal biopsy samples were obtained from patients with BE prospectively. Subjects were classified into three groups: BE, BE with dysplasia, and adenocarcinoma (AC) arising from BE. Biopsy specimens over normal esophageal epithelium and gastric cardiac epithelium of limited cases were done. Four cell lines, HETA1 (human esophageal epithelium), CA-A and CP-C (non-dysplastic metaplasia), and OE33 (AC) were analyzed for quantitative mRNA and Western blotting of TGF-β. RESULTS: All 30 subjects with BE were enrolled. Expression of TGF-β mRNA in BE were significantly (P < 0.01) lower than that in the normal esophagus and cardiac epithelium. The BE tissue showed a lower positive ratio of TGF-β immunohistochemical (IHC) stain than the cardiac epithelium. The expression of TGF-β mRNA in the cell lines CA-A, CP-3, OE-33, was significantly (P < 0.05) lower than that in the cell line HETA-1. The Western blotting result showed lower TGF-β protein expression of the cell lines CA-A, CP-3, and OE-33. CONCLUSIONS: The expression of TGF-β was lower in the tissue of BE.
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 | 0.000 |
| 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".