Serum levels of matrix metalloproteinase-2 and -9 and conventional tumor markers (CEA and CA 19-9) in patients with colorectal and gastric cancers
Bibliographic record
Abstract
BACKGROUND: Matrix metalloproteinases (MMPs), especially MMP-2 and MMP-9, play an important role in tumor invasion and metastasis. This study aimed to determine the serum levels of MMP-2, MMP-9, 130- and 225-kDa gelatinolytic bands and conventional tumor markers, carcinoembryonic antigen (CEA) and cancer antigen (CA) 19-9, in patients with gastrointestinal cancers. The relationship between these parameters and clinicopathological factors was also studied. METHODS: Sera from controls (n=19), and patients with colorectal (n=47) and gastric (n=34) cancer were collected prospectively. The gelatinolytic activities of MMP-2, MMP-9, 130- and 225-kDa bands were determined using gelatin zymography. CEA and CA 19-9 were determined using immunoradiometric assay (IRMA). RESULTS: Serum levels of MMP-9, 130- and 225-kDa gelatinolytic bands, CEA, and CA 19-9, but not MMP-2, in colorectal and gastric cancer were significantly higher than that of controls. No significant correlation was found between histological grade or clinical stage and levels of MMP-9, 130- and 225-kDa gelatinolytic bands, which were correlated (r=0.61-0.89, p<0.005). CONCLUSIONS: Our findings suggest that zymographic determination of MMP-9, 130- and 225-kDa gelatinolytic bands in colorectal and gastric cancer may be useful in studying these types of cancer in parallel with conventional tumor markers.
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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 | 0.000 | 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".