Predictive value of lymph node and tumor matrix metalloproteinase expression in the analysis of metastatic periampullary tumors
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Matrix metalloproteinases (MMP) are endogenous peptidases capable of degrading various components of the basement membrane. The ability of malignant epithelial cells to degrade extracellular matrix and basement membrane is an important step in the process of metastatic invasion. In this study, we prospectively compared the outcome of patients undergoing laparotomy for resection of periampullary malignancies with lymph node and tumor MMP expression to determine if there was a correlation between metalloproteinase expression and patient outcome. METHODS: Sixteen patients undergoing operation were followed prospectively. Expression of MMP-2 and -9 and their inhibitors TIMP (tissue inhibitor of matrix metalloproteinase) -1 and -2 were measured in lymph node and tumor samples by semiquantitative PCR analysis. RESULTS: All patients who died from their disease process had significantly greater MMP-2 expression in their lymph nodes relative to TIMP-2 expression. In contrast, patients with prolonged disease-free survival had decreased nodal MMP-2/TIMP-2 expression (P = 0.001). Patients with relatively higher MMP-2/TIMP-2 expression in their tumors also had a worse prognosis (P = 0.06). CONCLUSION: The data demonstrate that the mRNA expression of MMPs and TIMPs in the draining lymph node of patients with periampullary adenocarcinomas may hold prognostic significance for patient survival. This prognostic information may be of use in patients when planning future adjuvant therapies.
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 | 0.002 |
| 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".