Clinicopathological correlations of TIMP‐1 and TIMP‐2 in Hodgkin's lymphoma
Bibliographic record
Abstract
OBJECTIVES: The influence of matrix-tumour interactions in Hodgkin's lymphoma is poorly characterised, although a large part of the tumour often consists of reactive tissue. The aim of the present study was to assess the clinicopathological role of two main inhibitors of matrix metalloproteinases, TIMP-1 and TIMP-2, in Hodgkin's lymphoma. MATERIALS AND METHODS: The TIMP-1 and TIMP-2 protein expressions were studied from paraffin-embedded tumour sections of 68 patients with Hodgkin's lymphoma by using immunostaining with TIMP-1 and TIMP-2-specific antibodies. The results of the stainings were compared with the clinicopathological disease characteristics. RESULTS: A total of 33.3% of the tumour tissue sections expressed TIMP-1 and 46.8% expressed TIMP-2. The expression of the TIMP-1 protein was found to be strongly associated with the nodular sclerosis subtype (P = 0.015) and the existence of a bulky tumour (P = 0.004) in Hodgkin's lymphoma. The expression of the TIMP-2 protein, on the other hand, correlated with the occurrence of B symptoms (P = 0.032). CONCLUSIONS: These results provide the first clinical evidence suggesting that TIMP-1 could promote growth of Hodgkin's lymphoma, and may be linked to connective tissue turnover in the nodular sclerosis subtype. However, TIMP-2 is shown to correlate with systemic symptoms.
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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 | 0.001 | 0.002 |
| 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) | 0.002 | 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".