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Record W1740061842 · doi:10.25011/cim.v32i4.6621

Potential role of matrix metalloproteinase-2,-9 and tissue inhibitors of metalloproteinase-1,-2 in exudative pleural effusions

2009· article· en· W1740061842 on OpenAlexvenueno aff
Sezai Vatansever, Remise Gelışgen, Hafize Uzun, Sibel Yurt, Filiz Koşar

Bibliographic record

VenueClinical and investigative medicine · 2009
Typearticle
Languageen
FieldMedicine
TopicPleural and Pulmonary Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMatrix metalloproteinaseMatrix metalloproteinase 9MetalloproteinaseMatrix Metalloproteinase 3MedicineTissue inhibitor of metalloproteinasePathologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To investigate diagnostic values of pleural fluid matrix metalloproteinase-2 (MMP-2), MMP-9, tissue inhibitors of metalloproteinase-1 (TIMP-1) and TIMP-2 measurements in tuberculous pleurisy(TP) and malignat pleurisy (MP). METHODS: The study included 24 patients with TP, 22 patients with MP and 15 patients with pleural effusion of non-tuberculous and non-malignant origin as controls. MMP-2,-9 and TIMP-1,-2 levels in pleural fluid were measured by ELISA method. RESULTS: Pleural fluid MMP-2 and MMP-9 levels were higher (P < 0.001, P < 0.001, respectively) in TP than in MP and controls. MP patients have higher pleural fluid MMP-2 and MMP-9 levels (P < 0.01, P < 0.05, respectively) than controls. Pleural fluid TIMP-2 levels were higher (P < 0.01 and P < 0.001, respectively) in MP than in TP and controls. Pleural fluid MMP-9 levels were negatively correlated with pleural fluid TIMP-2 levels (r: 0.464, P=0.029) in patients with MP. CONCLUSIONS: Determination of TIMP-2 in pleural fluid may contribute to differentiate TP from MP. These results suggest that overproduction of MMP-9 and TIMP-2 is associated with accumulation of the pleural effusion in malignancy. Further studies with a greater number of patients are needed to confirm this hypothesis.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.341
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2009
Admission routes1
Has abstractyes

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