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Protease Expression in Interface Tissues Around Loose Arthroplasties

2004· article· en· W1981738773 on OpenAlexaff
Akira Kido, G. Pap, D K N gler, Edmund Ziomek, R M nard, H. Neumann, Albert Roessner

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2004
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsBiotechnology Research Institute
Fundersnot available
KeywordsMedicineSynovial membraneCathepsinRheumatoid arthritisMatrix metalloproteinaseOsteoarthritisAseptic processingPathologyMatrix Metalloproteinase 3ImmunologyInternal medicineSurgeryBiologyEnzymeBiochemistry

Abstract

fetched live from OpenAlex

To determine whether cathepsins and matrix metalloproteinase-1 are involved in accelerating tissue destruction, we examined, immunohistochemically, the expression of matrix metalloproteinase-1 and cathepsins B, D, L, and X in periprosthetic synovial-like interface tissues from 14 patients with failed prosthetic hips and in the synovial membranes of hips from 18 patients with rheumatoid arthritis and 25 patients with primary osteoarthritis. The expression levels of all these proteases in the interface tissue were higher than in the synovial membrane of osteoarthritis. The expression levels of cathepsins B and X in the interface tissue were higher than in the rheumatoid synovium. The results show similarities in the expression patterns of cathepsins D and L and matrix metalloproteinase-1 between aseptic prosthetic loosening and rheumatoid arthritis. In addition, these data suggest that the impact of cathepsins B and X on tissue degradation is more pronounced in aseptic prosthetic loosening than in rheumatoid arthritis.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.082
GPT teacher head0.432
Teacher spread0.350 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations14
Published2004
Admission routes1
Has abstractyes

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