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Record W2136097277 · doi:10.1111/wrr.12071

Circulating levels of matrix metalloproteinases and tissue inhibitors of metalloproteinases in patients with incisional hernia

2013· article· en· W2136097277 on OpenAlexaff
Nadia A. Henriksen, Lars Tue Sørensen, Lars Nannestad Jørgensen, Magnus S. Ågren

Bibliographic record

VenueWound Repair and Regeneration · 2013
Typearticle
Languageen
FieldMedicine
TopicHernia repair and management
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsMatrix metalloproteinaseIncisional herniaMedicineHerniaLaparotomyPathogenesisConnective tissuePathologyGastroenterologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Incisional hernia formation is a common complication to laparotomy and possibly associated with alterations in connective tissue metabolism. Matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) are closely involved in the metabolism of the extracellular matrix. Our aim was to study serum levels of multiple MMPs and TIMPs in patients with and without incisional hernia. Out of 305 patients who underwent laparotomy, 79 (25.9%) developed incisional hernia over a median follow-up period of 3.7 years. Pooled sera from a subset (n = 72) of these patients were screened for MMP-1, MMP-2, MMP-3, MMP-7, MMP-8, MMP-9, MMP-10, MMP-12, MMP-13, TIMP-1, TIMP-2, and TIMP-4 using a multiplex sandwich fluorescent immunoassay supplemented with gelatin zymography. The screening indicated differences in serum MMP-9 and TIMP-1 levels. Consequently, MMP-9 and TIMP-1 levels were measured in serum in the whole patient cohort with enzyme-linked immunosorbent assay. There were no significant differences in either MMP-9 (p = 0.411) or TIMP-1 (p = 0.679) levels between hernia and hernia-free patients. MMP-9 was significantly increased in smokers compared with nonsmokers (p = 0.016). In conclusion, a possible involvement of MMPs and TIMPs in the pathogenesis of incisional hernia formation was not reflected systemically.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.304
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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.

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

Citations18
Published2013
Admission routes1
Has abstractyes

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