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Fibrillin‐1 in incisional hernias: an immunohistochemical study in scar and non‐scar regions of human skin and muscle fasciae

2008· article· en· W2068321010 on OpenAlexaff
M.P. Fricke, Claus Langer, Edgar Brunner, Lynn Y. Sakai, L. Füzesi, Dieter P. Reinhardt, Fabio Quondamatteo

Bibliographic record

VenueJournal of Anatomy · 2008
Typearticle
Languageen
FieldMedicine
TopicHernia repair and management
Canadian institutionsMcGill University
FundersUniversity of Galway
KeywordsFibrillinMedicineFasciaExtracellular matrixIncisional herniaHerniaImmunohistochemistryPathologyConnective tissueAnatomySurgeryMarfan syndromeBiologyCell biology

Abstract

fetched live from OpenAlex

Incisional hernias represent one of the most common complications after laparotomy. Specific pre-operative risk factors have not yet been identified. Recent studies indicate that changes in extracellular matrix components such as collagen I and collagen III may be involved in hernia development. In the present study we have evaluated the significance of fibrillin-1 in hernia development as one of the main components of the extracellular matrix. Tissue samples from non-scar skin and muscle fascia of 12 patients with incisional hernias as well as from the respective scar tissues were obtained. Corresponding tissue samples of 10 patients with normal postoperative wound healing served as controls. Distribution of fibrillin-1 was evaluated immunohistochemically. Differences in fibrillin-1 distribution in the non-scar tissues of muscle fascia have been found in patients with incisional hernia, compared to those without hernia. In scar regions of both patient groups, slight differences in the pattern of fibrillin-1 were observed. A tendency to a differential deposition of fibrillin-1 in skin samples, although hardly quantifiable, was observed as well. Our results suggest that fibrillin-1 is a relevant factor contributing to tissue stability. Disturbances in its deposition, even before scar formation, may be an important factor to the development of incisional hernias.

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)0.000
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 score0.025
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.321
Teacher spread0.301 · 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

Citations7
Published2008
Admission routes1
Has abstractyes

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