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Record W2604469412 · doi:10.1111/wrr.12535

Impaired collagen synthesis in the rectum may be a molecular target in anastomotic leakage prophylaxis

2017· article· en· W2604469412 on OpenAlexaff
Anastasia S Buch, Peter Schjerling, Marie Kjær, Lars Nannestad Jørgensen, Peter‐Martin Krarup, Magnus S. Ågren

Bibliographic record

VenueWound Repair and Regeneration · 2017
Typearticle
Languageen
FieldMedicine
TopicColorectal Cancer Surgical Treatments
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsRectumMatrix metalloproteinaseAnastomosisMedicineColorectal surgeryColorectal cancerMessenger RNASurgeryInternal medicinePathologyCancerChemistryAbdominal surgeryBiochemistryGene

Abstract

fetched live from OpenAlex

The underlying molecular mechanisms for anastomotic leakage (AL) after colorectal surgery are unknown and there are no therapeutics for AL prevention. Our aim was to correlate endogenous matrix metalloproteinase (MMP) activity, collagen concentration, and collagen/MMP/cytokine mRNA levels with anatomic location in human colorectal tissue. We enrolled 22 patients in this prospective study: 7 underwent elective laparoscopic sigmoid resection and 15 underwent low anterior resection for colorectal cancer. Full-thickness intestinal tissue rings from anastomoses constructed with a circular stapler were used for the determination of the MMP activity, tissue collagen concentration and mRNA levels. COL1A1 (p = 0.017) and COL3A1 (p = 0.0013) mRNA levels were lower in rectal tissue than in colonic samples. Neither MMP activities nor collagen concentrations differed significantly between the two anatomic locations. By elucidating the factors responsible for the decreased collagen production we may identify specific molecular targets in AL prophylaxis.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.023
GPT teacher head0.285
Teacher spread0.262 · 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 designBench or experimental
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

Citations8
Published2017
Admission routes1
Has abstractyes

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