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Record W2052923207 · doi:10.1159/000158256

Arterial Glycosaminoglycans in Diabetic Dogs

2008· article· en· W2052923207 on OpenAlexaff
O. V. Sirek, A. Sirek, Eva Cukerman

Bibliographic record

VenueBlood Vessels · 2008
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Conditions and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineCoronary arteriesInternal medicineElastinHyaluronic acidGlycosaminoglycanThoracic aortaGround substanceDiabetes mellitusMesenteric arteriesAortaEndocrinologyDermatan sulfateAbdominal aortaCardiologyArteryAnatomyPathologyChondroitin sulfateConnective tissue

Abstract

fetched live from OpenAlex

The glycosaminoglycan (GAG) composition of a number of large and medium-sized arteries was studied in 6 alloxan-diabetic beagles and was compared with 6 normal, age-matched controls. Diabetic animals were maintained on diet and insulin for 100 days. The aortic arch, thoracic and abdominal segments, external iliac, superior mesenteric, renal, common carotid and coronary arteries were analyzed for hyaluronic acid (HA) and for heparan (HS), dermatan (DS), and chondroitin (CS) sulphates. All diabetic dogs displayed significant alterations. The HA content was reduced in iliac arteries, and together with HS, also in the thoracic aorta. HS or CS were increased in carotid, iliac and renal arteries, DS, a GAG constitutent with very high affinity for low density lipoproteins, was significantly increased in coronary arteries alone. 2 additional animals which are excluded from this series did not become diabetic after alloxanization and showed no change in arterial GAG content. Early changes in the chemistry of the arterial ground substance seem to provide a clue to the precocious development of atherosclerotic disease in diabetes.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.016
GPT teacher head0.237
Teacher spread0.221 · 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
Published2008
Admission routes1
Has abstractyes

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