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Record W2121203714 · doi:10.1369/jhc.7a7354.2008

Glomerular CD34 Expression in Short- and Long-term Diabetes

2008· article· en· W2121203714 on OpenAlexafffund
Luz M. Acevedo, Irene Londoño, Malika Oubaha, Lucian Ghitescu, Moı̈se Bendayan

Bibliographic record

VenueJournal of Histochemistry & Cytochemistry · 2008
Typearticle
Languageen
FieldMedicine
TopicRenal Diseases and Glomerulopathies
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsDiabetes mellitusImmunogold labellingCD34EndocrinologyInternal medicineGlycationKidneyBiologyImmunocytochemistryMedicineImmunologyCell biologyStem cellAntibody

Abstract

fetched live from OpenAlex

Aging and diabetes are associated with exacerbated expression of adhesion molecules. Given their importance in endothelial dysfunction and their possible involvement in the alteration of glomerular permeability occurring in diabetes, we have evaluated expression of the sialomucin-type adhesion molecule CD34 in renal glomerular cells of normal and diabetic animals at two different ages by colloidal gold immunocytochemistry and immunoblotting. CD34 labeling was mostly assigned to the plasma membranes of glomerular endothelium and mesangial processes. Podocyte membranes were also labeled, but to a lesser degree. Short- and long-term diabetes triggers a substantial increase in immunogold labeling for CD34 in renal tissues compared with young normoglycemic animals. However, the level of labeling in old diabetic and healthy control rats is similar, suggesting that the effect of diabetes and aging on CD34 expression is similar but not synergistic. Western blotting of isolated glomerular fractions corroborated immunocytochemical results. Increased expression of CD34 may reflect its involvement in the pathogenesis of glomerular alterations related to age and diabetes. Alterations present in early diabetes, resembling those occurring with age, strengthen the concept that diabetes is an accelerated form of aging.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.257
Teacher spread0.244 · 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 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

Citations13
Published2008
Admission routes2
Has abstractyes

Explore more

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