MétaCan
Menu
Back to cohort
Record W145412264 · doi:10.1096/fasebj.20.5.a1107-c

Gender‐related differences in advanced glycation endproducts and oxidative stress markers in rats

2006· article· en· W145412264 on OpenAlexafffund
Xiaoxia Wang, Kaushik Desai, B. H. J. Juurlink, JACQUES de CHAMPLAIN, Lingyun Wu

Bibliographic record

VenueThe FASEB Journal · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Glycation End Products research
Canadian institutionsUniversité de MontréalUniversity of Saskatchewan
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsEndocrinologyInternal medicineKidneyOxidative stressBlood pressureMedicineStainingGlycationPathologyDiabetes mellitus

Abstract

fetched live from OpenAlex

A blood pressure-associated increase in MG-induced AGEs including CEL and CML in the kidney of SHR has been shown. In the present study, gender-related changes in AGEs and NOS were investigated in SD rats and SHRsp. The systolic blood pressure of SHRsp was significantly higher than that of SD rats. Male SD rats had more intense kidney staining for CEL than female SD rats. Both male and female SHRsp had more marked CEL and CML staining localized to kidney tubules, as opposed to SD rats. Nuclei containing NF-êB p65 and activated macrophages were seen in the kidney from SHRsp, not so much in SD rats, localized to renal tubules in male and glomerular vessels in female SHRsp. Higher protein level of NF-êB p65 was found in SHRsp than in SD rats. SD rats had more intense kidney nNOS staining than SHRsp. Intensity of iNOS staining was significantly higher in SHRsp than in SD rats with no gender differences in either strain. SHRsp and male rats exhibit higher AGEs and oxidative stress than SD and female rats, respectively. These differences might partly account for development of hypertension in SHRsp and higher vulnerability of male animals to renal pathology. (Supported by CIHR&HSFC)

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 score0.230
Threshold uncertainty score0.303

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 head0.016
GPT teacher head0.274
Teacher spread0.258 · 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

Citations0
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicAdvanced Glycation End Products researchFrench-language works237,207