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Record W2054633164 · doi:10.1080/07435800500229011

Leptin Increases Expression and Activity of Matrix Metalloproteinase-2 and Does Not Alter Collagen Production in Rat Glomerular Mesangial Cells

2005· article· en· W2054633164 on OpenAlexaff
Marian Pit-Siu Lee, Siham Madani, Dominika Sekula, Gary Sweeney

Bibliographic record

VenueEndocrine Research · 2005
Typearticle
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsYork University
Fundersnot available
KeywordsLeptinExtracellular matrixEndocrinologyInternal medicineMatrix metalloproteinaseMesangial cellNephropathyChemistryZymographyProteasesKidneyBiologyMedicineBiochemistryEnzymeObesityDiabetes mellitus

Abstract

fetched live from OpenAlex

Obesity is a leading risk factor for the development of nephropathy. In nephropathy, one of the major structural alterations found in the kidney is the increase in, or altered profile of, extracellular matrix (ECM) proteins such as collagen. Excessive synthesis and decreased degradation of matrix proteins by proteases such as matrix metalloproteinases (MMPs) may contribute to this process. We hypothesized that alterations observed in nephropathy may be due to alterations in direct effects of leptin, the product of the obesity gene. Here, we investigate the effect of leptin on collagen synthesis and MMP-2 production in rat glomerular mesangial cells. Using quantitative real-time PCR we showed that leptin does not alter the expression of collagen type I and IV mRNA. In keeping with this observation, proline incorporation was not altered by leptin. We also demonstrate that leptin induces MMP-2 expression in glomerular mesangial cells, assessed by quantitative real-time PCR. Analysis of conditioned media by gelatin zymography indicated increased activity at a molecular weight corresponding with that of MMP-2 in leptin-treated samples. In summary, our results indicate that leptin induces MMP-2 expression and activity without altering collagen synthesis, suggesting that normal leptin function has the potential to prevent ECM accumulation.

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)0.001
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 score0.005
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.040
GPT teacher head0.352
Teacher spread0.313 · 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

Citations19
Published2005
Admission routes1
Has abstractyes

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