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Record W2171399490 · doi:10.2337/dc08-1609

Effect of Protein Kinase Cβ Inhibition on Renal Hemodynamic Function and Urinary Biomarkers in Humans With Type 1 Diabetes: A Pilot Study

2008· article· en· W2171399490 on OpenAlexaff
David Z.I. Cherney, Ana Konvalinka, Bernard Zinman, Eleftherios P. Diamandis, Anton Soosaipillai, Heather N. Reich, Joanne Lorraine, Vesta Lai, James W. Scholey, Judith Miller

Bibliographic record

VenueDiabetes Care · 2008
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsEli Lilly (Canada)Lunenfeld-Tanenbaum Research InstituteMount Sinai HospitalUniversity Health Network
Fundersnot available
KeywordsMedicineDiabetes mellitusRenal functionHemodynamicsUrinary systemType 2 diabetesInternal medicineEndocrinologyUrologyPharmacology

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to examine the effect of protein kinase Cbeta inhibition with ruboxistaurin on renal hemodynamic function and urinary biomarkers (monocyte chemoattractant protein-1 [MCP-1] and epidermal growth factor) in renin angiotensin system blockade-treated type 1 diabetic subjects. RESEARCH DESIGN AND METHODS: Albuminuric subjects were randomized (2:1) to ruboxistaurin (32 mg daily; n = 13) or placebo (n = 7) for 8 weeks. Renal hemodynamic function was measured during clamped euglycemia or hyperglycemia and before and after ruboxistaurin or placebo. RESULTS: Ruboxistaurin was not associated with between-group differences during clamped euglycemia or hyperglycemia. In a post hoc analysis comparing hyperfilterers with normofilterers during euglycemia, glomerular filtration rate and MCP-1 decreased, whereas the epidermal growth factor-to-MCP-1 ratio increased in hyperfilterers versus normofilterers (all P < 0.05). CONCLUSIONS: The effect of ruboxistaurin is modest and dependent, at least in part, on the level of ambient glycemia and baseline glomerular filtration rate.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.805

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 head0.009
GPT teacher head0.232
Teacher spread0.223 · 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 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

Citations40
Published2008
Admission routes1
Has abstractyes

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