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Record W2808789194 · doi:10.2337/db18-503-p

PBI-4050 Improves Metabolic Regulation and Diabetic Nephropathy through Reduction of ER Stress, Pro-Inflammatory/Fibrotic Markers, Galectin-3 Expression, and Inflammatory Cell Infiltration in ob/ob Mouse Model

2018· article· en· W2808789194 on OpenAlexaff
Jean-Christophe Simard, Marie-Pier Cloutier, Alexandre Laverdure, Jonathan Richard, Liette Gervais, A. Felton, Brigitte Grouix, Pierre Laurin, Martin Leduc, François A. Leblond, Lyne Gagnon

Bibliographic record

VenueDiabetes · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective Tissue Growth Factor Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineCTGFInternal medicineEndocrinologyAdiponectinKidneyDiabetic nephropathyFibrosisAdipose tissueDiabetes mellitusInsulin resistanceGrowth factorReceptor

Abstract

fetched live from OpenAlex

Background: PBI-4050 displays anti-inflammatory/fibrotic properties with metabolic regulation. PBI-4050 has been shown to reduce glycated hemoglobin levels, and different biomarkers related to kidney and heart injury in a phase II open label clinical trial in patients suffering from type 2 diabetes with metabolic syndrome. PBI-4050 has also completed with success an open label phase II clinical trial in patients with idiopathic pulmonary fibrosis (IPF) and in patients with Alström Syndrome. The aim of this study was to investigate the effect of PBI-4050 on diabetic nephropathy in leptin deficient ob/ob mice, a model of type 2 diabetes and metabolic syndrome. Methods: ob/ob mice were treated with vehicle or PBI-4050 (200 mg/kg, oral once a day) from day 1 to 105. OGTT, triglycerides, adiponectin and serum insulin levels, as well as inflammatory cell infiltration and pro-inflammatory/fibrotic gene expression and histology of kidney and white adipose tissue (WAT) were examined. Results: PBI-4050 improved glucose metabolism, reduced serum triglycerides and insulin levels, and increased serum adiponectin levels. In kidneys, PBI-4050 reduced ER stress (p-PERK, ATF-6, CHOP and p-IRE1). Furthermore, PBI-4050 reduced α-SMA, fibronectin and CTGF gene expression. Pro-inflammatory/remodeling markers expression (MRC-1, MCP-1 and MMP-2) were also reduced with PBI-4050 treatment. Moreover, histological analysis revealed that PBI-4050 reduced collagen deposition in glomeruli. Specific immunostaining also showed that PBI-4050 significantly reduced galectin-3 expression in kidney and WAT. Finally, PBI-4050 reduced the expression levels of inflammatory cell infiltration markers (Ly6G and F4/80) in kidney. Conclusions: These results suggest that PBI-4050 is a strong potential candidate for the treatment of metabolic diseases and related diabetic nephropathy. Disclosure J. Simard: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. M. Cloutier: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. A. Laverdure: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. J. Richard: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. L. Gervais: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. A. Felton: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. B. Grouix: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. P. Laurin: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc.. Board Member; Self; Prometic Life Sciences Inc. M. Leduc: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. F.A. Leblond: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. L. Gagnon: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc..

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 score0.031
Threshold uncertainty score0.914

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 head0.238
Teacher spread0.229 · 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
Published2018
Admission routes1
Has abstractyes

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