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Developments and Issues in the Canadian Market for Asset-Backed Commercial Paper

2003· article· en· W26878796 on OpenAlexaboutno aff
John Kiff, Paula Toovy

Bibliographic record

VenueThe Journal of Nutritional Biochemistry · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCredit Risk and Financial Regulations
Canadian institutionsnot available
FundersAgence Nationale de la Recherche
KeywordsBusinessAsset (computer security)FinanceCommerce

Abstract

fetched live from OpenAlex

Insulin resistance (IR), which is a leading cause of the metabolic syndrome, results in early brain function alterations which may alter brain mitochondrial functioning. Previously, we demonstrated that rats fed a control diet and submitted to an acute restraint stress exhibited a delayed mitochondrial permeability transition pore (mPTP) opening. In this study, we evaluated the combined effects of dietary and emotional stressors as found in western way of life. We studied, in rats submitted or not to an acute stress, the effects of diet-induced IR on brain mitochondria, using a high fat/high fructose diet (HF(2)), as an IR inducer, with addition or not of cinnamon as an insulin sensitizer. We measured Ca(2+) retention capacity, respiration, ROS production, enzymatic activities and cell signaling activation. Under stress, HF(2) diet dramatically decreased the amount of Ca(2+) required to open the mPTP (13%) suggesting an adverse effect on mitochondrial survival. Cinnamon added to the diet corrected this negative effect and resulted in a partial recovery (30%). The effects related to cinnamon addition to the diet could be due to its antioxidant properties or to the observed modulation of PI3K-AKT-GSK3β and MAPK-P38 pathways or to a combination of both. These data suggest a protective effect of cinnamon on brain mitochondria against the negative impact of an HF(2) diet. Cinnamon could be beneficial to counteract deleterious dietary effects in stressed conditions.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.026
GPT teacher head0.240
Teacher spread0.215 · 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 designNot applicable
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

Citations4
Published2003
Admission routes1
Has abstractyes

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