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Record W1480383518

Cystic fibrosis-related diabetes: from CFTR dysfunction to oxidative stress.

2009· article· en· W1480383518 on OpenAlexaff
Thierry Ntimbane, Blandine Comte, Geneviève Mailhot, Yves Berthiaume, Vincent Poitout, Marc Prentki, Rémi Rabasa‐Lhoret, Emile Levy

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsCystic fibrosisOxidative stressCystic fibrosis transmembrane conductance regulatorMedicineInflammationDiabetes mellitusMalabsorptionPopulationFibrosisImmunologyInternal medicineEndocrinology
DOInot available

Abstract

fetched live from OpenAlex

Cystic fibrosis (CF) represents the most common lethal autosomal recessive disorder in the Caucasian population. It is caused by mutations in the CF transmembrane conductance regulator (CFTR) gene, resulting in abnormal Na(+) and Cl(-) transport in several tissues. Its main clinical manifestations include bronchopulmonary infections along with gastrointestinal and nutritional disorders. Intense and recurrent inflammation ultimately leads to an overabundance of activated neutrophils and macrophages that contribute to free radical generation. Furthermore, CFTR defects directly affect glutathione transport and homeostasis, while intestinal fat malabsorption limits uptake of endogenous antioxidant vitamins. Collectively, these abnormal events disturb the balance between pro- and anti-oxidants and promote oxidative stress, which may play a significant role in CF-related diabetes (CFRD), a severe complication associated with a drastic increase of morbidity and mortality. This review will focus on the involvement of oxidative stress in CF pathology, especially its role in the occurrence of CFRD. The multiple abnormalities in the oxidant/antioxidant balance could be a potential target for a new therapeutic approach.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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

Citations69
Published2009
Admission routes1
Has abstractyes

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