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Record W3117198608 · doi:10.3233/jcb-209011

DIBI, a novel polymeric iron chelator modulates IL-6 and IL-8 secretion from Cystic Fibrosis airway epithelial cells in response to endotoxin induction

2020· article· en· W3117198608 on OpenAlexaff
Maral Aali, Alexa Caldwell, Audrey Li, Bruce E. Holbein, Valérie Chappe, Christine Lehmann

Bibliographic record

VenueJournal of Cellular Biotechnology · 2020
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsDalhousie University
Fundersnot available
KeywordsInflammationSecretionCystic fibrosisLipopolysaccharideFibrosisImmune systemImmunologyMedicineChemistryInternal medicine

Abstract

fetched live from OpenAlex

Iron chelators have been utilized clinically to treat patients with iron overload conditions. There is a growing body of evidence linking iron dysregulation and reactive oxygen species (ROS) overproduction as underlying factors in Cystic Fibrosis (CF) disease. The chronic inflammation can lead to progressive airway destruction. Alleviation of this chronic inflammation is a potential target for CF treatment and thus, this research investigated the dose-response effects of DIBI, a novel iron chelator, on inflammation in CF nasal epithelial cells. Polarized CF cells were stimulated with, lipopolysaccharide (LPS), co-treated with DIBI (LPS+DIBI), or DIBI alone (DIBI). We demonstrated that DIBI modulated the release of IL-6 and IL-8 in CF cells in a dose-dependent manner. Reduction of extracellular iron with the lower doses of DIBI (25 and 50μM), increased IL-6 secretion in non-induced cells. LPS challenge increased IL-6 and IL-8 secretion which was suppressed by high dose (200μM) DIBI administration. This study demonstrates the therapeutic potential of iron chelation therapy to treat the dysregulation of the immune response in CF patients.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.817

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.246
Teacher spread0.235 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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