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Record W1489363258 · doi:10.1155/2015/547928

Epithelium‐Specific Ets‐Like Transcription Factor 1, ESE‐1, Regulates ICAM‐1 Expression in Cultured Lung Epithelial Cell Lines

2015· article· en· W1489363258 on OpenAlexafffund
Zhiqi Yu, Jun Xu, Jinbao Liu, Jing Wu, Chan Mi Lee, Li Yu, Jim Hu

Bibliographic record

VenueMediators of Inflammation · 2015
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsUniversity of TorontoHospital for Sick Children
FundersCanadian Institutes of Health Research
KeywordsTranscription factorEpitheliumCell biologyICAM-1BiologyCell cultureCellLungCancer researchMolecular biologyGeneMedicineCell adhesion moleculeGeneticsInternal medicine

Abstract

fetched live from OpenAlex

Cystic fibrosis (CF) patients suffer from chronic airway inflammation with excessive neutrophil infiltration. Migration of neutrophils to the lung requires chemokine and cytokine signaling as well as cell adhesion molecules, such as intercellular adhesion molecule-1 (ICAM-1), which plays an important role in mediating adhesive interactions between effector and target cells in the immune system. In this study, we investigated the relationship between ICAM-1 and epithelium-specific ETS-like transcription factor 1 (ESE-1) and found that ICAM-1 expression is upregulated in cell lines of CF (IB3-1) as well as non-CF (BEAS-2B and A549) epithelial origin in response to inflammatory cytokine stimulation. Since ESE-1 is highly expressed in A549 cells without stimulation, we examined the effect of ESE-1 knockdown on ICAM-1 expression in these cells. We found that ICAM-1 expression was downregulated when ESE-1 was knocked down in A549 cells. We also tested the effect of ESE-1 knockdown on cell-cell interactions and demonstrate that the knocking down ESE-1 in A549 cells reduce their interactions with HL-60 cells (human promyelocytic leukemia cell line). These results suggest that ESE-1 may play a role in regulating airway inflammation by regulating ICAM-1 expression.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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

Citations6
Published2015
Admission routes2
Has abstractyes

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