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Record W2916656765 · doi:10.1016/j.biopha.2019.108669

Citrus alkaline extracts prevent fibroblast senescence to ameliorate pulmonary fibrosis via activation of COX-2

2019· article· en· W2916656765 on OpenAlexfundno aff
Fanchao Feng, Zhichao Wang, Ruofei Li, Qi Wu, Cheng Gu, Yong Xu, Wenpan Peng, Di Han, Xianmei Zhou, Jing Wu, Hailang He

Bibliographic record

VenueBiomedicine & Pharmacotherapy · 2019
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsnot available
FundersNational Aerospace Science Foundation of ChinaNanjing UniversityNational Natural Science Foundation of ChinaAGE-WELL
KeywordsSenescenceIdiopathic pulmonary fibrosisPulmonary fibrosisFibroblastCancer researchFibrosisMedicineMyofibroblastLungPharmacologyBiologyPathologyInternal medicineIn vitroBiochemistry

Abstract

fetched live from OpenAlex

and P21, concomitant with down-regulation of the myofibroblasts marker α-SMA, and the number of senescence-associated β-galactosidase (SA-β-Gal) positive cells were decreased by CAE treatment, indicating a significant inhibitory effect of CAE on fibroblast senescence. Additionally, CAE down-regulated the expression of the senescence-associated secretory phenotype (SASP) in etoposide-induced senescent fibroblasts. Further studies indicated that COX-2 activation was required for CAE to inhibit the lung fibroblast senescence through a P53-dependent pathway. Results showed that the anti-senescence effect of CAE was abrogated when COX-2 was knocked down or inhibited by COX-2 inhibitor NS-398 or indomethacin in lung fibroblasts. Meanwhile, the anti-fibrotic and anti-senescence effect of CAE were abolished due to disruption of COX-2 in vivo. Collectively, our results provided a novel insight into the potential mechanism of CAE to inhibit the fibroblasts activation through preventing cellular senescence.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.045
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.293
Teacher spread0.282 · 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.

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

Citations43
Published2019
Admission routes1
Has abstractyes

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