Citrus alkaline extracts prevent fibroblast senescence to ameliorate pulmonary fibrosis via activation of COX-2
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a progressive and ultimately fatal lung disease with a poor prognosis and limited treatment options. The incidence of IPF increases with age, and the mechanisms related to aging such as cellular senescence have been strongly implicated in disease pathology. Therefore, a better understanding of fibroblasts senescence might provide a new therapeutic strategy to prevent and treat pulmonary fibrosis. In this study, we aimed to explore the effects of citrus alkaline extracts (CAE) on the fibroblasts senescence, and elucidate the underlying mechanism to ameliorate pulmonary fibrosis. We demonstrated that CAE mitigated the collagen deposition by the initial early treatment, suggesting a potential preventive effect of CAE on pulmonary fibrosis. The expression of senescence biomarkers P16 INK4a 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".